Variable-complexity aerodynamic optimization of a high-speed civil transport wing

Hutchison, M. G.; Unger, Eric; Mason, William H.; Grossman, Bernard; Haftka, Raphael T.

doi:10.2514/3.46462

Cited by 97 publications

(40 citation statements)

References 12 publications

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“…The choice of gross weight as the objective function directly incorporates both aerodynamic and structural considerations, in that the structural design directly affects aircraft empty weight and drag, while aerodynamic performance dictates drag and thus the required fuel weight. This HSCT design problem is described in detail elsewhere (Baker et al (1998), Golovidov (1997, Hutchison et al (1993), Hutchison et al (1994), MacMillin et al (1996), MacMillin et al (1997)), and thus only a few pertinent details will be repeated here.…”

Section: Hsct Configuration Design Applicationmentioning

confidence: 99%

“…The methods used to calculate the drag components used in the drag calculation and their corresponding ranges are described in Hutchison et al (1993) and Hutchison et al (1994). The …”

Section: Hsct Configuration Design Applicationmentioning

confidence: 99%

“…A simple strip boundary layer friction estimate is implemented as in Hutchison et al (1994). A vortex lattice method with vortex lift and ground effects included (Bertin et al (1989)) is used to calculate landing angle of attack.…”

mentioning

confidence: 99%

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A fully‐distributed parallel global search algorithm

Watson

Baker²

2001

Engineering Computations

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The n-dimensional direct search algorithm DIRECT of Jones, Perttunen, and Stuckman has attracted recent attention from the multidisciplinary design optimization community. Since DIRECT only requires function values (or ranking) and balances global exploration with local refinement better than n-dimensional bisection, it is well suited to the noisy function values typical of realistic simulations. While not efficient for high accuracy optimization, DIRECT is appropriate for the sort of global design space exploration done in large scale engineering design. Direct and pattern search schemes have the potential to exploit massive parallelism, but efficient use of massively parallel machines is nontrivial to achieve. This paper presents a fully distributed control version of DIRECT that is designed for massively parallel (distributed memory) architectures. Parallel results are presented for a multidisciplinary design optimization problem-configuration design of a high speed civil transport.

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Section: Hsct Configuration Design Applicationmentioning

confidence: 99%

“…The methods used to calculate the drag components used in the drag calculation and their corresponding ranges are described in Hutchison et al (1993) and Hutchison et al (1994). The …”

Section: Hsct Configuration Design Applicationmentioning

confidence: 99%

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A fully‐distributed parallel global search algorithm

Watson

Baker²

2001

Engineering Computations

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“…4. rmse of the 20 clustered localized models for variable levels of residual error tolerances, while taking the original airfoil analysis code as the reference model (i.e., with a fidelity of 1) case, this implies the correlation between the predicted fitness values is of utmost importance. For greater details, the reader is referred to [16][17][18], where it was shown that the correlation metric represents a more useful metric for estimating the quality of an approximation model over one that is based on absolute error.…”

Section: Multi-fidelity Models In the Aerodynamic Airfoil Design Problemmentioning

confidence: 99%

“…They are typically expressed in the form of mathematical equations and then implemented as computer simulation codes. A motivating real world example for us is the aerodynamic aircraft wing analysis and design where various multi-fidelity models ranging from low-fidelity simple-physics models to high-fidelity detailed simulation models have been studied [1][2][3][4][5][6]. In this field, Navier-Stokes and Euler equations are examples of two computational models with different fidelity, where the latter is obtained by removing the viscosity terms from the Navier-Stokes equation.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Optimization with Dynamic Fidelity Computational Models

Lim

Ong

Jin

et al. 2008

Lecture Notes in Computer Science

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Abstract. In optimization, it is now a common practice to use lower fidelity computational models in place of the original model when dealing with problems with computationally expensive objective functions. In this paper, we present a study on evolutionary optimization with dynamic fidelity computational models capable of acclimatizing to localized complexity, for enhancing design search efficiency. In particular, we propose an evolutionary framework for model fidelity control that decides, at runtime, the appropriate fidelity level of the computational model, which is deemed to be computationally less expensive, to be used in place of the exact analysis code as the search progresses. Empirical study on an aerodynamic airfoil design problem based on a Memetic Algorithm with Dynamic Fidelity Model (MA-DFM) demonstrates that improved quality solution and efficiency are obtained over existing evolutionary schemes.

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Kriging meta‐model assisted calibration of computational fluid dynamics models

et al. 2016

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Computational fluid dynamics (CFD) is a simulation technique widely used in chemical and process engineering applications. However, computation has become a bottleneck when calibration of CFD models with experimental data (also known as model parameter estimation) is needed. In this research, the kriging meta-modelling approach (also termed Gaussian process) was coupled with expected improvement (EI) to address this challenge. A new EI measure was developed for the sum of squared errors (SSE) which conforms to a generalised chi-square distribution and hence existing normal distribution-based EI measures are not applicable. The new EI measure is to suggest the CFD model parameter to simulate with, hence minimising SSE and improving match between simulation and experiments. The usefulness of the developed method was demonstrated through a case study of a single-phase flow in both a straight-type and a convergent-divergent-type annular jet pump, where a single model parameter was calibrated with experimental data.Keywords: Calibration, computational fluid dynamics, expected improvement, Gaussian process, parameter estimation Correspondence concerning this article should be addressed to Tao Chen at t.chen@surrey.ac.uk. 2 IntroductionComputational fluid dynamics (CFD) simulations are usually large complex computer programs representing real life fluid systems. The complexity of such simulations can make them quite a herculean task to solve, with the run time associated with the simulation being of great concern. Such simulation run time could be for hours or days.1,2 In addition, large scale or complex systems usually place a great demand on computer memory. 3 Computation is especially a problem when the CFD simulation is required to run for a large number of times, for example when sensitivity analysis is carried out 1,4 , or the models are used within a certain optimisation problem. 4,5 Of particular interest in this research project is calibration of CFD models (also known as model parameter estimation), which can be cast into an optimisation problem.CFD model calibration can be defined as the process of adjusting numerical or physical parameters in the CFD model which, on obtaining the optimal values 6 , helps to improve agreement with experimental data. 7,8,9 The parameters in this context are typically turbulence model coefficients or turbulence model choice. The default values given by CFD software providers for turbulence parameters may apply only in standard circumstances. In this paper, calibration is formulated as an optimisation problem to minimise the mismatch between simulated and experimental outputs (responses). The calibration process may need tens to hundreds of simulation runs to evaluate the objective function, the sum of squared errors (where the errors quantify the differences between the simulated and the experimental outputs) over the region of interest, a requirement that is often infeasible for CFD.These difficulties faced by CFD simulations have led to the application of meta-modelling, whi...

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Variable-complexity aerodynamic optimization of a high-speed civil transport wing

Cited by 97 publications

References 12 publications

A fully‐distributed parallel global search algorithm

A fully‐distributed parallel global search algorithm

Evolutionary Optimization with Dynamic Fidelity Computational Models

Kriging meta‐model assisted calibration of computational fluid dynamics models

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