دانلود رایگان مقاله لاتین بهینه سازی شکل آیرودینامیکی از سایت الزویر
عنوان فارسی مقاله:
بهینه سازی شکل آیرودینامیکی برای حداقل درگ قوی و محدودیت قابلیت اطمینان آسانسور
عنوان انگلیسی مقاله:
Aerodynamic shape optimization for minimum robust drag and lift reliability constraint
سال انتشار : 2016
مقدمه انگلیسی مقاله:
The availability of powerful Computational Fluid Dynamics (CFD) models has allowed the scientific community to investigate and develop a variety of algorithms applied to shape optimization, optimal active flow control with suction-blowing jets, topology optimization, etc. However, the resulting optimal design lacks good performance when the values of some parameters of the problem are uncertain or may vary within a range. Optimal designs based on a single value of the models parameters are very sensitive to uncertainties in the parameters in the sense that the performance deteriorates considerably in the neighborhood region where the parameters are likely to take values. Thus, the optimal design should take into account the variability or uncertainties of such parameters [51,50,46,45] by minimizing an overall measure of the performance over all possible values of the uncertain parameters and the sensitivity of performance to uncertainties. A multi-point optimization approach has been introduced to account for uncertainties by computing the performance in multiple points in the uncertain parameter space [34,16,23,35].Probability distribution functions (PDFs) are often used to quantify uncertainties in simulations and probability calculus is applied to propagate the uncertainties in output quantities of interest (QoI). In design optimization the output QoI are associated with system performance measures involved in the objective function or the constraints. The mean and standard deviation are conveniently used as simple measures of uncertainty in QoI. Thus, the obvious choice in design optimization under uncertainties would be to minimize the mean value of the performance function and the standard deviation over the range of possible values of these uncertain parameters [39,41]. The mean and standard deviation are formulated as multidimensional integrals in the uncertain parameter space. The computation of these multidimensional integrals may be based on deterministic or stochastic approaches, including derivative-based, sampling and grid-based approaches. The derivative-based robust design uses a Taylor or asymptotic expansion and the multidimensional integrals are approximated by expressions that involve the first and second derivative of the performance variables with respect to the uncertain parameters [39,41,42,27,36]. Such approaches are quite fast, but lack accuracy in cases of large uncertainties, or in cases where the linearization of the performance function in the uncertain parameter space is not adequate such as in the case of transonic flow.Stochastic approaches for the estimation of the statistical moments are usually based on advanced Monte-Carlo (MC) methods [27,49,19] which are costly due to the very large number of analyses on the sample points required to compute these integrals. In addition, the sample estimates of the integrals are non-smooth functions of the design variables due to the variability of the samples between design points, complicating the use of gradient-based optimization algorithms over the uncertain parameter space. The grid-based approaches  are more accurate for the estimation of the uncertainties but they usually require a large number of CFD evaluations on the predefined grid nodes. The sparse-grid approach [47,14,5] is a remedy to the numerous required evaluations, substantially reducing the number of grid points and thus the computational cost in relation to grid-based and Gauss quadrature techniques. Using grid-based techniques, the multi-dimensional integrals for the mean and standard deviation become smooth functions of the design variables.
Efficient Aerodynamic Shape Optimization - Semantic Scholar https://pdfs.semanticscholar.org/e5b4/d7ce91cb66c01ecfd62f9928919ae04fe2b1.pdf by A Jameson - 2004 - Cited by 34 - Related articles Aug 30, 2004 - theory for shape optimization had also been explored by Pironneau for ... minimum drag, while meeting other criteria such as low structure ... [PDF]Aerodynamic Shape Optimization Techniques Based On Control ... aero-comlab.stanford.edu/Papers/houston98.pdf by A Jameson - Cited by 141 - Related articles for aerodynamic shape design in both inviscid and ... tive results are presented for viscous optimization ... method could converge to a local minimum rather. aerodynamic shape optimization - NASA Technical Reports Server ... https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20040084446.pdf by TL Holst - 2004 - Cited by 11 - Related articles user-specified minimum limit on tS!e. Gene use;-specifi& .... using several three-dimensional aerodynamic shape optimization problems. Problem Statement: ... [PDF]Aerodynamic Shape Optimization - University of Toronto oddjob.utias.utoronto.ca/.../Christopher_Lee_Aerodynamic_Shape_Optimization_of_... by C Lee - Cited by 15 - Related articles is the twist optimization for minimum induced drag at fixed lift of a rectangular ... tool, designers are able to perform aerodynamic shape optimization, robustly ... [PDF]Hybrid method for aerodynamic shape optimization ... - Laurent Dumas https://dumas.perso.math.cnrs.fr/Art5.pdf by F ed erique Muyl - 2004 - Cited by 128 - Related articles An aerodynamic shape optimization tool for complex industrial flows is .... but only one global minimum, and the Griewank function defined respectively as. [PDF]Three-Dimensional Aerodynamic Shape Optimization Using Genetic ... www.ams.sunysb.edu/~qqshi/.../Three_Dimensional_ASO_Using_GA_and_GSA.pdf by GS Dulikravich’r - Cited by 90 - Related articles Three-Dimensional Aerodynamic Shape Optimization. Using Genetic and Gradient .... variables such that the cost function reaches its global minimum value.