46 citations · 51 across the 6 of their papers we have counts for
6 papers
Diff-NekRS: A Scalable Differentiable Framework for Multi-Timestep Solver-in-the-Loop Training
Junoh Jung, Riccardo Balin, Bethany Lusch +1
Hybrid physics-machine-learning solvers improve under-resolved simulations by embedding trainable corrections into the time integration. During autoregressive inference, repeated s…
In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD
Riccardo Balin, Filippo Simini, Cooper Simpson +7
Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for tra…
Turbulent boundary layer with strong favorable pressure gradient and curvature effects: Streamline coordinate and scaling analysis
Aviral Prakash, Riccardo Balin, John A. Evans +1
Direct numerical simulation (DNS) of a turbulent boundary layer over the Gaussian (Boeing) bump is performed. This boundary layer exhibits a series of adverse and favorable pressur…
Direct Numerical Simulation of a Turbulent Boundary Layer with Strong Pressure Gradients
Riccardo Balin, Kenneth E. Jansen
The turbulent boundary layer over a Gaussian shaped bump is computed by direct numerical simulation (DNS) of the incompressible Navier-Stokes equations. The two-dimensional bump ca…
Direct Numerical Simulation of a Turbulent Boundary Layer on a Flat Plate Using Synthetic Turbulence Generation
James R. Wright, Riccardo Balin, John W. Patterson +2
The turbulent boundary layer over a flat plate is computed by direct numerical simulation (DNS) of the incompressible Navier-Stokes equations as a test bed for a synthetic turbulen…
S-Frame Discrepancy Correction Models for Data-Informed Reynolds Stress Closure
Eric L. Peters, Riccardo Balin, Kenneth E. Jansen +2
Despite their well-known limitations, RANS models remain the most commonly employed tool for modeling turbulent flows in engineering practice. RANS models are predicated on the sol…