Developers of computer codes, analysts who use the codes, and decision makers who rely on the results of the analyses face a critical question: How should confidence in modeling and simulation be critically assessed? Verification and validation (V&V) of computational simulations are the primary methods for building and quantifying this confidence. Briefly, verification is the assessment of the accuracy of the solution to a computational model. Validation is the assessment of the accuracy of a computational simulation by comparison with experimental data. In verification, the relationship of the simulation to the real world is not an issue. In validation, the relationship between computation and the real world, i.e., experimental data, is the issue.

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Verification, Validation, and Predictive Capability in Computational Engineering and Physics

Anisotropic grid adaptation for functional outputs: application to two-dimensional viscous flows

The accuracy of the least-squares technique for gradient reconstruction on unstructured meshes is examined. While least-squares techniques produce accurate results on arbitrary isotropic unstructured meshes, serious difficulties exist for highly stretched meshes in the presence of surface curvature. In these situations, gradients are typically under-estimated by up to an order of magnitude. For vertex-based discretizations on triangular and quadrilateral meshes, and cell-centered discretizations on quadrilateral meshes, accuracy can be recovered using an inverse distance weighting in the least-squares construction. For cell-centered discretizations on triangles, both the unweighted and weighted least-squares constructions fail to provide suitable gradient estimates for highly stretched curved meshes. Good overall flow solution accuracy can be retained in spite of poor gradient estimates, due to the presence of flow alignment in exactly the same regions where the poor gradient accuracy is observed. However, the use of entropy fixes has the potential for generating large but subtle discretization errors.

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Revisiting the Least-squares Procedure for Gradient Reconstruction on Unstructured Meshes

Results from the Fourth AIAA Drag Prediction Workshop (DPW-IV) are summarized. The workshop focused on the prediction of both absolute and differential drag levels for wing-body and wing-body-horizontal-tail configurations that are representative of transonic transport air- craft. Numerical calculations are performed using industry-relevant test cases that include lift- specific flight conditions, trimmed drag polars, downwash variations, dragrises and Reynolds- number effects. Drag, lift and pitching moment predictions from numerous Reynolds-Averaged Navier-Stokes computational fluid dynamics methods are presented. Solutions are performed on structured, unstructured and hybrid grid systems. The structured-grid sets include point- matched multi-block meshes and over-set grid systems. The unstructured and hybrid grid sets are comprised of tetrahedral, pyramid, prismatic, and hexahedral elements. Effort is made to provide a high-quality and parametrically consistent family of grids for each grid type about each configuration under study. The wing-body-horizontal families are comprised of a coarse, medium and fine grid; an optional extra-fine grid augments several of the grid families. These mesh sequences are utilized to determine asymptotic grid-convergence characteristics of the solution sets, and to estimate grid-converged absolute drag levels of the wing-body-horizontal configuration using Richardson extrapolation.

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Summary of the Fourth AIAA CFD Drag Prediction Workshop

Fluid–structure interaction for aeroelastic applications

The results from the first AIAA CFD Drag Prediction Workshop are summarized. The workshop was designed specifically to assess the state-of-the-art of computational fluid dynamics methods for force and moment prediction. An impartial forum was provided to evaluate the effectiveness of existing computer codes and modeling techniques, and to identify areas needing additional research and development. The subject of the study was the DLR-F4 wing-body configuration, which is representative of transport aircraft designed for transonic flight. Specific test cases were required so that valid comparisons could be made. Optional test cases included constant-C(sub L) drag-rise predictions typically used in airplane design by industry. Results are compared to experimental data from three wind tunnel tests. A total of 18 international participants using 14 different codes submitted data to the workshop. No particular grid type or turbulence model was more accurate, when compared to each other, or to wind tunnel data. Most of the results overpredicted C(sub Lo) and C(sub Do), but induced drag (dC(sub D)/dC(sub L)(exp 2)) agreed fairly well. Drag rise at high Mach number was underpredicted, however, especially at high C(sub L). On average, the drag data were fairly accurate, but the scatter was greater than desired. The results show that well-validated Reynolds-Averaged Navier-Stokes CFD methods are sufficiently accurate to make design decisions based on predicted drag.

/pdf/summary-of-data-from-the-first-aiaa-cfd-drag-prediction-43ep13kd51.pdf

Summary of Data from the First AIAA CFD Drag Prediction Workshop

A highly automated method is presented for rapidly adjusting multiblock patched grids over complex configurations during aerodynamic shape optimization or aeroelastic analyses. A very low level of user-intervention is achieved by making the block boundaries transparent. This is accomplished by employing data structures as they are commonly found in unstructured computational fluid dynamics (CFD) methods, and by basing the relation between a "master" (or solid surface) node and its "slave" nodes solely on minimum distance. The "slave" nodes form the vertices of subgrids (typical size: 5x5 grid points) on grid block faces. These subgrids ensure maintenance of point-matched block interfaces. The "slave" nodes follow the movement of their "master" nodes, scaled by a Gaussian distribution function to preserve the integrity of grids in the presence of multiple body surfaces. Transfinite interpolation is used to transfer the displacement of the slave nodes throughout the entire computational domain. An automated surface regridding capability enhances the robustness of the methodology and improves the grid quality. Effectiveness and efficiency of the grid perturbation method are demonstrated for several applications to High-Speed Civil Transport (HSCT) wing/body (W/B) and wing/body/nacelle/diverter (W/B/N/D) configurations.

Method for perturbing multiblock patched grids in aeroelastic and design optimization applications

Drag prediction using nonlinear computational methods

Aerospace vehicle MDO shape optimization using ADIFOR 3.0 gradients

This paper is a discussion of the supersonic nonlinear point design optimization efforts at McDonnell Douglas Aerospace under the High-Speed Research (HSR) program. The baseline for these optimization efforts has been the M2.4-7A configuration which represents an arrow-wing technology for the High-Speed Civil Transport (HSCT). Optimization work on this configuration began in early 1994 and continued into 1996. Initial work focused on optimization of the wing camber and twist on a wing/body configuration and reductions of 3.5 drag counts (Euler) were realized. The next phase of the optimization effort included fuselage camber along with the wing and a drag reduction of 5.0 counts was achieved. Including the effects of the nacelles and diverters into the optimization problem became the next focus where a reduction of 6.6 counts (Euler W/B/N/D) was eventually realized. The final two phases of the effort included a large set of constraints designed to make the final optimized configuration more realistic and they were successful albeit with a loss of performance.

Shreekant Agrawal

Papers

Summary of Data from the First AIAA CFD Drag Prediction Workshop

Method for perturbing multiblock patched grids in aeroelastic and design optimization applications

Drag prediction using nonlinear computational methods

Aerospace vehicle MDO shape optimization using ADIFOR 3.0 gradients

Supersonic Aerodynamic Design Improvements of an Arrow-Wing HSCT Configuration Using Nonlinear Point Design Methods