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Optimization of Noise‐Reducing Orifices in Natural Gas Pressure‐Regulating Valves Based on a Query‐by‐Committee Kriging Model.

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Title: Optimization of Noise‐Reducing Orifices in Natural Gas Pressure‐Regulating Valves Based on a Query‐by‐Committee Kriging Model.
Authors: Sun, Wen1 (AUTHOR) sunwen@xsyu.edu.cn, Gao, Yuhui1 (AUTHOR), Tao, Weili2 (AUTHOR), Zeng, Wei1 (AUTHOR), Sen, Smritijit (AUTHOR) smsen@wiley.com
Source: Modelling & Simulation in Engineering. 7/23/2026, Vol. 2026, p1-14. 14p.
Subjects: Kriging, Surrogate-based optimization, Mathematical programming, Pressure regulators, Aeroacoustics, Valves, Noise control
Abstract: The quest for optimal noise‐reduction orifice plates in natural gas pressure regulating valves is often constrained by a fundamental dilemma: high‐fidelity fluid‐acoustic simulations, although accurate, impose prohibitive computational costs that render conventional optimization approaches impractical. To break this stalemate, this paper introduces a novel surrogate‐assisted framework featuring a Query‐by‐Committee (QBC) mechanism that enhances the adaptive capabilities of kriging modeling. Unlike traditional single‐criterion infill strategies, our framework constructs a diverse committee of kriging models using multiple kernel functions. By quantifying the level of "disagreement" among committee members regarding unobserved points, the algorithm intelligently selects the most informative samples, thereby maximizing global prediction accuracy while minimizing simulation calls. Extensive benchmarking against standard test functions demonstrates that the proposed QBC‐kriging achieves a 6.13% higher comprehensive prediction accuracy and an 18.72% lower computational expenditure compared with the classical expected improvement (EI) strategy. In a multiobjective engineering case study focusing on a gas regulator′s orifice plate, the new framework yields a 1.2% enhancement in optimization precision and a 39.6% boost in efficiency. Furthermore, a robustness analysis under varying inlet pressures reveals that the QBC‐derived design maintains stable acoustic performance (4.8%–5.3% reduction, SD = 0.21 dB), whereas the previously optimized design exhibits significantly wider fluctuations (4.1%–8.68%, SD = 1.42 dB). This work offers a practical and scalable optimization pathway for complex fluid‐acoustic systems burdened by high computational expenses. [ABSTRACT FROM AUTHOR]
Copyright of Modelling & Simulation in Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Optimization of Noise‐Reducing Orifices in Natural Gas Pressure‐Regulating Valves Based on a Query‐by‐Committee Kriging Model.
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  Data: <searchLink fieldCode="JN" term="%22Modelling+%26+Simulation+in+Engineering%22">Modelling & Simulation in Engineering</searchLink>. 7/23/2026, Vol. 2026, p1-14. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Kriging%22">Kriging</searchLink><br /><searchLink fieldCode="DE" term="%22Surrogate-based+optimization%22">Surrogate-based optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+programming%22">Mathematical programming</searchLink><br /><searchLink fieldCode="DE" term="%22Pressure+regulators%22">Pressure regulators</searchLink><br /><searchLink fieldCode="DE" term="%22Aeroacoustics%22">Aeroacoustics</searchLink><br /><searchLink fieldCode="DE" term="%22Valves%22">Valves</searchLink><br /><searchLink fieldCode="DE" term="%22Noise+control%22">Noise control</searchLink>
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  Data: The quest for optimal noise‐reduction orifice plates in natural gas pressure regulating valves is often constrained by a fundamental dilemma: high‐fidelity fluid‐acoustic simulations, although accurate, impose prohibitive computational costs that render conventional optimization approaches impractical. To break this stalemate, this paper introduces a novel surrogate‐assisted framework featuring a Query‐by‐Committee (QBC) mechanism that enhances the adaptive capabilities of kriging modeling. Unlike traditional single‐criterion infill strategies, our framework constructs a diverse committee of kriging models using multiple kernel functions. By quantifying the level of "disagreement" among committee members regarding unobserved points, the algorithm intelligently selects the most informative samples, thereby maximizing global prediction accuracy while minimizing simulation calls. Extensive benchmarking against standard test functions demonstrates that the proposed QBC‐kriging achieves a 6.13% higher comprehensive prediction accuracy and an 18.72% lower computational expenditure compared with the classical expected improvement (EI) strategy. In a multiobjective engineering case study focusing on a gas regulator′s orifice plate, the new framework yields a 1.2% enhancement in optimization precision and a 39.6% boost in efficiency. Furthermore, a robustness analysis under varying inlet pressures reveals that the QBC‐derived design maintains stable acoustic performance (4.8%–5.3% reduction, SD = 0.21 dB), whereas the previously optimized design exhibits significantly wider fluctuations (4.1%–8.68%, SD = 1.42 dB). This work offers a practical and scalable optimization pathway for complex fluid‐acoustic systems burdened by high computational expenses. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Modelling & Simulation in Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1155/mse/2114540
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      – Code: eng
        Text: English
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        PageCount: 14
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      – SubjectFull: Kriging
        Type: general
      – SubjectFull: Surrogate-based optimization
        Type: general
      – SubjectFull: Mathematical programming
        Type: general
      – SubjectFull: Pressure regulators
        Type: general
      – SubjectFull: Aeroacoustics
        Type: general
      – SubjectFull: Valves
        Type: general
      – SubjectFull: Noise control
        Type: general
    Titles:
      – TitleFull: Optimization of Noise‐Reducing Orifices in Natural Gas Pressure‐Regulating Valves Based on a Query‐by‐Committee Kriging Model.
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            NameFull: Sun, Wen
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              M: 07
              Text: 7/23/2026
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              Y: 2026
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