Neural Network and Regression Approximations in High-Speed Civil Aircraft Design Optimization

Patnaik, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.

doi:10.2514/2.2395

Cited by 16 publications

(13 citation statements)

References 5 publications

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“…In order to raise the optimization rate and reduce the analysis time of the CFD, the surrogate model is established based on the improved BP neural network [9] . The surrogate model builds the response relationship between the Mach number, design variables, and aerodynamics, which lays the foundation for the robust design.…”

Section: Surrogate Modelmentioning

confidence: 99%

Robust airfoil optimization based on improved particle swarm optimization method

Wang

Zhang

Chen

2011

Appl. Math. Mech.-Engl. Ed.

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A robust airfoil optimization platform is constructed based on the modified particle swarm optimization method (i.e., the second-order oscillating particle swarm method), which consists of an efficient optimization algorithm, a precise aerodynamic analysis program, a high accuracy surrogate model, and a classical airfoil parametric method. There are two improvements for the modified particle swarm method compared with the standard particle swarm method. First, the particle velocity is represented by the combination of the particle position and the variation of position, which makes the particle swarm algorithm a second-order precision method with respect to the particle position. Second, for the sake of adding diversity to the swarm and enlarging the parameter searching domain to improve the global convergence performance of the algorithm, an oscillating term is introduced to the update formula of the particle velocity. At last, taking two airfoils as examples, the aerodynamic shapes are optimized on this optimization platform. It is shown from the optimization results that the aerodynamic characteristic of the airfoils is greatly improved in a broad design range.

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Section: Surrogate Modelmentioning

confidence: 99%

Robust airfoil optimization based on improved particle swarm optimization method

Wang

Zhang

Chen

2011

Appl. Math. Mech.-Engl. Ed.

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“…3 , and an allowable strength of 30 000 psi. Critical design loads were generated from a variety of shuttle accelerations and maneuvers (Ref.…”

Section: A Subproblem Solution Strategymentioning

confidence: 99%

“…An approximation module (Ref. 3) with neural network and regression methods, available in CometBoards, can be used to reduce the number of calculations. The deterministic version of CometBoards can accommodate several different disciplines, each of which can be further divided into subproblems.…”

Section: Introductionmentioning

confidence: 99%

Optimization Test-Bed CometBoards Extended into Stochastic Domain

Patnaik

Pai

Coroneos

2009

50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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“…CometBoards has been successfully used to solve a number of problems: structural design of space station components, 7 the design of nozzle components for air-breathing engines, 2 design of supersonic aircraft, 3 mixed flow turbofan engines, 8 and wave rotor concepts in jet engines. 9 The regression method and neural-networkbased aircraft analysis tools have been incorporated into CometBoards.…”

Section: Introductionmentioning

confidence: 99%

Subsonic Aircraft Design Optimization with Neural Network and Regression Approximators

Patnaik

Coroneos

Guptill

et al. 2005

Journal of Aircraft

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Neural Network and Regression Approximations in High-Speed Civil Aircraft Design Optimization

Cited by 16 publications

References 5 publications

Robust airfoil optimization based on improved particle swarm optimization method

Robust airfoil optimization based on improved particle swarm optimization method

Optimization Test-Bed CometBoards Extended into Stochastic Domain

Subsonic Aircraft Design Optimization with Neural Network and Regression Approximators

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