Linear logistic regression with weight thresholding for flow regime classification of a stratified wake

• Published in: Theoretical and applied mechanics letters Vol. 13; no. 2; p. 100414
• Main Authors: Huang, Xinyi L.D., Kunz, Robert F., Yang, Xiang I.A.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2023; Elsevier
• Subjects: Classification; Full Reynolds stress modelling; Stratified wake; Supervised learning; Stratified wake; Full Reynolds stress modelling; Supervised learning; Classification
• ISSN: 2095-0349
• DOI: 10.1016/j.taml.2022.100414

•Build a DNS database with O(100) realizations for stratified wake.•Develop a machine learning tool to classify the flow regimes in a stratified wake.•The physics governing stratified wake is universal with respect to Re and Fr.•The classifier captures the universal physics and generalize well. A stratified wake has multiple flow regimes, and exhibits different behaviors in these regimes due to the competing physical effects of momentum and buoyancy. This work aims at automated classification of the weakly and the strongly stratified turbulence regimes based on information available in a full Reynolds stress model. First, we generate a direct numerical simulation database with Reynolds numbers from 10,000 to 50,000 and Froude numbers from 2 to 50. Order (100) independent realizations of temporally evolving wakes are computed to get converged statistics. Second, we train a linear logistic regression classifier with weight thresholding for automated flow regime classification. The classifier is designed to identify the physics critical to classification. Trained against data at one flow condition, the classifier is found to generalize well to other Reynolds and Froude numbers. The results show that the physics governing wake evolution is universal, and that the classifier captures that physics.