Search Results

The thermal comfort inside automotive cabin has been extensively studied for decades. Traditional CFD models provide accurate simulation results of the air temperature distributions inside cabins but at a relatively high computation cost. In order to reduce the computational cost while still providing reasonable accuracy in simulating the air temperature profile inside a mid-sized sedan cabin, this paper introduces a new simulation tool that utilizes a proper orthogonal decomposition (POD) method. The POD method, an interpolation technique, requires only one set of multiple CFD simulations to produce a set of “snapshots”. Later, any simulations that require CFD runs to solve algorithm equation sets can be simplified by using interpolation between the snapshots provided that the geometry of the cabin keeps the same. As a result, the computation time can be reduced to only a few minutes.

Complex engineering design optimization often requires multiple executions of computationally expensive simulation tools such as those based on Computational Fluid Dynamics (CFD). Some CFD simulations can take several hours to complete, thus potentially making the design optimization task infeasible. In this paper, a combination of two powerful methodologies is presented that has the potential of reducing the engineering time required for CFD based design by more than 90%. The first methodology, termed as Parallel Parameterized CFD (PPCFD) allows for speeding up multiple CFD runs to explore a given design space very efficiently. The second approach is Approximation Assisted Optimization (AAO). AAO techniques are used to reduce the time and effort involved in conducting optimization with computationally expensive simulations. The PPCFD methodology needs to be tailored or customized for an individual geometry of interest.