Balancing Ventilation Performance and Energy Conservation: An Integrated Multi-Objective Optimization and Preference Decision-Making Model for Optimizing Impinging Jet Ventilation

Abstract

Balancing the requirements for thermal comfort (TC) and indoor air quality (IAQ) while minimizing energy consumption (EC) is a challenging trade-off problem in impinging jet ventilation (IJV), which has great potential for energy savings. Multi-criteria decision-making (MCDM) technology is a rapid and effective approach to address this issue in response to changing outdoor weather conditions. However, conflicting ventilation performances, such as TC (e.g. draught rate (DR), predictive mean vote (PMV)), IAQ (e.g. air change efficiency (ACE), local mean age of air (LMAA)) and EC, may present challenges in identifying optimal ventilation parameters. This study therefore proposes an integrated multi-objective optimization (MOO) and MCDM model to overcome this limitation. The former, which yields Pareto solutions, is to maintain ventilation performance indicators with good TC or IAQ standards within an acceptable range (e.g. -0.5 < |PMV| < 0.5, DR < 20%, 0.95 < ACE), while resolving only the trade-off among the remaining indicators (e.g. LMAA and EC). To enhance the computational efficiency of the proposed optimization framework, we have developed a response surface model for ventilation performance based on experimentally validated computational fluid dynamics (CFD) simulations. Compared to the typical MCDM method (i.e. TOPSIS), the proposed method reduces EC by up to 16.99% on average while still meeting TC and IAQ requirements. Moreover, this study determines the optimal return vent height and operation mode to meet various decision preferences with changing weather conditions. Our findings provide insights into accurately designing and controlling ventilation parameters of IJV and contribute to the practical applications of integrated MOO and MCDM in various ventilation scenarios.

Keywords: Impinging jet ventilation, Energy saving, Multi-objective optimization, Multi-criteria decision making, Operation control