Author ORCID Identifier
Date Available
6-30-2017
Year of Publication
2017
Document Type
Doctoral Dissertation
Degree Name
Doctor of Philosophy (PhD)
College
Engineering
Department/School/Program
Mechanical Engineering
Advisor
Dr. Sean Bailey
Co-Director of Graduate Studies
Dr. Jesse Hoagg, Dr. Alexandre Martin
Abstract
The data-driven adaptive algorithms are explored as a means of increasing the accuracy of Reynolds-averaged turbulence models. This dissertation presents two new data-driven adaptive computational models for simulating turbulent flow, where partial-but-incomplete measurement data is available. These models automatically adjust (i.e., adapts) the closure coefficients of the Reynolds-averaged Navier-Stokes (RANS) k-ω turbulence equations to improve agreement between the simulated flow and a set of prescribed measurement data.
The first approach is the data-driven adaptive RANS k-ω (D-DARK) model. It is validated with three canonical flow geometries: pipe flow, the backward-facing step, and flow around an airfoil. For all 3 test cases, the D-DARK model improves agreement with experimental data in comparison to the results from a non-adaptive RANS k-ω model that uses standard values of the closure coefficients.
The second approach is the Retrospective Cost Adaptation (RCA) k-ω model. The key enabling technology is that of retrospective cost adaptation, which was developed for real-time adaptive control technology, but is used in this work for data-driven model adaptation. The algorithm conducts an optimization, which seeks to minimize the surrogate performance, and by extension the real flow-field error. The advantage of the RCA approach over the D-DARK approach is that it is capable of adapting to unsteady measurements. The RCA-RANS k-ω model is verified with a statistically steady test case (pipe flow) as well as two unsteady test cases: vortex shedding from a surface-mounted cube and flow around a square cylinder. The RCA-RANS k-ω model effectively adapts to both averaged steady and unsteady measurement data.
Digital Object Identifier (DOI)
https://doi.org/10.13023/ETD.2017.280
Recommended Citation
Li, Zhiyong, "Data-Driven Adaptive Reynolds-Averaged Navier-Stokes k - ω Models for Turbulent Flow-Field Simulations" (2017). Theses and Dissertations--Mechanical Engineering. 93.
https://uknowledge.uky.edu/me_etds/93
