A two-step parametric generative method for heat exchangers design in additive manufacturing

Abstract

Additive Manufacturing(AM) liberates a lot for the hands of designers to create complex geometry and avoid many traditional manufacturing constrains. A set of advanced generative design and topological optimization tools are emerging to support the design for AM. However, many existing methods are based on traditional topological optimization methods, which are focusing more on lightweight design but have difficulty to optimize component’s functions. In heat exchanger design, the functional features, e.g. fluid tunnels, are usually fixed without changing during the lightweight optimization. This cannot guarantee an optimal functional performance for the design. To solve this problem, this paper proposes a two-step generative design method that can both help optimizing the functional features for performance improvement and lightweight design. This method has two generative optimization loops, including functional feature group optimization and lightweight optimization. A set of finite optimized candidate alternative solutions from the first optimization loop are inputs for the second lightweight optimization loop to form boundary conditions and freezing regions in lightweight design. With the topological optimization in the second loop, the pre-selected functional features can be combined with the optimal volume layouts in the original design space to form a final component with improved function performance and reduced mass. To demonstrate the proposed method, a design case of a heat exchanger with conformal cooling channels is presented. The method can be adopted for other similar design cases in additive manufacturing applications.