| Title: |
A clustering-based domain decomposition framework for reduced-order modeling: Application to atmospheric boundary layer flows |
| Authors: |
Li, Haoyan; Procacci, Alberto; Raghunathan Srikumar, Sampath Kumar; Mosca, Gabriele M.G.; Gambale, Alessandro; Parente, Alessandro |
| Source: |
Physics of fluids, 37 (9 |
| Publication Year: |
2025 |
| Collection: |
DI-fusion : dépôt institutionnel de l'Université libre de Bruxelles (ULB) |
| Subject Terms: |
Physique de l'état condense [struct. électronique; etc.]; Physique de l'état condense [struct. propr. thermiques; Physique de l'état condense [supraconducteur] |
| Description: |
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. ; SCOPUS: ar.j ; info:eu-repo/semantics/published |
| Document Type: |
article in journal/newspaper |
| File Description: |
1 full-text file(s): application/pdf |
| Language: |
English |
| Relation: |
uri/info:doi/10.1063/5.0281638; uri/info:scp/105016620414 |
| Availability: |
https://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/397242; https://dipot.ulb.ac.be/dspace/bitstream/2013/397242/3/POF25-AR-08484.pdf |
| Accession Number: |
edsbas.41C521A |
| Database: |
BASE |