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A clustering-based domain decomposition framework for reduced-order modeling: Application to atmospheric boundary layer flows

par Li, Haoyan ;Procacci, Alberto ;Raghunathan Srikumar, Sampath Kumar;Mosca, Gabriele M.G.;Gambale, Alessandro ;Parente, Alessandro
Référence Physics of fluids, 37, 9, 095186
Publication Publié, 2025-09-01

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Résumé :

Reduced-order models emerge as a solution to efficiently predict atmospheric boundary layer flows. However, global reduced-order models, applying dimensionality reduction to the whole domain, struggle with accuracy in domains disturbed by buildings. To address this limitation, this study proposed an unsupervised domain-decomposition approach for reduced-order models using clustering to divide the domain into building-influenced and uninfluenced subdomains. Clustering enables the localized dimensionality reduction via principal component analysis and predictive mapping using Gaussian process regression in each subdomain. Two clustering methods, K-means and vector quantization principal component analysis, were used as the comparison to individually develop domain-decomposition reduced-order models. A steady atmospheric boundary layer flow over a building array across 64 operating conditions served as the test case. The development of the reduced-order models was based on computational fluid dynamics simulations using k-ω shear stress transport model. Compared to the global reduced-order model, the clustering-based domain decomposition reduced-order models reduced root mean square error by 9.5% for the stream-wise velocity field and 18.2% for the turbulent kinetic energy field and achieved a speed-up of approximately 10 5 times over full-order computational fluid dynamics simulations.