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To address the challenges of low computational efficiency, poor solution quality, and the difficulty in accurately and synergistically optimizing heat transfer and reducing flow loss in multi-objective topology optimization of microchannel heat sinks, this study innovatively proposes a multi-objective topology optimization model based on ε-constraint algorithm. Moreover, the multi-objective optimization functions are constructed using different heat transfer single-objectives: heat transfer amount JQ and temperature variance JTV. For model improvement methods, a double-interpolation concept improved on the q-parameterized interpolation function is used to alter the continuity distribution state of density design variable ξ. The adjoint-based discrete sensitivity model and Global Convergent Moving Asymptotic Algorithm are used to implement the iterative update of optimization structure. The result shows: the optimized structures and its performance parameters evolve regularly with the weight coefficients of multi-objective functions, revealing the optimization mechanism of microchannel and state variables, and the trade-off game between structure and performance; The convergence stability of ε-constraint algorithm is significantly improved compared to traditional normalized Simple Additive Weighting model, and the computational efficiency of the representative case is relatively improved by 40.4%. The ε-constraint algorithm effectively suppresses the grayscale area and intermediate density range, thereby achieving higher-quality solutions and the state variable distribution more consistent with physical laws. The optimization model responds significantly to different JQ and JTV, and corresponding optimized structures can achieve maximum heat exchange and optimal temperature uniformity under minimum fluid energy consumption, respectively.
Wang et al. (Fri,) studied this question.