Optimization Design of Indoor Environmental Ventilation in Buildings Based on Improved SVR-PSO Model

Mengmeng Han, Chunxiao Zhang, Sihui Yin, Jingjing Jia and Chulsoo Kim ()
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Mengmeng Han: Department of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea
Chunxiao Zhang: Department of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea
Sihui Yin: Department of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea
Jingjing Jia: Department of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea
Chulsoo Kim: Department of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea

Sustainability, 2024, vol. 16, issue 12, 1-14

Abstract: As the growth of society and the continuous upgrading of people’s living standards increase, people’s requirements for indoor environment are also increasing. To optimize the ventilation methods inside buildings, a numerical simulation method was used to construct numerical simulations of airflow organization and aerosol diffusion, and based on this model, better ventilation methods were determined. To optimize the determined better ventilation method, a multi-constraint optimization model was constructed using infection probability, thermal comfort, energy utilization coefficient, and velocity non-uniformity coefficient. The ventilation method was optimized through multi-objective constraints. To solve the optimization model, an optimized particle swarm algorithm was studied and designed. The results showed that under the “air rain” flow field, the maximum values of aerosol particles at the human body, bed surface, and outlet were 171, 769, and 19,973, respectively, while the minimum values were 4, 169, and 2197, respectively. The “air rain” flow field is a better ventilation method. The maximum and minimum values of the original non-uniformity coefficient were 0.44 and 0.08, respectively. After optimization by the particle swarm optimization algorithm, the maximum and minimum predicted non-uniformity coefficients were 0.457 and 0.08, respectively. The original value and predicted value are very close. The numerical model and algorithm constructed by the research institute are effective. The algorithm designed by the research institute can provide technical support for multi-objective optimization of indoor ventilation methods.

Keywords: support vector regression; particle swarm optimization; indoor; ventilation; optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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