Inverse Prediction and Optimization of Flow Control Conditions for Confined Spaces using a CFD-Based Genetic Algorithm

Optimizing an indoor flow pattern according to specific design goals requires systematic evaluation and prediction of the influences of critical flow control conditions such as flow inlet temperature and velocity. In order to identify the best flow control conditions, conventional approach simulates a large number of flow scenarios with different boundary conditions. This paper proposes a method that combines the genetic algorithm (GA) with computational fluid dynamics (CFD) technique, which can efficiently predict and optimize the flow inlet conditions with various objective functions. A coupled simulation platform based on GenOpt (GA program) and Fluent (CFD program) was developed, in which the GA was improved to reduce the required CFD simulations. A mixing convection case in a confined space was used to evaluate the performance of the developed program. The study shows that the method can predict accurately the inlet boundary conditions, with given controlling variable values in the space, with fewer CFD cases. The results reveal that the accuracy of inverse prediction is influenced by the error of CFD simulation that need be controlled within 15%. The study further used the Predicted Mean Vote (PMV) as the cost function to optimize the inlet boundary conditions (e.g., supply velocity, temperature, and angle) of the mixing convection case as well as two more realistic aircraft cabin cases. It presents interesting optimal correlations among those controlling parameters. Introduction With rapid developments in fluid dynamics, numerical science and computer technologies, computational fluid dynamics (CFD) has become an efficient tool for indoor environment study and system design. Optimizing an indoor flow pattern according to specific design goals requires systematic evaluation and prediction of the influences of critical flow control conditions such as flow inlet temperature, velocity and angle. In order to identify the best flow control conditions, conventional approach simulates a large number of flow scenarios with different boundary conditions. Previous studies reveal advanced search and optimization algorithms such as genetic algorithm (GA) can effectively reduce the total number of iterations to reach an (or a group of) optimal solution(s) [1]. GA is an optimization algorithm that simulates natural evolution in the search of optimal solutions [2]. It has been applied to a variety of engineering design, parameter identification and system optimization. Efforts of