Optimization of multi-staged Tesla valve using response surface methodology

Abstract

The multi-stage Tesla valve (MSTV), which consists of multiple identical TVs in series, enhances the effectiveness of the TV. To further improve the performance of the MSTV, an improved MSTV has been proposed by designing each arch channel in the typical MSTV as two separate arch channels: the inner arch channel and the outer arch channel. Response surface methodology is used to optimize the improved MSTV, with the maximum mass flow rate in forward flow and the minimum mass flow rate in reverse flow as two optimization objectives. The non-dominated sorting genetic algorithm is employed to obtain the Pareto solution set, resulting in the optimized design for the improved MSTV (named short-baffle improved MSTV). Theoretical simulations and experimental research are conducted on a typical MSTV, an improved MSTV, and a short-baffle improved MSTV, and their flow resistance ratios (FRRs) are obtained. The FRR of the short-baffle improved MSTV has improved by an average of 8.70% compared to that of typical MSTV. At low inlet pressures, the increase in FRR is approximately 1.4% higher than that at high inlet pressures. The research results indicate that the FRR of the shot-baffle improved MSTV is greater than that of a typical MSTV, and to some extent, the performance of an MSTV is enhanced under low inlet pressure.