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

Xinyi L.D. Huang, Robert F. Kunz, Xiang I.A. Yang

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number100414
JournalTheoretical and Applied Mechanics Letters
Volume13
Issue number2
DOIs
StatePublished - Mar 2023

All Science Journal Classification (ASJC) codes

  • Computational Mechanics
  • Environmental Engineering
  • Civil and Structural Engineering
  • Biomedical Engineering
  • Aerospace Engineering
  • Ocean Engineering
  • Mechanics of Materials
  • Mechanical Engineering

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