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Physics-informed acoustic leak detection in R32 multi-split systems via evolutionary machine learning.

Auteurs : MA X., YANG Z., LIU X., WANG Y.

Type d'article : Article de la RIF

Résumé

The transition to flammable R32 refrigerant requires rapid and interpretable leak detection. Traditional acoustic diagnostics frequently fail under non-stationary conditions because they overlook the effect of Oil Circulation Rate (OCR) fluctuations on acoustic source mechanisms. A decrease in OCR shifts the flow regime from oil-filmdamped two-phase flow to a high-frequency, turbulence-dominated pure gas jet. This phase transition and the resulting spectral drift compromise models trained on steady-state features. We propose a hierarchical framework that integrates multi-objective feature selection with explainable machine learning. Using an acoustic database covering the evolution from normal circulation to oil-depleted states, an improved ensemble NSGA-II algorithm identifies 15 features insensitive to oil content variations. A Triangular Topology Aggregation Optimizer then tunes Support Vector Machine hyperparameters. The model achieves 31.8% dimensionality reduction and 97.27% accuracy, mitigating recognition errors caused by OCR fluctuations. A recall rate exceeding 94% is maintained for near-field micro-leaks, enabling alerts before refrigerant accumulation reaches the Lower Flammability Limit (LFL). SHAP analysis demonstrates consistency between model decisions and the physics of acoustic attenuation and fluid phase transitions, offering a physically interpretable solution for HVAC safety monitoring.

Documents disponibles

Format PDF

Pages : 11 p.

Disponible

  • Prix public

    20 €

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    Gratuit

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Détails

  • Titre original : Physics-informed acoustic leak detection in R32 multi-split systems via evolutionary machine learning.
  • Identifiant de la fiche : 30034912
  • Langues : Anglais
  • Sujet : Technologie
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 186
  • Date d'édition : 06/2026
  • DOI : http://dx.doi.org/https://doi.org/10.1016/j.ijrefrig.2026.02.030

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