Abstract
To create a comfortable thermal environment in the passenger compartment, designers usually redesign the parameters of the HVAC system and simulate a large number of flow fields with different boundary conditions including the inlet velocity and temperature. However, the traditional method is often inefficient and ineffective. This paper presents an inversed design method based on back propagation neural network (BPNN) and particle swarm optimization (PSO). The BPNN is used to obtain more computational fluid dynamics (CFD) cases by fitting some known case results. The PSO method is used to identify the best flow control conditions. Besides, this paper used the predicted mean vote (PMV) and energy consumption as the objective function to optimize the inlet boundary conditions. The results show that the proposed method can reduce CFD calculation time and achieve optimized design of the HVAC system in-vehicle cabins.
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References
Xue Y, Zhai Z, Chen Q (2013) Inverse prediction and optimization of flow control conditions for confined spaces using a CFD-based genetic algorithm. Build Environ 64:77–784
Fayazbakhsh MA, Bagheri F, Bahrami M (2015) An inverse method for calculation of thermal inertia and heat gain in air conditioning and refrigeration systems. Appl Energy 138:496–504
Wei L, Ran D, Chen C, Lin CH, Chen Q (2015) Inverse design of the thermal environment in an airliner cabin by use of the CFD-based adjoint method. Energy Build 104:147–155
Lei L, Wang S, Zhang T (2016) Inverse design of underfloor heating power rates and air-supply temperature for an aircraft cabin. Appl Therm Eng 95:70–78
Chen Y, Tong Z, Wu W, Samuelson H, Malkawi A, Norford L (2019) Achieving natural ventilation potential in practice: control schemes and levels of automation. Appl Energy 235:1141–1152
Cheng Z, Tong S, Tong Z (2019) Bi-directional nozzle control of multistage radial-inflow turbine for optimal part-load operation of compressed air energy storage. Energy Convers Manag 181:485–500
Tong S, Cheng Z, Cong F, Tong Z, Zhang Y (2018) Developing a grid-connected power optimization strategy for the integration of wind power with low-temperature adiabatic compressed air energy storage. Renew Energy 125:73–86
Tong Z, Baldauf RW, Isakov V, Deshmukh P, Zhang KM (2016) Roadside vegetation barrier designs to mitigate near-road air pollution impacts. Sci Total Environ 541:920–927
Tong Z, Chen Y, Malkawi A, Adamkiewicz G, Spengler JD (2016) Quantifying the impact of traffic-related air pollution on the indoor air quality of a naturally ventilated building. Environ Int 89–90:138–146
Tong Z, Yang B, Hopke PK, Zhang KM (2017) Microenvironmental air quality impact of a commercial-scale biomass heating system. Environ Pollut 220:1112–1120
Zhang D, Du W, Zhuge L, Tong Z, Freeman RB (2019) Do financial constraints curb firms’ efforts to control pollution? Evidence from Chinese manufacturing firms. J Clean Prod 215:1052–1058
Wu W, Yoon N, Tong Z, Chen Y, Lv Y, Ærenlund T et al (2019) Diffuse ceiling ventilation for buildings: a review of fundamental theories and research methodologies. J Clean Prod 211:1600–1619
Tong Z, Whitlow TH, Landers A, Flanner B (2016) A case study of air quality above an urban roof top vegetable farm. Environ Pollut 208(Part A):256–260
Tong Z, Chen Y, Malkawi A (2016) Defining the influence region in neighborhood-scale CFD simulations for natural ventilation design. Appl Energy 182:625–633
Tong Z, Zhang KM (2015) The near-source impacts of diesel backup generators in urban environments. Atmos Environ 109:262–271
Tong Z, Wang YJ, Patel M, Kinney P, Chrillrud S, Zhang KM (2012) Modeling spatial variations of black carbon particles in an urban highway-building environment. Environ Sci Technol 46:312–319
Tong Z, Whitlow TH, MacRae PF, Landers AJ, Harada Y (2015) Quantifying the effect of vegetation on near-road air quality using brief campaigns. Environ Pollut 201:141–149
Acknowledgements
We would like to acknowledge the National Natural Science Foundation of China (51708493), National Research Program for Key Issues in Air Pollution Control (DQGG0207), Zhejiang Provincial Natural Science Foundation (LR19E050002), Zhejiang Province Key Science and Technology Project (2018C01020, 2018C01060, 2019C01057), and the Youth Funds of State Key Laboratory of Fluid Power & Mechatronic Systems (SKLoFP_QN_1804).
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Tong, Z., Liu, H., Tong, S., Xu, J. (2020). Inverse Design for Thermal Environment and Energy Consumption of Vehicular Cabins with PSO–CFD Method. In: Long, S., Dhillon, B. (eds) Man–Machine–Environment System Engineering . MMESE 2019. Lecture Notes in Electrical Engineering, vol 576. Springer, Singapore. https://doi.org/10.1007/978-981-13-8779-1_57
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DOI: https://doi.org/10.1007/978-981-13-8779-1_57
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