Accurate Multiblock Navier-Stokes Solver for Complex Aerodynamic Configurations

Epstein, Boris; Rubin, T.; Seror, S.

doi:10.2514/2.2012

Cited by 38 publications

(19 citation statements)

References 14 publications

(11 reference statements)

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“…The OPTIMAS optimizations conducted here cost about three orders-ofmagnitude more than an objective-function evaluation. For more information on OPTIMAS and NES, see References [2][3][4][5].…”

Section: Optimization and Analysis Methodsmentioning

confidence: 99%

A Pilot Project in Preparation of an Aerodynamic Optimization Workshop with Lessons Learned

Vassberg

Jameson

Peigin

et al. 2008

26th AIAA Applied Aerodynamics Conference

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This paper is an addendum to a recent publication on a Comparative Study of 3D Wing Drag Minimization by Different Optimization Techniques; with a focus on a path forward towards the development of a larger-scale Aerodynamic Optimization Workshop. Three independent optimization efforts were conducted concurrently, each without knowledge of the others' results. The resulting "optimal" wings were then cross analyzed by each of the CFD methods used in the optimization phase, and by a fourth CFD method which was not used to develop any of the geometries. The incremental improvements, as predicted by these cross analyses, are compared to establish a level of uncertainty in the performance improvements and identify any unusual trends. The baseline geometry of this pilot project is the DPW-W1 wing, which was placed into the public domain by the Drag Prediction Workshop as a test case for the third workshop of June 2006. Lessons learned from this collaborative effort are documented and suggestions are provided to aid in the preparation of a possible future workshop on aerodynamic shape optimization.

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Section: Optimization and Analysis Methodsmentioning

confidence: 99%

A Pilot Project in Preparation of an Aerodynamic Optimization Workshop with Lessons Learned

Vassberg

Jameson

Peigin

et al. 2008

26th AIAA Applied Aerodynamics Conference

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“…The mesh sizes in the fine level (used in the analysis runs) were chosen based on the results of grid convergence studies for the corresponding RANS solvers (see [27][28][29]); these data are presented in Table 1.…”

Section: Cfd Driversmentioning

confidence: 99%

“…Numerical solutions of RANS equations in the framework of the optimization tool OPTIMAS were based on the structured multiblock/multiface code NES [28,34], which employs the essentially nonoscillatory (ENO) concept with a flux interpolation technique that allows accurate estimation of sensitive aerodynamic characteristic such as lift, pressure drag, friction drag, and pitching moment. The effects of turbulence are modeled through an eddyviscosity hypothesis with the Baldwin-Lomax turbulence model [35].…”

Section: Cfd Driversmentioning

confidence: 99%

Comparative Study of Three-Dimensional Wing Drag Minimization by Different Optimization Techniques

Epstein

Jameson

Peigin

et al. 2009

Journal of Aircraft

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The main goal of this paper is to document a comparative study of different computational-fluid-dynamics-based optimization techniques applied to the solution of a three-dimensional wing drag minimization problem. To achieve this objective, three optimization tools were used: SYN107 (Intelligent Aerodynamics International), MDOPT (The Boeing Company), and OPTIMAS (Israel Aerospace Industries). The first tool employs gradient-based search techniques using the continuous adjoint equation, the second one is a response-surface method, and the last one uses a floating-point genetic algorithm as its search engine. As the starting geometry, the public domain DPW-W1 wing (a test case for the Third AIAA Drag Prediction Workshop) was used. The comparisons included herein are provided in three stages: cross analysis of the initial geometry by the computational fluid dynamics tools employed in the optimizations, optimization of the initial geometry to minimum drag, and cross analysis of optimal shapes achieved by the optimization tools using all computational fluid dynamics tools employed. The cross analysis also includes results from an independent computational fluid dynamics method that was not used in any of the optimization efforts. These results help quantify the level of variation that is inherent in, and can be expected from, application of the current state-of-the-art aerodynamic optimization methods. The present work may be regarded as a move toward the construction of reliable test cases for an aerodynamic shape optimization problem. Another goal of this collaborative investigation is to collect lessons learned from this pilot project to help develop a model for an aerodynamic optimization workshop.

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“…Moreover, the basic core codes may be written in di erent programming languages. For example, in our case, the CFD sub-algorithms (employing the core code NES [21,22]) were written in the C language, while the GAs sub-algorithms employ FORTRAN-77.…”

Section: Implementation Of the Algorithmmentioning

confidence: 99%

“…As a gas-dynamic model for calculating C D and C L values, the full Navier-Stokes equations are used. Numerical solution of the full Navier-Stokes equations was based on the code NES [21,22].…”

Section: Problem Statementmentioning

confidence: 99%

Robust handling of non‐linear constraints for GA optimization of aerodynamic shapes

Peigin¹,

Epstein²

2004

Numerical Methods in Fluids

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SUMMARYA new approach to the robust handling of non-linear constraints for GAs (genetic algorithms) optimization is proposed. A speciÿc feature of the approach consists of the change in the conventional search strategy by employing search paths which pass through both feasible and infeasible points (contrary to the traditional approach where only feasible points may be included in a path). The method (driven by full Navier-Stokes computations) was applied to the problem of multiobjective optimization of aerodynamic shapes subject to various geometrical and aerodynamic constraints. The results demonstrated that the method retains high robustness of conventional GAs while keeping CFD computational volume to an acceptable level, which allowed the algorithm to be used in a demanding engineering environment.

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Accurate Multiblock Navier-Stokes Solver for Complex Aerodynamic Configurations

Cited by 38 publications

References 14 publications

A Pilot Project in Preparation of an Aerodynamic Optimization Workshop with Lessons Learned

A Pilot Project in Preparation of an Aerodynamic Optimization Workshop with Lessons Learned

Comparative Study of Three-Dimensional Wing Drag Minimization by Different Optimization Techniques

Robust handling of non‐linear constraints for GA optimization of aerodynamic shapes

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