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.
翻译:寻找流体动力学或空气动力学表面的最佳设计往往是不可能的,因为评估成本功能(比如计算流体动态)的成本成本成本成本成本成本成本成本成本成本成本成本成本的成本成本成本成本评估成本成本成本(计算流流动态 ) 。 此外,由于强加的几何限制、常规参数化方法和用户偏差,设计空间本身固有的设计局限性,可以在选定的设计空间中限制设计设计的所有 ~ lt all} 不论使用传统优化方法还是新颖的机器学习数据驱动设计算法搜索设计空间。我们用这种形状优化战略来应对这些困难: 我们提议了(1) 一种方法,用我们称之为“逐个设计”的变形来创建设计空间设计空间空间的配置空间设计空间。 我们同时用一种优化算法来搜索使用新颖的Bayesian Optimin化(OBO) 战略,即我们称之为 ~混合变量、 多动性Bayesian Opitimization } (MixMOBO) 。我们应用这种形状优化战略来最大限度地增加一个水力动力涡轮的输出输出。我们同时应用了这两种战略,我们用这些战略,我们用了一个模型模型模型模型的模型设计成本模型的模型的构造中心,我们用了一个测试的模型的模型的模型的模型的模型的模型的模型的构造中心,我们可以同时设计成本要求。