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Bayesian parameter estimation of a k-ε model for accurate jet-in-crossflow simulations

AIAA Journal

Ray, Jaideep R.; Lefantzi, Sophia L.; Arunajatesan, Srinivasan A.; DeChant, Lawrence J.

Reynolds-averaged Navier–Stokes models are not very accurate for high-Reynolds-number compressible jet-in-crossflow interactions. The inaccuracy arises from the use of inappropriate model parameters and model-form errors in the Reynolds-averaged Navier–Stokes model. In this study, the hypothesis is pursued that Reynolds-averaged Navier–Stokes predictions can be significantly improved by using parameters inferred from experimental measurements of a supersonic jet interacting with a transonic crossflow.

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TYPE Journal Article YEAR 2016
OSTIDOI

A robust technique to make a 2D advection solver tolerant to soft faults

Procedia Computer Science

Strazdins, Peter; Harding, Brendan; Lee, Chung; Mayo, Jackson M.; Ray, Jaideep R.; Armstrong, Robert C.

We present a general technique to solve Partial Differential Equations, called robust stencils, which make them tolerant to soft faults, i.e. bit flips arising in memory or CPU calculations. We show how it can be applied to a two-dimensional Lax-Wendroff solver. The resulting 2D robust stencils are derived using an orthogonal application of their 1D counterparts. Combinations of 3 to 5 base stencils can then be created. We describe how these are then implemented in a parallel advection solver. Various robust stencil combinations are explored, representing tradeoff between performance and robustness. The results indicate that the 3-stencil robust combinations are slightly faster on large parallel workloads than Triple Modular Redundancy (TMR). They also have one third of the memory footprint. We expect the improvement to be significant if suitable optimizations are performed. Because faults are avoided each time new points are computed, the proposed stencils are also comparably robust to faults as TMR for a large range of error rates. The technique can be generalized to 3D (or higher dimensions) with similar benefits.

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TYPE Conference Poster YEAR 2016
ScopusOSTIDOI

Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting

Computer Methods in Applied Mechanics and Engineering

Carlberg, Kevin T.; Ray, Jaideep R.; van Bloemen Waanders, Bart G.

Implicit numerical integration of nonlinear ODEs requires solving a system of nonlinear algebraic equations at each time step. Each of these systems is often solved by a Newton-like method, which incurs a sequence of linear-system solves. Most model-reduction techniques for nonlinear ODEs exploit knowledge of a system's spatial behavior to reduce the computational complexity of each linear-system solve. However, the number of linear-system solves for the reduced-order simulation often remains roughly the same as that for the full-order simulation.We propose exploiting knowledge of the model's temporal behavior to (1) forecast the unknown variable of the reduced-order system of nonlinear equations at future time steps, and (2) use this forecast as an initial guess for the Newton-like solver during the reduced-order-model simulation. To compute the forecast, we propose using the Gappy POD technique. The goal is to generate an accurate initial guess so that the Newton solver requires many fewer iterations to converge, thereby decreasing the number of linear-system solves in the reduced-order-model simulation.

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TYPE Journal Article YEAR 2015
ScopusOSTIDOI
Results 76–100 of 197
Results 76–100 of 197