Optimization technique for design of automotive air filter housing with improved fluid dynamic performance

Scope and method of study. Automotive air filter housings often are designed with major consideration given to fitting the available space rather than to providing the filter with a well-behaved, uniform flow. The development of a filter housing design technique that determines the housing geometry required to provide a user-specified velocity distribution through the filter was accomplished. Computations were performed for the case of a uniform velocity distribution through the filter. The uniform velocity distribution corresponds to a uniform, constant pressure drop across the filter from the upstream to the downstream side. Computational Fluid Dynamic (CFD) calculations of the viscous laminar flow upstream and downstream of the filter were performed using 2-D Navier-Stokes equations. A computational optimization method was applied to minimize the variation in the pressure drop along the filter by changing the geometry of the upper wall. As the upper wall is moved, the CFD solution for the computations is repeated and the pressure drop variation is re-evaluated. An experimental verification was performed using a model filter housing constructed with the geometry specified by the results of the computational design technique. A Laser Doppler Anemometer (LDA) was used to measure the velocity distribution above the filter.; Findings and conclusions. The optimization results have produced a pressure distribution that is very close to the specified uniform distribution. The measured velocity distributions in this housing were compared with measured velocity distributions for different housing models. The model designed with the computational technique shows a much more uniform flow distribution above the filter than the other housings. The pressure distribution across the filter was measured using a pressure transducer. A filtration model was used with the measured velocity distributions, predicting local efficiency distributions and overall filter efficiencies. The results show that filter resistance and housing geometry can have large effects on the flow field. The filter efficiency is strongly dependent on the flow field. Hence the housing geometry and filter resistance may have significant effects upon filter efficiency for both smaller and larger particles.