Performance analysis of an air quality CFD model in complex environments: Numerical simulation and experimental validation with EMU observations

Accepted Manuscript Performance analysis of an air quality CFD model in complex environments: Numerical simulation and experimental validation with EMU observations Pramod Kumar, Amir-Ali Feiz PII:

S0360-1323(16)30308-0

DOI:

10.1016/j.buildenv.2016.08.013

Reference:

BAE 4597

To appear in:

Building and Environment

Received Date: 3 May 2016 Revised Date:

10 August 2016

Accepted Date: 11 August 2016

Please cite this article as: Kumar P, Feiz A-A, Performance analysis of an air quality CFD model in complex environments: Numerical simulation and experimental validation with EMU observations, Building and Environment (2016), doi: 10.1016/j.buildenv.2016.08.013. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Pramod Kumara,∗, Amir-Ali Feiza

´ LMEE, Universit´e d’Evry Val-d’Essonne, 40 Rue Du Pelvoux 91080 Courcouronnes, France.

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Performance Analysis of an Air Quality CFD Model in Complex Environments: Numerical Simulation and Experimental Validation with EMU Observations

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Abstract

Assessment of air quality in complex industrial and urban regions is essential in setting effective policies for sustainable development of society and assisting to improve the environment and thereby support health and safety. In this study, a CFD model fluidyn-PANACHE, dedicated to the dispersion

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of hazardous gases in complex industrial and urban sites, is critically vali-

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dated with tracer observations from EMU experiment for two distinct types of sources in two geometrically different environments: (i) Case-A1 (release from an open door in courtyard area of a simple L-shaped building), and

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(ii) Case-C1 (continuous release over the larger distances around an industrial site featuring numerous buildings and complex local topography). The

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steady RANS solution is used first to get quick results and comparison with the experimental data shows a good agreement between the modelled and measured concentrations. Nevertheless, in order to examine the performance of the solver, unsteady RANS simulations are performed. A detailed statisti∗

Corresponding author Email address: [email protected] (Pramod Kumar )

Preprint submitted to Building and Environment

August 10, 2016

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cal analysis shows that the performance of fluidyn-PANACHE against both

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Cases of EMU observations is well within the acceptable bounds of statistical measures for air quality applications. It is observed that in a simple

geometry with lesser complexity in Case-A1, steady and unsteady simulations give similar performance against the tracer observations and predicts

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≈ 68% points within a factor of two. In a more complex industrial site in Case-C1, unsteady RANS is performing slightly better than the steady sim-

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ulation and it predicts ≈ 80.0% points within factor of two, which is higher than ≈ 67.0% points from the steady simulation.

Keywords: CFD modeling, EMU experiment, fluidyn-PANACHE, Model evaluation, RANS and URANS, Urban dispersion modelling.

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1. Introduction

Air quality assessment in complex urban or industrial regions with new

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and existing techniques is imperative to provide the information needed to

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estimate the population exposure to hazardous airborne matter and to assist

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regulators, urban planners or emergency authorities to outline the evacuation

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plans in a case of the natural disasters, accidental or deliberated release. In

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industrial safety and environment programmes, near-field (< 10 km) disper-

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sion of toxic and hazardous gases in complex chemical sites and surrounding

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the buildings and other structures are often predicted with the Computational Fluid Dynamic (CFD) codes. A CFD model solves the Navier-Stokes (N-S) fluid dynamics equations using a small grid size (of order 1 m or even

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less) [1] over the complex terrains. With rapid advances in computer re-

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sources and methods, CFD models provide accurate flow fields and pollutant

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dispersion modelling around buildings and other structures in complex urban

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or industrial regions for various kind of release scenarios.

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The unsteady flow effect triggered by the presence of buildings and other

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geometries is an influencing factor on the dispersion in urban or industrial

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regions. Compared with simple Gaussian dispersion models or other analyti-

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cal or empirical approximations, CFD models efficiently predict the obstacles

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influence on wind patterns and pollutant cloud shapes in complex urban envi-

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ronments [2]. Nevertheless, the CFD model validation against experimental

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datasets is one critical point to examine its capability to provide reliable and

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valuable information in emergency planing or chronic impact assessment.

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Many CFD codes were successfully validated against experimental data for

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various types of releases in diverse geometric environments and atmospheric

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conditions [1, 3, 4, 5, 2, 6, 7, 8, etc.]. Some recent comprehensive review

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studies, e.g., [9, 10, 11, 12, 13, 14, 15, etc.], highlight the applicability, ad-

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vantages, disadvantages, limitations, and problems in the CFD modelling for

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atmospheric dispersion in urban or industrial environments. However, to ob-

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tain the reliable results for emergency preparedness and air quality analysis,

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CFD models are required to be set up, tested, and evaluated correctly with

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the experimental observations in different geometric environments and atmo-

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spheric conditions [16, 9, 17, 18]. The validation of a CFD model is also an

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essential part to utilize it with an inversion method for reconstruction of the unknown atmospheric tracer sources in urban or complex industrial regions [19, 20, 21].

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Complexities in flow fields and pollutant transport and dispersion in ur-

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ban or industrial regions make difficult to assess the contaminant plume

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concentrations due to local topography, terrain conditions, buildings and

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other geometrical structures. Buildings in an urban region alter the flow-field

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and deflect the wind, causing updrafts and downdrafts, channelling between

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buildings, areas of calm winds adjacent to strong winds, and horizontally

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and vertically rotating-eddies between buildings, at street corners, and other

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places within the urban canopy [22]. The physics in urban regions is often

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characterized by unsteadiness due to the presence of a complex assembly

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of bluff bodies and to the variability of approaching winds [23, 12]. Often,

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a question also arises as to whether the steady Reynolds-averaged Navier-

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Stokes (RANS) solution would perform equally well with the unsteady RANS

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(URANS) in various geometrically different environments. The basic equa-

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tions of the URANS are formally derived by applying ensemble averaging.

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Only with ensemble averaging the resulting equations comply with the steady

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RANS equations now containing the partial time derivatives in URANS codes

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[24]. URANS models are driven by time-dependent boundary conditions and

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account, e.g., for different land uses with different radiation budgets and pro-

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vide time dependent predictions [24]. This type of model is used in standard

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meteorological meso- and macro-scale applications (weather forecasts etc.).

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However, selection of proper solution strategies for complex urban disper-

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sion applications includes choices between the steady RANS and URANS

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approaches [12]. Thus, one of the objective of this study is to evaluate the comparative performance of the steady and unsteady RANS solutions for pollutant dispersion in two geometrically different regions.

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This study is a step towards the development of a methodology that will

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utilize a CFD model fluidyn-PANACHE to retrieve an unknown atmospheric

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tracer source or leak in an industrial region from a finite set of the concen-

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tration measurements. However, due to uncertainties in the synoptic wind,

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boundary conditions, model errors or parametrization uncertainties, a pre-

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cise validation of the CFD models is a necessity to obtain the reliable results

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for emergency preparedness. For this, fluidyn-PANACHE required to be first

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validated for forward dispersion in an industrial region so that we can utilize

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the information based on this in a inversion process for source reconstruction

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in an industrial or urban region. Thus, this study concerns a comprehensive

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validation of the fluidyn-PANACHE against the experimental observations

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in two geometrically different environments.

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In order to evaluate the CFD simulations of urban flows and pollutant

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dispersion, completeness and reliability of an experimental data is a neces-

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sity. CFD validation in turn requires high-quality experimental data to be

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compared with the simulation results. This study utilizes the extensive field

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observations involving tracer gas releases in a wind tunnel from the Evalua-

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tion of Modelling Uncertainty (EMU) project. The EMU project provides an

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unique and useful dataset for validation of dispersion models around build-

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ings, industrial regions and over complex topography [25]. To demonstrate

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the fluidyn-PANACHE model’s capabilities to simulate the flow and disper-

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sion patterns in the near field but also at larger distances, two cases (i) the

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single L-shaped building case and (ii) the real industrial site case are selected. These EMU cases are also useful to analyse the CFD model performance from two different types of releases, such as from area and point sources.

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In this study, three-dimensional numerical simulations are performed to

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evaluate a CFD model fluidyn-PANACHE with tracer observations from an

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experiment, for dispersion of toxic and hazardous gaseous pollutants around

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buildings and in geometrically complex chemical sites. The steady RANS

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solution is used first to get quick results and comparison with experimental

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data. Nevertheless, in order to improve performance of the solver, unsteady

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RANS solution is tried and compared with results from steady simulations.

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2. Description of the CFD model

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Various CFD approaches, e.g. large-eddy simulation (LES), direct numer-

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ical simulation (DNS), steady RANS, URANS, and hybrid URANS/LES, etc.

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have been used for basic research on urban flow and dispersion. However,

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deciding which CFD approach is most appropriate for a given problem is not

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always straightforward, as each approach has specific advantages and disad-

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vantages and up to now, RANS has been the most commonly used approach

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in CFD for urban physics [11, 12]. Thus, a CFD model fluidyn-PANACHE,

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which includes both steady and unsteady RANS solutions, is applied here.

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2.1. fluidyn-PANACHE

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A CFD model fluidyn-PANACHE was developed for the simulation of

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atmospheric flows and pollutant dispersion from various types of single and

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multiple sources in complex environments. The fluidyn-PANACHE is a self-

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contained fully 3-dimensional (3-D) fluid dynamics, commercial CFD code, designed to simulate accidental and industrial pollutant dispersion into the complex terrains in the presence of obstacles. A description of the model and its applications are given in [26, 27, 28, 2]. The fluidyn-PANACHE

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solves the N-S equations along with the equations describing conservation

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of species concentration, mass, heat transfer and energy for a mixture of 6

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ideal gases using finite volume numerical techniques. The CFD model solves

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the Reynolds averaged forms of these governing differential equations in 3-D

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space and time for turbulent flow and dispersion in a computational domain.

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It includes solutions of both steady and unsteady forms of the RANS equa-

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tions. The steady RANS refers to time-averaging of the N-S equations and

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yields statistically steady descriptions of turbulent flow. However, flow in the

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atmospheric boundary layer (ABL) is inherently unsteady, and therefore, an

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unsteady approach may required. URANS refers to ensemble-averaging of

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the N-S equations and resolves only the unsteady mean-flow structures, while

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it model the turbulence. URANS can be a good option when the unsteadiness

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is pronounced and deterministic.

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The Reynolds stresses are modelled using a linear eddy viscosity model

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[29]. Ideal gas law is used for the thermodynamic model of mixture of gases.

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Air is modelled as the moist air with effective properties of the mixture of dry

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air and water vapour. The ABL processes are built-in the CFD code with

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different numerical models. Dispersion of gases is modelled by solving the full

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conservation equations governing the transport of species concentration. It

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includes a built-in automatic 3-D mesh generator that can create the finite-

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volume mesh around obstacles and body-fitting the terrain undulations. A

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detailed description of the fluidyn-PANACHE’s features is given in section

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S1 of the Supplementary Information (SI). 2.2. Turbulence model Turbulent structure in the computational domain is resolved using a mod-

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ified standard k −  turbulence model in fluidyn-PANACHE. The k −  model

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is a 2-equation linear eddy viscosity model and it’s implementation is de7

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rived from the standard high-Reynolds number (Re) form with corrections

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for buoyancy and compressibility [30, 31]. It solves the transport equations

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for turbulent kinetic energy, k, and its dissipation rate,  for adapted con-

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stants. The k −  model computes the length and time scales from the local

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turbulence characteristics. Thus, it can model the turbulent flows subjected

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to both mechanical shear (obstacles, terrain undulations, canopy) as well as

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buoyancy (stability and buoyant/heavy gas plumes). The k −  model is an

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isotropic model of turbulence. Thus, it results in turbulent diffusivities that

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are same in both horizontal and vertical directions at a location.

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2.3. Boundary conditions

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Appropriate boundary conditions are required on the main computational

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domain boundary, the ground, and on the obstacles. The top boundary is

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treated as an outflow boundary. The lateral boundaries of the domain are

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treated as inflow and outflow boundaries based on the direction of the wind

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with respect to the domain boundary. At the inflow boundary, velocity, tem-

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perature, species concentrations and turbulence variables are specified. Pres-

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sure is extrapolated from inside the domain. Species concentrations are set

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according to the specified background concentrations. The inflow boundary

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conditions for wind, temperature, and turbulence profiles and wall functions

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are briefly described in following subsections. 2.3.1. Wind and Temperature profiles The vertical wind profile is an important factor defining the structure of

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the ABL in CFD simulations. In this study, the log-law profile in neutral

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stability condition has been used to parametrize the inflow boundary condi8

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tion for the EMU Cases A1 and C1. The vertical wind profile u(z) is given by the Monin-Obukhov similarity theory,     z u∗ ln − ψ(ζ) u(z) = κ z0

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(1)

where u∗ is the friction velocity, κ(= 0.41) is the von Karman constant, z

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is the vertical height above the ground surface, z0 is the surface roughness

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length, ψ is the similarity function depends on the atmospheric stability, and

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ζ = z/L is the stability parameter in which L is the Obukhov length. For

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neutral stability condition, ψ can be assumed to zero, that transformed the

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wind profile u(z) in Eq. (1) to the standard log-law profile.

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2.3.2. Turbulence profile

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Several parametrizations in the fluidyn-PANACHE are implemented for

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the inflow turbulence profiles. The profiles of k and  used in this study is a

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semi-empirical model based on the similarity theory and turbulence measure-

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ments [32]. In neutral and stable regimes (z/L ≥ 0), the turbulence profiles

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according to [32] are determined as follows:   6u2∗ , for z/zi ≤ 0.1 k(z) =  6u2 (1 − z/zi )1.75 , for z/zi > 0.1 ∗

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(z) =

   u3∗ (1.24 + 4.3z/L) , κz

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  u3∗ (1.24 + 4.3z/L) (1 − 0.85z/zi )1.5 , for z/zi > 0.1 κz

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where zi is height of the ABL. In neutral stability condition, z/L → 0 and accordingly these profiles are transformed to apply in neutral regimes.

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2.3.3. Wall functions Standard wall functions in fluidyn-PANACHE [31] are used to compute

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the drag forces on solid walls in a turbulent boundary layer. The wall func-

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tions result from a solution of the N-S equations for the turbulent boundary

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layer in equilibrium. A no-slip boundary condition, where velocity compo-

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nents are set to zero at the ground surface, is defined for the bottom boundary

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condition. Following log law of the wall function for momentum is used:    1 ln(Ey + ), for y + > 11.63 κ + u = (4)  + y + , for y < 11.63

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where u+ = Up /u∗ is the non-dimensional velocity, in which Up is the fluid

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velocity parallel and relative to wall, y + = ρu∗ y/µ is the non-dimensional

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wall to cell-centre distance, in which y is the cell-centre to wall distance and

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µ is the dynamic viscosity of fluid and E is a function of wall roughness [33].

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2.4. CFD solver

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For both scenarios (Cases A1 & C1) of the EMU experiment, a fully im-

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plicit NT CFD solver in fuidyn-PANACHE is used for flow-field computations

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in the domains with unstructured mesh. The NT solver is a pressure-based

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fully implicit segregated method on unstructured meshes that solves all gov-

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erning equations separately and uses an iterative procedure for both steady state and transient cases. The steady RANS solution is used first to get quick results and comparison with the EMU experimental data in both the

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Cases. The selected parameters have been chosen for a fast convergence of

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the simulations. Nevertheless, in order to improve performance of the solver,

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unsteady RANS solver is used for simulations of both EMU Cases. The solver

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options have been chosen similar except the unsteady RANS solution.

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To couple the pressure and momentum equations in the numerical compu-

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tations, the Semi-Implicit Method for Pressure Linked Equations-Consistent

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(SIMPLEC or SIMPLE-Consistent) algorithm [34] is utilized. The buoy-

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ancy model is used to parametrize the body force term in the N-S equations.

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Dispersion of the gaseous pollutants is modelled by solving the standard Eu-

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lerian advection-diffusion equation governing the transport and diffusion in

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a computational domain. Residuals were used to check the convergence of

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the solution during a computation and also to stop the simulations. For a

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CFD simulation, the scaled residuals for all variables were considered to be

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equal or less than O(10−4 ).

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3. Description of the EMU experiment

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The Evaluation of Modelling Uncertainty (EMU) project, funded by the

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European Commission, involved a comprehensive evaluation of the models

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to simulate flow and dispersion patterns in different realistic geometric envi-

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ronments. The objective of this tracer experiment was to evaluate the spread

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in results due to the way that CFD codes are applied and the accuracy of

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such codes in complex gas dispersion situations [35, 25]. It records a com-

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prehensive set of the concentration and other observations and is useful to setting up and validate the CFD models for dispersion in various realistic environments for different release scenarios. The EMU project consisted in

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14 test cases of industrial scenarios, which ranged from single building on flat

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terrain right through to the cases associated with a specific, complex topog11

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raphy industrial site. Stage A in the EMU project comprised three cases, Al

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to A3, involving a simple building on flat ground, neutral atmosphere and

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isothermal conditions. Stage B incorporated increases in complexity of the

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geometry (i.e. terrain, obstacles and number of buildings), release conditions

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(i.e. two-phase and non-isothermal releases) and meteorology (i.e. stabil-

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ity and wind speed). Stage C concerned an actual industrial site, featuring

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numerous buildings and complex local topography. Experiments were per-

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formed at the University of Surrey [35] in a large stratified wind tunnel (20 m

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× 3.5 m × 1.5 m) at a model scales between 1/133 and 1/250. Continuous jet

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releases of dense, buoyant and neutrally-buoyant gases have been simulated

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in neutral or stable atmosphere.

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In the present work, two test cases A1 and C1 of the EMU project are

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simulated using fluidyn-PANACHE. Case A1 involves a release from an open

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door in the courtyard area of a simple L-shaped building on a flat ground.

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Whereas, Case C1 comprises a continuous point release over the larger dis-

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tances around an industrial site featuring numerous buildings and complex

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local topography. The complete description of each case is described in fol-

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lowing sections for the numerical simulations.

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3.1. Description of Case A1, computational domain, and grid structure

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Case A1 of the EMU project entailed the numerical simulation of a passive

release from an L-shaped building (Figure 1) located on a flat surface into the ABL flow. The EnFlo wind-tunnel was modelled at the University of Surrey and neutral ambient conditions were assumed during the experiment

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[35]. In Case A1, a continuous steady release of a neutrally buoyant gas took

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place from a relatively large “courtyard” door in the side of building (Figure 12

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1). The source in this case is an areal source of 20 m2 area that releases the

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neutrally buoyant gas into horizontal direction. In this case, a mixture of

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2.96% ethylene (C2 H4 ) in a nitrogen (N2 ) balance was used for the source

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gas, and was essentially neutrally stable. Properties of the source are listed

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in Table 1. Velocity UH at building height was 5 ms−1 , with wind direction

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θ = 0o . The ground roughness length (z0 ) of the experimental site in Case

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A1 is 0.12 m [35]. In this case, the concentrations at crosswind locations

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on the cross-section at five distances downwind (x) of the lee edge of the

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L-shaped building were measured at x/H = 0.5, 1, 2, 5, 10; where H = 10

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m is height of the building. At each sampling line, sensors were deployed

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in crosswind and vertical directions (Figure 1). The height (z/H) of the

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sensors were varying (z/H = from 0.11 to 3.0) at all crosswind arc (y/H) of

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the downwind distances (x/H) from the source. Receptors information for

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the available concentration measurements in the EMU Case A1 is given in

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SI section S2 & Table S1.

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Figure 1 also represents the computational domain considered for the

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EMU Case A1. The dimensions of the computational domain have been set

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as follows: 300 m long, 180 m wide and 120 m high. The distance between

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the source and the inlet flow boundary condition is 85 m and the source

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is located in the middle of the width of the domain. The computational

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domain is discretized in 39 irregular vertical levels. In order to select the appropriate mesh, a grid sensitivity analysis is performed with simulation test-cases using six different mesh sizes (SI section S3, Table S3, Figure S1).

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The unstructured grid cells numbers in these six test-cases of the investigated

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meshes were varying from 1461876 to 2740725 (Table S3). The grid mesh

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in the simulation domain is more refined at close to the buildings, source

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and receptor locations. Based on this grid sensitivity analysis, a mesh size

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consists a total of 2134080 unstructured grid cells in the 3-D computational

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domain with 54720 cells on each horizontal plane is adapted for the current

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study. For Case A1, min/max of dx (or dy) are 0.2 m/2.5 m; min/max of dz

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are 0.3 m/20 m.

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3.2. Description of Case C1, computational domain, and mesh structure

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Case C1 of the EMU project entails a continuous, passive jet release from

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the side of a building within a real chemical site. The surrounding terrain

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in this Case C1 is complex, with steep hills, trench-like features and cliffs at

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the edge of the sea. The site comprises a large number of irregularly shaped

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buildings, most of which however are conveniently aligned with each other

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(Figure 2). Cowan [36] reported four categories of the surface roughness in

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the domain: sea, land, village/town and industrial site. The atmospheric

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stability classes encountered are neutral and stable. In this case, we take our

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origin to be at sea-level, directly below the source position. Three types of

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concentration data (126 observations) were recorded: (i) cross-stream profiles

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at ground-level, (ii) at (z−zg )/H = ∼ 2−3 (where zg is the height of ground-

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level), and (iii) vertical profiles through the ground-level maxima. A number

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of other ground-level concentration measurements were also made during the experiment [35]. The source height in the Case C1 is 2.0 m and the tracer was released from a point source tilted in horizontal direction with 327.5◦ (Table 1). The product released of Case C1 is a mixture of 77.3% by volume C2 H4 in

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N2 , giving a mixture density ratio α = 1.0. Mass flux, release temperature,

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release duration, exit velocity and emission direction have been considered 14

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as inputs for a point source continuous release. The source characteristics of

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this EMU Case C1 for a passive tracer are tabulated in Table 1. Receptors

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information for the available concentration measurements in the EMU Case

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C1 is given in SI section S2 & Table S2.

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Figure 2 also represents the computational domain for Case C1. The

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domain dimensions have been set as 800 m long, 1200 m wide and 200 m

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high. 3-D unstructured mesh was generated in domain for the Case C1

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with 2041182 cells, which contains 52338 cells on each horizontal plane. 39

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irregular vertical planes were created in the vertical direction. A grid sensi-

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tivity analysis with more refined and coarse meshes was also carried out to

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adapt the present mesh for further CFD simulations. The min/max of dx

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(or dy) are 0.7 m/12 m; min/max of dz are 0.8 m/40 m for the EMU Case

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C1. For comparison with the observations (volume fractions of the source

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fluid), the predicted concentrations are normalized with Cn = UH H 2 /Cs Qs

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and converted into volume fractions [36]. In this computation of the normal-

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ization conversion in Case C1: UH = 5 ms−1 , H = 10 m, Cs = 0.773 and

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Qs = πDs2 Us /4 = 17.8 m3 s−1 , in which source diameter (Ds ) = 1.74 m, and

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exit velocity Us = 7.5 ms−1 .

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4. Model performance validation methods

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The variables to evaluate for an air quality analysis depend on the in-

tended goals of a model validation, e.g., the highest and second highest hourly-averaged ground level concentrations are of interest for regulatory

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applications to standard pollutants and for military applications, it is typi-

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cally the hazard areas of certain concentration or dosage thresholds [37]. The 15

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quantities such as the maximum concentration over the entire receptor net-

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work, and the maximum concentration across a given sampling arc or along

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a sampling line are the potential outputs of an air quality model that should

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be validated. The mean concentration at some desired locations in a region

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is also required to analyze the extent of exposure from a release. These are

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the potential outputs of an air quality model that should be validated. The

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cumulative distribution functions (cdf) where the prediction of probability

330

to have a particular health effect above a threshold have also been utilized

331

for air quality analysis [38, 6, 39, etc.].

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The performance validation of the CFD model is produced both via the

333

result analysis and direct comparison of numerical results versus experimental

334

observations. The validation is performed both qualitatively and quantita-

335

tively by the scatter plots, quantile-quantile (Q − Q) plots, and computing

336

the statistical performance measures. In a scatter plot, the paired observa-

337

tions and predicted concentrations are plotted against each other and the

338

magnitude of the model’s over- or under-predictions can be visible by visual

339

inspection. On the other hand, Q − Q plot begins with the same paired data

340

as the scatter plots, but removes the pairing and instead ranks each of the

341

observed and predicted data separately from lowest to highest [37]. A Q − Q

342

plot is often useful to find out whether a model can generate a concentration

344

345

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distribution that is similar to the observed distribution. Biases at the low or high concentration values are quickly visible in the Q − Q plots. For model validation, standard statistical performance measures: Nor-

346

malized Mean Square Error (NMSE), Fractional Bias (FB), Factor of Two

347

(FAC2), Pearson correlation coefficient (COR), Geometric Mean bias (MG),

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Geometric Variance (VG), and Index of Agreement (IA) are computed and

349

analyzed. These measures define the agreement between the model pre-

350

dicted concentrations with the experimental observations. NMSE defines

351

the degree of scattering in a sample. The positive and negative values of FB

352

respectively indicate the overall under- or over-prediction of the simulated

353

concentration from the observations. FAC2 is the most robust measure, be-

354

cause it is not overly influenced by the outliers in observed and predicted

355

concentrations. Since the distribution is close to lognormal for most atmo-

356

spheric pollutant concentrations, the linear measures FB and NMSE may be

357

overly influenced by infrequently occurring high observed and/or predicted

358

concentrations, whereas the logarithmic measures MG and VG may provide

359

a more balanced treatment of extreme high and low values. However, MG

360

and VG are overly influenced by the very small and zero values, which are not

361

uncommon in dispersion modelling [37]. COR reflects the linear relationship

362

between two variables and sensitive to a few aberrant data pairs.

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A perfect model would present the ideal values of NMSE, FB = 0; and

364

FAC2, COR, MG, VG, IA = 1 [37]. However, due to influence of random

365

atmospheric processes, these values are not attainable and minimum perfor-

366

mance measures for a model to be defined as acceptable [37]. For an acceptable

367

model performance, [37] provided following bounds of these measures:

369

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NMSE < 4.0, −0.3 < FB < 0.3, FAC2 > 0.5, VG < 1.6, 0.7 < MG < 1.3. Computation of these performance measures assumes that the validation

dataset contains pairs of predicted and observed concentrations. For a contin-

371

uous and constant release during an experimental period, distribution of the

372

modelled concentrations reaches steady-state in a computational domain and

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thus, the averaged values and end-time simulation concentrations are simi-

374

lar. Accordingly, the result of both RANS and URANS is the time-averaged

375

value of the concentrations in a duration at the ending of simulations. In

376

both EMU Cases, available concentration observations are representative of

377

average values over the duration of the experiment in steady-state flows that

378

means the pairing used for the validation is in space only for all the sensors.

379

5. Results and discussion

380

5.1. EMU project simulation, Case A, Phase I (Case A1)

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The simulations with both steady and unsteady RANS solutions were

382

performed on 20 processors (Intel(R) Xeon(R) CPU E52695 [email protected] GHz)

383

on a cluster CentOS release 6.5. The steady RANS solution took ≈ 5 hours

384

to complete the simulation for the Case A1, whereas, the computational time

385

in URANS simulation was ≈ 8 hours.

386

5.1.1. Overall performance

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The predicted concentrations at all the sensor positions are compared

388

with the experimental observations. Also, the approximate dimensions of

389

the predicted recirculation zones are compared with the measurements. The

390

predicted concentrations from both (i) steady and (ii) unsteady RANS simu-

392

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lations are analysed with the EMU observations. A comparative performance analysis with both types of the simulations is performed by computing and comparing the statistical evaluation measures.

394

Figure 3 shows isopleths of the ground level concentration from steady

395

RANS simulations in the computational domain. The contour plot shows

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influence of the elongated building on plume deflection. It shows that the

397

concentrations from the source exit are advected by the recirculation flow

398

behind the L-shaped building, and a low concentration region appears in

399

wake of the building in downwind direction. A high concentration region

400

appears close to the source, even in upwind direction of the source exit. The

401

high concentration in upwind of the source exit is due to the back reflection

402

of the plume concentration from the L-shaped building at close to the source.

403

The concentration contour in Figure 3 exhibits the ability of CFD model to

404

capture the effects of an building geometry on plume dispersion. Similar

405

characteristics of plume behavior are also observed from URANS simulation.

406

For steady RANS simulation, Figure 4 shows the zoom on velocity vectors

407

in x and z cross-section, behind the L-shaped building in Case A1. The wind

408

tunnel observations and empirical formulas in [40] suggested that the length

409

of the re-circulating wake or cavity in the edge of the L-shaped building is

410

about 1.0 to 1.5 times H. From the FLACS CFD model prediction, Hanna et

411

al. [1] predicted this length about 1.5-2.0 times H, whereas this length from

412

the fluidyn-PANACHE prediction in Figure 4 is observed about 1.0-2.0 times

413

H. The unsteady RANS simulations also observed the similar characteristics

414

of velocity vectors in this Case A1.

416

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For comparison with the observations (volume fractions of the source

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fluid), the CFD model simulated concentrations are normalized by a source condition parameter, Cn = UH H 2 /Cs Qs , where Qs is the release rate. In this computation for Case A1: UH = 5 ms−1 , H = 10 m, Qs = 20 m3 s−1 and Cs =

419

0.0296. Normalized values of the concentrations are used for computing the

420

statistical performance measures and for further analysis of the results. Fig-

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ures 5(a1)&(b1) show the scatter diagrams between the normalized predicted

422

and observed concentrations for steady and unsteady RANS simulations, re-

423

spectively, for all 256 measurement points in Case A1. These figures show

424

a good agreement between the wind tunnel observations and the simulated

425

concentrations in both type of simulations by fluidyn-PANACHE. For un-

426

paired analysis, Q − Q plots in Figures 5(a2)&(b2)) show that the predicted

427

concentrations are close to one-to-one line in both steady and unsteady sim-

428

ulations. It was observed that the simulated concentrations in both steady

429

and unsteady RANS simulations are approximately similar in this Case A1.

430

The statistical performance measures between the predicted and observed

431

normalized concentrations are given in Table 2 for both steady and unsteady

432

RANS simulations. Table 2 also shows the statistical performance measures

433

computed from the normalized concentrations at each sampling line (x/H =

434

0.5, 1.0, 2.0, 5.0, 10.0) downwind from the source. As NMSE characterizes the

435

extent of scattering in a sample, NMSE value for the concentrations at all

436

receptor points is 0.95 for steady RANS simulation (Table 2), which is smaller

437

than its acceptable bound (NMSE < 4). A slightly smaller, but similar

438

value of NMSE (= 0.92) is observed for the concentrations from unsteady

439

RANS simulation (Table 2). Negative values of FB (= -0.13 (steady), -0.12

440

(unsteady)) show that the CFD model exhibits an overall overprediction

442

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with the observations in both type of simulations. Higher values of COR and IA in both the simulations show a good one-to-one correlation between the simulated and observed concentrations. The values of COR, MG, and

444

VG are 0.87, 1.31, and 1.93, respectively for steady simulation (Table 2)

445

and it predicts ≈ 68% of points within a factor of two of the observations.

20

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Approximately similar values of these statistical indices (COR = 0.87, FAC2

447

= 67.97, MG = 1.31, VG = 1.88) are observed for URANS in this Case A1.

448

5.1.2. Performance at each sampling line

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One can observe a slightly overprediction tendency of higher concentra-

450

tions at near-field of the release at some locations (Figures 5(a)&6, Table 2).

451

This trend of the overprediction can be exhibited by the negative values of

452

FB at close to the source at x/H = 0.5, 1.0, 2.0 (Table 2, Figure 6), for both

453

steady and unsteady RANS simulated concentrations. However, the extent

454

of overprediction decreases with increasing downwind distance from the re-

455

lease and the CFD model slightly underpredicts at far from the source at

456

x/H = 5.0, 10.0 (Table 2). At all sampling lines downwind from the release,

457

both CFD solutions predict more than ≈ 64% points within a factor of two of

458

the observations. A good one-to-one correlation between the predicted and

459

measured concentrations is also observed at all sampling lines (Figure 6).

460

NMSE values decrease with increasing downwind distances from the release

461

in both steady and unsteady RANS simulations (Figure 6, Table 2).

462

5.1.3. Performance validation of the crosswind concentrations

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The prediction of the normalized concentrations in crosswind direction at

464

two different vertical levels can be assessed from Figures 7(a)&(b) at several

465

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downwind distance from the release. These figures show the simulated crosswind results against the observed profiles at x/H = 1.0, 2.0, 5.0, 10.0 for (a) steady and (b) unsteady RANS simulations. It was observed that close to the

468

building (i.e. x/H ≤ 2, z/H ≤ 0.34), the crosswind CFD plumes have high

469

peaks in comparison to its experimental counterpart. In fact, the simulated 21

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crosswind plumes exhibit the normal distributions which is slightly different

471

from the observed long right tailed plume in the experimental concentrations

472

at x/H = 1.0 and z/H = 0.16 (Figure 7(a1)&(b1)). It is noticed that both

473

simulated and observed crosswind plumes have approximately similar char-

474

acteristics with increasing downwind distances from the release and also at

475

higher vertical levels. However, agreement between the simulated and exper-

476

imental data at sensor positions is good at a given distance from the source

477

in both steady and unsteady simulations (Figure 7(a)&(b)).

478

5.1.4. Performance validation of the mean arc-maximum concentrations

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The methodology has been traditionally used to study whether the air

480

quality models can correctly predict quantities such as the maximum con-

481

centration over the entire receptor network, and the maximum concentration

482

across a given sampling arc or along a sampling line [37]. Thus, the nor-

483

malized predicted and observed mean arc-maximum concentrations across

484

a given crosswind (y/H) sampling arc at a given downwind sampling line

485

(x/H) and vertical level (z/H) are compared and discussed. The analysis of

486

these mean arc-maximum concentration at several vertical levels at a given

487

downwind distance is advantageous to quantifying the maximum exposure at

488

the different vertical levels which can be equivalent to the different storeys

490

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of the buildings in an urban environment. Figures 8(a)&(b) show the scatter plots between the normalized predicted

and observed mean arc-maximum concentrations at all vertical levels and at each downwind sampling lines in the EMU Case A1. These figures show

493

the normalized arc-maximum concentrations predicted from both (a) steady

494

and (b) unsteady RANS simulations. The ratio of normalized predicted and 22

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observed mean arc-maximum concentrations across a given crosswind (y/H)

496

sampling arc at all downwind distances and vertical levels are also presented

497

in Table 3. In general, both steady and unsteady RANS simulated results of

498

the mean arc-maximum predicted concentrations at each sampling line are

499

in good agreement with the wind-tunnel observations (Figure 8(a)&(b)). In

500

most of the cases, the concentrations are slightly overpredicted where the

501

heights are less than 10 m, but slightly underpredicted at the heights more

502

than 10 m (Table 3). In most of the cases (≈ 88%), the predicted mean

503

arc-maximum concentrations are within a factor of two of the observations

504

in both steady and unsteady RANS solutions (Figure 8, Table 3).

505

5.1.5. Comparison with a previous validation study

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In order to analyze the relative performance of the present CFD model–

507

fluidyn-PANACHE with other CFD models, a comparison of the present

508

simulation results is carried out with the previous simulation results from a

509

CFD model FLACS [1] at 36 locations for the EMU Case A1. Table 4 presents

510

a comparison of FLACS predicted concentrations (F Lro/p = Cobs /Cpred ) [1]

511

with present fluidyn-PANACHE model predicted concentrations (F P ro/p =

512

Cobs /Cpred ) from the unsteady RANS solution at x/H = 1.0 (i.e., one building

513

height downwind of the lee of the building) for six different heights (z/H =

515

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517

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0.16, 0.37, 0.67, 1.02, 1.47, 1.96), and for six different lateral positions (y/H = -2.0 -1.5, -1.0, -0.5, 0.0, 0.5) in this Case A1. It shows that the present model is simulating slightly better than the FLACS. At y/H = -1.5, -1.0, FLACS predicts 33% of points within a factor of two, whereas FAC2 points

518

predicted by the present model are 100% and 67%, respectively (Table 4).

519

Overall, 72% of the FLACS predictions are within a factor of two of the 23

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observations for cross-wind and vertical profiles [1]. Whereas, present model

521

predicts 83% points within a factor of two of all these observations, which

522

is more than the FAC2 value predicted by the FLACS. This comparison

523

shows that the crosswind and vertical profiles were well and slightly better

524

simulated by the fluidyn-PANACHE.

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In summary, based on the above statistical performance analysis, we can

526

conclude that the fluidyn-PANACHE exhibits good performance in both

527

steady and unsteady RANS simulations. It is also observed that in a simple

528

geometry with lesser complexity in the Case A1, steady and unsteady RANS

529

simulations give approximately similar performance against the observations.

530

5.2. EMU project simulation, Case C, Phase I (Case C1)

531

5.2.1. Overall performance

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Both steady and unsteady RANS simulations were performed for this

533

Case C1 of “passive” plume dispersion in a real chemical site of the EMU

534

project. Figure 9 presents isopleths of the steady RANS simulated tracer

535

concentrations at ground level within the site. The plume centreline for the

536

“passive” release is displaced by more than 5H across-stream, even though its

537

nominal emission velocity ratio is Us /UH = 1.5. This is probably due to the

538

shielding effect of the tall rectangular building upstream of the source, which

540

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reduces the background flow velocity around the source, thereby increasing the actual velocity ratio of the emission. The predicted and observed concentrations at all receptors are presented

in form of the scatter plots (Figures 10(a1)&(b1)) and the Q − Q plots (Fig-

543

ures 10(a2)&(b2)), for both steady and unsteady RANS simulations. In

544

scatter plots (Figures 10(a1)&(b1)), it is observed that the simulated con24

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centrations by the CFD model in both type of simulations have good agree-

546

ment with the observations. However, in comparison to the steady solution

547

(Figure 10(a1)), small scatter is observed in unsteady RANS simulated con-

548

centrations (Figure 10(b1)). The simulated higher concentrations in steady

549

solution at the receptors near to the source are close to one-to-one line, how-

550

ever, comparably more scatter is observed for lower concentrations at far

551

away from the source. However, in unsteady solution, even the lower con-

552

centrations at the receptors far away from the release are well predicted and

553

have smaller scatteredness. These trends of predicted concentrations in both

554

solutions are more visible in Q − Q plots in Figures 10(a2)&(b2), which show

555

a comparison of the concentration distributions.

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In order to quantitatively analyze the CFD model performance, the sta-

557

tistical performance measures are separately calculated for (i) all measure-

558

ments, (ii) at each downwind distance (x/H) from the release, (iii) at each

559

cross-stream profile (y/H), (iv) each vertical profile ((z − zg )/H), and (v)

560

ground-level maximum (GL − M AX) concentrations (Table 5). The com-

561

puted statistical indices for both steady and unsteady RANS simulations

562

show that the CFD model is preforming good with the observations. Over-

563

all, smaller value of NMSE (=0.34) in unsteady solution shows the lesser

564

scatter in comparison to the steady simulation (NMSE = 0.43). The un-

566

567

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steady RANS solution predicts 79.5% of points within a factor of two of the observations, which is higher than the number of points (FAC2 = 67.0%) simulated with in a factor of two from the steady simulation (Table 5). The

568

value of FB shows the similar degree of slightly overprediction (FB = -0.10)

569

in both solutions. Good one-to-one agreement are observed by the higher

25

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values of COR and IA from both solutions (Table 5). The smaller values

571

of MG (=1.83) and VG(=29.7) in unsteady solutions show a smaller scatter

572

in comparison to the steady solution (MG = 2.0, VG = 43.8) and it is also

573

visible from the scatter and Q − Q plots in Figures 10(a1)&(b1).

574

5.2.2. Performance at each horizontal line

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The variation of the statistical indices with downwind distance (x/H)

576

from the release is analysed to study the near- and far-field dispersion from

577

both steady and unsteady RANS solutions (Figure 11). Table 5 and Fig-

578

ure 11 show that both CFD solutions overpredict at the receptors close to

579

the source (at x/H = 11.0, steady: FB = -0.16, unsteady: FB = -0.04).

580

However, the overprediction is smaller in unsteady simulation. Positive and

581

close to the ideal values of FB (≤ 0.10) at x/H = 30.0, 45.0, & 67.5 show

582

a good, however, slightly underpredictions at far from the source in both

583

simulations. Close to the release at x/H = 11.0, steady RANS predicts only

584

29.4% of points within a factor of two of the observations, whereas, compara-

585

bly large no. of points (FAC2 = 58.8%) were simulated within a factor of two

586

with the unsteady simulation (Table 5). It is also observed that more num-

587

ber of points are predicted within a factor of two with increasing downwind

588

distances from the source in both types of the solutions (Figure 11, Table

590

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5). The degree of scatter between the predicted and observed concentrations decreases with increasing downwind distances from the source in both CFD simulations. This fact can be clearly seen with the decreasing value of NMSE with increasing downwind distances from the release (Figure 11, Table

593

5). However, NMSE values in unsteady simulations were observed smaller

594

than the steady solution at each horizontal line downwind from the source. 26

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The values of COR and IA are also slightly better in unsteady simulation,

596

especially at far from the source (Figure 11, Table 5).

597

5.2.3. Performance of crosswind and vertical concentrations

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Observed and predicted crosswind concentration profiles are analysed for

599

both steady and unsteady RANS simulations. Figures 12(a)&(b) show the

600

experimental and numerical results for cross-stream profiles at (i) ground

601

level, i.e. at (z − zg )/H = 0.0, and (ii) (z − zg )/H = 3.6, for all four

602

downwind distances from the release. The statistical indicies are also sepa-

603

rately computed at all horizontal and crosswind points at two vertical levels

604

(z − zg )/H = 0.0 & 3.6. For both solutions, the crosswind concentrations are

605

slightly overpredicted at ground-level, while an underprediction is observed

606

at (z − zg )/H = 3.6 (Table 5). For (z − zg )/H = 0.0 & 3.6, steady solution

607

predicts respectively 65.6% and 55.6% points within a factor of two of the

608

observations. Whereas, FAC2 values for the unsteady RANS solutions are

609

62.5% and 80.6% at (z − zg )/H = 0.0 & 3.6, respectively. NMSE values for

610

the unsteady RANS are also smaller than the steady RANS (Table 5).

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The statistical measures are also separately computed for the concen-

612

trations at all downwind distances and verticals receptors at two crosswind

613

points y/H = 5.0 & 6.3 (Table 5). It shows that URANS is predicting compa-

615

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rable better than the steady solution. The unsteady solution predicts 100.0% and 83.3% points within a factor of two at y/H = 5.0 & 6.3, respectively, which are better than FAC2 = 75.0% & 66.7% from the steady solution. The vertical concentration profiles were measured and predicted at y/H =

618

5.0 for x/H = 30.0, and at y/H = 6.3 for x/H = 11.0, 45.0, 67.5 (sensors

619

were deployed on 8 vertical levels at each of these x/H corresponds to these 27

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respective crosswind points (SI Table S2)). The vertical concentration pro-

621

files at near- and far-field are shown in Figures 13(a)&(b) for y/H = 5.0 and

622

y/H = 6.3. In general, the simulated concentrations have overprediction

623

tendency at receptors near to the ground-level, however, they are underpre-

624

dicted at the higher height above the ground surface. The values of these

625

performance measures in Table 5 exhibits that the unsteady RANS solution

626

is performing better than the steady RANS simulation in complex urban

627

geometry of the EMU Case C1.

628

5.2.4. Performance of the ground-level maximum concentrations

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The ratios of observed and modelled ground level maximum (GL − M ax)

630

concentrations at each sampling line are presented in Table 6 for both steady

631

and unsteady RANS solutions. It shows a comparison of the modelled and

632

experimental results of ground-level maximum concentration at each down-

633

wind distance from the source. It exhibits a good agreement of the simulated

634

concentrations with the experimental results. The model shows an overpre-

635

diction (steady: FB = -0.27, unsteady: FB = -0.28) of the ground level

636

maximum concentrations at (z − zg )/H = 0.0. Both solutions predict 100%

637

points within a FAC2 for the GL − M ax concentrations (Table 5). All these

638

simulated concentrations in both steady and unsteady RANS solutions have

640

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642

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629

good one-to-one correlation with the observed concentrations. In summary, predictions from both steady and unsteady RANS solutions

in both EMU cases are in good agreement with observations. Nevertheless, it is not surprising that the differences between steady and unsteady RANS are

643

relatively small as at heart the methods are very similar as they only describe

644

mean statistics in terms of (parameterized) Reynolds stresses. However, a 28

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validation of this theoretical remark with an experimental dataset is always

646

advantageous to avoid the uncertainty in selecting among these solutions,

647

especially in emergencies scenarios, and also for air quality and atmospheric

648

dispersion studies. In fact, a conclusion from the present study to utilize the

649

unsteady RANS instead of steady RANS in more complex urban regions can

650

be useful.

651

6. Conclusions

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This study presents the 3-D CFD simulations for near-field dispersion

653

of toxic and hazardous gases near buildings and in a geometrically complex

654

chemical site. A CFD model fluidyn-PANACHE is validated using two cases

655

of the EMU project: (i) Case A1 and (ii) Case C1. In both EMU cases,

656

the steady RANS solver is used first to get quick results and comparison

657

with the experimental data. This first comparison shows a good agreement

658

between the predicted and measured concentrations. Nevertheless, in order

659

to improve the performance of the solver, the unsteady mode has been tried.

660

A comprehensive statistical analysis of the results is performed to analyze

661

the performance of the CFD model in complex urban or industrial regions.

662

The performance in each EMU case is validated at (i) all receptors (overall)

663

(ii) each sampling line downwind from the release (iii) crosswind and vertical

665

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concentrations, and (iv) maximum concentrations. In both cases of the EMU experiment, predicted concentrations from both

steady and unsteady RANS simulations by fluidyn-PANACHE are in good

667

agreement with observations. Based on the statistical analysis of results, it is

668

observed that in a simple geometry with lesser complexity in the EMU Case 29

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A1, both steady and unsteady RANS solutions give approximately similar

670

performance from the observations. In this Case A1, overall ≈ 68% of points

671

are predicted within a factor of two of observations from both solutions and

672

an overprediction tendency of the concentrations is observed at the heights

673

close to ground surface. The statistical measures are within the acceptable

674

range at each sampling line. In Case A1, at all sampling lines downwind from

675

the release, CFD model predicts more than ≈ 64% points within a factor of

676

two. Crosswind and mean arc-maximum concentrations from both solutions

677

are in good agreement with observations.

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In a comparably more complex and real urban geometry in the EMU

679

Case C1, CFD model with both steady and unsteady solvers shows a good

680

agreement between the simulated and measured concentrations. However, it

681

was observed that the unsteady RANS solution is performing slightly bet-

682

ter than the steady solution. In Case C1, unsteady RANS solution predicts

683

79.5% of points within a factor of two, which is higher than 67.0% points

684

simulated with in a factor of two from the steady simulation. All points

685

(100%) were predicted within a factor of two for the ground-level maximum

686

concentrations in both simulations. NMSE values in unsteady simulation

687

were observed smaller than the steady solution at each horizontal line down-

688

wind from the source. Close to the release at x/H = 11.0, steady solution

690

691

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EP

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689

D

678

predicts only 29.4% of points within a factor of two, whereas, comparably large no. of points (FAC2 = 58.8%) were predicted with the unsteady simulation. For both solutions, crosswind concentrations are slightly overpredicted

692

at ground-level, while a slightly underprediction is observed at higher vertical

693

level above the ground surface. The crosswind and vertical profiles in the

30

ACCEPTED MANUSCRIPT

near- and far-field show the overprediction tendency at the receptors near to

695

the source but slightly underprediction at far away from the release.

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694

The statistical evaluation results with two cases of the EMU experiment

697

show an overall good performance of the CFD model fluidyn-PANACHE in

698

complex urban environments. It shows that the fluidyn-PANACHE is well

699

suited for the air pollution and emergency planning in industrial or urban

700

areas. This study critically examines the real predictive capability of fluidyn-

701

PANACHE in contexts of an accidental or deliberate airborne release in ur-

702

ban regions and strengthen the evidence that it is capable of dealing properly

703

with dispersion phenomena in complex urban or industrial environments.

704

Acknowledgment

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A sincere thanks to Liying Chen, Dr. Malo Le Guellec and Dr. Claude

706

Souprayen from Fluidyn France, for discussions and helping in CFD simu-

707

lation. Authors gratefully acknowledge Fluidyn France for the use of CFD

708

model fluidyn-PANACHE. The EMU dataset was obtained from the CED-

709

VAL at Hamburg University, by the team of Prof. Michael Schatzmann.

710

References

712

713

714

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EP

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D

705

[1] S. R. Hanna, O. R. Hansen, S. Dharmavaram, FLACS CFD air quality model performance evaluation with Kit Fox, MUST, Prairie Grass, and EMU observations, Atmospheric Environment 38 (28) (2004) 4675–4687. doi:http://dx.doi.org/10.1016/j.atmosenv.2004.05.041.

715

31

ACCEPTED MANUSCRIPT

[2] P. Kumar, A.-A. Feiz, P. Ngae, S. K. Singh, J.-P. Issartel, CFD sim-

717

ulation of short-range plume dispersion from a point release in an ur-

718

ban like environment, Atmospheric Environment 122 (2015) 645 – 656.

719

doi:http://dx.doi.org/10.1016/j.atmosenv.2015.10.027. [3] J.

Labovsky,

Jelemensky,

using

dynamic

CFD

simulations

boundary

of

conditions,

ammo-

722

nia

Process

723

Safety and Environmental Protection 88 (4) (2010) 243 – 252.

724

doi:http://dx.doi.org/10.1016/j.psep.2010.03.001.

M AN U

dispersion

L.

SC

720 721

RI PT

716

[4] P. Gousseau, B. Blocken, T. Stathopoulos, G. van Heijst, CFD sim-

727

ulation of near-field pollutant dispersion on a high-resolution grid:

728

A case study by LES and RANS for a building group in down-

729

town montreal, Atmospheric Environment 45 (2) (2011) 428 – 438.

730

doi:http://dx.doi.org/10.1016/j.atmosenv.2010.09.065.

TE

D

725 726

[5] C. Gromke, B. Blocken, Influence of avenue-trees on air qual-

733

ity at the urban neighborhood scale. Part I: Quality assur-

734

ance studies and turbulent schmidt number analysis for RANS

735

CFD simulations, Environmental Pollution 196 (2015) 214–223.

737 738

739

740

741

AC C

736

EP

731 732

doi:http://dx.doi.org/10.1016/j.envpol.2014.10.016.

[6] Prediction of high concentrations and concentration distribution of a continuous point source release in a semi-idealized urban canopy using CFD-RANS modeling, Atmospheric Environment 100 48–56, year =.

[7] J. Liu, J. Niu, CFD simulation of the wind environment around 32

ACCEPTED MANUSCRIPT

an isolated high-rise building:

An evaluation of SRANS, LES

743

and DES models, Building and Environment 96 (2016) 91 – 106.

744

doi:http://dx.doi.org/10.1016/j.buildenv.2015.11.007.

RI PT

742

[8] P.-Y. Cui, Z. Li, W.-Q. Tao, Buoyancy flows and pollutant dis-

747

persion through different scale urban areas: CFD simulations and

748

wind-tunnel measurements, Building and Environment (2016) –

749

doi:http://dx.doi.org/10.1016/j.buildenv.2016.04.028.

SC

745 746

[9] Y. Tominaga, T. Stathopoulos, CFD simulation of near-field pollu-

752

tant dispersion in the urban environment: A review of current mod-

753

eling techniques, Atmospheric Environment 79 (0) (2013) 716 – 730.

754

doi:http://dx.doi.org/10.1016/j.atmosenv.2013.07.028.

M AN U

750 751

[10] S. D. Sabatino, R. Buccolieri, P. Salizzoni, Recent advancements in nu-

757

merical modelling of flow and dispersion in urban areas: a short review,

758

International Journal of Environment and Pollution 52 (3-4) (2013) 172–

759

191. doi:10.1504/IJEP.2013.058454.

TE

D

755 756

[11] B. Blocken, 50 years of computational wind engineering: Past, present

762

and future, Journal of Wind Engineering and Industrial Aerodynamics

763

129 (2014) 69 – 102. doi:http://dx.doi.org/10.1016/j.jweia.2014.03.008.

766

767

AC C

764 765

EP

760 761

[12] B. Blocken, Computational fluid dynamics for urban physics: Importance, scales, possibilities, limitations and ten tips and tricks towards accurate and reliable simulations, Building and Environment 91

768

(2015) 219–245, fifty Year Anniversary for Building and Environment.

769

doi:http://dx.doi.org/10.1016/j.buildenv.2015.02.015.

770

33

ACCEPTED MANUSCRIPT

[13] R. Meroney, R. Ohba, B. Leitl, H. Kondo, D. Grawe, Y. Tominaga,

772

Review of CFD guidelines for dispersion modeling, Fluids 1 (2) (2016)

773

14. doi:10.3390/fluids1020014.

RI PT

771

[14] M. Lateb, R. Meroney, M. Yataghene, H. Fellouah, F. Saleh, M. Bo-

776

ufadel, On the use of numerical modelling for near-field pollutant dis-

777

persion in urban environments – a review, Environmental Pollution 208,

778

Part A (2016) 271 – 283, special Issue: Urban Health and Wellbeing.

779

doi:http://dx.doi.org/10.1016/j.envpol.2015.07.039. [15] Y.

Tominaga, of

T.

M AN U

780 781

SC

774 775

Stathopoulos,

near-field

questions

782

modeling

783

vironment,

784

doi:http://dx.doi.org/10.1016/j.buildenv.2016.06.027.

Building

pollutant

Ten

and

dispersion

Environment

in

concerning

the

(2016)

built –In

en-

press.

[16] G. Antonioni, S. Burkhart, J. Burman, A. Dejoan, A. Fusco, R. Gaas-

787

beek, T. Gjesdal, A. Jppinen, K. Riikonen, P. Morra, O. Parmhed,

788

J. Santiago, Comparison of CFD and operational dispersion models in

789

an urban-like environment, Atmospheric Environment 47 (2012) 365 –

790

372. doi:http://dx.doi.org/10.1016/j.atmosenv.2011.10.053.

793

794

795

796 797

798

TE

EP

[17] C. Yuan, E. Ng, L. K. Norford, Improving air quality in high-density

AC C

791 792

D

785 786

cities by understanding the relationship between air pollutant dispersion and urban morphologies, Building and Environment 71 (2014) 245 – 258. doi:http://dx.doi.org/10.1016/j.buildenv.2013.10.008.

[18] COST ES1006, Best Practice Guidelines, COST Action ES1006, no. ISBN: 987-3-9817334-0-2, 2015.

34

ACCEPTED MANUSCRIPT

[19] I. V. Kovalets, S. Andronopoulos, A. G. Venetsanos, J. G. Bartzis, Iden-

800

tification of strength and location of stationary point source of atmo-

801

spheric pollutant in urban conditions using computational fluid dynam-

802

ics model, Mathematics and Computers in Simulation 82 (2) (2011) 244

803

– 257. doi:http://dx.doi.org/10.1016/j.matcom.2011.07.002.

RI PT

799

[20] P. Kumar, A.-A. Feiz, S. K. Singh, P. Ngae, G. Turbelin, Reconstruction

806

of an atmospheric tracer source in an urban-like environment, Journal

807

of Geophysical Research: Atmospheres 120 (24) (2015) 12589–12604.

808

doi:10.1002/2015JD024110.

M AN U

SC

804 805

[21] P. Kumar, S. K. Singh, A.-A. Feiz, P. Ngae, An urban scale inverse

811

modelling for retrieving unknown elevated emissions with building-

812

resolving simulations, Atmospheric Environment 140 (2016) 135 – 146.

813

doi:http://dx.doi.org/10.1016/j.atmosenv.2016.05.050.

816

[22] M. J. Brown, Urban dispersionchallenges for fast response modeling, in:

TE

814 815

D

809 810

Fifth AMS Symposium on the Urban Environment, 2004. [23] L. Margheri, P. Sagaut, An uncertainty quantification analysis in

818

a simplified problem of urban pollutant dispersion by means of

819

anova-pod/kriging-based response surfaces, in:

820

Joint US-European Fluids Engineering Division Summer Meeting and

822

823

ASME 2014 4th

AC C

821

EP

817

12th International Conference on Nanochannels, Microchannels, and Minichannels, American Society of Mechanical Engineers, 2014, pp. V01DT28A004–V01DT28A004.

824

[24] J. Franke, A. Hellsten, H. Schl¨ unzen, B. Carissimo, Best practice guide-

825

line for the CFD simulation of flows in the urban environment. COST 35

ACCEPTED MANUSCRIPT

Action 732: Quality Assurance and Improvement of Microscale Meteo-

827

rological Models (2007) , Hamburg, Germany.

RI PT

826

[25] R. C. Hall, Evaluation of Model Uncertainty (EMU)-CFD modelling

829

of near-field atmospheric dispersion. Project EMU Final Report, Tech.

830

Rep. WS Atkins Doc No. WSA/AM5017/R7, European Commission,

831

WS Atkins, Woodcote Grove, Ashley Road, Epsom, Surrey KT18 5BW,

832

UK (1997).

M AN U

SC

828

833

[26] A. Mazzoldi, T. Hill, J. J. Colls, CFD and gaussian atmospheric disper-

834

sion models: A comparison for leak from carbon dioxide transportation

835

and storage facilities, Atmospheric Environment 42 (34) (2008) 8046 –

836

8054. doi:http://dx.doi.org/10.1016/j.atmosenv.2008.06.038.

839

[27] Fluidyn-PANACHE, User Manual, FLUIDYN France / TRANSOFT International, version 4.0.7 Edition (2010).

D

837 838

[28] R. Hill, A. Arnott, P. Hayden, T. Lawton, A. Robins, T. Parker, Eval-

841

uation of cfd model predictions of local dispersion from an area source

842

on a complex industrial site, International Journal of Environment and

843

Pollution 44 (1-4) (2011) 173–181.

845

846

847

EP

[29] J. H. Ferziger, M. Peric, Computational Methods for Fluid Dynamics,

AC C

844

TE

840

Springer Berlin Heidelberg, 2002. doi:10.1007/978-3-642-56026-2.

[30] B. Launder, Turbulence modelling of buoyancy-affected flows, in: Singapore Turbulence Colloquium, 2004.

36

ACCEPTED MANUSCRIPT

[31] K. Hanjalic, Turbulence and transport phenomena: Modelling and simu-

849

lation, in: Turbulence Modeling and Simulation (TMS) Workshop, Tech-

850

nische Universitt Darmstadt, 2005.

RI PT

848

[32] J. Han, S. P. Arya, S. Shen, Y.-L. Lin, An estimation of turbulent

852

kinetic energy and energy dissipation rate based on atmospheric bound-

853

ary layer similarity theory, no. NASA/CR-2000-210298, National Aero-

854

nautics and Space Administration, Langley Research Center, Hampton,

855

Virginia 23681-2199, 2000.

M AN U

SC

851

856

[33] C. V. Jayatilleke, The influence of prandtl number and roughness on

857

the resistance of the laminar sublayer to momentum and heat transfer,

858

Progress in Heat and Mass Transfer 1 (1969) 193–329. [34] J. P. Van Doormaal, G. D. Raithby, Enhancements of the simple method

860

for predicting incompressible fluid flows, Numerical Heat Transfer 7 (2)

861

(1984) 147–163. doi:10.1080/01495728408961817.

TE

D

859

[35] I. R. Cowan, A. G. Robins, I. P. Castro, Project EMU Experimental

864

data, Case A, Release 1; Case B, Release 3 and Case C, Release 1, Tech.

865

rep., EnFlo Research Centre, Department of Mechanical Engineering,

866

University of Surrey, Guildford, Surrey GU2 5XH, UK (1995).

868

869

AC C

867

EP

862 863

[36] I. R. Cowan, Project EMU Simulations, Stage C, Tech. rep., EnFlo Research Centre, Department of Mechanical Engineering, University of Surrey, Guildford, Surrey GU2 5XH, UK (1996).

870

[37] J. C. Chang, S. R. Hanna, Air quality model performance evalua-

871

tion, Meteorology and Atmospheric Physics 87 (1-3) (2004) 167–196. 37

ACCEPTED MANUSCRIPT

doi:10.1007/s00703-003-0070-7.

872

[38] E. Yee, B.-C. Wang, F.-S. Lien, Probabilistic model for concentra-

875

tion fluctuations in compact-source plumes in an urban environment,

876

Boundary-layer meteorology 130 (2) (2009) 169–208.

RI PT

873 874

[39] G. C. Efthimiou, S. Andronopoulos, I. Tolias, A. Venetsanos, Prediction

878

of the upper tail of concentration distributions of a continuous point

879

source release in urban environments, Environmental Fluid Mechanics

880

(2016) 1–23doi:10.1007/s10652-016-9455-2.

M AN U

SC

877

[40] S. R. Hanna, G. A. Briggs, R. P. Hosker Jr, Handbook on atmospheric

882

diffusion, Tech. Rep. DOE/TIC-11223, National Oceanic and Atmo-

883

spheric Administration, Oak Ridge, TN (USA). Atmospheric Turbulence

884

and Diffusion Lab. (1982).

887

1 2

888 889 890

892 893 894

3

Source data for the EMU project: Case A1 and Case C1. . . . 41 Statistical performance measures of the concentrations from both steady and unsteady RANS simulations for all and at each downwind distance of the EMU project Case A1. FAC2 is given in percentage (%). . . . . . . . . . . . . . . . . . . . . 41 Comparison of the normalized observed and predicted mean arc-maximum concentrations (ro/p = Cobs /Cpred ) across a given crosswind (y/H) sampling arc at a given downwind sampling line (x/H) and vertical level (z/H). . . . . . . . . . . . . . . . 42

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899 900 901

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903 904 905

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List of Figures 1

910 911

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912 913 914

3

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917 918 919

5

921 922 923 924 925 926 927 928

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Layout and the dimension of the computational domain considered for the EMU Case A1. . . . . . . . . . . . . . . . . The site features and dimension of the computational domain considered for the EMU project Case C1. The black lines are terrain height contours close to the site. . . . . . . . . . . . Ground level concentration contours from steady RANS solution for the EMU Case A1. . . . . . . . . . . . . . . . . . . Zoom on velocity vectors computed from steady RANS solution in vertical plane (x−z) behind the building in EMU Case A1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scatter (first row) and Q-Q (second row) plots between the normalized predicted and observed concentrations at all vertical levels of each sampling line for the EMU Case A1. First and second columns are respectively for steady and unsteady RANS simulated concentrations. The middle solid line is oneto-one line between observed and simulated concentrations whereas the dotted lines correspond to factor of two. . . . . The statistical performance measures (a) NMSE, (b) FB, (c) COR, and (d) FAC2 with increasing downwind distances from the source (at each horizontal lines) in EMU Case A1 . . .

D

906

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902

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A Comparison of FLACS predicted concentrations (F Lro/p = Cobs /Cpred ) [1] with present fluidyn-PANACHE predicted concentrations (F P ro/p = Cobs /Cpred ) from the unsteady RANS solution at x/H = 1.0 for six different heights (z/H), and for six different lateral positions (y/H) in the EMU Case A1. y/H = 0.0 is along the center of the lee building wall and y/H is positive towards the longer side of the building. . . . . . . 43 Statistical performance measures of the concentrations from both steady and unsteady RANS simulations for all and at each downwind distance, and also for other profiles of the EMU project Case C1. FAC2 is given in percentage (%). . . . . . . 44 Comparison of modeled and experimental results (ro/p = Cobs /Cpred ) of ground-level maximum concentrations (Case C1). . . . . . 44

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933 934

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9

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940 941 942 943 944

11

945 946 947

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948 949 950 951 952

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Comparison of the measured and simulated normalized concentrations for cross-stream profiles at two vertical levels for x/H = 1.0, 2.0, 5.0, 10.0 in Case A1. First and second columns are respectively for steady and unsteady RANS simulated concentrations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Scatter plot between the normalized predicted and observed mean arc-maximum concentrations at all vertical levels of each sampling line for the EMU Case A1. . . . . . . . . . . . . . . 51 Ground level concentration contours computed from steady RANS solution for the EMU Case C1. . . . . . . . . . . . . . 52 Scatter (first row) and Q-Q (second row) plots between the normalized predicted and observed concentrations at all vertical levels of each sampling line for the EMU Case C1. First and second columns are respectively for steady and unsteady CFD simulated concentrations. . . . . . . . . . . . . . . . . . 53 The statistical performance measures (a) NMSE, (b) FB, (c) COR, and (d) FAC2 with increasing downwind distances from the source (at each horizontal lines) in EMU Case C1 . . . . 54 Comparison of measured and simulated normalized concentrations for cross-stream profiles at two vertical levels ((z − zg )/H = 0.0, 3.6) for four downwind distances (x/H = 11.0, 30.0, 45.0, 67.5) from the releases in Case C1. First and second columns are respectively for steady and unsteady RANS simulated concentrations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Comparison of measured and simulated concentrations for vertical profiles in the near-field (left) and in the far-field (right) (Case C1). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

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C2 H4 2.96%

N2 97.04% 20 2.5 23.68 25 Continuous

Type of source Exit velocity (m/s) Chemical species Composition

Height of source (m) Mass flux (Kg/s) Temperature (o C) Release direction

Point 7.5

C2 H4 77.3%

N2 22.7%

2.0 5.46 15 Horizontal 327.5o

SC

Area 1.0

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Type of source Exit velocity (m/s) Chemical species Fraction volume Source surface (As ) (m2 ) Height of source’s center (m) Mass flux (Kg/s) Temperature (o C) Release duration (s)

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Table 1: Source data for the EMU project: Case A1 and Case C1. Case A1 Case C1

Table 2: Statistical performance measures of the concentrations from both steady and unsteady RANS simulations for all and at each downwind distance of the EMU project Case A1. FAC2 is given in percentage (%).

All x/H = 0.5 x/H = 1.0 x/H = 2.0 x/H = 5.0 x/H = 10.0 All x/H = 0.5 x/H = 1.0 x/H = 2.0 x/H = 5.0 x/H = 10.0

NMSE

FB

COR

FAC2

MG

VG

IA

0.95 0.95 0.83 0.52 0.17 0.16 0.92 0.91 0.81 0.51 0.17 0.16

-0.13 -0.21 -0.18 -0.09 0.09 0.17 -0.12 -0.20 -0.17 -0.08 0.10 0.18

0.87 0.85 0.84 0.87 0.93 0.95 0.87 0.86 0.84 0.87 0.93 0.95

67.58 63.89 72.92 70.83 66.67 65.00 67.97 65.28 70.83 70.83 68.75 65.00

1.31 1.17 1.22 1.29 1.39 1.64 1.31 1.17 1.22 1.28 1.39 1.62

1.93 2.54 1.68 1.59 1.74 1.96 1.88 2.45 1.65 1.57 1.71 1.89

0.85 0.83 0.81 0.85 0.95 0.97 0.86 0.84 0.81 0.85 0.95 0.97

EP

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Sim. type

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unsteady

41

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Table 3: Comparison of the normalized observed and predicted mean arc-maximum concentrations (ro/p = Cobs /Cpred ) across a given crosswind (y/H) sampling arc at a given

z/H steady (ro/p ) unsteady (ro/p )

0.13 0.45 0.46

0.33 0.48 0.48

0.50 0.54 0.55

0.66 0.50 0.51

1.03 0.69 0.68

1.37 1.23 1.19

x/H = 1.0

z/H steady (ro/p ) unsteady (ro/p )

0.16 0.44 0.45

0.37 0.48 0.48

0.67 0.68 0.68

1.02 0.84 0.84

1.47 1.62 1.58

1.99 1.78 1.72

x/H = 2.0

z/H steady (ro/p ) unsteady (ro/p )

0.11 0.50 0.50

0.34 0.57 0.58

0.66 0.67 0.68

1.03 1.03 1.02

1.51 2.55 2.50

1.99 1.84 1.79

x/H = 5.0

z/H steady (ro/p ) unsteady (ro/p )

0.11 0.69 0.70

0.34 0.74 0.75

0.67 0.88 0.88

0.98 1.02 1.03

1.49 1.48 1.47

1.97 1.71 1.69

0.16 0.74 0.76

0.66 0.87 0.88

0.98 1.03 1.05

1.83 1.30 1.30

3.00 1.30 1.26

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x/H = 0.5

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downwind sampling line (x/H) and vertical level (z/H).

z/H steady (ro/p ) unsteady (ro/p )

AC C

x/H = 10.0

42

1.45 1.62 1.56

1.69 1.56 1.50

1.93 1.64 1.58

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Table 4: A Comparison of FLACS predicted concentrations (F Lro/p = Cobs /Cpred ) [1] with present fluidyn-PANACHE predicted concentrations (F P ro/p = Cobs /Cpred ) from

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the unsteady RANS solution at x/H = 1.0 for six different heights (z/H), and for six different lateral positions (y/H) in the EMU Case A1. y/H = 0.0 is along the center of

0.16

0.37

0.67

1.02

1.47

1.99

FAC2

y/H = −2.0

F Lro/p F P ro/p

1.40 1.25

1.82 1.48

2.08 1.31

1.47 0.83

1.00 0.49

1.25 0.69

0.83 1.00

y/H = −1.5

F Lro/p F P ro/p

3.06 0.51

3.17 0.82

2.27 1.10

3.16 1.39

1.43 1.12

1.11 0.51

0.33 1.00

y/H = −1.0

F Lro/p F P ro/p

2.44 0.34

2.59 0.40

2.17 0.77

1.91 1.06

2.17 1.40

0.65 0.70

0.33 0.67

F Lro/p F P ro/p

1.66 0.55

1.66 0.61

1.75 0.64

1.71 0.78

1.88 1.37

0.63 1.24

1.00 1.00

F Lro/p F P ro/p

0.67 1.22

0.94 1.13

1.00 0.77

0.93 0.85

0.85 1.60

0.83 1.88

1.00 1.00

F Lro/p F P ro/p

0.50 2.74

0.60 2.24

0.61 1.38

0.59 1.74

0.57 2.67

0.61 2.78

1.00 0.33

y/H = −0.5

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y/H = 0.0

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z/H

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the lee building wall and y/H is positive towards the longer side of the building.

AC C

y/H = 0.5

43

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Table 5: Statistical performance measures of the concentrations from both steady and

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unsteady RANS simulations for all and at each downwind distance, and also for other profiles of the EMU project Case C1. FAC2 is given in percentage (%). COR

FAC2

0.43 0.77 0.42 0.36 0.16 0.59 0.62 0.21 0.10 0.21 0.34 0.34 0.22 0.20 0.07 0.29 0.22 0.14 0.08 0.21

-0.10 -0.16 0.06 0.04 0.04 -0.24 0.46 -0.14 0.12 -0.27 -0.10 -0.04 0.06 0.10 0.06 -0.08 0.25 -0.15 -0.04 -0.28

0.97 0.97 0.88 0.82 0.84 0.97 0.77 0.95 0.97 0.98 0.99 0.99 0.93 0.90 0.93 0.99 0.91 0.96 0.99 0.99

67.0 29.4 64.7 64.7 82.4 65.6 55.6 75.0 66.7 100.0 79.5 58.8 70.6 64.7 94.1 62.5 80.6 100.0 83.3 100.0

EP

TE

D

unsteady

All x/H = 11.0 x/H = 30.0 x/H = 45.0 x/H = 67.5 (z − zg )/H = 0.0 (z − zg )/H = 3.6 y/H = 5.0 y/H = 6.3 GL − M ax All x/H = 11.0 x/H = 30.0 x/H = 45.0 x/H = 67.5 (z − zg )/H = 0.0 (z − zg )/H = 3.6 y/H = 5.0 y/H = 6.3 GL − M ax

FB

MG

VG

IA

2.00 7.15 4.00 1.25 1.17 3.11 2.12 1.32 1.81 0.85 1.83 6.18 3.45 1.36 1.18 1.53 1.52 1.04 1.48 0.84

43.8 62781 69340 1.74 1.25 15411 9.75 1.48 3.34 1.07 29.7 24442 24566 2.34 1.22 3.75 3.75 1.09 2.01 1.06

0.96 0.94 0.91 0.88 0.91 0.94 0.75 0.91 0.98 0.94 0.97 0.97 0.95 0.93 0.96 0.90 0.90 0.94 0.99 0.94

SC

steady

NMSE

M AN U

Sim. type

AC C

Table 6: Comparison of modeled and experimental results (ro/p = Cobs /Cpred ) of ground-

level maximum concentrations (Case C1).

x/H y/H steady (ro/p ) unsteady (ro/p )

5.5 5.0 0.77 0.73

11.0 5.6 0.76 0.73

11.0 5.1 0.58 0.63

19.5 6.0 0.65 0.67

44

30.0 6.3 0.73 0.76

30.0 7.0 0.91 0.91

37.5 7.0 1.01 1.01

45.0 6.8 0.88 0.84

45.0 7.0 1.02 0.97

56.3 7.0 1.02 0.98

67.5 6.8 0.93 0.91

67.5 7.0 1.08 1.04

M AN U

SC

RI PT

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Figure 1: Layout and the dimension of the computational domain considered for the EMU

AC C

EP

TE

D

Case A1.

45

AC C

EP

TE

D

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SC

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Figure 2: The site features and dimension of the computational domain considered for the EMU project Case C1. The black lines are terrain height contours close to the site.

46

0

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C2H4 (kg/m3) 0.002

0.001

0.003

0.004

D

Figure 3: Ground level concentration contours from steady RANS solution for the EMU

AC C

EP

TE

Case A1.

Figure 4: Zoom on velocity vectors computed from steady RANS solution in vertical plane (x − z) behind the building in EMU Case A1.

47

AC C

EP

TE

D

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SC

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Figure 5: Scatter (first row) and Q-Q (second row) plots between the normalized predicted and observed concentrations at all vertical levels of each sampling line for the EMU Case A1. First and second columns are respectively for steady and unsteady RANS simulated concentrations. The middle solid line is one-to-one line between observed and simulated concentrations whereas the dotted lines correspond to factor of two.

48

1.2

0.2

Steady Unsteady

(a)

SC

FB

0.0 0.6

M AN U

-0.1 0.3

Steady Unsteady

(b)

0.1

0.9

-0.2 -0.3

0.0 0.5

1.0

1.00

2.0 x/H

5.0

TE

COR

0.92

0.80

1.0

2.0 x/H

5.0

5.0

10.0 Steady Unsteady

(d)

70 68 66 64 62

10.0

AC C

0.5

EP

0.84

2.0 x/H

72

D

0.96

1.0

74

Steady Unsteady

(c)

0.88

0.5

10.0

FAC2

NMSE

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0.5

1.0

2.0 x/H

5.0

10.0

Figure 6: The statistical performance measures (a) NMSE, (b) FB, (c) COR, and (d) FAC2 with increasing downwind distances from the source (at each horizontal lines) in EMU Case A1

49

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Unsteady

Steady z/H = 0.16 z/H = 0.16 z/H = 1.02 z/H = 1.02

obs pred obs pred

2.0

1.5

1.0

1.5

1.0

SC

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

-3.0

1.0

(a2) EMU case A1: x/H = 2.0

z/H = 0.34 z/H = 0.34 z/H = 1.51 z/H = 1.51

1.4

obs pred obs pred

-2.0

-1.5

1.2

1.0 0.8 0.6

-1.0

-0.5

0.0

0.5

1.0

0.0

0.5

1.0

(b2) EMU case A1: x/H = 2.0

1.4

Normalized concentration

1.2

-2.5

1.6

z/H = 0.34 z/H = 0.34 z/H = 1.51 z/H = 1.51

obs pred obs pred

-2.0

-1.5

M AN U

1.6

1.0 0.8 0.6 0.4

0.4

0.2

0.2

0.0

0.0 -3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

-3.0

1.0

0.7

z/H = 0.34 z/H = 0.34 z/H = 1.49 z/H = 1.49

obs pred obs pred

TE

0.6

D

(a3) EMU case A1: x/H = 5.0

0.5 0.4

EP

0.3 0.2 0.1 0.0 -3

-2

-1

0.3

0.7

z/H = 0.34 z/H = 0.34 z/H = 1.49 z/H = 1.49

0.6

-0.5

obs pred obs pred

0.5 0.4 0.3 0.2

-4

2

obs pred obs pred

-2

z/H = 0.16 z/H = 0.16 z/H = 1.83 z/H = 1.83

0.3

0.2

-3

-1

0

1

2

(b4) EMU case A1: x/H = 10.0

0.4

Normalized concentration

z/H = 0.16 z/H = 0.16 z/H = 1.83 z/H = 1.83

-1.0

(b3) EMU case A1: x/H = 5.0

0.0 1

(a4) EMU case A1: x/H = 10.0

0.4

-2.5

0.1

0

AC C

-4

Normalized concentration

Normalized concentration

2.0

0.0

0.0

Normalized concentration

obs pred obs pred

0.5

0.5

Normalized concentration

z/H = 0.16 z/H = 0.16 z/H = 1.02 z/H = 1.02

2.5

Normalized concentration

Normalized concentration

2.5

(b1) EMU case A1: x/H = 1.0

3.0

RI PT

(a1) EMU case A1: x/H = 1.0

3.0

obs pred obs pred

0.2

0.1

0.1

0.0

0.0 -6

-4

-2

0 y/H

2

-6

4

50

-4

-2

0

2

y/H

Figure 7: Comparison of the measured and simulated normalized concentrations for crossstream profiles at two vertical levels for x/H = 1.0, 2.0, 5.0, 10.0 in Case A1. First and second columns are respectively for steady and unsteady RANS simulated concentrations.

4

Steady x/H = 0.50 x/H = 1.00 x/H = 2.00 x/H = 5.00 x/H = 10.0

10

(a)

Unsteady

x/H = 0.50 x/H = 1.00 x/H = 2.00 x/H = 5.00 x/H = 10.0

(b)

M AN U

1

1

0.1

0.1

TE

D

0.1

1

0.01 0.01

EP

Normalized observed concentration (Cobs/Cn)

10

0.1

1

Normalized observed concentration (Cobs/Cn)

Figure 8: Scatter plot between the normalized predicted and observed mean arc-maximum concentrations at all vertical levels of each sampling line for the EMU Case A1.

AC C

0.01 0.01

Normalized predicted concentration (Cpred/Cn)

Normalized predicted concentration (Cpred/Cn)

10

SC

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51

10

AC C

EP

TE

D

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SC

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0

0.005

C2H4 (kg/m3) 0.01

0.015

0.02

Figure 9: Ground level concentration contours computed from steady RANS solution for the EMU Case C1.

52

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Steady

Unsteady

0.1

Normalized observed concentration (Cobs/Cn)

0.001 0.001

Normalized predicted concentration (Cpred/Cn)

0.1

AC C 0.01

0.1

Normalized observed concentration (Cobs/Cn)

0.01

0.1

1

0.1

1

Normalized observed concentration (Cobs/Cn)

x/H = 11.0 x/H = 30.0 x/H = 45.0 x/H = 67.5

(b2)

0.1

0.01

EP

0.01

0.001 0.001

1

1

(a2)

x/H = 11.0 x/H = 30.0 x/H = 45.0 x/H = 67.5

(b1)

M AN U

0.1

TE

Normalized predicted concentration (Cpred/Cn)

1

0.01

D

0.001 0.001

0.1

0.01

0.01

x/H = 11.0 x/H = 30.0 x/H = 45.0 x/H = 67.5

SC

(a1)

x/H = 11.0 x/H = 30.0 x/H = 45.0 x/H = 67.5

RI PT

1

Normalized predicted concentration (Cpred/Cn)

Normalized predicted concentration (Cpred/Cn)

1

1

0.001 0.001

0.01

Normalized observed concentration (Cobs/Cn)

Figure 10: Scatter (first row) and Q-Q (second row) plots between the normalized predicted and observed concentrations at all vertical levels of each sampling line for the EMU Case C1. First and second columns are respectively for steady and unsteady CFD simulated concentrations.

53

0.9

0.2

Steady Unsteady

(a)

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M AN U

FB

NMSE

0.3

0.0

SC

0.1 0.6

Steady Unsteady

(b)

-0.1

0.0 11.0

30.0

45.0 x/H

1.00

30.0

100

Steady Unsteady

(c)

0.96

45.0

67.5

x/H Steady Unsteady

(d)

TE

COR

0.88

EP

0.84

30.0

45.0

FAC2

D

80

0.92

0.80 11.0

-0.2 11.0

67.5

60

40

67.5

20 11.0

30.0

45.0

67.5

x/H

AC C

x/H

Figure 11: The statistical performance measures (a) NMSE, (b) FB, (c) COR, and (d) FAC2 with increasing downwind distances from the source (at each horizontal lines) in EMU Case C1

54

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Steady 0.16

obs pred obs pred

0.08

0.04

-5

0 5 10 15 20 (a2) EMU case C1: (z-zg)/H = 0.0

0.040

0.04

0.032

0.024

0.016

0.008

-10

obs pred obs pred

-5

obs pred obs pred

0.032

0 5 10 15 20 (b2) EMU case C1: (z-zg)/H = 0.0

M AN U

x/H = 45.0 x/H = 45.0 x/H = 67.5 x/H = 67.5

25

x/H = 45.0 x/H = 45.0 x/H = 67.5 x/H = 67.5

0.024

obs pred obs pred

0.016

0.008

0.000

0.000 -10

-5

0

5

10

15

(a3) EMU case C1: (z-zg)/H = 3.6

0.025

obs pred obs pred

EP

0.010

0.005

0.000 -4

-2

0

2

4

6

8

10

5

10

15

20

x/H = 11.0 x/H = 11.0 x/H = 30.0 x/H = 30.0

0.020

obs pred obs pred

0.015

0.010

0.000 12

14

AC C

0.012

0

(b3) EMU case C1: (z-zg)/H = 3.6

0.005

(a4) EMU case C1: (z-zg)/H = 3.6 x/H = 45.0 x/H = 45.0 x/H = 67.5 x/H = 67.5

0.016

Normalized concentration

TE

0.015

-5

0.025

D

x/H = 11.0 x/H = 11.0 x/H = 30.0 x/H = 30.0

0.020

-10

20

0.008

0.004

-4

16 obs pred obs pred

-2

0

2

4

6

8

10

12

14

(b4) EMU case C1: (z-zg)/H = 3.6 x/H = 45.0 x/H = 45.0 x/H = 67.5 x/H = 67.5

0.016

Normalized concentration

Normalized concentration

0.08

25

Normalized concentration

-10

Normalized concentration

0.12

0.00

0.00

Normalized concentration

x/H = 11.0 x/H = 11.0 x/H = 30.0 x/H = 30.0

RI PT

0.12

(b1) EMU case C1: (z-zg)/H = 0.0

SC

x/H = 11.0 x/H = 11.0 x/H = 30.0 x/H = 30.0

Normalized concentration

0.16

Normalized concentration

Unsteady

(a1) EMU case C1: (z-zg)/H = 0.0

0.012

16 obs pred obs pred

0.008

0.004

0.000

0.000 -2

0

2

4

6

y/H

8

10

12

14

16

-2

0

2

4

6

y/H

8

10

12

14

55

Figure 12: Comparison of measured and simulated normalized concentrations for crossstream profiles at two vertical levels ((z − zg )/H = 0.0, 3.6) for four downwind distances (x/H = 11.0, 30.0, 45.0, 67.5) from the releases in Case C1. First and second columns are respectively for steady and unsteady RANS simulated concentrations.

16

RI PT

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Steady

0.02

0.04

0.06

0.08

normalized concentration

10

EP

4

2

0.005

0.010

0.015

0.020

0.02

0.04

0.06

0.08

0.10

0.12

normalized concentration

D

TE

6

0 0.000

0 0.00

0.10

(a2) EMU case C1: vertical concentration (far-field) x/H = 45.0 obs x/H = 45.0 pred x/H = 67.5 obs x/H = 67.5 pred

AC C

(z-zg)/H

8

4

2

2

10

6

(a1) EMU case C1: vertical concentration (near-field) x/H = 11.0 obs x/H = 11.0 pred x/H = 30.0 obs x/H = 30.0 pred

SC

4

0 0.00

8

(z-zg)/H

6

10

M AN U

(z-zg)/H

8

(a1) EMU case C1: vertical concentration (near-field) x/H = 11.0 obs x/H = 11.0 pred x/H = 30.0 obs x/H = 30.0 pred

(z-zg)/H

10

Unsteady

8

(a2) EMU case C1: vertical concentration (far-field) x/H = 45.0 obs x/H = 45.0 pred x/H = 67.5 obs x/H = 67.5 pred

6

4

2

0.025

0.030

0 0.000

0.005

normalized concentration

0.010

0.015

0.020

0.025

normalized concentration

Figure 13: Comparison of measured and simulated concentrations for vertical profiles in the near-field (left) and in the far-field (right) (Case C1).

56

0.030

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TE D

M AN U

SC

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% !

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AC C

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