Analysis and Optimization of Multistage Tesla Valves by Computational Fluid Dynamics and a Multi‐Objective Genetic Algorithm. Issue 12 (7th November 2022)

• Record Type: Journal Article
• Authors: Babaoğlu, Nihan Uygur; Parvaz, Farzad; Foroozesh, Jamal; Hosseini, Seyyed Hossein; Ahmadi, Goodarz; Elsayed, Khairy
• Is Part Of: Chemical engineering & technology. Volume 45:Issue 12(2022)
• Page Start: 2245
• Page End: 2253
• Publication Date: 2022-11-07
• Subjects: Diodicity -- Flow control -- Multistage Tesla valve -- Surrogate‐based optimization -- Tesla valve; Chemical engineering -- Periodicals; Chemical processes -- Periodicals; 660.05
• DOI: 10.1002/ceat.202200247
• Languages: English
• ISSNs: 0930-7516

Abstract

Abstract: The multistage Tesla valve was optimized by minimizing the forward pressure drop ( FPD ) and maximizing the reverse pressure drop ( RPD ). First, computational fluid dynamics simulations were conducted; then, surrogate‐based optimization was done. Finally, an explicit correlation between the objective functions and the variables was developed to estimate the target functions directly. The results showed that decreasing the valve‐to‐valve distance over the hydraulic diameter ( G / D h ) maximizes the diodicity ( Di ) and minimizes the FPD . When an increase in the RPD is desired, the Reynolds number ( Re ) should be increased, which leads to an increase in the FPD . The maximized Di value is 1.811 for D h = 0.821, G / D h = 0.993, N = 16, tan( α ) = 0.848, and Re = 173.03. However, the minimum value of the FPD and the maximum RPD were found as 194.72 Pa and 352.69 Pa, respectively. Abstract : Using computational fluid dynamics results and the multi‐objective genetic algorithm technique, the performance of multistage Tesla valves was optimized by minimizing the forward pressure drop and maximizing the reverse pressure drop and the diodicity.