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Heat Exchanger Design through Large-scale Three-dimensional Thermal-fluid Topology Optimization
ISSN: 0148-7191, e-ISSN: 2688-3627
To be published on April 06, 2021 by SAE International in United States
Event: SAE WCX Digital Summit
Due to the inherent computational cost of multiphysics topology optimization methods, particularly those with fluid-flow models, it is a common practice to implement these methods in two-dimensions. However many real-world heat exchangers can have significant performance improvements when optimized in three-dimensions. This paper presents the application of a large scale three-dimensional thermal-fluid topology optimization method for the design of heat exchangers under forced convection. The weak forms of the fluid-flow and thermal finite element models are derived and fully coupled through the velocity field. The fluid-flow model is governed by the Navier-Stokes equations modified with a velocity-dependent damping term. The thermal model is governed by the convection-diffusion equation. Sensitivity analysis is done with the discrete form of the adjoint method. The optimization process is updated using the method of moving asymptotes so that the thermal compliance of the heat exchanger is minimized, while satisfying the weak form of the governing equations. The finite element models are solved using a trust region Newton’s method. The solution is scaled across several supercomputer nodes using the Portable, Extensible Toolkit for Scientific Computation (PETSc) developed by Argonne National Laboratory. The finite element models, sensitivity analysis, optimization algorithm, and solver are all implemented in an in-house, density-based, thermal-fluid topology optimization code written in C++. The code is applied to solve three heat exchanger optimization problems with a corresponding discussion on the result for each problem. All of the resulting designs feature well connected fluid-flow channels. For reference, the computational cost of solving the three optimization problems is provided.