Development of an integrated approach for the inverse design of built environment by a fast fluid dynamics-based generic algorithm

Abstract It is essential to further design built environments with improved thermal comfort level, air quality, and reduced energy consumption of the HVAC system. The CFD-based GA was able to identify the global optimal design, but this method requires numbers of CFD simulations which is time consuming. Besides, there is no general rule in determining the critical parameters of GA, such as population size, mutation rate, and crossover rate. Therefore, this study adopted the FFD instead of CFD and developed the FFD-based GA in OpenFOAM. By testing the FFD-based GA in designing the thermal environment in an office with displacement ventilation, it was found that the FFD-based GA had the similar performance with that of the CFD-based GA and saved more than 75% computational effect. Making use of the efficiency of the FFD-based GA, this investigation tested the effect of population size, mutation rate, and crossover rate on the inverse design by GA. In the same design case, the appropriate population size was n = 16 and mutation rate was m = 0.1 , while the crossover rate had no general effect on the inverse design.

[1]  Tan Zhu,et al.  Developing Indoor Air Quality Related Standards in China , 2003 .

[2]  W. Cai,et al.  China building energy consumption: Situation, challenges and corresponding measures , 2009 .

[3]  Wei Liu,et al.  Inverse design of the thermal environment in an airliner cabin by use of the CFD-based adjoint method , 2015 .

[4]  Hongye Su,et al.  Optimization of ventilation system operation in office environment using POD model reduction and genetic algorithm , 2013 .

[5]  K. Goda,et al.  A multistep technique with implicit difference schemes for calculating two- or three-dimensional cavity flows , 1979 .

[6]  Kobayashi Shuichiro,et al.  An Optimization Method of District Heating and Cooling Plant Operation Based on Genetic Algorithm , 1998 .

[7]  Zhiqiang Zhai,et al.  Inverse Design Methods for the Built Environment , 2017 .

[8]  Kathleen M. Swigger,et al.  An Analysis of Genetic-Based Pattern Tracking and Cognitive-Based Component Tracking Models of Adaptation , 1983, AAAI.

[9]  A. Staniforth,et al.  Semi-Lagrangian integration schemes for atmospheric models - A review , 1991 .

[10]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Yu Xue,et al.  Inverse prediction and optimization of flow control conditions for confined spaces using a CFD-based genetic algorithm , 2013 .

[12]  Saleh Nabi,et al.  Adjoint-based optimization of displacement ventilation flow , 2017 .

[13]  R. Courtney,et al.  The Health Consequences of Smoking-50 Years of Progress: A Report of the Surgeon General, 2014Us Department of Health and Human Services Atlanta, GA: Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for: Critique , 2015 .

[14]  Shugang Wang,et al.  Prompt design of the air-supply opening size for a commercial airplane based on the proper orthogonal decomposition of flows , 2016 .

[15]  Q Chen,et al.  Real-time or faster-than-real-time simulation of airflow in buildings. , 2009, Indoor air.

[16]  John D. Spengler,et al.  Indoor Air Quality Handbook , 2000 .

[17]  Tian-Hu Zhang,et al.  Inverse design of aircraft cabin environment by coupling artificial neural network and genetic algorithm , 2014 .

[18]  A. Chorin Numerical solution of the Navier-Stokes equations , 1968 .

[19]  Liang Zhou,et al.  Optimization of ventilation system design and operation in office environment , 2009 .

[20]  Wei Liu,et al.  Development of a fast fluid dynamics-based adjoint method for the inverse design of indoor environments , 2017 .

[21]  Jerald D. Parker,et al.  Heating, Ventilating, and Air Conditioning: Analysis and Design , 1977 .

[22]  Edward Arens,et al.  Air Quality and Thermal Comfort in Office Buildings: Results of a Large Indoor Environmental Quality Survey , 2006 .

[23]  Zhiqiang John Zhai,et al.  Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature , 2007 .

[24]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[25]  Bin Zhao,et al.  Review of relationship between indoor and outdoor particles: I/O ratio, infiltration factor and penetration factor , 2011 .

[26]  S. Orszag,et al.  Renormalization group analysis of turbulence. I. Basic theory , 1986, Physical review letters.

[27]  K. Deb An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[28]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[29]  Fariborz Haghighat,et al.  Optimization of ventilation systems in office environment, Part II: Results and discussions , 2009 .

[30]  Standard Ashrae Thermal Environmental Conditions for Human Occupancy , 1992 .

[31]  Wei Liu,et al.  State-of-the-art methods for inverse design of an enclosed environment , 2015, Building and Environment.

[32]  John R. Koza,et al.  Genetic programming as a means for programming computers by natural selection , 1994 .

[33]  S. Patankar Numerical Heat Transfer and Fluid Flow , 2018, Lecture Notes in Mechanical Engineering.