Modelling of ammonia emissions from naturally ventilated livestock buildings: Part 2, air change modelling

b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 4 6 e2 5 8

Available online at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/issn/15375110

Special Issue: Emissions from naturally ventilated livestock buildings Review

Modelling of ammonia emissions from naturally ventilated livestock buildings: Part 2, air change modelling5 Bjarne Bjerg a,*, Paolo Liberati b, Alvaro Marucci c, Guoqiang Zhang d, Thomas Banhazi e, Thomas Bartzanas f, Giovanni Cascone g, In-Bok Lee h, Tomas Norton i a

Department of Large Animal Sciences, University of Copenhagen, Groennegaardsvej 2, DK1870 Frederiksberg C, Denmark b Department of Agricultural and Food Sciences (DISTAL), University of Bologna, Viale G. Fanin 50, 40127 Bologna, Italy c Department of Science and Technology for Agriculture, Forestry, Nature and Energy (DAFNE), Tuscia University, Via San Camillo de Lellis, s.n.c. e 01100 Viterbo, Italy d Department of Engineering, Aarhus University, Blichers Alle´, DK8830 Tjele, Denmark e National Centre for Engineering in Agriculture, University of Southern, Queensland, West Street, Toowoomba QLD 4350, Australia f Institute of Technology and Management of Agricultural Ecosystems, Center for Reseach and Technology-Thessaly, 1st Industrial Area of Volos, 38500 Volos, Greece g Department of Agricultural Engineering, University of Catania, Via S. Sofia, 100, 95133 Catania, Italy h Department of Rural Systems Engineering, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea i Engineering Department, Harper Adams University College, Newport, Shropshire, TF10 8NB, United Kingdom

article info This review shows the theoretical background for development of lumped ventilation models Article history:

that can be integrated into models that aim to aid either design or operation of low emission

Received 23 December 2011

naturally-ventilated buildings. The strength of lumped parameter modelling methods is that

Received in revised form

they have the potential to include effects of varying outdoor climate conditions, varying heat

12 November 2012

production from animals and the building design, which allow estimation of ventilation rate

Accepted 21 January 2013

and indoor air temperature and humidity, with acceptable calculation times. With regard to design of low emission buildings, significant challenges still exist in reflecting the spatial distribution of ammonia emission surfaces and the influence of air velocity above these surfaces. In relation to operation of natural ventilation systems, it is obvious that lumped parameter methods have the potential to aid automatic control systems that aim to optimise the adjustment of automatically controlled openings for natural ventilation in a way

5

Developed from a presentation in the session “Emission from naturally ventilated buildings” at the XVIIth World Congress of CIGR, “Sustainable Biosystems through Engineering”. June 13e17, 2010, Quebec City, Canada. * Corresponding author. Tel.: þ45 35333585. E-mail addresses: [email protected], [email protected] (B. Bjerg). 1537-5110/$ e see front matter ª 2013 IAgrE. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biosystemseng.2013.01.010

b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 4 6 e2 5 8

247

that prevents over-ventilation and, thereby, minimises ammonia emission, without compromising indoor aerial conditions. It is also foreseen that lumped parameter methods have the potential to optimise opening adjustment and exhaust strategies in hybrid ventilation systems. In these contexts, ventilation is can be combined with a partial pit exhaust ventilation system which makes it possible to collect a significant fraction of the entire ammonia emission in a limited air stream. This may make it affordable to utilise air cleaning technologies in conjunction with naturally ventilated animal buildings. ª 2013 IAgrE. Published by Elsevier Ltd. All rights reserved.

Nomenclature A Cd H

R U V c hi qi

1.

building surface area (m2) discharge coefficient (depending on the opening geometry) inside enthalpy balance due to: sensible and latent heat exchange from ventilation; sensible and latent heat production from animals and slurry; sensible heat exchange from the building envelope (W) volumetric ventilation rate (m3 s1) mean heat transfer coefficient (W m2 K1) building volume (m3) specific heat of air (J kg1 K1) inside air enthalpy (J kg1 dry air) mass flow rate of the gas released in the building by the animals (kg kg1 dry air)

Introduction

Determination of ammonia emission from livestock buildings is most frequently based on a calculated mass balance. The procedure requires determination of the rate of air exchange, which can be dealt with relatively easily in mechanically ventilated buildings, but which includes significant challenges for naturally ventilated buildings. From a measurement point of view, the challenges are associated with the large spatial and temporal variations in ammonia concentrations and air velocities in the ventilation openings. A thorough review of measurement methods lies beyond the scope of this article but can be found in Ogink, Mosqueraa, Calvet, and Zhang (2013) and Calvet et al. (2013). From a modelling point of view, large challenges are associated with the representation of the variations in weather conditions and the limited access to reliable data for validation. This article is the second part of a tripartite review on modelling ammonia emissions from naturally ventilated livestock buildings. Part one of the series reviews models that focus on ammonia evaporation processes depending on different animal species, floor types, manure handling systems, properties in the manure, and in the air (Bjerg, Norton, et al., 2013), and the third part reviews studies relevant for utilisation of Computational Fluid Dynamic (CFD) methods (Bjerg, Cascone, et al., 2013). CFD methods used in relation to ammonia emission from naturally ventilated buildings include modelling of geometrical details and spatial distribution of both outdoor and indoor air parameters and are, consequently, associated with

qv t u r Dm DP DT Fa Fb Fs Fv

ventilation rate (kgdry air s1) time (s). air velocity (m s1) air density (kgdry air m3) gas concentration difference between inside and outside air (kg kg1 dry air) pressure drop (Pa) difference between inside and outside air temperature (K) sensible heat produced by the animals (W) envelope building heat loss (W) supplementary heat (W) heat loss by air exchange (W)

Subscript i inside

significant computational time, especially if the result is to reflect the emission over periods with varying weather and operation conditions. By contrast, this second part of the series focuses on prediction of air change by models that do not require details of the spatial distribution of the air parameters. Models that ignore the spatial distribution of physical parameters are frequently denoted as lumped parameter models (Homepage, 2012; Maloszewski, Zuber, 1982; Sarak, Onur, & Satman, 2005). In this work we use a broad definition of the term lumped parameter models where we include all models that treat the air inside the building as one or a few lumps and neglect differences in air conditions inside the lump(s), regardless of whether a steady state or a dynamic approach is used. Fig. 1 shows a simplified flow-chart for a lumped parameter model to calculate air change in a naturally ventilated livestock building. The purpose of this article is to review studies on lumped parameters ventilation modelling and to assess the possibility to utilise these methods to model the release of ammonia in naturally ventilated livestock buildings.

2. Ventilation rate determination with lumped models 2.1.

Balance methods

Methods for determination of ventilation rate in naturally ventilated livestock building can be based on steady-state balance methods (heat and mass) (ASHRAE, 1972; Albright, 1990; CIGR,

248

b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 4 6 e2 5 8

where A is the building surface area (m2), U is the mean heat transfer coefficient (W m2 K1) and DT ¼ difference between inside and outside air temperature (K). Heat losses by air exchange can be obtained by: Fv ¼ cDTqv

(6) 1

1

where c is the specific heat of air (J kg K ) Replacing Equations (5) and (6) in Equation (4), we can calculate the ventilation rate as: qv ¼ ½Fs þ Fa  ðAUDTÞ=ðcDTÞ

(7)

If the calculation is referred to warm weather periods, Fs ¼ 0 and Fb will be negligible, so Equation (7) becomes: qv ¼ Fa =ðcDTÞ

Fig. 1 e Simplified flow-chart for a lumped parameters model to calculate air change in a naturally ventilated livestock buildings.

1984; Pedersen et al., 1998) or by a pressure-based modelling techniques considering both thermal buoyancy and wind effect (Bruce, 1975, 1978, 1982, 1986; Boulard & Baille, 1995; Brockett & Albright, 1987; Choinie`re, Tanaka, Munroe, & SuchorskiTremblay, 1992; Foster & Down, 1987; Shrestha, Cramer, Holmes, & Kummel, 1993; van ’t Ooster & Both, 1988; Roy, Boulard, Kittas, & Wang, 2002; Zhang, Janni, & Jacobson, 1989). Three steady-state balances can be used to calculate ventilation rate of livestock buildings based on heat, moisture, and carbon dioxide balances. In particular, for mass balance (based on moisture and carbon dioxide content), we can use the following equation (CIGR, 1984): qi ¼ Dmqv

(1)

where qi is the mass flow rate of the gas released in the building by the animals (kg kg1 dry air), qv is the ventilation rate (kgdry air s1) and Dm is the gas concentration difference between inside and outside air (kg kg1 dry air); The ventilation rate can be calculated as: qv ¼ qi =Dm

(2)

and R ¼ qv =r

(3) 3

1

where R ¼ volumetric ventilation rate (m s ) and r is the air density (kgdry air m3) Heat balance can be written as (CIGR, 1984): Fs þ Fa ¼ Fb þ Fv

(8)

Pedersen et al. (1998) applied these balance methods to buildings for fattening pigs, dairy cattle and laying hens. A useable prediction of the ventilation rate was possible on a 24 h basis when the inside to outside differences of air temperature, absolute humidity, and carbon dioxide concentrations were larger than 2  C, 0.5 g water m3 dry air, and 200 ppm, respectively. Correlation between ventilation rates predicted by the heat/moisture balance and predicted by the carbon dioxide balance were 0.67 ( p < 0.01) for fattening pigs, and 0.9 ( p < 0.001) for cows and laying hens, for those cases where inside to outside temperature differences were larger than 5  C (see Pedersen et al., 1998). This threshold excluded all five natural ventilated buildings from the data set. Correction was necessary for animal heat, moisture and carbon dioxide production if the balance calculation was for periods shorter than 24 h, considering the pattern of the animals’ diurnal activity. Sensitivity analysis showed that the estimations were more precise in cold than in warm periods and that the carbon dioxide balance resulted in more precise estimations than the heat or moisture balances. In addition it was concluded that heat and moisture balances were not applicable for calculations of ventilation rate in uninsulated buildings (Pedersen et al., 1998). Pedersen et al. (2008) reviewed theoretical, respiration chamber, and house-level calculations of CO2 production. They stated that: 1) CO2 production at animal-level ranges from 0.16 to 0.21 m3 h1 hpu1 (1 hpu ¼ heat production unit, a quantity of animals producing 1000 W of total heat at 20  C), depending on the species, the live weight, the feed intake level and animal activity; 2) when using CO2 production from respiration chambers to calculate the building ventilation rate, the data need to be corrected by adding þ10% to take into account the CO2 produced by the manure in the building (correction valid for manure stored in the house for not more than 3 weeks, but not valid for deep litter systems).

2.2.

Pressure-based modelling - thermal buoyancy effect

(4)

where Fa is the sensible heat produced by the animals (W), Fs is the supplementary heat (W), Fb is the envelope building heat loss (W) and Fv is the heat loss by air exchange (W) Envelope building losses can be calculated as:

Pressure-based modelling refers to the Bernoulli equation relating air speed (u) across an opening to the pressure drop (DP) across the opening (Roy et al., 2002):

Fb ¼ AUDT

where Cd is the discharge coefficient (depending on the opening geometry), and r is the fluid density. Pressure drop

(5)

u ¼ Cd ð2rDPÞ0:5

(9)

b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 4 6 e2 5 8

can be generated by a temperature gradient between inside and outside air (called the chimney or stack or buoyancy effect) and/or by the wind (wind effect). The stack effect is driven by the density gradient originating from vertical buoyancy forces. In a building subjected to wind, areas of positive and negative pressure are created on the external building surface. As a consequence the internal pressure also changes. Bruce (1978) stated that the internal pressure is constant throughout a building with no internal partitions. As reported by Foster and Down (1987), there are two theories of the stack effect: the first, a simplified theory, requires that: 1) all inlets are located at same height; 2) all outlets are located in the same height; and 3) no openings acts both as inlet and outlets. The second one, a more generalised theory, is not limited by these requirements. It takes into account the possibility to consider at the same time more openings, either vertical (windows) or horizontal (i.e. open ridge). Further, this approach considers the possibility that a vertical opening can act as an inlet and an outlet at the same time. This theory introduces the concept of the neutral plane, a height at which internal and external pressures will be equal, so no airflow will take place (Bruce, 1978). With regard to thermal buoyancy, the generalised theory proposed by Bruce (1978) has been validated by Bruce (1982). Bruce used Timmons and Baughman’s (1981) data, collected from a half-scale model of a typical free stall dairy barn with side openings and an open ridge, to measure the velocity at the open ridge induced by thermal buoyancy. Four different widths of the ridge were used. The theory predicted the measured velocities through the different ridges with a coefficient of variation of 7.4%. Even though this validation result is good, Foster and Down (1987) argued the need for more experimental validations of the theory with a wider set of building types. In the pressure-based approach, it is possible to calculate the ventilation rate by referring only to outdoor climate data. If the thermal buoyancy effect becomes significant, the calculation of the natural ventilation that includes the wind effect requires the determination of the heat balance of the building. In setting up the energy balance, various approaches are presented in the literature. Regarding the thermal behaviour of the building envelope, some authors use steady-state models, with some including the effects of solar heat load. In other works thermal behaviour is modelled in a transient way (Axaopoulos, Panagakis, Pitsillis, Kyritsis, 1994; Liberati & Zappavigna, 2007). Furthermore, some research has also considered evaporation from manure removing sensible heat from the air as latent heat (Axaopoulos, Panagakis, & Kyritsis, 1992; Liberati & Zappavigna, 2007) or simulated the internal surface temperatures including the floor (Liberati & Zappavigna, 2007). In the ventilation design program StaldVent (Strøm & Morsing, 2004), the geometric parameters and the insulation condition of the livestock building investigated, the type and numbers of the animals in the building, the locations of the ventilation openings and the local climate conditions may be used as the modelling inputs. Based on the animal heat production models and the required indoor thermal condition, the necessary ventilation openings required for the natural

249

ventilation system based on buoyancy forces can be computed.

2.3.

Pressure-based modelling - wind effect

Wind-induced ventilation can be estimated by means of the wind pressure coefficients (Cp) (Bruce, 1986; van ’t Ooster & Both, 1988). This approach shows two weaknesses, mainly linked to the poor correspondence between pressure differences measured in the field and those calculated by mean of pressure coefficients and discharge coefficients (Demmers et al., 2001). This last depends only on the geometry of the openings, while the pressure coefficients depend on the overall geometry of the building, the location of the openings on the building itself, and wind incidence on the building. Regarding large openings, Li, Delsante, and Symons (2000) indicate how to calculate appropriate discharge coefficients. In the literature, several sets of pressure coefficients exist for different building shapes and dimensions (Brockett & Albright, 1987; Bruce, 1975; Buffington & Skinner, 1980; Choinie`re et al., 1992; Demmers et al., 2001; Shrestha et al., 1993). Pressure coefficients can be calculated both through field measurements and by CFD simulation (e.g. Shrestha et al., 1993; LaFrance & Brugger, 2006; respectively). LaFrance and Brugger (2006) always used their own CFD calculated pressure coefficients applied to the air ventilation simulation model developed by Brockett and Albright (1987), and obtained good agreement with ventilation rate (R2 ¼ 0.98). CFD calculation of pressure coefficients can also be done by considering the so-called sealed-body, i.e. simulating the building without the openings, thus reducing the modelling effort. This approximation works because the pressure distribution on the building is not affected by the presence of the openings if these openings are not too large (Ramponi & Blocken, 2012). Wang and Chen (2007) coupled multi-zone (i.e. lumped models) and CFD models with zero-equation turbulence model (to calculate pressure coefficients) to determine air ventilation rate. From this theoretical study, Wang, Dols, and Chen (2010) developed the software CONTAM 3.0 to simulate airflow and contaminant transport in and around buildings. Wang and Wong (2006) stated that coupling CFD, to predict external pressure coefficients, with a building simulation program would allow “accurately and quickly predicting natural ventilation and indoor thermal behaviour”. In Swami and Chandra (1987) an extended procedure is reported to calculate natural air ventilation in buildings, and also many empirical equations to calculate pressure coefficients as a function of wind direction and the aspect ratio of the building; distinction has be made between low-rise and high-rise buildings. Validation is done with measured air ventilation data for three wind directions (only one building). The reported error ranged between 11% and þ0.4%. Co´stola, Blocken, and Hensen (2009) give an overview of pressure coefficient data. They distinguish between primary sources (data from full scale measurements, in wind tunnel measurements, and from CFD simulations) and secondary sources such as analytical models. Differences in the secondary sources are due mainly to position on the wall and the

250

b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 4 6 e2 5 8

degree of exposure/sheltering. A very interesting web application for pressure coefficient generation from the Dutch institution TNO (http://cpgen.bouw.tno.nl) has been recently published. It allows pressure coefficient calculation, eventually considering outside obstructions (only for block shaped buildings). Co´stola et al. (2009) compared calculated with measured pressure coefficients and highlighted remarkable deviations for some experimental conditions (particular locations on the building and wind directions), and also reported other secondary sources (AIVC, ASHRAE, Swami & Chandra (1987), CpCalcþ). Pressure coefficients calculated from full-scale experiments for natural ventilation prediction show very scattered values for low wind speeds (due to the influence of the buoyancy process driven by solar radiation) (Co´stola et al., 2009). By using a computer simulation program incorporating the model reported in Liberati and Zappavigna (2007), it is possible to assess the overall ventilation rate by considering buoyancy effect and wind effects separately, and then combining them together, as functions of wind speed and inside-outside air temperature difference. Fig. 2 shows the ventilation rate for a simulation of a naturally ventilated piggery with 300 finishing pigs. The stack effect adds to the wind effect in the combined ventilation mechanism when the wind speed is less than 3 m s1. Above this limit the stack effect can be considered irrelevant for the global ventilation. It should be noted, also, that the air change induced only by the stack effect is inadequate at the lower air temperature differences.

2.4.

Dynamic air exchange modelling

Several researchers have suggested models that estimate air exchange over time by following a steady-state approach, with a time-advancing update of the building heat and mass

balances (van ’t Ooster & Both, 1988; Axaopoulos et al., 1994; van’t Klooster, Bontsema, & Salomons, 1995; Cooper, Parsons, & Demmers, 1998; Liberati & Zappavigna, 2007). In general, dynamic models can be synthesised with the following equation (simplified from van’t Klooster et al., 1995): dðVri Di hi Þ=dt ¼ H

where H is the inside enthalpy balance due to: sensible and latent heat exchange from ventilation; sensible and latent heat production from animals and slurry; sensible heat exchange from the building envelope (W). V is the building volume (m3), ri is the inside air density (kg m3), hi is the inside air enthalpy (J kg1 dry air) and t is time (s). Solving Equation (10) dynamically by means a computer program, time t is advanced in a discrete manner, and at each small time step heat balance (H ) is updated and consequently the inside enthalpy (hi) will be calculated. Normally, heat exchange from the building envelope is calculated by means of steady-state equations, or by simulating the thermal behaviour of the wall by means of finite difference method, with appropriate inside and outside boundary conditions (Liberati & Zappavigna, 2007). Ventilation rate can be calculated by means of pressure-based modelling (thermal buoyancy effect and wind effect) or by simplified models (Cooper et al., 1998). Panagakis and Axaopoulos (2004) compared the steady state and dynamic model based on the experiment in a pig building and proved the accuracy and efficiency of applying the dynamic transient method to tune a PID controller. Dynamic models allow simulation considering historical weather data (daily, monthly, or yearly), and the inclusion of sub-models (also stochastic models), without overloading the computation time. Another dynamic approach is the zonal model technique assumes that the indoor space can be divided into some wellmixed macroscopic homogeneous zones. Modelling is similar to the pressure-based techniques, considering that mass balances between adjacent zones must be guaranteed (Li et al., 2000). Jessen (2007) and Wu, Stoustrup & Hieselberg (2008) have applied this approach in a dynamic multi-zone model ¨ zcan, Vranken, and for livestock indoor climate control. O Berckmans (2007), in a laboratory scale test, showed that both temperature and tracer gas concentration give an error of 15% if multiple zone modelling is used.

2.5.

Fig. 2 e Comparison of the ventilation rates due to buoyancy (lower surface), wind (gridded surface), and their combined effects (upper surface), for different wind speed and inside-outside air temperature differences.

(10)

Uncertainty and model validation

A very topical problem lies in the validation of the ventilation model, which requires reliable reference data coming from experimental measurements. Indirect measuring techniques of airflow rate can lead to erroneous ventilation rate ¨ zcan, Vranken, & Berckmans, 2005; Van determination (O Buggenhout et al., 2009), mainly because of imperfect mixing of air inside the ventilated space. The tracer gas method (TGM) is often used as a reference for validation but its accuracy could be improved by introducing information about ¨ zcan et al., 2005). imperfect mixing within the building (O Van Buggenhout et al. (2009), in a laboratory experimental setting, analyse the effect of the position of the sampling point on accuracy of TGM for ventilation rate estimation in

b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 4 6 e2 5 8

a scale-model building with forced ventilation. The best position among 36 considered in the experiment was the one placed at the outlet, giving an error less than 10%, while the worst estimation, using other sampling points, could rise up to 86%, because of the imperfect mixing of the air. So, in forced ventilated buildings, the best estimation is obtained by positioning the gas injector at the air inlet and the sampling point at the air outlet. If considering naturally ventilated buildings, inlet and outlet openings are not always well defined and can also change depending on outside and inside boundary conditions (e.g. changing the ratio between wind and buoyancy effect, or the wind direction). So, in this context, TGM can lead to results far from reality and with a high degree of randomness. Van Buggenhout et al. (2009) presented a synoptic table showing the accuracy of different techniques for measuring ventilation rate in naturally ventilated buildings, as derived from the literature. Values are highly variable. For example, for methods linked to balance equations the following error values were reported: CO2 balance, 15e40%; moisture balance, 5e40%; heat balance, 31e101%. In the same way, however, more sophisticated approaches can also give substantial errors: CFD models, 15e65%; free impelling turbine, 5e25%; hot wire anemometer, 25%. In conclusion, Van Buggenhout et al. (2009) call for the development of a reliable standardised method to be used in field measurement, although many authors use tracer gas as a reference method to validate airflow models. A possible way to determine a reliable ventilation rate ¨ zcan could be by measuring the airflow at each opening (O et al., 2007). Costola, Blocken, Ohba, and Hensen (2010) highlight the uncertainty in airflow rate calculations due to the use of surface-averaged pressure coefficients in place of local coefficients. In this study a database of 145 intensive wind tunnel tests carried out by the Tokyo Polytechnic University (http://wind.arch.t-kougei.ac.jp/system/eng/contents/code/ tpu) was used. In this research, the uncertainty was greater for small airflow rates than for the largest airflow rate, when averaged pressure coefficients were used. Air ventilation models to be used in estimating ammonia emission should be dynamic to better characterise diurnal and seasonal variability. Dekock, Vranken, Gallmann, Hartung, and Berckmans (2009) modelled yearly ammonia emission for a specific building, using data at an acquisition interval of 12e15 min. In particular, using two linear models (one for each of the two sub-periods into which the fattening stage was divided) in which emission was linked to inside/ outside temperature difference, air ventilation, and animal weight, it was possible to estimate yearly ammonia emission with a maximum deviation from the measured values of less than 10%. To fit the linear models, only two day’s measured data per sub-period were used (applying so-called intermittent measurements). In an earlier work, using a fixed model for the whole year, the error was to 25%.

3.

Weather conditions

Modelling air exchange in naturally ventilated buildings require assumptions about data regarding the main external

251

climatic parameters such as solar radiation, air temperature and humidity, wind speed and direction. Climatic data to be used in the model can be acquired directly from the location or estimated. In both cases, data should be representative of the place where the building is located and take into account any historical trends. The estimation of solar radiation has been explored in several surveys that are based on empirical or semi-empirical deterministic approaches (Donatelli, Carlini, & Bellocchi, 2006). Other methods are based on stochastic processes that are capable of producing one or more weather variables with the same statistical properties as naturally occur for a given location (Wilks & Wilby, 1999). Usually these methods are implemented in software (GSRad, Donatelli et al., 2006; WGEN, Richardson & Wright, 1984; ClimGen, Sto¨ckle, Nelson, Donatelli, & Castellvı‘, 2001). GSRad supplies a collection of alternative deterministic or stochastic methods to estimate/ generate synthetic daily and hourly radiation data (Donatelli et al., 2006). The solar radiation outside the earth’s atmosphere is calculated according to the solar geometry (Stine & Harrigan, 1985). The transmission of solar radiation through the earth’s atmosphere can be calculated by diverse methods (Thornton & Running, 1999; Winslow, Hunt, & Piper, 2001; Woodward, Barker, & Zyskowski, 2001). The solar radiation at ground level can be estimated from alternative sets of weather inputs based on physical relationships or stochastic procedures ˚ ngstro¨m, 1924; Bristow & Campbell, 1984; Donatelli & (A Bellocchi, 2001; Richardson, 1981; Supit & Van der Goot, 2003). The most simplified models (Bristow & Campbell, 1984; Donatelli & Bellocchi, 2001) relate diurnal temperature range to solar energy transmission through the earth’s atmosphere. Clouds are very important for limiting solar radiation at the earth’s surface; Supit and van Kappel (1998) consider cloud cover in their model to estimate the transmissivity of the earth’s atmosphere. ˚ ngstro¨m (1924) is the most common The model from A choice to estimate global solar radiation when sunshine measurements are available. As an alternative, there are stochastic procedures (Woodward et al., 2001). These models are based on the dependence structure of daily maximum and minimum temperature and solar radiation (Richardson, 1981). The global solar radiation includes direct beam and diffuse radiation (Liu & Jordan, 1960). The estimation of diffuse radiation on a horizontal surface depends on the extra-terrestrial irradiance and a transmission function, while the direct fraction of solar radiation is the complement to global solar radiation. In order to determine air temperature, data measured and recorded for a sufficient number of years from weather stations can be used. By analysing these data, it is possible to reconstruct the daily trend through specific mathematical models (Goudriaan & van Laar, 1994, 256 pp; Marucci, Gusman, & Pagniello, 2005; Parton & Logan, 1981). Daily maximum and minimum air temperatures are considered a stochastic process conditioned by precipitation status (Danuso, 2002; Richardson, 1981). Using simulations, hourly air temperature values can be generated by using daily values of maximum and minimum air temperatures, according to different methods (Cesaraccio, Spano, Duce, & Snyder, 2001). A further approach predicts hourly air temperatures

b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 4 6 e2 5 8

from solar radiation (Dumortier, 2002). The air relative humidity, daily and hourly, is often not available in historical series. In these cases it is possible to estimate the air relative humidity using meteorological models, starting from commonly measured variables. For example the CLIMA weather generator (Donatelli, Bellocchi, Habyarimanaa, Confalonieri, & Micale, 2009) allows the user to select specific modelling options to generate meteorological variables including the air relative humidity. Long-term measured records of wind speed are only available for a few locations. The wind speed distribution is not described well by a normal distribution and the twoparameter Weibull distribution is the most widely used model of wind behaviour (Corotis, Sigl, Klien, 1978; Takle & Brown, 1978). Daily mean values of wind speed are generated by sampling from probability distribution functions. Alternative approaches are also available to estimate daily maximum and minimum wind speeds (Ephrath, Goudriaan, & Marani, 1996; Porter, Pickering, Jones, & Hoogenboom, 2000; Tatarko, Skidmore, & Wagner, 1997). Wind speed tends to be both random and cyclic, like most climatic variables. In general, surface wind speed tends to show random variations superimposed on a varying diurnal cycle related to atmospheric stability and geostrophic wind (Carlson, 1998, 507 pp). Probability distribution functions are used to randomly apportion daily mean wind speed within each hour of the day. As an alternative to random approaches, deterministic wave functions (Ephrath, Goudriaan, & Marani, 1996; Goudriaan & van Laar, 1994, 256 pp; Gregory, Peterson, Lee, & Wilson, 1994; Hoffmann, 2002; Tatarko et al., 1997) can be used to describe diurnal variations between maximum and minimum wind speeds during the day (Donatelli et al., 2009). In order to obtain external climatic parameters in places lacking weather stations, some models to calculate maximum and minimum monthly temperatures have been developed by correlating these values with some physical parameters (distance from the sea, altitude, latitude) (Marucci & Leone, 1989). The correlations obtained were highly significant for places belonging to the region considered when determining the correlation coefficients, while for the surrounding areas the results were not so satisfactory. The addition of data concerning the exposure (slope and orientation) of the land surrounding the weather station in the correlation led to an appreciable improvement in the estimation particularly in areas where the slope of the land is significant (Marucci & Leone, 1994). Similar elaborations have been successively proposed in METEONORM software (see meteonorm.com), which is a comprehensive meteorological reference, incorporating a catalogue of meteorological data and calculation procedures for solar applications and system design at any desired location in the world. In METEONORM, several databases from different parts of the world have been combined and checked for their reliability; mean monthly climatological data are available for the following 8 parameters: global radiation, environmental air temperature, humidity, precipitation, number of days with precipitation, wind speed and direction, sunshine duration. For many regions of the world, the measured data may only be applied within a radius of 50 km around the weather stations. This makes it necessary to interpolate

parameters between stations. Interpolation models for solar radiation, temperature and additional parameters, allowing application at any site in the world, are included in the software. In the Staldvent design software for livestock building ventilation (Strøm & Morsing, 2004), the normalised weather conditions in different climate zones or areas can be integrated to simulate the performance of the ventilation system. The normalised climate data were derived based on statistical weather data and provide hourly-averages over a year. Utilising these data for simulation, the system can be used over different seasons (Strøm & Morsing, 2004). Liberati and Zappavigna (2007) used measured climatic data (recording interval 1 min) including: outside air temperature and humidity, wind speed and direction, solar radiation. Among the outputs of their simulation model,, for example, is the ventilation rate for a naturally ventilated piggery with 300 finishing pigs, as calculated from real weather data as input (Fig. 3). From this figure, it is possible to see that the combined ventilation rate is not the algebraic sum of chimney and wind effect calculated separately. Locally acquired weather data are necessary to validate specific air ventilation models. Dynamic models need hourly (or similar interval) estimates of weather parameters.

4.

Geometry

The relation between wind velocity and direction, and the velocity of air flows inside the livestock building depend on many factors such as the size, shape, and location in the wall of ventilation openings, the shape of the roof of the building, the orientation of the building, its internal design, and the presence or absence of neighbouring structures, etc. Research concerning the role of the geometry of the building for dynamic ventilation modelling has been focused on the influence of shade height on the physiological response of cattle during hot weather (Garret, Bond, & Pereira, 1967), the geometric factors for thermal radiation exchange between

40000 35000

Ventilation rate (m 3 h -1)

252

30000 25000 20000 15000 10000 5000 0 01/08 0.00

01/08 12.00

02/08 0.00

02/08 12.00

03/08 0.00

03/08 12.00

04/08 0.00

04/08 12.00

05/08 0.00

Time

Fig. 3 e Combined air ventilation rate for a naturally ventilated piggery with 300 finishing pigs calculated from Wind effect, chimney real weather data. combined air ventilation. effect,

b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 4 6 e2 5 8

cows and their surroundings (Liberati, 2008; Perry & Speck, 1962) and a model estimating the effectiveness of shade structures on the production of dairy cows in hot climates (Swierstra & Van Ouwerkerk, 1985). A dynamic model may take into account the building geometry among the inputs (building size and orientation, roof slope, shed or gabled roof, openings size and location) to optimise internal climate with respect to the geometrical characteristics (Liberati & Zappavigna, 2007). In particular, in this model, geometrical data allow calculation of the solar load on walls and roof (given the surface area, the azimuth and the slope) and air exchange due both to buoyancy and wind effects. For evaluating buoyancy effects, the model needs the height and width of the openings and their height above the floor, while for wind effect calculation the azimuth of each opening, and their position in the wall/roof are also required. This is because the pressure coefficients depend on wind direction and on building location. In the model developed by Liberati and Zappavigna (2007) the orientation of the

253

openings is provided by associating each opening with the wall in which it is located. Fig. 4 shows a screenshot of the program interface used to enter geometrical and material data for the model. Liberati and Zappavigna (2010) use this model for three different types of stockbreeding buildings: a building for fattening pigs on a slatted floor with a shed (mono-pitch) roof or a gabled roof; a replacement building for 15 calves and 35 young heifers with a gabled roof; and a simple shelter for cows with a gabled or multiple shed roofing. The best solution found was the single shed roof, to provide the best possible combination of the wind and solar load though it can also be the worst solution if badly designed. The roof geometry can be important only if it is not insulated. In fact the insulation tends to make the thermal exchanges uniform for all the different solutions (Liberati & Zappavigna, 2010). Roof shape (gabled and shed) is important, particularly with greater roof slope. To predict ammonia emission from naturally livestock buildings, besides building construction details used in

Fig. 4 e Screenshot of the computer program interface used to input geometrical and material data of the simulation model described in Liberati and Zappavigna (2007).

254

b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 4 6 e2 5 8

lumped parameters models, it is also necessary to include a sub-model able to characterise the spatial distributions of ammonia emission surfaces. But, to date, this literature review did not reveal any attempts to solve that challenge.

utilisation, calculated as the fraction of solar irradiation on the building envelope that is transferred to sensible heat in the air volume, depends on the ventilation airflow through the building. A higher airflow results in a higher utilisation.

5. Thermal insulation of building construction

6.

The influence of thermal insulation of the building shell can easily be included in lumped parameter models if a steadystate approach to the heat balance for the building is followed, e.g. Strøm and Morsing (2004). But the steady-state approach neglects the influence of the heat capacity of the building construction on air temperature and air exchange. Inclusion of this influence requires a dynamic approach. The dynamic model of Liberati and Zappavigna (2010) takes into account the heat capacity or, as the phenomenon also is denoted, the thermal-inertial characteristics of the building materials. The model enables the user to choose between various roof insulation levels. Roof geometry can be important when the roof is not insulated. On the other hand, when there is no insulation in the roof, the height of the eaves and the slope are relevant factors (the higher and greater, respectively, the better), but the shape is even more important. Some authors have investigated the thermal behaviour of some roof- covering materials depending on their physical properties. Single and multi-layer materials have been tested and specific software has been developed to simulate the thermal behaviour of the whole building (Gusman, Marucci, & Bibbiani, 2004). The thermal insulation of roofs in hot climate areas is essential to reduce the impact of solar radiation on the livestock environment. However this can have a negative effect since the insulation limits the discharge of the internal heat when the outside temperature is lower than the inside temperature, as in the case of closed and crowded livestock buildings, especially during the night in summer. Some experimental tests have been carried out in summer to investigate the cooling effect of perforated roofs (Zappavigna & Liberati, 2005) and ventilated roofs (Liberati, Spiga, & Zappavigna, 2009). In dynamic conditions the heat transmission loss must take into account the thermal capacity of the building. For instance in the morning, when the surfaces are cooler than in the evening, the heat loss will be relatively higher. Furthermore, the condensation of water on the internal surfaces of the building envelope at low temperatures and relative high indoor humidity will convert latent heat to sensible heat, which acts as an additional heat source. This process will later be reversed as the wet surfaces dry and heat will then be recovered from the indoor air. These aspects have been discussed in more depth but are estimated to have only a minor influence on the calculated transmission heat loss over 24 h (Pedersen et al., 1998). Research has been carried out to determine the solar heat load, the ratio of solar heat utilisation, the absorbance factor, the transmittance-absorbance factor and the overall heat transfer coefficient per floor area for an un-insulated building (Jeppsson & Gustafsson, 2001). The ratio of solar heat

Heat release from animals

In the literature there are many models of animal heat production. Many of these were obtained under laboratory conditions, and though they are very thorough and detailed (Bruce & Clark, 1979; Quiniou, Noblet, van Milgen, & Dubois, 2001; Strøm, 1978), they are not always adequate to be used at animal housing level. ASAE Standards (2008) report heat production based mostly on data recorded several years ago. Because of changes in management of piggeries, feeding composition and pig genetics, heat production at animal level has increased. There has also been an increase in the lean percentage, by 1.55% in the last ten years, corresponding to an increase in heat production of about 15% due to less fat for insulation (Brown-Brandl, Nienaber, Xin, & Gates, 2004). The CIGR 4th Report of the Working Group on Climatization of Animal Houses (CIGR, 2002), based also on the work of Pedersen (2002), by contrast, seeks to provide information at the animal house level, which is useful for building design. Early-weaned pigs or lightweight pigs in general show overestimation of heat production, as reported by Brown-Brandl et al. (2004) analysing literature data of fed animals (unfasted) from 1957 to 2002. Moreover, these authors also stated that heat production estimated using the CIGR (2002) formulae for pigs with live weight greater than 90 kg and less than 20 kg needs additional research, and “values are not adequate to accurately design modern swine housing facilities”. From this study, “the new genetic lines have a maximum increase in total heat production of approximately 15%”. In multiple regression analyses Brown-Brandl et al. (2004) compared data from 1957 to 1988 and from 1988 to 2003 as a function of temperature and body mass. They estimated an increase of 12.4%e35.3% in heat production for the newer genetic lines and observed largest differences at the higher temperatures. Morsing, Pedersen, Strøm, and Jacobsen (2005) compared between old and new values (CIGR, 2002) at 20  C, highlighting that the old total heat production model at 20  C underestimated heat production for body masses greater than 90 kg. The CIGR (2002) formulae take into account body live weight, daily feed energy intake, air temperature, milk production, and diurnal variation of animal activity (Blanes & Pedersen, 2005). Moreover, using field experimentation, diagrams are provided in relation to indoor air temperature to improve partition between sensible and latent heat at housing level (for fattening pigs and weaners on partially slatted floors only for the range of about 17e26  C). A correction factor has also been introduced to consider the part of sensible heat that is used to evaporate water from feed, spilt drinking water and manure. CIGR (2002) gives parameters to calculate heat production for cattle, pigs, poultry, sheep, goats, rabbit, horses, and mink. In this last report, the Working Group on Climatization of Animal Houses intended to give indications on heat and moisture production at animal and house levels, although

b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 4 6 e2 5 8

experimental data used are limited to Northern European production and housing conditions. Theoretical models can describe heat balance at animal level considering heat exchanges by convection, conduction, long-wave radiation (and short wave from sun, eventually), evaporation at skin surface and in the respiratory tract (e.g. McGovern and Bruce (2000) for cattle; Fialho, Bucklin, Zazueta, and Myer (2004) for pigs).

7.

Discussion and conclusions

This review shows the theoretical background for development of lumped parameter models to calculate air ventilation rate, which can be integrated into models to aid either design or operation of low emission naturally ventilated buildings. In particular, the strength of dynamic lumped parameter modelling is that it has the potential to include effects of changing outdoor climate conditions, and varying heat production from animals, building design and thermal-inertia characteristics of the building materials, thus allowing time-distributed estimation of ventilation rate, and indoor air temperature and humidity. In relation to use in models to aid the design of low emission buildings, significant challenges still exist regarding the spatial distribution of ammonia emission surfaces and the influence of air velocity and turbulence above these surfaces. It can be anticipated that both full-scale and model-scale measurement as well as CFD can provide data that may assist such developments, but there is no doubt that it will require a major effort to solve this challenge. By simulating, in a parametric way (varying one parameter at a time), some of the most widespread manure removal systems (e.g. different slurry pits) using CFD, it could be possible to identify empirical sub-models to be included in a lumped parameter model. Another challenge is requirement for pressure coefficients as inputs for the pressure method. In practice, it is very difficult to find these coefficients for different buildings in the literature. So, in many cases, measured data are required from the real building. The decision on the number of measurements and their locations on the building can be a difficult task due to the complex characteristics of a natural ventilated building, especially with large openings under varied wind conditions. Therefore, large uncertainty is unavoidable. More research is therefore needed to see if a simplified measurement method can be found. To do this, we may need more comprehensive research based on both experimental and CFD methods. As stated above, a parametric study of a certain number of typical buildings using CFD could allow the calculation of the pressure coefficients from empirical formulae as function of wind direction and opening location on the building. In this way, with accurate pressure coefficients, it could be possible to simulate also buildings with large openings (probably also without a wall). Furthermore, with similar formulae, it could also be possible to calculate reliable discharge coefficients to improve the quality of air ventilation determination. Statistical modelling, as developed at the University of Southern Queensland (Banhazi, Rutley & Pitchford, 2010; Banhazi, Seedorf, Rutley, & Pitchford, 2008), has the ability to document how the ventilation rate is affected by easily

255

determined parameters based on data from relative large numbers of herds, and can provide knowledge that can be utilised in general recommendations in relation to design and operation of low emission naturally ventilated buildings. However, large uncertainties in field measurements constitute a major obstacle to achieving reliable data for validation of models that aim to predict ventilation rate in naturally ventilated buildings, and, therefore, make it difficult to assess the reliability of the models. Technological methods to reduce ammonia emissions from naturally ventilated buildings include air cleaning of a minor part of the total air exchange exhausted through a pit ventilation system (Bjerg & Andersen, 2010). To be able to model such cases it is obviously crucial to predict the distribution of ammonia outflow between the pit exhaust system, and the openings in walls and roof of the building itself. Due to the dominating influence of the spatial distribution of the aerial parameters, it is difficult to imagine how lumped ventilation models can be utilised in relation to aid design for such cases. For that task CFD methods will undoubtedly be a better choice. Thus it is also obvious that ventilation modelling can be a significant part of automatic control systems that optimise the use of a partial pit exhaust system e e.g. by prioritising the most efficient exhaust points in relation to the current weather conditions, or by automatic control of the openings for natural ventilation in a way that prevents overventilation but secures suitable indoor aerial conditions and optimal performance of the pit exhaust cleaning system. Lumped parameters models, such as the one proposed by Liberati and Zappavigna (2007), are considered to constitute a very useful background for progress in future research on predictive methods aimed at the design of livestock buildings to provide appropriate thermal conditions for the animals and limit the emission of gases, especially ammonia. This will require development and incorporation of an ammonia emission module. The first approach to develop such a module could be to utilise already existing knowledge on how ammonia emission generally is influenced by circumstances such as floor design, animal weight, animal production, air temperature, air exchange and feed composition. The development of such first generation ammonia emission modules might reveal ways to account for more of the specific conditions in the buildings analysed. Another significant potential improvement would be to include steering strategies for control of adjustable openings. Together these extensions would enable both design and control strategies to be identified for individual buildings or specific geographical locations that lead to appropriate thermal conditions for the animals and minimised ammonia emission.

references

Albright, L. D. (1990). Environment control for animals and plantsIn ASAE Textbook Nr. 4, . St. Joseph: American Society of Agricultural Engineers. (2012). Homepage: http://en.wikipedia.org/wiki/Lumped_ element_model. Assessed 11.05.12. ASHRAE. (1972). Handbook of fundamentals (Chapter 8)In Environment control for animals and plantsePhysiological

256

b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 4 6 e2 5 8

considerations, . Am. Soc. of Heating, Refrigeration and AirConditioning Engineers. ASAE Standards. (2008). EP270.5: Design of ventilation systems. St. Joseph, Mich: ASAE. Axaopoulos, P., Panagakis, P., & Kyritsis, S. (1992). Computer simulation assessment of the thermal microenvironment of growing pigs under summer conditions. Trans. ASAE, 35(3), 1005e1009. Axaopoulos, P., Panagakis, P., Pitsilis, G., & Kyritsis, s (1994). AGRISIM: a PC user-friendly transient simulation program for growing-finishing swine buildings. Applied Engineering in Agriculture, 10(5), 735e738. ˚ ngstro¨m, A. (1924). Solar and terrestrial radiation. Quarterly A Journal of the Royal Meteorological Society, 50, 121e125. Banhazi, T. M., Seedorf, J., Rutley, D. L., & Pitchford, W. S. (2008). Identification of risk factors for sub-optimal housing conditions in Australian piggeries e Part III: environmental parameters. Journal of Agricultural Safety and Health, 14(1), 41e52. Banhazi, T. M., Rutley, D. L., & Pitchford, W. S. (2010). Validation and fine-tuning of a predictive model for air quality in livestock buildings. Biosystems Engineering, 105(3), 395e401. Bjerg, B., & Andersen, M. (2010). Numerical simulation of a pit exhausts system for reduction of ammonia emission from a naturally ventilated cattle building. In The XVIIth world Congress of CIGR. Quebec 12e17 June, Canada. Bjerg, B., Cascone, G., Lee, I. -B, Bartzanas, T., Norton, T., Hong, S. -W, et al. (2013). Modelling of ammonia emissions from naturally ventilated livestock buildings e part three: CFD modelling. Biosystems Engineering, 116(3), 259e275. Bjerg, B., Norton, T., Banhazi, T., Zhang, G., Bartzanas, T., Liberati, P., et al. (2013). Modelling of ammonia emissions from naturally ventilated livestock buildings e part one: ammonia release modelling. Biosystems Engineering, 116(3), 232e245. Blanes, V., & Pedersen, S. (2005). Ventilation flow in pig houses measured and calculated by carbon dioxide, moisture and heat balance equations. Biosystems Engineering, 92(4), 483e493. Boulard, T., & Baille, A. (1995). Modelling of air exchange rate in a greenhouse equipped with continuous roof vents. Journal of Agricultural Engineering Research, 61(1), 37e48. Bristow, K. L., & Campbell, G. S. (1984). On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agricultural and Forest Meteorology, 31, 159e166. Brockett, B., & Albright, L. (1987). Natural ventilation in single airspace buildings. Journal of Agricultural Engineering Research, 37, 141e154. Brown-Brandl, T. M., Nienaber, J. A., Xin, H., & Gates, R. S. (2004). A literature review of swine heat production. Trans. ASAE, 47(1), 259e270. Bruce, J. M. (1975). The open ridge as a ventilator in livestock buildings. Farm buildings research and development studies. November 1e8. Aberdeen, U.K.: Scottish Farm Building Investigation Unit. Bruce, J. M. (1978). Natural convection through openings and its application to cattle building ventilation. Journal of Agricultural Engineering Research, 23(2), 151e167. Bruce, J. M. (1982). Ventilation of a model livestock building by thermal buoyancy. Trans. ASAE, 25(6), 1724e1726. Bruce, J. M. (1986). Theory of natural ventilation due to thermal buoyancy and wind. In Proc. CIGR Section II Seminar of pig housing, rabbit and small Birds species housing. Rennes, France. Bruce, J. M., & Clark, J. J. (1979). Models of heat production and critical temperature for growing pigs. Animal Production, 28, 353e369. Buffington, D. E., & Skinner, T. C. (1980). Solar radiation and wind effects as functions of building orientation. Trans. ASAE, 23(6), 1482e1488. Calvet, S., Gates, R. S., Zhang, G., Estelle´s, F., Ogink, N. W. M., Pedersen, S., et al. (2013). Measuring gas emissions from

livestock buildings: a review on uncertainty analysis and error sources. Biosystems Engineering, 116(3), 221e231. Carlson, T. N. (1998). Mid-latitude weather systems. Boston, MA, USA: The American Meteorological Society. Cesaraccio, C., Spano, D., Duce, P., & Snyder, R. L. (2001). An improved model for determining degree-day values from daily temperature data. International Journal of Biometeorology, 45, 161e169. Choinie`re, Y., Tanaka, H., Munroe, J. A., & Suchorski-Tremblay, A. (1992). Prediction of wind-induced ventilation for livestock housing. Journal of Wind Engineering & Industrial Aerodynamics, 41-44, 2563e2574. CIGR. (1984). Climatization of animal houses. Aberdeen: Scottish Farm Buildings Investigation Unit. CIGR. (2002). Heat and moisture production at animal and house levels. In S. Pedersen, & K. Sa¨llvik (Eds.), Fourth report of CIGR working group on climatization of animal houses. Cooper, K., Parsons, D. J., & Demmers, T. (1998). Thermal balance models of livestock buildings for use in climate change studies. Journal of Agricultural Engineering Research, 69(1), 43e52. Corotis, R. B., Sigl, A. B., & Klein, J. (1978). Probability models of wind velocity magnitude and persistence. Solar Energy, 20, 483e493. Co´stola, D., Blocken, B., & Hensen, J. L. M. (2009). Overview of pressure coefficient data in building energy simulation and airflow network programs. Building and Environment, 44(10), 2027e2036. Costola, D., Blocken, B. J. E., Ohba, M., & Hensen, J. L. M. (2010). Uncertainty in airflow rate calculations due to the use of surface-averaged pressure coefficients. Energy and Buildings, 42(6), 881e888. Danuso, F. (2002). CLIMAK: a stochastic model for weather data generation. Italian Journal of Agronomy, 6, 57e71. Dekock, J., Vranken, E., Gallmann, E., Hartung, E., & Berckmans, D. (November 2009). Optimisation and validation of the intermittent measurement method to determine ammonia emissions from livestock buildings. Biosystems Engineering, 104(3), 396e403. Demmers, T., Phillips, V., Short, L., Burgess, L., Hoxey, R., & Wathes, C. (2001). Validation of ventilation rate measurement methods and the ammonia emission from naturally ventilated dairy and beef buildings in the United Kingdom. Journal of Agricultural Engineering Research, 79(1), 107e116. Donatelli, M., & Bellocchi, G. (2001). Estimate of daily global solar radiation: new developments in the software RadEst 3.00. In M. Bindi, M. Donatelli, J. R. Porter, & M. K. van Ittersum (Eds.), Proceedings of the second international symposium on modelling cropping systems. Florence, Italy (pp. 213e214). Donatelli, M., Bellocchi, G., Habyarimanaa, E., Confalonieri, R., & Micale, F. (2009). An extensible model library for generating wind speed data. Computers and Electronics in Agriculture, 69, 165e170. Donatelli, M., Carlini, L., & Bellocchi, G. (2006). A software component for estimating solar radiation. Environmental Modelling & Software, 21, 411e416. Dumortier, D. (2002). Prediction of air temperatures from solar radiation. Tech. Rep. SoDa-5-2-4, CNRS-ENTPE. Ephrath, J. E., Goudriaan, J., & Marani, A. (1996). Modelling diurnal patterns of air temperatures, radiation, wind speed and relative humidity by equations for daily characteristics. Agricultural Systems, 51, 377e393. Fialho, F. B., Bucklin, R. A., Zazueta, F. S., & Myer, R. O. (2004). Theoretical model of heat balance in pigs. Animal Science, 79, 121e134. Foster, M. P., & Down, M. J. (1987). Ventilation of livestock buildings by natural convection. Journal of Agricultural Engineering Research, 37(1), 1e13. Garret, W. N., Bond, T. E., & Pereira, N. (1967). Influence of shade height on physiological responses of cattle during hot weather. Trans. ASAE, 10(4), 156e167.

b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 4 6 e2 5 8

Goudriaan, J., & van Laar, H. H. (1994). Modelling potential crop growth processes. London, United Kingdom: Kluwer Academic Publishers. Gregory, J. M., Peterson, R. E., Lee, J. A., & Wilson, G. R. (1994). Modeling wind and relative humidity effects on air quality. In Proceedings of the international specialty conference on aerosols and atmospheric optics: Radiative balance and visual air quality, Vol. A, snowbird, UT, USA (pp. 757e766). Gusman, A., Marucci, A., & Bibbiani, C. (2004). A transient heat conduction model to simulate thermal behaviour of roofs and walls in hot climates. In International symposium “New trends in Farm buildings”, 2e6 May, E´vora, Portugal. Hoffmann, R., (2002). A Comparison of control concepts for wind turbines in terms of energy Capture. Darmstadt, Germany: Department of Power Electronics and Control of Drives. D17 Darmsta¨dter Dissertation. 134 pp. Jeppsson, K.-H., & Gustafsson, G. (2001). Solar heat load in uninsulated livestock buildings. Journal of Agricultural Engineering Research, 78(2), 187e197. Jessen, J. J. (2007). Embedded Controller design for pig Stable ventilation systems. Automation and Control, Department of Electronic systems, Aalborg University. PhD thesis. LaFrance, T., & Brugger, M. (2006). Determining pressure coefficients for natural ventilation purposes by computational fluid dynamics modeling. In An ASAE meeting presentation, Paper No: 064093, St. Joseph, MI. Li, Y., Delsante, A. E., & Symons, J. (2000). Prediction of natural ventilation in buildings with large openings. Building and Environment, 35(3), 191e206. Liberati, P. (2008). Analysis of the effect of the roofing design on heat stress in dairy cow housing. Journal of Agricultural Engineering, 4, 21e27. Liberati, P., Spiga, M., & Zappavigna, P. (2009). Optimization of ventilated roofs for livestock housing. International Communications in Heat and Mass Transfer, 36, 432e437. Liberati, P., & Zappavigna, P. (2010). A simulation model to predict the internal climatic conditions in livestock houses as a tool for improving the building design and management. In XVIIth world Congress of the International Commission of Agricultural and Biosystems Engineering (CIGR), Que´bec City, Canada June 13e17. Liberati, P., & Zappavigna, P. (2007). A dynamic computer model for optimization of the internal climate in swine housing design. Transactions of the ASABE, 50(6), 2179e2188. Liu, B. Y. H., & Jordan, R. C. (1960). The interrelationship and characteristic distribution of direct, diffuse and total solar radiation. Solar Energy, 4, 1e19. Maloszewski, P., & Zuber, A. (1982). Determining the turnover time of groundwater systems with the aid of environmental tracers. 1. Models and their applicability. Journal of Hydrology, 57(3e4), 207e231. Marucci, A., Gusman, A., & Pagniello, B. (2005). Modelli matematici per la previsione della temperatura dell’aria. In National Conference AIIA “L’ingegneria agraria per lo sviluppo sostenibile dell’area mediterranea”. Catania (Italy), June 27e30, 2005. Marucci, A., & Leone, A. (1989). Metodi automatici per la stima puntuale della temperatura dell’aria in una regione. Genio Rurale, 6, 59e67. Marucci, A., & Leone, A. (1994). L’influenza dei parametri fisici del territorio sulle grandezze climatiche: il ruolo dell’esposizione nella stima indiretta della temperatura media mensile di un luogo. Rivista di Ingegneria Agraria, 2, 74e82. Swami, M. V., & Chandra, S. (March, 1987). Procedures for calculating natural ventilation airflow rates in buildings. FSEC-CR163e86. Cape Canaveral, Florida: Florida Solar Energy Center. McGovern, R. E., & Bruce, J. M. (2000). A model of the thermal balance for cattle in hot conditions. Journal of Agricultural Engineering Research, 77, 81e92.

257

Morsing, S., Pedersen, S., Strøm, J. S., & Jacobsen, L. (2005). Energy Consumption and air quality in growing-finishing pig houses for three climate regions using CIGR 2002 heat production equations. Agricultural Engineering International: The CIGR EJournal, VII, Manuscript BC 05 007. Ogink, N. W. M., Mosqueraa, J., Calvet, S., & Zhang, G. (2013). Methods for measuring gas emissions from naturally ventilated livestock buildings: developments over the last decade and perspectives for improvement. Biosystems Engineering, 116(3), 297e308. ¨ zcan, S. E., Vranken, E., & Berckmans, D. (2007). A critical O comparison of existing ventilation rate determination techniques for building opening and prospect for future. ASABE Publication number 701P0907cd Proceeding of the international symposium on air quality and waste management for agriculture. St-Joseph, MI: ASABE. ¨ zcan, S. E., Vranken, E., & Berckmans, D. (2005). Review on O ventilation rate measuring and modelling techniques in naturally ventilated buildings. In AIVC 26th conference e Brussels, Belgium, 21e23 September 2005 (pp. 111e121). Panagakis, P., & Axaopoulos, P. (2004). Comparison of two modeling methods for the prediction of degree-hours and heat-stress likelihood in a swine building. Transactions of the ASAE, 47(2), 585e590. Parton, W. J., & Logan, J. A. (1981). A model for diurnal variation in soil and air temperature. Agricultural Meteorology, 23, 205e216. Pedersen, S. (2002). Heat and moisture production for pigs on animal and house level. USAdPaper No. 024178 ASAE annual international meeting; CIGR XVth world congress. Hyatt Regency Chicago, IL. St. Joseph, MI: ASAE. Pedersen, S., Blanes-Vidal, V., Jørgensen, H., Chwalibog, A., Haeussermann, A., Heetkamp, M. J. W., et al. (2008). Carbon dioxide production in animal houses: a literature review. Manus BC 08 008. Agricultural Engineering International: CIGR Journal, X, 19. Pedersen, S., Takai, H., Johnsen, J. O., Metz, J. H. M., Groot, P. W. G., Koerkamp, G. H., et al. (1998). A comparison of three balance methods for calculating ventilation rates in livestock buildings. Journal of Agricultural Engineering Research, 70, 25e37. Perry, R. L., & Speck, E. P. (1962). Geometric factors for thermal radiation exchange between cows and their surroundings. Trans. ASAE, 5(1), 31e37. Porter, C. H., Pickering, N. B., Jones, J. W., & Hoogenboom, G. (2000). Weather module in DSSAT v. 4.0 Documentation and source code Listing. Report No. 2000-1203 Agricultural and biological engineering department research (pp. 25). Gainesville, FL, USA: University of Florida. Quiniou, N., Noblet, J., van Milgen, J., & Dubois, S. (2001). Modelling heat production and energy balance in grouphoused pigs exposed to low or high ambient temperatures. British Journal of Nutrition, 85, 97e106. Ramponi, R., & Blocken, B. (2012). CFD simulation of crossventilation for a generic isolated building: impact of computational parameters. Building and Environment, 53(2012), 34e48. Richardson, C. W. (1981). Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resources Research, 17, 182e190. Richardson, C. W., & Wright, D. A. (1984). WGEN: A model for generating daily weather variables. U.S. Department of Agriculture, Agricultural Research Service, ARS-8. Roy, J. C., Boulard, T., Kittas, C., & Wang, S. (2002). Convective and ventilation transfers in greenhouses: Part 1: the greenhouse considered as a perfectly stirred tank. Biosystems Engineering, 83(1), 1e20. Sarak, H., Onur, M., & Satman, A. (2005). Lumped-parameters models for low-temperature geothermal fields and their application. Geothermics, 34(6), 728e755.

258

b i o s y s t e m s e n g i n e e r i n g 1 1 6 ( 2 0 1 3 ) 2 4 6 e2 5 8

Shrestha, G. L., Cramer, C. O., Holmes, B. J., & Kummel, D. W. (1993). Wind-induced ventilation of an enclosed livestock building. Trans. ASAE, 36(3), 921e932. Stine, W. B., & Harrigan, R. W. (1985). Solar energy fundamentals design. New York, USA: John Wiley and Sons, Inc. Sto¨ckle, C. O., Nelson, R. L., Donatelli, M., & Castellvı‘, F. (2001). ClimGen: a flexible weather generation program. In M. Bindi, M. Donatelli, J. R. Porter, & M. K. Van Ittersum (Eds.), Proceedings of the second international symposium on modelling cropping systems, Florence, Italy (pp. 229e230). Strøm, J. S. (1978). Heat loss from cattle, swine and poultry as basis for design of environmental control systems in livestock buildings (in Danish). SBI-Landbrugsbyggeri 55. Denmark: Danish Building Research Institute. Strøm, J. S., & Morsing, S. (2004). StaldVent 5.0 simulation and climate. Bygholm, Horsens, Denmark: Department of Agricultural Engineering, Danish Institute of Agricultural Sciences. Supit, I., & van Kappel, R. R. (1998). A simple method to estimate solar radiation. Solar Energy, 63, 147e160. Supit, I., & Van der Goot, E. (2003). Updated system description of the Wofost crop growth simulation model as implemented in the crop growth monitoring system applied by the European Commission. The Netherlands: Treemail, Heelsum. Swierstra, D., & Van Ouwerkerk, E. N. J. (1985). A model estimating the effectiveness of shade structures on the production of dairy cows in hot climates. In Seminar of the 2nd technical section of the CIGR agricultural buildings in hot climate countries, Catania, Italy. Takle, E. S., & Brown, J. M. (1978). Note on the use of Weibull statistics to characterize wind speed data. Journal of Applied Meteorology, 17, 556e559. Tatarko, J., Skidmore, E. L., & Wagner, L. E. (1997). Wind erosion prediction system: weather generator submodel. In Wind erosion: An international symposium/workshop commemorating the 50th anniversary of USDA-ARS wind erosion research, Manhattan, KS, USA. http://www.weru.ksu.edu/symposium/proceedings/ tatarko.pdf. Thornton, P. E., & Running, S. W. (1999). An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity and precipitation. Agricultural and Forest Meteorology, 93, 211e228. Timmons, M. B., & Baughman, G. R. (1981). Similitude analysis of ventilation by the stack effect from an open ridge livestock structure. TRANSACTIONS of the ASAE, 24(4), 1030e1034. ¨ zcan, S., Vranken, E., Van Buggenhout, S., Van Brecht, A., Eren O Van Malcot, W., & Berckmans, D. (October 2009). Influence of

sampling positions on accuracy of tracer gas measurements in ventilated spaces. Biosystems Engineering, 104(2), 216e223. van’t Klooster, C. E., Bontsema, J., & Salomons, L. (1995). Dynamic model to tune a climatic control algorithm in pig houses with natural ventilation. Trans. ASAE, 38, 911e918. van ’t Ooster, A., & Both, A. J. (1988). Towards a better understanding of relations between building design and natural ventilation in livestock buildings. In Proc. 3rd intl. livestock environment symposium, 8e21. St. Joseph, Mich: ASAE. Wang, L., & Chen, Q. (2007). Theoretical and numerical studies of coupling multizone and CFD models for building air distribution simulations. Indoor Air, 17(5), 14. Wang, L. L., Dols, W. S., & Chen, Q. (2010). Using CFD capabilities of CONTAM 3.0 for simulating airflow and contaminant transport in and around buildings. HVAC and R Research. ISSN: 1078-9669, 16(6). ISSN: 1078-9669, 749e763. Wang, L., & Wong, N. H. (2006). Natural ventilation simulation with coupling program between building simulation (BS) and computational fluid dynamics (CFD) simulation program for accurate prediction of indoor thermal environment. In PLEA2006-The 23rd conference on passive and low energy architecture, Geneva, Switzerland, 6e8 September 2006. Wilks, D. S., & Wilby, R. L. (1999). The weather generation game: a review of stochastic weather models. Progress in Physical Geography, 23, 329e357. Winslow, J. C., Hunt, E. R., & Piper, S. C. (2001). A globally applicable model of daily solar irradiance estimated from air temperature and precipitation data. Ecological Modelling, 143, 227e243. Woodward, S. J. R., Barker, D. J., & Zyskowski, R. F. (2001). A practical model for predicting soil water deficit in New Zealand pastures. New Zealand Journal of Agricultural Research, 44, 91e109. Wu, Z., Stoustrup, J., & Heiselberg, P. (2008). Parameter estimation of dynamic multizone models for livestock indoor climate control. In 29th AIVC conference on advanced building ventilation and environmental technology for addressing climate change issues, Kyoto, Japan, October 2008. International Energy Agency, Air Infiltration and Ventilation Center, Operatine Agent and Management, INIVE EEIG. Zappavigna, P., & Liberati, P. (2005). Slotted roofs as a tool for improving the housing conditions in hot climateIn Proceedings of the seventh international symposium “Livestock Environment VII” A.S.A.E., Beijing, China, 18e20 May, 1-6, , ISBN 1-892769-48-4. Zhang, J., Janni, K. A., & Jacobson, L. D. (1989). Modelling for natural ventilation induced by thermal buoyancy and wind. The Transactions of the ASAE, 32(6), 2165e2174.