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<p>Corrugated micro-perforated panels (CMPPs) under limited-space installation conditions exhibit complex acoustic behavior due to strong multi-parameter coupling, which makes broadband and high-efficiency sound absorption design challenging. To address this issue, this study proposes a Kriging-based DEU adaptive sampling multi-objective optimization strategy for CMPP absorbers. The sound absorption coefficients of the sample points are calculated by finite element simulations and used for surrogate modeling and optimization. Based on a limited initial sample set generated by LHS, the proposed DEU method performs directional enrichment to improve surrogate model accuracy efficiently. Compared with conventional LHS sampling, the DEU method achieves higher surrogate modeling efficiency, better optimization performance, and improved predictive reliability with fewer samples. MOPSO is then employed to obtain the Pareto front of total absorption area and effective absorption area, and the knee-point configuration is selected as the optimal design. Experimental validation further confirms the effectiveness and practical potential of the proposed strategy for broadband and high-efficiency sound absorption design of CMPP absorbers.</p>

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Multi-objective optimization of corrugated microperforated panels for broadband sound absorption using adaptive sampling method

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月14日

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  • Zhengping Wu,
  • Lu Ean Ooi,
  • Yuanbo Liu,
  • Pengwei Lyu

摘要

Corrugated micro-perforated panels (CMPPs) under limited-space installation conditions exhibit complex acoustic behavior due to strong multi-parameter coupling, which makes broadband and high-efficiency sound absorption design challenging. To address this issue, this study proposes a Kriging-based DEU adaptive sampling multi-objective optimization strategy for CMPP absorbers. The sound absorption coefficients of the sample points are calculated by finite element simulations and used for surrogate modeling and optimization. Based on a limited initial sample set generated by LHS, the proposed DEU method performs directional enrichment to improve surrogate model accuracy efficiently. Compared with conventional LHS sampling, the DEU method achieves higher surrogate modeling efficiency, better optimization performance, and improved predictive reliability with fewer samples. MOPSO is then employed to obtain the Pareto front of total absorption area and effective absorption area, and the knee-point configuration is selected as the optimal design. Experimental validation further confirms the effectiveness and practical potential of the proposed strategy for broadband and high-efficiency sound absorption design of CMPP absorbers.