Feasibility Study of Constant Eddy-Viscosity Assumption in Gradient-Based Design Optimization

2003 ◽  
Vol 40 (6) ◽  
pp. 1168-1176
Author(s):  
Chang Sung Kim ◽  
Chongam Kim ◽  
Oh Hyun Rho
Keyword(s):  
Design Optimization ◽  
Feasibility Study ◽  
Eddy Viscosity ◽  
Gradient Based

Feasibility Study of Constant Eddy-Viscosity Assumption in Gradient-Based Design Optimization

2003 ◽  
Vol 40 (6) ◽  
pp. 1168-1176 ◽  
Author(s):  
Chang Sung Kim ◽  
Chongam Kim ◽  
Oh Hyun Rho
Keyword(s):  
Design Optimization ◽  
Feasibility Study ◽  
Eddy Viscosity ◽  
Gradient Based

A Comprehensive Investigation of Ensembles of Gaussian-Based and Gradient-Based Transformed Reliability Analyses: When and How to Use Them

2016 ◽  
Author(s):  
Po Ting Lin ◽  
Wei-Hao Lu ◽  
Shu-Ping Lin
Keyword(s):  
Design Optimization ◽  
Design Space ◽  
Optimization Problems ◽  
Nonlinear Problems ◽  
Kernel Functions ◽  
Sensitivity Analyses ◽  
Normal Space ◽  
Highly Nonlinear ◽  
Standard Normal ◽  
Gradient Based

In the past few years, researchers have begun to investigate the existence of arbitrary uncertainties in the design optimization problems. Most traditional reliability-based design optimization (RBDO) methods transform the design space to the standard normal space for reliability analysis but may not work well when the random variables are arbitrarily distributed. It is because that the transformation to the standard normal space cannot be determined or the distribution type is unknown. The methods of Ensemble of Gaussian-based Reliability Analyses (EoGRA) and Ensemble of Gradient-based Transformed Reliability Analyses (EGTRA) have been developed to estimate the joint probability density function using the ensemble of kernel functions. EoGRA performs a series of Gaussian-based kernel reliability analyses and merged them together to compute the reliability of the design point. EGTRA transforms the design space to the single-variate design space toward the constraint gradient, where the kernel reliability analyses become much less costly. In this paper, a series of comprehensive investigations were performed to study the similarities and differences between EoGRA and EGTRA. The results showed that EGTRA performs accurate and effective reliability analyses for both linear and nonlinear problems. When the constraints are highly nonlinear, EGTRA may have little problem but still can be effective in terms of starting from deterministic optimal points. On the other hands, the sensitivity analyses of EoGRA may be ineffective when the random distribution is completely inside the feasible space or infeasible space. However, EoGRA can find acceptable design points when starting from deterministic optimal points. Moreover, EoGRA is capable of delivering estimated failure probability of each constraint during the optimization processes, which may be convenient for some applications.

scholarly journals An Arbitrary Hybrid Turbulence Modeling Approach for Efficient and Accurate Automotive Aerodynamic Analysis and Design Optimization

Fluids ◽  
2021 ◽  
Vol 6 (11) ◽  
pp. 407
Author(s):  
Saule Maulenkul ◽  
Kaiyrbek Yerzhanov ◽  
Azamat Kabidollayev ◽  
Bagdaulet Kamalov ◽  
Sagidolla Batay ◽  
...  
Keyword(s):  
Flow Field ◽  
Design Optimization ◽  
Automotive Industry ◽  
Turbulence Modeling ◽  
Turbulence Models ◽  
Analysis And Design ◽  
Eddy Simulation ◽  
Gradient Based ◽  
Automotive Aerodynamics ◽  
Single Flow

The demand in solving complex turbulent fluid flows has been growing rapidly in the automotive industry for the last decade as engineers strive to design better vehicles to improve drag coefficients, noise levels and drivability. This paper presents the implementation of an arbitrary hybrid turbulence modeling (AHTM) approach in OpenFOAM for the efficient simulation of common automotive aerodynamics with unsteady turbulent separated flows such as the Kelvin–Helmholtz effect, which can also be used as an efficient part of aerodynamic design optimization (ADO) tools. This AHTM approach is based on the concept of Very Large Eddy Simulation (VLES), which can arbitrarily combine RANS, URANS, LES and DNS turbulence models in a single flow field depending on the local mesh refinement. As a result, the design engineer can take advantage of this unique and highly flexible approach to tailor his grid according to his design and resolution requirements in different areas of the flow field over the car body without sacrificing accuracy and efficiency at the same time. This paper presents the details of the implementation and careful validation of the AHTM method using the standard benchmark case of the Ahmed body, in comparison with some other existing models, such as RANS, URANS, DES and LES, which shows VLES to be the most accurate among the five examined. Furthermore, the results of this study demonstrate that the AHTM approach has the flexibility, efficiency and accuracy to be integrated with ADO tools for engineering design in the automotive industry. The approach can also be used for the detailed study of highly complex turbulent phenomena such as the Kelvin–Helmholtz instability commonly found in automotive aerodynamics. Currently, the AHTM implementation is being integrated with the DAFoam for gradient-based multi-point ADO using an efficient adjoint solver based on a Sparse Nonlinear optimizer (SNOPT).

A Post-Flutter Response Constraint for Gradient-Based Aircraft Design Optimization

2021 ◽  
Author(s):  
Cristina Riso ◽  
Carlos E. S. Cesnik ◽  
Bogdan I. Epureanu ◽  
Patrick Teufel
Keyword(s):  
Design Optimization ◽  
Aircraft Design ◽  
Gradient Based

Reliability-Based Design Optimization of Complex Problems With Multiple Design Points via Narrowed Search Region

2019 ◽  
Vol 142 (6) ◽  
Author(s):  
Yutian Wang ◽  
Peng Hao ◽  
Zhendong Guo ◽  
Dachuan Liu ◽  
Qiang Gao
Keyword(s):  
Design Optimization ◽  
Risk Function ◽  
Computational Cost ◽  
Reliability Based Design Optimization ◽  
Search Region ◽  
Complex Problems ◽  
Reliability Based Design ◽  
Training Samples ◽  
Gradient Based ◽  
Similar Accuracy

Abstract The expensive computational cost is always a major concern for reliability-based design optimization (RBDO) of complex problems. The performance of RBDO can be lowered by the inaccuracy of reliability analysis (RA) which is caused by multiple local optimums and multiple design points in highly non-linear space. In order to reduce the computational burden and guarantee the accuracy of RA (and thus to improve the RBDO performance), a global RBDO algorithm by adopting an improved constraint boundary sampling (GRBDO-ICBS) method is proposed. Specifically, the GRBDO-ICBS method first narrows the concerned search region by using a Kriging-based global search. The accuracies of the design points are verified by the expected risk function (ERF), and the corresponding inaccurate design points are added into training samples to update Kriging. Then a multi-start gradient-based sequential RBDO is carried out, which tries to find out all multiple design points in the concerned search region. The performance of GRBDO-ICBS is demonstrated by four examples. All results have shown that the proposed method can achieve similar accuracy as Monte Carlo simulation (MCS)-based RBDO but with a much lower computational cost.

Design Optimization by Integration of Evolutionary Search and Cumulative Approximation

2005 ◽  
Author(s):  
Kikuo Fujita ◽  
Tomoki Ushiro ◽  
Noriyasu Hirokawa
Keyword(s):  
Design Optimization ◽  
Computational Cost ◽  
Optimal Solution ◽  
Response Surfaces ◽  
Evolutionary Search ◽  
Design Problems ◽  
Real Coded Genetic Algorithm ◽  
Cumulative Function ◽  
Gradient Based ◽  
New Framework

This paper proposes a new design optimization framework by integrating evolutionary search and cumulative function approximation. While evolutionary algorithms are robust even under multi-peaks, rugged natures, etc., their computational cost is inferior to ordinary schemes such as gradient-based methods. While response surface techniques such as quadratic approximation can save computational cost for complicated design problems, the fidelity of solution is affected by density of samples. The new framework simultaneously performs evolutionary search and constructs response surfaces. That is, in its early phase the search is performed over roughly but globally approximated surfaces with the relatively small number of samples, and in its later phase the search is performed intensively around promising regions, which are revealed in the preceded phases, over response surfaces enhanced with additional samples. This framework is expected to be able to robustly find the optimal solution with less sampling. An optimization algorithm is implemented by combining a real-coded genetic algorithm and a Voronoi diagram based cumulative approximation, and it is applied to some numerical examples for discussing its potential and promises.

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