Geometric Optimization of a Tesla Valve Through Machine Learning to Develop Fluid Pressure Drop Devices

Thorough investigation into Tesla valve (TV) design was conducted across a large design of experiments (DOE) consisting of four varying geometric parameters and six different Reynolds number regimes in order to develop an optimized pressure drop device utilizing machine learning (ML) methods. A non-standard TV design was geometrically parameterized, and an automation suite was created to cycle through numerous combinations of parameters. Data were collected from completed computational fluid dynamics (CFD) simulations. TV designs were tested in the restricted flow direction for overall differential pressure, and overall minimum pressure with consideration to the onset of cavitation. Qualitative observations were made on the effects of each geometric parameter on the overall valve performance, and particular parameters showed greater influence on the pressure drop compared to classically optimized parameters used in previous TV studies. The overall minimum pressure demonstrated required system pressure for a valve to be utilized such that onset to cavitation would not occur. Data were utilized to train an ML model, and an optimized geometry was selected for maximized pressure drop. Multiple optimization efforts were made to meet design pressure drop goals versus traditional diodicity metrics, and two geometries were selected to develop a final design tool for overall pressure drop component development. Future work includes experimental validation of the large dataset, as well as further validation of the design tool for use in industry.