Optimization of the Shape of a Hydrokinetic Turbine's Draft Tube and Hub Assembly Using Design-by-Morphing with Bayesian Optimization

TL;DR

This paper introduces a novel shape optimization framework combining Design-by-Morphing and a Bayesian Optimization strategy called MixMOBO, enabling efficient, unconstrained design space exploration for hydrokinetic turbines with minimal computational cost.

Contribution

It presents a new methodology for creating unconstrained design spaces via morphing and a Bayesian optimization algorithm for efficient multi-objective shape optimization.

Findings

Successfully optimized turbine draft tube and hub shapes for maximum power output.

Demonstrated reduction in computational cost through minimal function evaluations.

Framework applicable to various fluid dynamic shape optimization problems.

Abstract

Finding the optimal design of a hydrodynamic or aerodynamic surface is often impossible due to the expense of evaluating the cost functions (say, with computational fluid dynamics) needed to determine the performances of the flows that the surface controls. In addition, inherent limitations of the design space itself due to imposed geometric constraints, conventional parameterization methods, and user bias can restrict {\it all} of the designs within a chosen design space regardless of whether traditional optimization methods or newer, data-driven design algorithms with machine learning are used to search the design space. We present a 2-pronged attack to address these difficulties: we propose (1) a methodology to create the design space using morphing that we call {\it Design-by-Morphing} (DbM); and (2) an optimization algorithm to search that space that uses a novel Bayesian Optimization (BO) strategy that we call {\it Mixed variable, Multi-Objective Bayesian Optimization} (MixMOBO). We apply this shape optimization strategy to maximize the power output of a hydrokinetic turbine. Applying these two strategies in tandem, we demonstrate that we can create a novel, geometrically-unconstrained, design space of a draft tube and hub shape and then optimize them simultaneously with a {\it minimum} number of cost function calls. Our framework is versatile and can be applied to the shape optimization of a variety of fluid problems.