Aeroheating Prediction Using the Hybrid Flow Solver ICAT

Liu, Zhining; Ramakrishnan, S.; Bihari, Barna L.; Olling, Charles R.; Dominik, D.

doi:10.2514/6.2002-2936

Cited by 5 publications

(3 citation statements)

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“…A very versatile 3-D flow solver was used for solving aerodynamics and aerothermodynamics problems governed by the Euler or Reynolds-averaged Navier-Stokes (RANS) equations [49][50][51][52][53]. The discretization was based on a total variation diminishing (TVD) [54][55][56] formulation for the inviscid fluxes and second-order central differencing for the viscous fluxes using a finite-volume framework.…”

Section: Cfd Simulation On Atlas II Rocket Plumementioning

confidence: 99%

“…We accounted for 9 gas species, H 2 , O 2 , H 2 O, OH, O, H, CO, CO 2 , and N 2 , and considered 12 reactions steps. The CFD solver used a second-order Rusanov scheme with 25% of the numerical dissipation added and standard minmod slope limiting with compression factor 1.25 [49]. The CFD solver can be used for a wide variety of situations, including (1) unsteady and steady flows, (2) low-speed, subsonic, transonic, supersonic, and hypersonic flows, (3) perfect gas, equilibrium air curve-fit, frozen, equilibrium, and finite-rate chemistry, (4) viscous and inviscid flows, (5) simple and complex geometries, and (6) internal and external flows.…”

Section: Cfd Simulation On Atlas II Rocket Plumementioning

confidence: 99%

“…In this section, we present a case study of rocket plume radiation simulation using the flow fields computed using a computational fluid dynamics solver described in [49].…”

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confidence: 99%

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A High-Order-Accurate GPU-Based Radiative Transfer Equation Solver for Combustion and Propulsion Applications

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Wilcox

et al. 2013

Numerical Heat Transfer, Part B: Fundamentals

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In this article we present a high-order-accurate solver for the radiative transfer equation (RTE) which uses the discontinuous Galerkin (DG) method and is designed for graphics processing units (GPUs). The compact nature of the high-order DG method enhances scalability, particularly on GPUs. High-order spatial accuracy can be used to reduce discretization errors on a given computational mesh, and can also reduce the mesh size needed to achieve a desired error tolerance. Computational efficiency is a key concern in solutions to radiative heat transfer problems, due to potentially large problem sizes created by (a) the presence of participating nongray media in a full-spectrum analysis, (b) the need to resolve a large number of angular directions and spatial extent of the domain for an accurate solution, and (c) potentially large variations in material and flow properties in the domain. We present here a simulation strategy, as well as a set of physical models, accompanied by a number of case studies, demonstrating the accuracy and superior performance in terms of computational efficiency of this approach.

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