Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization

Simpson, Timothy W.; Mauery, Timothy M.; Korte, John J.; Mistree, Farrokh

doi:10.2514/2.1234

Cited by 884 publications

(143 citation statements)

References 31 publications

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“…Surrogate models have been proposed to improve computation times. Surrogate models that replaced finite-element models include polynomial regression (Hemez et al, 2002), multivariate regression spines (Friedman, 1991), and Kriging estimates (Simpson et al, 2001) as reviewed by Rutherford et al (2006). Worden and Cross (2018) presented the utility of using surrogate models to predict bridge response under the influence of environmental conditions such as temperature.…”

Section: Introductionmentioning

confidence: 99%

Comparing Structural Identification Methodologies for Fatigue Life Prediction of a Highway Bridge

Pai

Nussbaumer

Smith

2018

Front. Built Environ.

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Accurate measurement-data interpretation leads to increased understanding of structural behavior and enhanced asset-management decision making. In this paper, four datainterpretation methodologies, residual minimization, traditional Bayesian model updating, modified Bayesian model updating (with an L -norm-based Gaussian likelihood func-∞ tion), and error-domain model falsification (EDMF), a method that rejects models that have unlikely differences between predictions and measurements, are compared. In the modified Bayesian model updating methodology, a correction is used in the likelihood function to account for the effect of a finite number of measurements on posterior probability-density functions. The application of these data-interpretation methodologies for condition assessment and fatigue life prediction is illustrated on a highway steel-concrete composite bridge having four spans with a total length of 219 m. A detailed 3D finite-element plate and beam model of the bridge and weigh-in-motion data are used to obtain the time-stress response at a fatigue critical location along the bridge span. The time-stress response, presented as a histogram, is compared to measured strain responses either to update prior knowledge of model parameters using residual minimization and Bayesian methodologies or to obtain candidate model instances using the EDMF methodology. It is concluded that the EDMF and modified Bayesian model updating methodologies provide robust prediction of fatigue life compared with residual minimization and traditional Bayesian model updating in the presence of correlated nonGaussian uncertainty. EDMF has additional advantages due to ease of understanding and applicability for practicing engineers, thus enabling incremental asset-management decision making over long service lives. Finally, parallel implementations of EDMF using grid sampling have lower computations times than implementations using adaptive sampling.

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Section: Introductionmentioning

confidence: 99%

Comparing Structural Identification Methodologies for Fatigue Life Prediction of a Highway Bridge

Pai

Nussbaumer

Smith

2018

Front. Built Environ.

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“…, β p T denotes the least square estimator of regression coefficients; and z(x) is a Gaussian random process. The detailed calculation process of these variables can be found in [35] and, in this paper, a MATLAB tool box called DACE [36] is used to construct the Kriging model. …”

Section: Kriging Modelmentioning

confidence: 99%

A Kriging Model Based Optimization of Active Distribution Networks Considering Loss Reduction and Voltage Profile Improvement

Wang

Tang

et al. 2017

Energies

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Optimal operation of the active distribution networks (ADN) is essential to keep its safety, reliability and economy. With the integration of multiple controllable resources, the distribution networks are facing more challenges in which the optimization strategy is the key. This paper establishes the optimal operation model of the ADN considering a diversity of controllable resources including energy storage devices, distributed generators, voltage regulators and switchable capacitor banks. The objective functions contain reducing the power losses and improving the voltage profiles. To solve the optimization problem, the Kriging model based Improved Surrogate Optimization-Mixed-Integer (ISO-MI) algorithm is proposed in this paper. The Kriging model is applied to approximate the complicated distribution networks, which speeds up the solving process. Finally, the accuracy of the Kriging model is validated and the efficiency among the proposed method, genetic algorithm (GA) and particle swarm optimization (PSO) is compared in an unbalanced IEEE-123 nodes test feeder. The results demonstrate that the proposed method has better performance than GA and PSO.

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“…In such a simulation-based design environment, the search for a feasible design space that satisfies the given performance requirements usually involves numerous iterations among several simulation tools. Depending on the fidelity of these analyses, they are computationally expensive and extremely timeconsuming, thus limiting the exploration of broad design space and its optimization [37,47,48]. In addition, these simulation tools are used by specialist in individual disciplines and in the later design stages.…”

Section: Meta-modeling Techniques For Simulation-based Design Environmentioning

confidence: 99%

Novel space-based design methodology for preliminary engineering design

Nahm

Ishikawa

2005

Int J Adv Manuf Technol

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This work is motivated from set-based concurrent engineering (SBCE) paradigm. In contrast to the traditional design practice, SBCE considers a broader range of design possibilities (i.e., design space) from the outset, explicitly communicates and reasons about sets of design alternatives, and gradually narrows the sets to eliminate inferior alternatives until a final solution remains. Thus, the key to success of SBCE is the proper implementation of the space representation method to define the possible design region, the space mapping method to obtain the performance space achievable by the initial design space, and the space narrowing method to eliminate infeasible subspaces from the initial design space. This paper proposes a novel spacebased design methodology for preliminary engineering design. The main characteristic features of our approach are to incorporate the designer's preference structure with degrees of desirability in specifying both design space and performance space, and to find a ranged set of design solutions that satisfy changing sets of performance requirements through set-to-set space mapping from design space to performance space and space narrowing to eliminate infeasible design subspaces. Central to the proposed design methodology is the integration of meta-modeling techniques, modified fuzzy arithmetic, design of experiment (D o E), robust design techniques, and uncertainty analysis. A preliminary parametric design example of a vehicle side impact beam is demonstrated to show the effectiveness of the proposed methodology and its availability in the large-scale multi-objective design synthesis problem.Keywords Fuzzy arithmetic · Measure of uncertainty · Robust and flexible design · Set-based concurrent engineering · Simulation-based design

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Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization

Cited by 884 publications

References 31 publications

Comparing Structural Identification Methodologies for Fatigue Life Prediction of a Highway Bridge

Comparing Structural Identification Methodologies for Fatigue Life Prediction of a Highway Bridge

A Kriging Model Based Optimization of Active Distribution Networks Considering Loss Reduction and Voltage Profile Improvement

Novel space-based design methodology for preliminary engineering design

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