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multi-objective optimization and machine learning for the temperature distribution measurement using acoustic tomography

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Applied Thermal Engineering 2025年 277卷

作者： Yan Li Jing Lei Siyuan Ren Shaanxi Coal Industry New Energy Technology Co. Ltd. Chang’an District Xi’an 710100 Shaanxi China School of Energy Power and Mechanical Engineering North China Electric Power University Changping District Beijing 102206 China School of Electronic and Control Engineering North China Institute of Aerospace Engineering Langfang 065000 Hebei China

The limited accuracy of reconstruction restricts the practical application of acoustic tomography in temperature distribution measurement. To alleviate this challenge, this study focuses on transforming reconstruction methodology by introducing a novel temperature distribution reconstruction method to mitigate the challenges posed by insufficient measurement information and inaccurate model. In the first stage, a multi-objective reconstruction model is established to reconstruct a preliminary temperature distribution. This model addresses measurement inaccuracies and model uncertainties, integrates the low-rank prior, and automatically balances the data fidelity term and the regularization term, thereby alleviating the challenge of selecting regularization parameters. In the second stage, the multi-objective random vector functional link network is proposed to infer the fine temperature distribution. This new multi-objective optimization learning engine addresses uncertainties in data and model, integrates the sparsity prior of the weight vector, overcomes the challenge of parameter tuning, and enhances the model’s robustness and generalization performance. The performance evaluation results confirm that, in comparison to widely-used reconstruction algorithms, the proposed reconstruction method not only achieves innovation in the reconstruction paradigm but also improves reconstruction accuracy and robustness. This new reconstruction methodology fuses measurement physics, machine learning, and multi-objective optimization, providing new perspectives for designing more effective reconstruction algorithms and unleashing the potential of acoustic tomography.

关键词： Acoustic tomography multi-objective optimization reconstruction model Random vector functional link network Temperature distribution measurement

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