Inverse design methods for indoor ventilation systems using CFD-based multi-objective genetic algorithm

Conventional designers typically count on thermal equilibrium and require ventilation rates of a space to design ventilation systems for the space. This design, however, may not provide a conformable and healthy micro-environment for each occupant due to the non-uniformity in airflow, temperature and ventilation effectiveness as well as potential conflicts in thermal comfort, indoor air quality (IAQ) and energy consumption. This study proposes two new design methods: the constraint method and the optimization method, by using advanced simulation techniques—computational fluid dynamics (CFD) based multi-objective genetic algorithm (MOGA). Using predicted mean vote (PMV), percentage dissatisfied of draft (PD) and age of air around occupants as the design goals, the simulations predict the performance curves for the three indices that can thus determine the optimal solutions. A simple 2D office and a 3D aircraft cabin were evaluated, as demonstrations, which reveal both methods have superior performance in system design. The optimization method provides more accurate results while the constraint method needs less computation efforts.