Air quality and thermal comfort analysis of kitchen environment with CFD simulation and experimental calibration

Journal Pre-proof Air quality and thermal comfort analysis of kitchen environment with CFD simulation and experimental calibration Zhenlei Chen, Jianjian Xin, Penyong Liu PII:

S0360-1323(20)30049-4

DOI:

https://doi.org/10.1016/j.buildenv.2020.106691

Reference:

BAE 106691

To appear in:

Building and Environment

Received Date: 3 December 2019 Revised Date:

18 January 2020

Accepted Date: 20 January 2020

Please cite this article as: Chen Z, Xin J, Liu P, Air quality and thermal comfort analysis of kitchen environment with CFD simulation and experimental calibration, Building and Environment, https:// doi.org/10.1016/j.buildenv.2020.106691. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. 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. © 2020 Published by Elsevier Ltd.

Air quality and thermal comfort analysis of kitchen environment with CFD simulation and experimental calibration Zhenlei Chena, b, Jianjian Xina, b, *, Penyong Liua, b

a

Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China

b

Jiangsu Province Collaborative Innovation Center of Modern Urban, Traffic

Technologies, Southeast University Road #2, Nanjing, 211189, P.R.China

*Corresponding author name: Jianjian Xin Affiliation: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China Detailed permanent address: Ningbo University (Meishan Campus) Email address: [email protected] (Zhenlei Chen) Email address: [email protected] (Jianjian Xin) Email address: [email protected] (Penyong Liu)

1

Abstract: During cooking fume particulate and high temperature are unavoidable in indoor environment and could pose serious threat to human health. This paper presents the theoretical formulas with experimental calibration and establishes an improved computational fluid dynamics (CFD) model based on Fluent V19.04. The present CFD model can accurately reflect the variation of fume concentration and assess air pollution in kitchen so as to improve thermal environment. In the present model, a hybrid grid by combing structured and unstructured grids is used to discretize the computational domain. The standard k-e turbulence model combined with a wall function is utilized to analyze the violent turbulent movement of particulate matter. The model is calibrated by the test data obtained from fume concentration experiments during cooking in kitchen. A relationship between the measured cooking fume concentration and the simulated steam vapor concentration is established approximately. Based on the improved CFD model, the movement of particulate matter under the effect of range hood with air curtain is simulated. The effects of exhaust volume on the particulate matter movement, air age, PMV and PPD indices are also investigated. The present CFD model provides a practical tool to evaluate the air quality and thermal comfort of the kitchen environment. Keywords: Kitchen environment, Air quality, Thermal comfort, Computational fluid dynamics, Experimental calibration

1. Introduction 2

It is found that there are more than 300 kinds of volatile organic compounds detected in cooking fumes. The fume condensations at high temperature are carcinogenic and mutagenic, posing a direct threat to human health. In modern houses, the kitchens are in the narrow and confined spaces and have poor ventilation capacity. Consequently, the indoor temperature cannot cool quickly and the cooking oil fumes cannot be discharged timely. Especially, Chinese cooking is characterized by stir-frying and braising, producing a large number of cooking oil fumes. The accumulating particulate matter deteriorates the indoor air quality. Exposure to particulate matter negatively affects the health of kitchen staff, leading to high risk of cerebrovascular disease, respiratory infections, cardiopulmonary disorders, ischemic heart disease, and lung cancer. Therefore, increasing ventilation efficiency, improving indoor air quality and thermal comfort are the burning issues of kitchen design for personnel health [1]. To analysis indoor air quality and thermal comfort, experiment is the common technique. For example, Atthajariyakul and Leephakpreeda [2] achieved the real-time determination of optimal indoor-air condition by considering thermal comfort, air quality and energy usage simultaneously. Predicted mean vote (PMV) and CO2 concentration can be preserved to the desired levels by their present method with less energy consumption. Akoua et al. [3] measured volatile organic compounds (VOC) concentration released from flooring material in a tested house. The test results showed that the VOC tracer concentration field is generally homogeneous, except near the flooring. Also, Hang et al. [4] conducted full-scale experiments to assess the 3

inter-cubicle airborne transmission in a shared room. Experimental analysis is reliable and it is widely used in various environment evaluations [5-7]. However, transient effects are obvious in the diffusion of cooking fume. The measurement is difficult, because the effective measurement time is very short. Moreover, the testing equipment can be blocked by the particulate matter, leading to incorrect measurement results, and even equipment damage. In addition, the error of human disturbance is large, leading to high testing cost and low accuracy. With the rapid development of computer technology and numerical algorithms, computational fluid dynamics (CFD) techniques provide an attractive alternative. Many CFD models have been successfully applied to the design and application of kitchen environmental engineering. In term of air quality study, Nielsen et al. [8] was the first ones to apply the CFD technique to the field of heating ventilating and air conditioning. They measured and calculated the velocity characteristics of ventilated rooms by laser-Doppler anemometry. Li et al. [9] studied the capture efficiency of a kitchen range hood in a confined domain by a two-zone mixing model. The results showed that the capture efficiency equals to the ratio of captured flow rate to the total plume flow rate at the front canopy height. Akoua et al. [10] analyzed the impact of volatile organic compounds emission on the concentration field in a ventilated room using a method of combining experiment and numerical simulation (Fluent 6.0). Andrey Livchak et al. [11] compared traditional mixing ventilation system and thermal displacement ventilation system in a typical kitchen by using a CFD model. Lai and Chen [12] analyzed the particle deposition and distribution in a chamber by 4

using Lagrangian method. They adopted the eddy interaction model to generate the instantaneous turbulent fluctuating velocity field and conducted a parametric analysis on the particle size. Lai and Chen [13] also developed a new Eulerian model to simulate particle dispersion in a small chamber. They applied a new near wall treatment to treat the anisotropic turbulence. Gao et al. [14] quantitatively studied the infection risks of SARS by CFD technique (Fluent). Tham [15] reviewed the challenge and development of indoor air quality and its effect on humans in last three decades. On the other hand, thermal comfort is also a critical index for the assessment of indoor environment. Many researchers studied thermal comfort from multiple aspects such as thermal comfort models [16], individual difference [17, 18] and thermal comfort index [19]. Xiang and Wang [20] evaluated the thermal comfort in a passenger compartment from several aspects such as three-dimensional (3D) temperature, PMV/PPD (predicted mean vote/predicted percentage of dissatisfied) index distributions and flow field by using FLUENT software. Yang et al. [21] analyzed indoor air quality parameters such as wind velocity, temperature field and air age by means of CFD technology. They found that good indoor thermal comfort was obtained. However, some local areas are not ventilated, and thus toxic gases cannot be released in time. Mady et al. [22] conducted the exergy analysis of human body. They developed the assessment indicators of thermal comfort conditions according to the concepts of destroyed exergy rate, exergy transfer rate to environment and exergy efficiency. Putra [23] visualized air velocity and indoor temperature distributions by 5

using COMSOL. Also, thermal comfort of building’s occupants was investigated by using a questionnaire method. Poshtiri and Mohabbati [24] assessed the thermal comfort of a shower cooling system by using ANSYS (Fluent 6.3) software and a thermodynamic model. Also, they performed a parametric analysis on geometric parameters and different environmental conditions. At present, many researchers have studied the dispersion of indoor pollutants by using experimental and numerical simulations. However, many studies emphasize the effects of air distribution on indoor pollutants. For the simulation of kitchen environment, indoor pollutants are usually replaced by pollutants such as benzene and formaldehyde. The physical properties of cooking oil fumes are usually neglected. Moreover, few studies attempted to establish comprehensive evaluation indices by combining air quality and thermal comfort. The present study aims to establish a CFD model to conduct environment analysis of range hood with air curtain from multiple aspects including air quality and thermal comfort. Therefore, the variation of fume concentration in kitchen can be well predicted. The numerical model is established using software Fluent and is calibrated against the experimental data. Based on the present model, the effects of exhaust volume on the particulate matter movement, air age, PMV and PPD indices are examined. The evaluation standard with respect to the air quality and thermal comfort for kitchen environment is developed. 2. Mathematical model and CFD Simulation 2.1. Mathematical model The zero equation turbulence model is used to conduct the transient calculation of 6

kitchen environment. The governing equations for the turbulent motion of air [25] are expressed as: ∂ui =0 ∂xi

(1)

(

∂ − ρ ui′u ′j ∂ ( ρ ui ) ∂ ( ρ ui u j ) ∂ 2ui ∂p + =− +µ + ∂t ∂x j ∂xi ∂x j ∂x j ∂x j

)

2 ∂ ( ρ h) ∂ ( ρ hui ) (k + kt )∂ u j + = + Sh ∂t ∂xi ∂xi ∂x j

(2)

(3)

Eqs. (1-3) are the continuity, momentum, and energy equations, respectively, where u is the fluid velocity; i, j = 1, 2, and 3 are the x-, y-, and z-directions, respectively; p is the pressure; t is the time; ρ is the density; µ is the dynamic viscosity coefficient; T

− ρ ui′u ′j is the Reynolds stresses; h is the enthalpy with h = ∫T C p dT , C p is the heat ref

capacity, T is the temperature, Tref = 298.15 K is the reference temperature; kt = C p µt / Prt , Prt is the Plante number; Sh is the source term; k is the turbulent

kinetic energy per unit mass of fluid, and it is defined as: 1 k = ui′u ′j 2

(4)

Based on the Boussinesq assumption, the Reynolds stresses induced by turbulent fluctuation can be represented in the same way as the viscous stresses:

 ∂u ∂u j  2  ∂u  − ρ ui′u ′j = µt  i +  −  ρ k + µt i  δ ij  ∂x  ∂xi   j ∂xi  3 

(5)

where µt is the turbulence viscosity and it represents the velocity fluctuation:

µt = 0.03874 ρ u0 L

(6)

where u0 is the local average velocity; L is the characteristic dimensions of turbulent fluctuations. Eq. (6) is the zero equation turbulence model proposed by Chen et al. 7

[26]. The governing equation for concentration diffusion is the component transport equation: ∂ ( ρYi ) ∂ ( ρYi u j ) ∂J + = − i + Si ∂t ∂x j ∂x j

(7)

where Yi is the component mass fraction; Si is the mass source term; Ji is defined as:

 µ J i = −  ρ Di + t Sct 

  ∇Yi 

(8)

where Di is the mass diffusion coefficient of component i, Sct = µt / ( ρ Dt ) is the turbulent Schmidt number, and Dt is the turbulent diffusion term which is set as 0.7 in the present study. 2.2. Geometry model and grid generation According to the actual dimensions of a residential kitchen and the distribution of kitchenware equipment, the geometry model of the kitchen is established and shown in Fig. 1. Its dimensions are 4.2 m in length, 2.4 m in width, and 2.6 m in height. Considering the diversity and complexity of kitchenware, the kitchen model is simplified reasonably. Firstly, the radiation effect of the sun is ignored and the windows are not considered. Secondly, the blade rotation of the range hood is not considered to simplify the computational model. Thirdly, a human model is established to consider the effect of the kitchen environment on the human body.

8

Fig. 1. Kitchen layout diagram

The dimensions of the range hood are 0.89 m in length, 0.4 m in width, and 0.39 m in height. The range hood is 1.43 m away from the ground, and its geometry model is shown in Fig. 2. A piping is laid on the flue system to intake fresh air from outside. The ventilation of the range hood is 8 m3·min-1. The flare angle of the smoke screen is 110o. The kitchen model is discretized on a hybrid grid by combing both structured and unstructured grids. The computational grid is shown in Fig. 3. The maximum and minimum grid sizes in the x-, y-, and z-directions are 80 mm and 1 mm, respectively. Local grid refinement is applied around the ventilation outlet, boiler, and human body. The grid number is set as 930, 000 to achieve a balance between the grid convergence and computational efficiency.

Fig. 2. Range hood (fresh air supply available)

Fig. 3. Computational grid of the kitchen model 9

2.3. Boundary conditions of CFD model The water vapor is used to model the produced oil fume in the kitchen since the density of the water vapor is similar to the oil fume. The difference of the diffusion property between oil fume and water vapor is not considered. The indoor fluid is viewed as the steady turbulent flow. Therefore, the steady-state standard k-turbulence model is adopted to model the kitchen environment. The SIMPLE (Semi-Implicit Method for Pressure Linked Equations) algorithm is used to decouple the velocity and pressure. The pressure term is treated with body force weighted scheme. The convective and viscous terms are discretized with second-order upwind scheme. Boiler #1 is the generation source of oil fume. The variation trends of inlet velocity and concentration percentage with time are shown in Fig. 4. Boiler #1 is turned on at the period of 56~95 s and 956~995 s. The velocity of the water vapor at the inlet is 2 m·s-1. The concentration percentage is set to 100% and the temperature is set to 70 oC. The simulation time is set to 1800 s, in consistent with the actual testing time. Meanwhile, the air curtain is turned on at the period of 0~900 s, and off at the period of 901~1800 s. Boiler #2 is turned off. The pressure boundary condition (-360 Pa) is applied at the smoke vent. The velocity boundary condition is applied at upper and lower air curtains, and the outflow velocity is 2.0 m·s-1. In addition, the metabolic rate of human body is set as 58.15 W·m-2. Other boundaries are set as adiabatic, non-permeable, and non-slip boundary conditions.

10

(a) Inlet velocity (b) Concentration percentage Fig. 4. Variation trends of inlet velocity and concentration percentage

2.4. Assessment of kitchen cooking environment The evaluation criteria for kitchen environment usually includes the air quality and thermal comfort. The air quality indicates the level of air pollution, and it is usually evaluated by the pollutant concentration. In the present study, the air quality is evaluated by the fume concentration and air age [27]. In addition, thermal comfort is a satisfaction evaluation index of human to the surrounding thermal environment, and is expressed by PMV and PPD indices as listed below. (1) Fume particulates concentration Pollutants produced by cooking are emitted in the form of fume particulates. The particulates contain hazardous substances such as carbon dioxide, carbon monoxide, and nitrogen oxide, as shown in Table 1, which could threaten human health seriously. Table 1 Typical harmful substances threatening human health Harmful substances

Harmfulness to the human health

Carbon dioxide

Damaging lung function when its concentration is more than 2%

Carbon monoxide

Binding with hemoglobin, causing hypoxia of the human body

Nitrogen oxide

Damaging respiratory system

(2) Air age The air age reflects the freshness of indoor air. The tracer gas method is usually used to measure the air age. The variation of tracer gas concentration with the time is 11

measured at a point to obtain the frequency distribution function of the air age. The air age τ at a point is expressed as: ∞

∞

∞

0

0

0

τ = ∫ tϕ (t )dt = ∫ (1 − φ (t ))dt = ∫

C (t )dt C (0)

(9)

where ϕ (t ) is the frequency distribution function [28], φ (t ) is the cumulative distribution function, C (t ) is the concentration of tracer gas at instant t. When the fresh air is turned on, the concentration of the trace gas is gradually reduced. (3) PMV and PPD indices PMV (predicted mean vote) and PPD (predicted percentage of dissatisfied) are proposed by Fanger [29] based on the thermal comfort equation. Compared with a single evaluation method such as the temperature and velocity, PMV and PPD indices are more comprehensive and appropriate. The PMV equation is expressed as:

PMV = ( 0.0303e0.036 M

( M − W ) − 0.42 ( M − W ) − 58.15 −    3.05 ×10 −3 × 5733 − 6.99 ( M − W ) − Pa  −  + 0.028 ) ×   −5 1.7 × 10 M ( 5867 − Pa ) − 0.0014 M ( 34 − ta )   4 4 −8    −3.96 × 10 f cl × ( tcl × 273) − ( tr + 273)  

(10)

where M is the human metabolic rate, W is the mechanical work made by the person; tr is the average radiation temperature; ta is the air temperature; fcl is the ratio of the surface area of human body on dress to the surface area exposed; Icl is the thermal resistance of the dress; tcl is the surface temperature of the dress; Pa is the steam partial pressure and it is related to the air relative humidity. PPD is expressed as:

PPD = 100 − 95 × e−0.03353×PMV

4

− 0.2179× PMV 2

(11)

The smaller the PPD value, the lower the the unsatisfactory rate of human body. 12

Considering the physiological and psychological differences, PPD can well reflect the unsatisfactory rate of human body to kitchen thermal environment.

3. Calibration analysis of the CFD model To calibrate the computational model, the oil fume concentration is tested in the process of making Chinese stir-fried shredded potato, as shown in Fig. 5. Lighthouse ambient particulate monitors in Fig. 6 are placed on two positions. One gauging point P1 is 188 cm away from the ground, 107 cm away from the rear wall of the cupboard, and 40 cm away from the left side of the range hood. Another gauging point P2 is 166 cm away from the ground, 77 cm away from the real wall of the cupboard, and 28 cm away from the left side of the range hood. The testing time is 1800 s and the monitoring data are collected each 5 s. The experiment is strictly conducted according to the testing procedure, and it is repeated 20 times. The mass concentration of the oil fume ftest at two gauging points are shown in Fig. 7.

Fig. 5. Test the scene graph

Fig. 6. Lighthouse particle sampler

13

(a) Gauging point P1 (b) Gauging point P2 Fig. 7. Groups of soot mass concentration curve 3.1. Initial calibration analysis Considering the deviations produced in the testing procedure, the data are treated with the 6σ method. The outliers are deleted when their value are among the maximum 5% or the minimum 5%. Then, the remained data are averaged. Also, 18 group effective data are remained when the outliers are deleted from the 20 group original data. Thus, the mass concentrations of the oil fume attained by the experiment are obtained, as shown in Fig. 8, in which the simulation results fCFD are also compared with the experimental data ftest. The simulation results are generally consistent with the experimental data, both exhibit two successive peaks. Moreover, the rear peak, corresponding to the moment when the air curtain is turned off, is a little higher than the front peak, corresponding to the moment when the air curtain is turned on. The reason is that the fume concentration at the gauging point is diluted by the fresh air. The simulation results reasonably reflect the variation trend of the fume concentration.

14

(a) P1 (b) P2 Fig. 8. 6σ processed test data with time curve Note that the fume concentration ftest measured by the experiment slightly deviates the simulated fCFD modeled with the water vapor. To quantitatively analyze the present CFD model, an approximate formula is established to reflect the relationship between ftest and fCFD, as listed below:

ftest = n∆pfCFD + m

(12)

where n and m are the constants, ∆p is the pressure difference at gauging point P1 or P2 between the two situations when the air curtain is open or closed. The pressure difference is 1.02 Pa, as shown in Fig. 9. According to the experimental and simulated fume concentrations at two instants, it is obtained that n = 1062.09 µg·m-3·Pa-1, and m = 26.25 µg·m-3.

Fig. 9. Static pressure at gauging points P1 and P2

15

Note that the pressure deviations between ftest and fCFD at three moments, two peaks and the moment of 1800 s, should be emphasized. Table 2 shows the average errors of the three moments between ftest and fCFD at two gauging points. The average errors are 44% and 43% at two gauging points, respectively. Since the present CFD model is simplified to the sealing treatment, the model does not agree with the reality. Therefore, modification on the present CFD model is needed. Table 2 Initial calibration analysis at two gauging points P1 and P2

Gauging Data point source P1 P2

ftest fCFD ftest fCFD

Calibration values/(µg·m-3·Pa-1) Open air Closed air Rear curtain curtain 91 112 25 65 128 63 65 79 22 55 94 62

Average errors /% 44 43

3.2. Modification of the CFD model The effects of the door seam are considered in the modified CFD model, as shown in Fig. 10. The boundary condition at this door seam is set as standard atmospheric pressure. Other boundary conditions are kept the same as those used in Section 2.3. Then, the fume concentration fCFD obtained by the modified CFD model is compared with the ftest obtained by the experiment, which is shown in Fig. 11. Better agreements are achieved between the results of the modified CFD model and the experimental data when the effect of door seam is considered. The calculated air intake flow at the door seam is 5.7 m3·min-1 and the flow at the inlet of the range hood is 14.0 m3·min-1. The results show that the flow at the door seam has a significant effect on the simulation of the kitchen environment. Hence, the effect of the door seam cannot be 16

ignored.

Fig. 10. Modified CFD model considering the door seam effect

(a) P1 (2) P2 Fig. 11. Comparison of fume concentrations between the modified CFD model and experiment

Fig. 12. Static pressure at two gauging points P1 and P2

Fig. 12 shows the static pressure at two gauging points P1 and P2 for two cases when the air curtain is open and closed. It is observed that the pressure difference is 2.9 Pa. Similarly, according to the values in the y-axis at two instants of 0 s and 1800 s in Fig. 11, it is obtained that n = 1109.2 µg·m-3·Pa-1, and m = 26.3 µg·m-3. Table 3 17

shows the simulated and experimental fume concentrations at two gauging points for three moments, two peaks and the moment of 1800 s. The average error of the three moments at the gauging point P1 reduces to 10% from 44%, and the average error at the gauging point P2 reduces to 15% from 43%. Therefore, the modified CFD model can reasonably predict the real kitchen environment. Table 3 Calibration analysis modified by the CFD model at two gauging points P1 and P2

Gauging Data point source P1 P2

ftest fCFD ftest fCFD

Calibration values/(µg·m-3·Pa-1) Open air Closed air Rear curtain curtain 91 112 25 86 119 31 65 79 22 53 84 28

Average errors /% 10 15

Table 4 Kitchen environment simulation conditions Velocity of fresh Cases Exhaust volume / m3· min-1 air/m·s -1 1 8 2.415 2 11 2.415 3 14 2.415 4 20 2.415

4. Results and discussions In section 3, a calibrated CFD model is established with improved accuracy. To study the effects of the ventilation system on the air quality of kitchen environment, this section discusses the effects of the exhaust volume on four evaluation indices, fume particulates movement, air age, PMV and PPD. Table 4 shows the various working conditions considered in the present study. Based on the four evaluation indices, we intend to appropriately assess the air quality and thermal environment in kitchen, thus to find the optimal exhaust volume. 4.1. Effects on the fume particulates movement 18

The air quality of the kitchen directly affects the health of the human body, where fume pollution problems are the most serious. This section examines the motion trajectory of the fume particulates from several aspects. Emphasis is put on the fume conditions around the human body. Figs. 13-16 show the motion trajectory of the fume particulate and the distribution of fume concentration under different exhaust air volumes for cases 1 to 4 to investigate the effects of the particulate matter on the kitchen environment under different exhaust air volumes. As seen in Fig. 13(a), most fume particulates are excluded outdoor by the range hood. However, some fume particulates are not removed timely, and they cruise indoor. Consequently, they adhere to the indoor wall and the human body, leading to the pollution. In Fig. 13(b), the movements of the fume particulates become more violent and complex under the effects of the range hood and fresh air. Moreover, the fume particulates exhibit random motion pattern under the effects of Brownian motion, and they are sensitive to the air distribution. Meanwhile, the fume particulates are highly accumulated around the vent of the range hood, due to the negative pressure there. The fume particulates blow to the ceiling under the effect of fresh air. Also, obvious fume accumulation phenomena occur around the breathing zone of the human body. Consequently, particulate exposure of the human body increases, which is detrimental to the health of the human body. For case 2, the spilled particulates are reduced distinctly. Therefore, their harm to the human body is weakened. When the exhaust volume increases to 14 m3·min-1, a small amount of fume particulates spill over and they are attached to the cupboard. As the exhaust volume increases to the maximum of 20 19

m3·min-1 (case 4), the optimum kitchen environment is obtained. The results indicate that, the larger the extract volume, the better the air quality, leading to the lower pollutant concentration.

(a) Motion trajectory of fume particulates (b) Distribution of fume concentration at Z = - 2.62 m Fig. 13. Working condition for case 1 (Exhaust air volume 8 m3·min-1)

(a) Motion trajectory of the oil fume (b) Distribution of fume concentration at Z = - 2.62 m Fig. 14. Working condition for case 2 (Exhaust air volume 11 m3·min-1)

(a) Motion trajectory of the oil fume (b) Distribution of fume concentration at Z = - 2.62 m Fig. 15. Working condition for case 3 (Exhaust air volume 14 m3·min-1)

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(a) Motion trajectory of the oil fume (b) Distribution of fume concentration at Z = - 2.62 m Fig. 16. Working condition for case 4 (Exhaust air volume 20 m3·min-1)

4.2. Effects on air age Air age is an important index identifying the air quality. It reflects the time length of air entering the room. The younger the air age, the better the air quality. Figs. 17-20 show the contours of air age at typical sections for case 1 to case 4, respectively. For example, contours of air age near the human body for case 1 are presented in Fig. 17(a). The air age adjacent to the ventilation system is small. However, the air age at some dead corners is relatively large. Fig. 17(b) presents the contours of air age near mouth and nose. The air age adjacent to the vent of fresh air is significant smaller than that near the cupboard. Also, the air age increases from the left to the right in the kitchen, because of the airflow pattern. From Figs. 17 to 20, it is found that the air age reduces obviously and the air quality is improved significantly as the exhaust volume increases.

(a) Contours of air age at Z = -2.62 m (b) Contours of air age at Y = 1.6 m Fig. 17. Working condition 1 (Exhaust air volume 8 m3·min-1)

(a) Contours of air age at Z = -2.62 m

(b) Contours of air age at Y = 1.6 m 21

Fig. 18. Working condition 2 (Exhaust air volume 11 m3·min-1)

(a) Contours of air age at Z = -2.62 m (b) Contours of air age at Y = 1.6 m Fig. 19. Working condition 3 (Exhaust air volume 14 m3·min-1)

(a) Contours of air age at Z = -2.62 m (b) Contours of air age at Y = 1.6 m Fig. 20. Working condition 4 (Exhaust air volume 20 m3·min-1)

4.3. Effects on the PMV and PPD index The present study programed UDF code for PMV and PPD equations. Also, we presented the contours of the PMV and PPD values on the kitchen environment by combining the user defined function of Fluent. Therefore, the kitchen thermal environment is quantitatively analyzed. The human body preserves the standing posture during the test, and the metabolic rate is set as 123 w·m-2. Fig. 21 shows the PMV contours on the vertical profiles of the kitchen. PMV values on the kitchen are larger than zero, because the cooking activities in the kitchen lead to a warm environment. Moreover, the PMV values on the upper area of the kitchen are larger than those on the lower area of the kitchen, due to the rising of hot air. As the exhaust volume of the range hood increases, the PMV values around the human body approach to the zero and the degree of thermal comfort increases. The PMV values on 22

the kitchen range region are the largest, because this area is the region of heat source. Also, the area of heat source decreases with the rising of exhaust volume. Therefore, the air environment around the human body is improved.

(a) Case 1 (8 m3·min-1)

(b) Case 2 (11 m3·min-1)

(c) Case 3 (14 m3·min-1) (d) Case 4 (20 m3·min-1) Fig. 21. Contours of PMV at Z = -2.62 m

The PMV values on all parts of the human body under different exhaust volumes (cases 1, 2, 3, and 4) are presented in Fig. 22. It can be found that PMV values of each part decrease with the increase of exhaust volume, except the ankle position. In other words, thermal sensation of each part is from hot to warm, and better thermal comfort is obtained. Meanwhile, the PMV values on the ankle position are high for a large exhaust volume. This is because the fresh air is taken in by the range hood and it cannot reach the ankle position. Consequently, the heat on the ankle position cannot be taken away. In addition,

23

the thermal comfort on the upper region of the human body increases gradually along the altitude direction, due to the movement of hot air.

Fig. 22. PMV values of various parts of the human body

Fig. 23 shows the PPD contours of kitchen environment for cases 1, 2, 3, and 4. The PPD values around the human body are reduced from approximately 55% to 35% when the exhaust volume increases from 8 m 3 ·min-1 to 11 m 3 ·min-1. However, dissatisfactory rate of thermal sensation is relatively large, as shown in Fig. 23(a), (b). For V e = 14 m3·min-1 (case 3), the PPD values of the kitchen are generally between 0% and 30%. Specifically, the

PPD

values

around

the

human

body

are

approximately

25%.

Dissatisfactory rate of thermal sensation is relatively low, and suitable environment is obtained. When the exhaust volume increases to the maximum of 20 m3·min-1 (case 4), the PPD values of the kitchen are generally between 0% and 25%, and those around the human body are about 25%. Again, relatively low dissatisfactory rate of thermal sensation is obtained. However, the PPD values around the chest region are slightly increased. The reason is

24

that the fresh air is taken into the exhaust vent due to the overlarge exhaust volume, leading to the disturbance of the fluid field.

(a) Case 1 (8 m3·min-1)

(b) Case 2 (11 m3·min-1)

(c) Case 3 (14 m3·min-1) (d) Case 4 (20 m3·min-1) Fig. 23. PPD contours at Z = -2.62 m

Fig 24 shows the PPD values around all parts of the human body under different exhaust volumes (cases 1, 2, 3, and 4). The variation trend of the PPD is consistent with that of the PMV. The results show that the PPD values of each part are significantly reduced as the exhaust volume increases except the ankle region, and they exhibit the variation trend of “L” pattern. Moreover, the PPD values of each part are gradually approaching. Specifically, the sensitivities of the thermal comfort are the highest around the nose and mouth. For V e = 8 m3·min-1, the PPD value around the nose and mouth is 58%. When the exhaust volume increases to 20 m3·min-1, the PPD value is 31%. Good thermal comfort on each part of the human body is obtained in the case 4. Also, the PPD 25

differences on each part are small, and the thermal comfort on each part is balanced. Good satisfactory rate is achieved. Thus, it can be concluded that the thermal comfort on the human body is effectively improved by increasing the exhaust volume.

Fig. 24. PPD values for various parts of the human body

5. Conclusions In this study, a CFD model by combining experimental calibration was established to simulate kitchen cooking environment. To validate the reliability of the present CFD method, an experiment was conducted to measure the fume concentration and temperature at the sensitive areas of the human body. Based on the experimental data, an improved CFD model was established in term of a calibrated formula. With this CFD model, the air quality and thermal comfort of the kitchen environment was studied under different exhaust volumes. The air quality standards with respect to the experimental kitchen was established. List below are the conclusions obtained from the present study. The results of the calibrated CFD model are consistent with the experimental data in term of the fume concentration. The average errors at two 26

gauging points are reduced to 10% with the new model from 40% with the original CFD model, approximately. Based on the calibrated CFD model, the effects of the exhaust volume on the air quality and the thermal comfort can be evaluated. It is found that kitchen fume can be easily discharged to outdoor when exhaust volume is 14 m3·min-1, approximately, and the fume concentration at mouth and nose area can be decreased to a satisfied level. On the other hand, the increasing of the exhaust volume would reduce the dissatisfactory rate of human thermal sensation to some extent. The dissatisfaction rate of thermal sensation around the human body decreases as “L” pattern, while the thermal comfort increases significantly. Indoor pollutant can be well controlled and good thermal comfort can be obtained when exhaust volume is around 11~14 -1

m3·min .

Acknowledgements The Project was supported by the National Science Foundation of China (Grant NO. 51909124), Ningbo University Talent Project (Grant NO. 421806920), and K.C. Wong Magna Fund in Ningbo University.

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Highlights An improved computational fluid dynamics (CFD) model is established by deriving a correction formula.

An experimental test is conducted to calibrate the present CFD model.

The effects of exhaust volume on the particulate matter movement, air age, PMV and PPD indices are investigated

We establish an evaluation standard with respect to the air quality and thermal comfort for kitchen environment.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: