Computational Analysis of the Transonic Dynamics Tunnel Using FUN3D

Chwalowski, Pawel; Quon, Eliot; Brynildsen, Scott E.

doi:10.2514/6.2016-1775

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…A Mars ascent vehicle with reaction control system (RCS) jets A parachute test vehicle A cone-cylinder-flare with jet in crossflow [45] A 65-degree sharp leading-edge delta wing [46] A hypersonic cylinder The benchmark supercritical wing [47] The Transonic Dynamics Tunnel [48] AEDC Tunnel D [49] ONERA M6 wing [50] The C25F and C25P supersonic aircraft configurations [51] The NASA Juncture Flow Model [52] The Acoustic Research Nozzle #2 [53] The Drag Prediction Workshop (DPW)-6 Common Research Model (CRM) [54] The High Lift Prediction Workshop (HLPW) HL-CRM(v2) configuration [55] The JAXA Standard High Lift configuration (with and w/o nacelle) [55] The Sonic Boom Prediction Workshop (SBPW3) C608 configuration The SBPW3 9×7 biconvex configuration [56] The sonic boom SEEB-ALR configuration [51] The AGARD-138 B4 wing body [57] A supersonic five-hole probe calibration database A supersonic retropropulsion configuration The X-38 [58] A smooth-body Orion heat shield The SLS with protuberances The Stardust sample return capsule An F-15 with shock sensing air-data probe [59] A Winnebago The NASA LOFTID configuration [60] The Langley Unitary Plan Wind Tunnel (LUPWT) [61] The LUPWT check standard model [62] The NASA HL-20 [63] The Oberkampf and Aeschliman sphere cone [64] A 70-degree sphere-cone capsule The OpenCSM lander [19] Mars InSight ground wind loads [65] The OpenCSM "dragon" capsule Several proprietary geometries Of course not all have been fully successful for a variety of reasons. The most common failing stems from the quality of the geometry model.…”

Section: Resultsmentioning

confidence: 99%

Sketch-to-Solution: An Exploration of Viscous CFD with Automatic Grids

Kleb

Park

Wood

et al. 2019

AIAA Aviation 2019 Forum

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Numerical simulation of the Reynolds-averaged Navier-Stokes (RANS) equations has become a critical tool for the design of aerospace vehicles. However, the issues that affect the grid convergence of three dimensional RANS solutions are not completely understood, as documented in the AIAA Drag Prediction Workshop series. Grid adaption methods have the potential for increasing the automation and discretization error control of RANS solutions to impact the aerospace design and certification process. The realization of the CFD Vision 2030 Study includes automated management of errors and uncertainties of physics-based, predictive modeling that can set the stage for ensuring a vehicle is in compliance with a regulation or specification by using analysis without demonstration in flight test (i.e., certification or qualification by analysis). For example, the Cart3D inviscid analysis package has automated Cartesian cut-cell gridding with output-based error control. Fueled by recent advances in the fields of anisotropic grid adaptation, error estimation, and geometry modeling, a similar work flow is explored for viscous CFD simulations; where a CFD application engineer provides geometry, boundary conditions, and flow parameters, and the sketch-to-solution process yields a CFD simulation through automatic, error-based, grid adaptation.

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Section: Resultsmentioning

confidence: 99%

Sketch-to-Solution: An Exploration of Viscous CFD with Automatic Grids

Kleb

Park

Wood

et al. 2019

AIAA Aviation 2019 Forum

View full text Add to dashboard Cite

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Transonic Flutter Dips of the AGARD 445.6 Wing

Stanford,

Jacobson

2024

Journal of Aircraft

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The AGARD 445.6 configuration is the most popular validation test case for transonic flutter predictions, but the actual extent of truly nonlinear transonic flow for this case is unclear, due to the sparsity of the experimental data and the thin profile of the wing. This work utilizes a combination of mesh adaptation and the linearized frequency-domain method to obtain high-quality viscous and inviscid flutter predictions; these solutions show a double flutter dip through the transonic Mach range driven by complex shock growth across the wing. A single experimental flutter point lies in this flutter dip area, which is not enough to assess the accuracy of these transonic flutter predictions. Modeling the boundary layer of the wind tunnel wall (as opposed to the commonly assumed symmetry wall assumption) has a very large impact on the predicted flutter boundary, though the size of the boundary layer during the original experiment is uncertain.

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Transonic Flutter Dips of the AGARD 445.6 Wing

Stanford

Jacobson

2023

AIAA SCITECH 2023 Forum

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Computational Analysis of the Transonic Dynamics Tunnel Using FUN3D

Cited by 6 publications

References 13 publications

Sketch-to-Solution: An Exploration of Viscous CFD with Automatic Grids

Sketch-to-Solution: An Exploration of Viscous CFD with Automatic Grids

Transonic Flutter Dips of the AGARD 445.6 Wing

Transonic Flutter Dips of the AGARD 445.6 Wing

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