An implicit algorithm for solving time dependent flows on unstructured grids

Tomaro, Robert; Strang, William Z.; Sankar, Lakshmi N.

doi:10.2514/6.1997-333

Cited by 150 publications

(55 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A Newton sub-iteration method is used to improve time accuracy of the point-implicit method; the resulting method is second-order accurate in time. Tomaro et al [59] converted the code from explicit to implicit, enabling CFL numbers as high as 10 6 .…”

Section: Cfd Solvermentioning

confidence: 99%

Computational approximation of nonlinear unsteady aerodynamics using an aerodynamic model hierarchy

Ghoreyshi

Jirasek

Cummings

2013

Aerospace Science and Technology

View full text Add to dashboard Cite

, "Computational approximation of nonlinear unsteady aerodynamics using an aerodynamic model hierarchy" (2013 Modeling nonlinear unsteady aerodynamic effects in the simulation of modern fighter aircraft is still a very challenging task. A framework for approximating nonlinear unsteady aerodynamics with a Radial Basis Function neural network is provided. Training data were generated from a hierarchy of aerodynamic models. At the highest level, solutions of the discretized Reynolds-Averaged Navier-Stokes (RANS) equations provide the quantitative and qualitative solution of flow around aircraft, although the results are expensive in terms of computational resources. The Euler simulations are less expensive and provide qualitative data up to moderate angles of attack. The integration of these data is promising for generating accurate aerodynamic models at moderate computational cost. To illustrate the method, an airfoil undergoing pitching and plunging motion is considered. The primary and secondary aerodynamic model data are computed using RANS and Euler equations, respectively. A description for a mapping between the aerodynamic loads and the motion parameters based on the implicit function theorem is described. The mapping is then augmented by adding the secondary data to the input dataset. The selection of training data is then discussed. Once the network is trained, it can compute the unsteady aerodynamic loads from motion descriptions on the order of a few seconds. The framework is examined for different motions, and in all cases, the ROM predictions closely represent the actual aerodynamic responses. It is also demonstrated that the aerodynamic hierarchy aids in the rapid development of a reduced-order model.

show abstract

Section: Cfd Solvermentioning

confidence: 99%

Computational approximation of nonlinear unsteady aerodynamics using an aerodynamic model hierarchy

Ghoreyshi

Jirasek

Cummings

2013

Aerospace Science and Technology

View full text Add to dashboard Cite

show abstract

“…Tomaro et al 3 converted the code from explicit to implicit, enabling CFL numbers as high as CF L ≈ 10 6 . Grismer et al 4 parallelized the code, with a demonstrated linear speed-up on as many as 4,000 processors.…”

Section: Cobaltmentioning

confidence: 99%

Assessment of Sting Effect on X-31 Aircraft Model Using CFD

Jirasek

Cummings

2010

48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

View full text Add to dashboard Cite

The article describes the computational evaluation of the effect a belly mounted sting on the aerodynamics characteristics of an X-31 wind tunnel model. The investigation includes results of steady and time-dependent CFD analysis of the X-31 model with and without sting. The values of the steady lift and pitching moment coefficients as well as the pitching moment of model undergoing a manuever are compared to each other and to the wind tunnel data. The results of this analysis show the detrimental effect of sting on stability of the vortical structure above the wing as well as a substantial increase of noise in pitching moment data.

show abstract

“…Strang, et al [3] validated the numerical method on a number of problems, including the Spalart-Allmaras (SA) model, which forms the core for the Detached Eddy Simulation (DES) model available in Cobalt. Tomaro, et al [4] converted the code from explicit to implicit, enabling CFL numbers as high as 10 6 . Grismer, et al [5] parallelized the code, yielding linear speed-up on as many as 2,800 processors.…”

Section: Flow Solvermentioning

confidence: 99%