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Applications of Automatic Differentiation in CFD
, 1994
"... Automated multidisciplinary design of aircraft requires the optimization of complex performance objectives with respect to a number of design parameters and constraints. The effect of these independent design variables on the system performance criteria can be quantified in terms of sensitivity deri ..."
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Cited by 11 (7 self)
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Automated multidisciplinary design of aircraft requires the optimization of complex performance objectives with respect to a number of design parameters and constraints. The effect of these independent design variables on the system performance criteria can be quantified in terms of sensitivity derivatives for the individual discipline simulation codes. Typical advanced CFD codes do not provide such derivatives as part of a flow solution. These derivatives are expensive to obtain by divided differences from perturbed solutions, and may be unreliable, particularly for noisy functions. In this paper, automatic differentiation has been investigated as a means of extending iterative CFD codes with sensitivity derivatives. In particular, the ADIFOR automatic differentiator has been applied to the 3D, thinlayer NavierStokes, multigrid flow solver called TLNS3D coupled with the WTCO wing grid generator. Results of a sequence of efforts in which TLNS3D has been successfully augmented to ...
Multilevel Decomposition Approach to Integrated Aerodynamic /Dynamic /Structural Optimization of Helicopter Rotor Blades
 American Helicopter Society Aeromechanics Specialists Conference
, 1994
"... This paper describes an integrated aerodynamic/dynamic/structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general purpose optimizer using multilevel decomposition techniques. At the upper level, the blade ..."
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Cited by 4 (0 self)
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This paper describes an integrated aerodynamic/dynamic/structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general purpose optimizer using multilevel decomposition techniques. At the upper level, the blade structure and response are represented in terms of global quantities (stiffnesses, mass, and average strains). At the lower level, the blade structure and response are represented in terms of local quantities (detailed dimensions and stresses). The upper level objective function is a linear combination of performance and dynamic measures. Upper level design variables include pretwist, point of taper initiation, taper ratio, root chord, blade stiffnesses, tuning masses,
QUANTIFICATION AND USE OF SYSTEM COUPLING IN DECOMPOSED DESIGN OPTIMIZATION PROBLEMS
"... Decompositionbased optimization strategies are used to solve complex engineering design problems that might be otherwise unsolvable. Yet, the associated computational cost can be prohibitively high due to the often large number of separate optimizations needed for coordination of problem solutions. ..."
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Cited by 1 (1 self)
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Decompositionbased optimization strategies are used to solve complex engineering design problems that might be otherwise unsolvable. Yet, the associated computational cost can be prohibitively high due to the often large number of separate optimizations needed for coordination of problem solutions. To reduce this cost one may exploit the fact that some systems may be weakly coupled and their interactions can be suspended with little loss in solution accuracy. Suspending such interactions is usually based on the analyst’s experience or experimental observation. This article introduces an explicit measure of coupling strength among interconnected subproblems in a decomposed optimization problem, along with a systematic way for calculating it. The strength measure is then used to suspend weak couplings and thus improve system solution strategies, such as the model coordination method. Examples show that the resulting strategy can decrease the number of required system optimizations significantly. NOMENCLATURE fi objective function associated with system i, fi: Rqi → R ∂ f /∂x gradient vector of f (x) a row vector F objective function representing the supersystem objective, F: RN+∑N i=1 ni → R gi
ENHANCED COLLABORATIVE OPTIMIZATION: A DECOMPOSITIONBASED METHOD FOR MULTIDISCIPLINARY DESIGN
, 2008
"... Astute choices made early in the design process provide the best opportunity for reducing the life cycle cost of a new product. Optimal decisions require reasonably detailed disciplinary analyses, which pose coordination challenges. These types of complex multidisciplinary problems are best addresse ..."
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Astute choices made early in the design process provide the best opportunity for reducing the life cycle cost of a new product. Optimal decisions require reasonably detailed disciplinary analyses, which pose coordination challenges. These types of complex multidisciplinary problems are best addressed through the use of decompositionbased methods, several of which have recently been developed. Two of these methods are collaborative optimization (CO) and analytical target cascading (ATC). CO was conceived in 1994 in response to multidisciplinary design needs in the aerospace industry. Recent progress has led to an updated version, enhanced collaborative optimization (ECO), that is introduced in this paper. ECO addresses many of the computational challenges inherent in CO, yielding significant computational savings and more robust solutions. ATC was formalized in 2000 to address needs in the automotive industry. While ATC was originally developed for objectbased decomposition, it is also applicable to multidisciplinary design problems. In this paper, both methods are applied to a set of test cases. The goal is to introduce the ECO methodology by comparing and contrasting it with ATC, a method familiar within the mechanical engineering design community. Comparison of ECO and ATC is not intended to establish the computational superiority of either method. Rather, these two methods are compared as a means of highlighting several promising approaches to the coordination of distributed design problems.
DEFINING PROPERTIES FOR DECOMPOSITION IN NONLINEAR PROGRAMMING
"... Five defining properties of an NLP problem determine which decomposition method is appropriate. The report reviews and compares prevalent methods in the context of these properties to develop a set of relationships between a given problem ..."
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Five defining properties of an NLP problem determine which decomposition method is appropriate. The report reviews and compares prevalent methods in the context of these properties to develop a set of relationships between a given problem
Generalized Coupling Management in Complex Engineering Systems Optimization
"... Decompositionbased design optimization strategies are used to solve complex engineering system problems that might be otherwise unsolvable. Yet, the associated computational cost can be prohibitively high due to the often large number of iterations needed for coordination of subproblem solutions. T ..."
Abstract
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Decompositionbased design optimization strategies are used to solve complex engineering system problems that might be otherwise unsolvable. Yet, the associated computational cost can be prohibitively high due to the often large number of iterations needed for coordination of subproblem solutions. To reduce this cost one may exploit the fact that some systems may be weakly coupled and their interactions can be suspended with little loss in solution accuracy. Suspending such interactions is usually based on the analyst’s experience or experimental observation. This article introduces an explicit measure of coupling strength among interconnected subproblems in a decomposed system optimization problem, along with a systematic way for calculating it. The strength measure is then used to suspend weak couplings and, thus, improve system solution strategies such as the model coordination method. Examples show that the resulting strategy may decrease the number of required function evaluations significantly. [DOI: 10.1115/1.4004541]