Shape optimization using the adjoint solver in computational fluid dynamics for additive manufacturing of a pollen filter

Abstract

Pollen filters play an essential role in protecting people from airborne allergens and ensuring indoor air quality. Allergic reactions to pollen can lead to discomfort, reduced productivity, and increased healthcare costs. A low pressure drop of these pollen filters not only enhances the comfort of individuals using the filters but also contributes to energy savings in ventilation systems, thereby promoting environmental sustainability. This research focuses on the shape optimization of pollen filters using the adjoint solver in computational fluid dynamics, aiming to enhance both human health and environmental sustainability. In a previous study, an approach using the adjoint solver was developed to optimize both the separation efficiency and the pressure drop. In the current work, a methodology is presented that exploits these findings and allows the design of initial structures, subsequent optimization, and detailed experimental and numerical comparisons with a reference filter using the example of a pollen filter. To validate the effectiveness of the optimized filter, the initial geometry and the optimized geometry were fabricated and tested on a test bench. Compared to a reference filter, our filter disk was able to separate 2.9% more particles of size 6 μm and the pressure drop was lower by 34.2%. This research work demonstrates that the developed method can effectively be used to improve the performance of pollen filters. The results obtained from the validation suggest that the optimized geometry of the filter exhibits higher separation efficiency while keeping the pressure drop low compared to state-of-the-art pollen filters.