Dimensionality reduction for regularization of sparse data-driven RANS simulations
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Publication:6078483
DOI10.1016/j.jcp.2023.112404OpenAlexW4385813957MaRDI QIDQ6078483
Oliver Brenner, Patrick Jenny, Pasha Piroozmand
Publication date: 27 September 2023
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2023.112404
Cites Work
- A consistent dual-mesh framework for hybrid LES/RANS modeling
- On the limited memory BFGS method for large scale optimization
- Representation of stress tensor perturbations with application in machine-learning-assisted turbulence modeling
- Efficient assimilation of sparse data into RANS-based turbulent flow simulations using a discrete adjoint method
- Flows over periodic hills of parameterized geometries: a dataset for data-driven turbulence modeling from direct simulations
- A discrete adjoint method for pressure-based algorithms
- Sequential Quadratic Programming Methods
- Automatic Differentiation of an Entire Design Chain for Aerodynamic Shape Optimization
- A Fast Total Variation Minimization Method for Image Restoration
- Conditioning and accurate solutions of Reynolds average Navier–Stokes equations with data-driven turbulence closures
- A data-assimilation method for Reynolds-averaged Navier–Stokes-driven mean flow reconstruction
- On explicit algebraic stress models for complex turbulent flows
- Linear and nonlinear sensor placement strategies for mean-flow reconstruction via data assimilation
- Nudging-based data assimilation of the turbulent flow around a square cylinder
- Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
- Data Assimilation
- The complex-step derivative approximation
- Fast convergence strategy for ambiguous inverse problems based on hierarchical regularization
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