Proper meshing is critical for accurate computational fluid dynamics (CFD) simulation. In the three stages of CFD simulation, pre-processing accounts for about 40–60% of the total time. In this study, the performance of the grid for a large number of numerical simulations was analyzed, and base mesh sizes were recommended for different indoor geometric models. The simulation results for temperature, velocity, mesh resolution and computation time were compared, and mesh types were recommended for different models. Considering factors such as wall function, wall y+, face quality of the mesh, grid volume change rate and prism layer setting, a semi-empirical mesh strategy was presented. This strategy was verified in three cases with different geometric complexity. The verification results showed that the mesh strategy can yield reasonable simulation results. The root-mean-square error of the velocity simulation results of the optimized grid can be reduced by nearly 20%. This strategy can save nearly 50% of the mesh adjustment time. Through analysis and fitting of the grid results, the evaluation criteria for this mesh strategy were obtained. The criteria can be used to evaluate the cases using this mesh strategy.
YuJFengX. A review of developments in computational fluid dynamics. Mod Manuf Technol Equip2013; 28: 25–26.
2.
NielsenPV. Berechnung der luftbewegung in einem zwangsbelüfteten raum. G I - Gesundheits Ingenieur1973; 94: 299–302.
3.
ShephardMS. Approaches to the automatic generation and control of finite element meshes. Appl Mech Rev1988; 41: 169–185.
4.
WangHZhaiZJ. Analyzing grid independency and numerical viscosity of computational fluid dynamics for indoor environment applications. Build Environ2012; 52: 107–118.
5.
SorensenDNNielsenPV. Quality control of computational fluid dynamics in indoor environments. Indoor Air2003; 13: 2–17.
6.
ZhaiZJWangH. Optimizing the trade-off between grid resolution and simulation accuracy: coarse grid CFD modeling. Atlanta, ASHRAE Research Project2011; ((RP-1418),): 1–147.
7.
LiYNielsenPV. CFD and ventilation research. Indoor Air2011; 21: 442–453.
8.
MurakamiSKatoSNaganoSTanakaY. Diffusion characteristics of airborne particles with gravitational settling in a convection-dominant door flow field. ASHRAE Trans1992; 98: 82–97.
9.
LuWHowarthATAdamNRiffatSB. Modeling and measurement of airflow and aerosol particle distribution in a ventilated two-zone chamber. Build Environ1996; 31: 417–423.
10.
ZhaoBLiXYanQ. A zero-equation turbulence model is used to simulate air flow in a ventilated air-conditioned room. J Tsinghua Univ (Sci & Tech)2001; 41: 109–113.
11.
ZhaiZChenQ. Numerical determination and treatment of convective heat transfer coefficient in the coupled building energy and CFD simulation. Build Environ2004; 39: 1001–1009.
12.
WangMChenQ. Assessment of various turbulence models for transitional flows in an enclosed environment (RP-1271). HVAC&R Res2009; 15: 1099–1119.
13.
WangMLinC-HChenQ. Advanced turbulence models for predicting particle transport in enclosed environments. Build Environ2012; 47: 40–49.
14.
ChenCLiuWLinC-HChenQ. Comparing the markov chain model with the eulerian and lagrangian models for indoor transient particle transport simulations. Aerosol Sci Tech2015; 49: 857–871.
15.
ZouYZhaoXChenQ. Comparison of STAR-CCM+ and ANSYS Fluent for simulating indoor airflows. Build Simul2018; 11: 165–174.
16.
Van HooffTBlockenB. Mixing ventilation driven by two oppositely located supply jets with a time-periodic supply velocity: a numerical analysis using computational fluid dynamics. Indoor Built Environ2020; 29: 603–620.
17.
XuWChenQ. Simulation of mixed convection flow in a room with a two-layer turbulence model. Indoor Air2000; 10: 306–314.
18.
SrebricJChenQ. An example of verification, validation, and reporting of indoor environment CFD analyses. ASHRAE Trans2002; 2: 185–194.
19.
HuangJMChenQRibotBRivoalenH. Modelling contaminant exposure in a single-family house. Indoor Built Environ2004; 13: 5–19.
20.
ChenQZhaiZJ. The use of CFD tools for indoor environment design. In: MalkawiAAugenbroeG (eds) Advanced Building Simulation. New York, NY: Spon Press, 2004, pp. 119–140.
21.
YangHZhangHYouS. CFD simulation of office thermal environment based on Airpak. J Shandong Jianzhu Univ2004; 19: 41–44.
22.
ZhangZChenQ. Experimental measurements and numerical simulations of particle transport and distribution in ventilated rooms. Atmos Environ2006; 40: 3396–3408.
23.
LauJChenQ. Floor-supply displacement ventilation for workshops. Build Environ2007; 42: 1718–1730.
24.
YaoTLinZ. An experimental and numerical study on the effect of air terminal layout on the performance of stratum ventilation. Build Environ2014; 82: 75–86.
25.
ZhangCKristensenMHJensenJSHeiselbergPKJensenRJPomianowskiM. Parametrical analysis on the diffuse ceiling ventilation by experimental and numerical studies. Energy Build2016; 111: 87–97.
26.
ZhangCHeidelbergPKChenQPomianowskiM. Numerical analysis of diffuse ceiling ventilation and its integration with a radiant ceiling system. Build Simul2017; 10: 203–218.
27.
ChengYLiZFongZ. Influence of air supply angle on heating effect of stratum ventilation arranged on the same side of the return tuyeres in winter. Build Sci2017; 33: 120–127.
28.
ZhaoXChenQ. Inverse design of indoor environment using an adjoint RNG k-ε turbulence model. Indoor Air2019; 29: 320–330.
29.
ZhangSLinZAiZHuanCChengYWangF. Multi-criteria performance optimization for operation of stratum ventilation under heating mode. Appl Energy2019; 239: 969–980.
30.
FanY. Performance evaluation of different air distraction systems on thermal uniformity and energy-saving: a case study of a Japanese detached house. Indoor Built Environ2019; 28: 186–194.
31.
ParkDYChangS. Numerical investigation of thermal comfort and transport of expiratory contaminants in a ventilated office with an air curtain system. Indoor Built Environ2019; 28: 401–421.
32.
KongXXiCLiHLinZ. Multi-parameter performance optimization for whole year operation of stratum ventilation in offices. Appl Energy2020; 268: 114966.
33.
BosbachJPennecotJWagnerCRaffelMLercheTReppS. Experimental and numerical simulations of turbulent ventilation in aircraft cabins. Energy2006; 31: 694–705.
34.
ZhangTChenQ. Novel air distribution systems for commercial aircraft cabins. Build Environ2007; 42: 1675–1684.
35.
ShiZChenQ. Thermal comfort analysis of displacement ventilation system coupled with passive chilled beams. In: 5th International High-Performance Buildings Conference at Purdue, West Lafayette, IN, 9–12 July 2018.
36.
ChakrounWAlotaibiSHabchiCGhaliKGhaddarN. Comparison of removal effectiveness of mixed versus displacement ventilation during vacuuming session. Build Environ2019; 155: 118–126.
37.
ShiZLuZChenQ. Indoor airflow and contaminant transport in a room with coupled displacement ventilation and passive-chilled-beam systems. Build Environ2019; 161: 106244.
38.
TanDLiBChengYLiuHChenJ. Airflow pattern and performance of wall confluent jets ventilation for heating in a typical office space. Indoor Built Environ2020; 29: 67–83.
39.
HuJKangYYuJZhongK. Numerical study on thermal stratification for impinging jet ventilation system in office buildings. Build Environ2021; 196: 107798.
40.
ZhouYJiS. Experimental and numerical study on the transport of droplet aerosols generated by occupants in a fever clinic. Build Environ2021; 187: 107402.
41.
CabléAMichauxGInardC. Addressing summer comfort in low- energy housings using the air vector: a numerical and experimental study. In: Proceedings of the 33th AIVC Conference, Brussels, Belgium, 10–11 October2012, p. 10.
42.
ZhuSSrebricJRudnickSNVincentRLNardellEA. Numerical modeling of the indoor environment with a ceiling fan and an upper-room ultraviolet germicidal irradiation system. Build Environ2014; 72: 116–124.
43.
RužićD. Influence of the ventilation system setting in the passenger car on the local thermal sensation of a driver. J Therm Sci Technol2015; 35: 125–134.
44.
LiuCZhouGLiH. Analysis of thermal environment in a hospital operating room. Procedia Eng2015; 121: 735–742.
45.
PeiGRimDSchiavonSVannucciM. Effect of sensor position on the performance of CO2-based demand controlled ventilation. Energy Build2019; 202: 109358.
46.
ZhangZChenXMazumdarSZhangTChenQ. Experimental and numerical investigation of airflow and contaminant transport in an airliner cabin mockup. Build Environ2009; 44: 85–94.
47.
WanMPSze ToGNChaoCYHFangLMelikovA. Modeling the fate of expiratory aerosols and the associated infection risk in an aircraft cabin environment. Aerosol Sci Tech2009; 43: 322–343.
48.
YanWZhangYSunYLiD. Experimental and CFD study of unsteady airborne pollutant transport within an aircraft cabin mock-up. Build Environ2009; 44: 34–43.
49.
GuptaJKLinC-HChenQ. Transport of expiratory droplets in an aircraft cabin. Indoor Air2011; 21: 3–11.
50.
RaiACChenQ. Simulations of ozone distributions in an aircraft cabin using computational fluid dynamics. Atmos Environ2012; 54: 348–357.
51.
EbrahimiKHosniMHZhengZ. Computational study of turbulent airflow in a full-scale aircraft cabin mockup: Part 1 - Determination of boundary conditions at the outlet of air diffusers. In: Proceedings of the ASME 2013 Fluids Engineering Division Summer Meeting, Incline Village,Nevada, 79–11 July 2013, p.
52.
RaiACLinC-HChenQ. Numerical modeling of volatile organic compound emissions from ozone reactions with human-worn clothing in an aircraft cabin. HVAC&R Res2014; 20: 922–931.
53.
LiuWDuanRChenCLinC-HChenQ. Inverse design of the thermal environment in an airliner cabin by use of the CFD-based adjoint method. Energy Build2015; 104: 147–155.
54.
YouRLinCHWeiDChenQ. Evaluating the commercial airliner cabin environment with different air distribution systems. Indoor Air2019; 29: 840–853.
55.
FišerJJíchaM. Impact of air distribution system on quality of ventilation in the small aircraft cabin. Build Environ2013; 69: 171–182.
56.
YouRLiuWChenJLinC-HWeiDChenQ. Predicting airflow distribution and contaminant transport in aircraft cabins with a simplified gasper model. J Build Perform Simul2016; 9: 699–708.
57.
YouRChenJShiZLiuWLinC-HWeiDChenQ. Experimental and numerical study of airflow distribution in an aircraft cabin mock-up with a gasper on. J Build Perform Simul2016; 9: 555–566.
YuanXChenQGlicksmanLR. Measurements and computations of room airflow with displacement ventilation. ASHRAE Trans1999; 105: 340–352.
60.
DuanRLiuWXuLHuangYShenXLinC-HLiuJChenQSasanapuriB. Mesh type and number for the CFD Simulations of air distribution in an aircraft cabin. Numer Heat Transf B Fundam2015; 67: 489–506.
61.
CaoQLiuMLiXLinC-HWeiDJiSZhangTChenQ. Influencing factors in the simulation of airflow and particle transportation in aircraft cabins by CFD. Build Environ2022; 207: 108413.
62.
DuanRHuangYShenXLiuJ. Influence of grid type on numerical calculation of flow field in empty aircraft cabin. Chin J Appl Mech2015; 32: 833–838.
63.
ChenHZhouXFengZCaoS. Application of polyhedral meshing strategy in indoor environment simulation: Model accuracy and computing time. Indoor Built Environ2022; 31(3): 719–731, doi:10.1177/1420326X211027620.
YaoZZhangXYangCHeF. Flow characteristics and turbulence simulation for an aircraft cabin environment. Procedia Eng2015; 121: 1266–1273.
66.
ZhaoYLiuZLiXZhaoMLiuY. A modified turbulence model for simulating airflow aircraft cabin environment with mixed convection. Build Simul2020; 13: 665–675.
67.
YaoTLinZ. An experimental and numerical study on the effect of air terminal types on the performance of stratum ventilation. Build Environ2014; 82: 431–441.
68.
ZhaoBYangCYangXLiuS. Particle dispersion and deposition in ventilated rooms: testing and evaluation of different Eulerian and Lagrangian models. Build Environ2008; 43: 388–397.
