Summary of the Third AIAA CFD Drag Prediction Workshop

Vassberg, John C.; Tinoco, Edward N.; Mani, Mori; Brodersen, Olaf; Eisfeld, Bernhard; Wahls, Richard A.; Morrison, Joseph H.; Zickuhr, Tom; Laflin, Kelly R.; Mavriplis, Dimitri J.

doi:10.2514/6.2007-260

Cited by 117 publications

(88 citation statements)

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“…Both figures show similar features to those found by Yoneta et al 17 There is a large low pressure area inboard of the pylon in Fig 4, with the pylon restricting this low pressure creeping along the span, an effect that was present in the AIAA drag prediction workshop results. 24 This effect is greater in the high drag case, with Fig. 5 showing a smaller low pressure area in the inboard region.…”

Section: Resultsmentioning

confidence: 90%

Performance and Noise Trade-Offs on a Civil Airliner with Over-the-Wing Engines

Powell

Sóbester

Joseph

2011

49th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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Community noise has become a major consideration in the design of new aircraft. The noise generated by the engines has decreased over the generations to the extent that a whole-airframe approach is required now to achieve further significant reductions. One option is to install the engines over the wings so the airframe reflects the fan noise away from on-the-ground observers. However, in addition to good noise shielding performance, the position of the engine also has to satisfy aerodynamic efficiency criteria. We investigate the sensitivity of aerodynamic and acoustic performance metrics with respect to the positioning of the engine relative to the wing. More specifically, we trade drag computed via Reynolds-Averaged Navier Stokes simulations versus noise shielding performance, obtained experimentally through scale model tests conducted in an anechoic chamber. Surrogate models of both metrics are constructed, enabling their Pareto analysis on the specific case of a modified DLR F6 airframe geometry.

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Section: Resultsmentioning

confidence: 90%

Performance and Noise Trade-Offs on a Civil Airliner with Over-the-Wing Engines

Powell

Sóbester

Joseph

2011

49th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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“…12 One might say that to at least some extent, this is the norm. 13 When multiple computational codes are evaluated, their predictions might be compared with some reference to determine how great a difference there is between the prediction of each code and that reference.…”

Section: B Validation Of Computational Modelsmentioning

confidence: 99%

“…(7b) with obvious extensions to the -not A‖ case, Eq. (11) becomes (12) and Bayes' Theorem as expressed in Eq. (9) becomes…”

Section: B Illustration Of Basic Conceptsmentioning

confidence: 99%

Bayesian Inference in the Modern Design of Experiments

DeLoach

2008

46th AIAA Aerospace Sciences Meeting and Exhibit

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= maximum acceptable inference error probability p 00 = probability that if a candidate model term was rejected in the last experiment, it will be rejected in the next replicate of that experiment p 01 = probability that if a candidate model term was rejected in the last experiment, it will be retained in the next replicate of that experiment p 10 = probability that if a candidate model term was retained in the last experiment, it will be rejected in the next replicate of that experiment p 11 = probability that if a candidate model term was retained in the last experiment, it will be retained in the next replicate of that experiment  0 = proportion of times that a candidate model term is rejected in a series of replicated experiments  1 = proportion of times that a candidate model term is retained in a series of replicated experiments RSM = Response Surface Methods/Models/Modeling X = design matrix alternative hypothesis = assertion that a significant difference exists between an estimated response and a given reference coding = linear transformation of variables into a range convenient for processing conditional probability = probability that one event will occur, given the occurrence of another event confidence interval = precision interval when n = infinity explained SS = sum of squares attributable to known causes F-Value = ratio of mean square for an effect to residual mean square factor = an independent variable; e.g. angle of attack factor level = a specific value for an independent variable; e.g., angle of attack = 2° graduating function = low-order approximation to true but unknown response function hierarchy = condition in which higher order terms are accompanied by component lower-order term inference = decision to reject either a null hypothesis or its corresponding alternative inference space = a coordinate system in which one axis is assigned to each independent variable * Senior Research Scientist, NASA Langley Research Center, Hampton, VA. AIAA Senior Member.https://ntrs.nasa.gov/search.jsp?R=20080008574 2018-05-12T00:15:31+00:00ZAmerican Institute of Aeronautics and Astronautics 2 interaction effect = change in effect due to change in factor level from low to high joint probability = probability of two events both occurring LOF = lack of fit main effect = change in response due to change in factor level from low to high marginal probability = probability that an event will occur, independent of whether another event occurs MDOE = Modern Design of Experiments mean square = ratio of sum of squares to degrees of freedom; variance null hypothesis = assertion than no difference exists between an estimated response and a given reference OFAT = One Factor At a Time orthogonal = state in which regressors are all mutually independent regressor = term in a regression model residual = difference between measurement and some reference residual mean square = residual sum of squares divided by residual degrees of freedom residual SS = difference between total sum of squares and explained sum of squares res...

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“…The collective DPW results have established some surprising facts concerning the sensitivity of aerodynamic drag prediction with respect to computational meshes. Over the course of the workshop series, the level of mesh resolution employed for calculations has steadily increased, from grids of several million points in DPW I, 6 up to grids of over 40 million points in DPW III, 8 with some individual studies employing grids up to 100 million points. 20,22,25 This steady increase in available grid resolution has been enabled largely due to advances in computational hardware and improved solution algorithms.…”

mentioning

confidence: 99%

“…Over the course of these three workshops [6][7][8] and numerous follow-on studies undertaken over these years, 9-27 a consensus has been emerging suggesting that the dominant sources of error in aerodynamic drag prediction are related to spatial discretization error, which in turn stems largely from grid resolution and grid quality issues. The gradual evolution of this consensus can be seen in the focus of the three DPW workshops.…”

mentioning

confidence: 99%

Grid Quality and Resolution Issues from the Drag Prediction Workshop Series

Mavriplis

Vassberg

Tinoco

et al. 2009

Journal of Aircraft

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The drag prediction workshop series (DPW), held over the last six years, and sponsored by the AIAA Applied Aerodynamics Committee, has been extremely useful in providing an assessment of the state-of-the-art in computationally based aerodynamic drag prediction. An emerging consensus from the three workshop series has been the identification of spatial discretization errors as a dominant error source in absolute as well as incremental drag prediction. This paper provides an overview of the collective experience from the workshop series regarding the effect of grid-related issues on overall drag prediction accuracy. Examples based on workshop results are used to illustrate the effect of grid resolution and grid quality on drag prediction, and grid convergence behavior is examined in detail. For fully attached flows, various accurate and successful workshop results are demonstrated, while anomalous behavior is identified for a number of cases involving substantial regions of separated flow. Based on collective workshop experiences, recommendations for improvements in mesh generation technology which have the potential to impact the state-of-the-art of aerodynamic drag prediction are given.

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Summary of the Third AIAA CFD Drag Prediction Workshop

Cited by 117 publications

References 0 publications

Performance and Noise Trade-Offs on a Civil Airliner with Over-the-Wing Engines

Performance and Noise Trade-Offs on a Civil Airliner with Over-the-Wing Engines

Bayesian Inference in the Modern Design of Experiments

Grid Quality and Resolution Issues from the Drag Prediction Workshop Series

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