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Reliability based design optimization with experiments on demand
KTH, School of Engineering Sciences (SCI), Solid Mechanics (Dept.), Solid Mechanics (Div.).
KTH, School of Engineering Sciences (SCI), Solid Mechanics (Dept.), Solid Mechanics (Div.).ORCID iD: 0000-0001-8068-2360
2012 (English)Report (Other academic)
Abstract [en]

In this paper, an algorithm for reliability based design optimization (RBDO) is presented. It incorporates a novel procedure in which experiments are performed one at a time where and when they are needed. The procedure is called experiments on demand. The experiment procedure utilizes properties specific to RBDO and the problem at hand augmented by the concept of D-optimality familiar from traditional design of experiments. Furthermore, an adaptive surrogate model fitting scheme is proposed which balances numerical stability and convergence rate as well as accuracy. Benchmarked against algorithms in the literature, the number of experiments needed for convergence was reduced by up to 80 % for a frequently used analytical problem and by up to 19 % for an application example. The accuracy of the reliability index is in line with the most efficient algorithm against which it was benchmarked but up to 3 % lower than the most accurate algorithm.

Place, publisher, year, edition, pages
2012. , 13 p.
Trita-HFL, ISSN 1104-6813 ; 519
Keyword [en]
Experiments on demand, Reliability based design optimization, Surrogate model
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-90800OAI: diva2:506559
QC 20120229Available from: 2012-02-29 Created: 2012-02-29 Last updated: 2012-03-01Bibliographically approved
In thesis
1. Methods for reliability based design optimization of structural components
Open this publication in new window or tab >>Methods for reliability based design optimization of structural components
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Cost and quality are key properties of a product, possibly even the two most important. Onedefinition of quality is fitness for purpose. Load-bearing products, i.e. structural components,loose their fitness for purpose if they fail. Thus, the ability to withstand failure is a fundamentalmeasure of quality for structural components. Reliability based design optimization(RBDO) is an approach for development of structural components which aims to minimizethe cost while constraining the probability of failure. However, the computational effort ofan RBDO applied to large-scale engineering problems has prohibited it from employment inindustrial applications. This thesis presents methods for computationally efficient RBDO.A review of the work presented on RBDO algorithms reveals that three constituentsof an RBDO algorithm has rendered significant attention; i ) the solution strategy for andnumerical treatment of the probabilistic constraints, ii ) the surrogate model, and iii) theexperiment design. A surrogate model is ”a model of a model”, i.e. a computationally cheapapproximation of a physics-based but computationally expensive computer model. It is fittedto responses from the physics-motivated model obtained via a thought-through combinationof experiments called an experiment design.In Paper A, the general algorithm for RBDO employed in this work, including the sequentialapproximation procedure used to treat the probabilistic constraints, is laid out. A singleconstraint approximation point (CAP) is used to save computational effort with acceptablelosses in accuracy. The approach is used to optimize a truck component and incorporatesthe effect that production related design variables like machining and shot peening have onfatigue life.The focus in Paper B is on experiment design. An algorithm employed to construct anovel experiment design for problems with multiple constraints is presented. It is based onan initial screening and uses the specific problem structure to combine one-factor-at-a-timeexperiments to a several-factors-at-a-time experiment design which reduces computationaleffort.In Paper C, a surrogate model tailored for RBDO is introduced. It is motivated by appliedsolid mechanics considerations and the use of the first order reliability method to evaluate theprobabilistic constraint. An optimal CAP is furthermore deduced from the surrogate model.In Paper D, the paradigm to use sets of experiments rather than one experiment at atime is challenged. A new procedure called experiments on demand (EoD) is presented. TheEoD procedure utilizes the core of RBDO to quantify the demand for new experiments andaugments it by a D-optimality criterion for added robustness and numerical stability.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2012. 86 p.
Trita-HFL. Report / Royal Institute of Technology, Solid mechanics, ISSN 1654-1472 ; 0520
National Category
Applied Mechanics
urn:nbn:se:kth:diva-90753 (URN)
Public defence
2012-03-12, F3, Linstedtsvägen 26, KTH, Stockholm, 10:00 (English)

QC 20120229

Available from: 2012-02-29 Created: 2012-02-28 Last updated: 2013-01-14Bibliographically approved

Open Access in DiVA

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