Data-driven turbulence modeling
arxiv(2024)
摘要
This chapter provides an introduction to data-driven techniques for the
development and calibration of closure models for the Reynolds-Averaged
Navier–Stokes (RANS) equations. RANS models are the workhorse for engineering
applications of computational fluid dynamics (CFD) and are expected to play an
important role for decades to come. However, RANS model inadequacies for
complex, non-equilibrium flows and uncertainties in modeling assumptions and
calibration data are still a major obstacle to the predictive capability of
RANS simulations. In the following, we briefly recall the origin and
limitations of RANS models, and then review their shortcomings and
uncertainties. Then, we provide an introduction to data-driven approaches to
RANS turbulence modeling. The latter can range from simple model parameter
inference to sophisticated machine learning techniques. We conclude with some
perspectives on current and future research trends.
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