An automated documentation and reporting system for CFD

Rodman, Laura C.; Reisenthel, Patrick; Childs, Robert E.

doi:10.2514/6.2002-986

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…11, for a model of the aerodynamic performance of an X-38-type forebody configuration as a function of Mach number and pitch angle upon atmospheric reentry. The X-38 data set consists of Euler CFD computations carried out by NASA Ames, and was used as a test case in the development of CFDExchange (20) , a system for the automated archival, search, and results retrieval of CFD solutions, using a relational database. In this system, the retrieved CFD solutions are considered as points in a multidimensional space.…”

Section: Correction Of Aerodynamic Databases Using Experimental Datamentioning

confidence: 99%

“…While the purpose of the uncertainty evaluation here is merely illustrative, giving a flavour what is possible, more quantitative uses of the prediction uncertainty have previously been suggested. For example (20) , in cases where the goal is to characterise the overall response surface within an acceptable uncertainty bound, the uncertainty prediction can be used to guide the further population of the parameter space, for example, by 'placing' further runs/cases in areas of highest uncertainty. On the other hand, if the goal is purely to optimise (rather than globally characterise), then, as pointed out in Ref.…”

Section: Correction Of Aerodynamic Databases Using Experimental Datamentioning

confidence: 99%

“…Neural networks (14,15) , support vector machines (16) , kernel methods (17,18) and multidimensional splines (19) all have proven capable of multidimensional data generalisation and of (partially) circumventing this difficulty. The particular approach used in this paper is based on self-training radial basis function networks which form the basis of the NEAR-RS (response surface) technology (20) .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cumulative global metamodels with uncertainty — a tool for aerospace integration

et al. 2006

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The integration of multidisciplinary data is key to supporting decisions during the development of aerospace products.Multidimensional metamodels can now be automatically constructed using limited experimental or numerical data, including data from heterogeneous sources. Recent progress in multidimensional response surface technology, for example, provides the ability to interpolate between sparse data points in a multidimensional parameter space. These analytical representations act as surrogates that are based on and complement higher fidelity models and/or experiments. These high-level representations or metamodels can include technical data from multiple fidelity levels and multiple disciplines, but also nontechnical data such as costs and schedules. Most importantly, these representations can be constructed on-the-fly and are cumulatively enriched as more data become available. The purpose of the present paper is to highlight applications of these Cumulative Global Metamodels (CGM), their ease of construction, and the role they can play in aerospace integration. This paper focuses on two types of applications, namely, design optimization and mutual data set enrichment via data fusion. I.Nomenclature II. BackgroundL s OBAL metamodels and response surface technology are increasing used in a variety of fields, including tructural reliability, instrument calibration, and aerodynamic and trajectory optimization, to name a few. 1-11Because of their analytical nature, these models can be used for automated searches and are naturally well-suited to the acceleration of optimization tasks and rapid strategy evaluation. A central issue to constructing appropriate response surface models is the so-called curse of dimensionality, in which the number of data points required to characterize/support the surface increases exponentially with the number of independent variables. This difficulty is well-known and, in effect, precludes the use of conventional schemes, such as polynomial and piecewise-polynomial (finite-element) approximations. Neural networks, support vector machines, and multidimensional splines all have proven capable of multidimensional data generalization and (partially) circumventing this difficulty. The particular approach used in this paper is based on self-training radial basis function networks which form the basis of the NEAR-RS (response surface) technology.

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Section: Correction Of Aerodynamic Databases Using Experimental Datamentioning

confidence: 99%

Section: Correction Of Aerodynamic Databases Using Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cumulative global metamodels with uncertainty — a tool for aerospace integration

et al. 2006

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“…However, a short discussion of RBF methodology is presented here for clarity. In general terms, radial basis function networks are analogous to solving a system of equations where each input data point has an "island of [16][17][18][19] used to develop the CFD-based A106 AFMA DB) constructs a global response surface across multiple dimensions based on a sparse set of input data.…”

Section: Response Surface Generation Using Radial Basis Functionsmentioning

confidence: 99%

Assessment of CFD-based Response Surface Model for Ares I Supersonic Ascent Aerodynamics

Hanke

2011

29th AIAA Applied Aerodynamics Conference

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The Ascent Force and Moment Aerodynamic (AFMA) Databases (DBs) for the Ares I Crew Launch Vehicle (CLV) were typically based on wind tunnel (WT) data, with increments provided by computational fluid dynamics (CFD) simulations for aspects of the vehicle that could not be tested in the WT tests. During the Design Analysis Cycle 3 analysis for the outer mold line (OML) geometry designated A106, a major tunnel mishap delayed the WT test for supersonic Mach numbers (M) greater than 1.6 in the Unitary Plan Wind Tunnel at NASA Langley Research Center, and the test delay pushed the final delivery of the A106 AFMA DB back by several months. The aero team developed an interim database based entirely on the already completed CFD simulations to mitigate the impact of the delay. This CFD-based database used a response surface methodology based on radial basis functions to predict the aerodynamic coefficients for M > 1.6 based on only the CFD data from both WT and flight Reynolds number conditions. The aero team used extensive knowledge of the previous AFMA DB for the A103 OML to guide the development of the CFD-based A106 AFMA DB. This report details the development of the CFD-based A106 Supersonic AFMA DB, constructs a prediction of the database uncertainty using data available at the time of development, and assesses the overall quality of the CFD-based DB both qualitatively and quantitatively. This assessment confirms that a reasonable aerodynamic database can be constructed for launch vehicles at supersonic conditions using only CFD data if sufficient knowledge of the physics and expected behavior is available. This report also demonstrates the applicability of non-parametric response surface modeling using radial basis functions for development of aerodynamic databases that exhibit both linear and non-linear behavior throughout a large data space. Nomenclature Acronyms and Abbreviations

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“…In additional work, a scheme was devised for inserting new CFD calculations into the database automatically, and for compiling data from related runs which could then be viewed within a multidimensional design space. 8 The goal for LVX is to combine expertise in launch vehicle aerodynamics with capabilities in information management to develop a practical tool for the aerodynamicist to employ in the design analysis of new launch vehicles. Useful and hardto-find information is identified and collected in the system to create an online knowledge base of aerodynamics of launch vehicles.…”

Section: Introductionmentioning

confidence: 99%

Integrated aerodynamic design and analysis of launch vehicles

Mendenhall

Hegedus

Whitlock

et al. 2001

39th Aerospace Sciences Meeting and Exhibit

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An integrated aerodynamic design and analysis procedure for launch vehicles which incorporates engineering prediction methods, historical knowledge, and corporate memory into a knowledge-based system is described. The objective is to provide an economical means to conduct the aerodynamic design and analysis of future launch vehicles to minimize the risk of aerodynamic-induced failures. The method will provide a guide to the aerodynamic-related information required during the conceptual and preliminary design stages. In addition, the user will have access to historical information on similar launch vehicles, the ability to set up geometric characteristics, and a capability to predict preliminary aerodynamics of simple launch vehicles. The method includes a number of searchable databases containing technical references on launch vehicle aerodynamics, engineering experiences and anecdotes on launch vehicle programs of the past, and information about various launch vehicles. The method can be used as a stand-alone tool for traditional design approaches or as an integral part of a multidisciplinary design method. It has archive capability, and it can be updated with new information as desired by the user.

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An automated documentation and reporting system for CFD

Cited by 6 publications

References 6 publications

Cumulative global metamodels with uncertainty — a tool for aerospace integration

Cumulative global metamodels with uncertainty — a tool for aerospace integration

Assessment of CFD-based Response Surface Model for Ares I Supersonic Ascent Aerodynamics

Integrated aerodynamic design and analysis of launch vehicles

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