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Machine learning-assisted design and optimization of thermoacoustic devices: Progress and perspectives
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DOI：10.1016/j.rser.2026.117255.png)
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Abstract
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• Machine learning-assisted design and optimization of thermoacoustic devices are systematically reviewed. • Applications in thermoacoustic refrigerators, and engines are analyzed. • Surrogate modeling, multi-objective optimization, inverse design, and DRL methods are summarized. • Current challenges including data scarcity and model generalization are discussed. • Future research opportunities for intelligent thermoacoustic system design are identified.
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16.3
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1.6W
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18.5W
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