A Fusion-Based Multi-Information Source Optimization Approach using Knowledge Gradient Policies

Ghoreishi, Seyede Fatemeh; Allaire, Douglas

doi:10.2514/6.2018-1159

Cited by 19 publications

(4 citation statements)

References 44 publications

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“…It is important that the improvement function contains the maximum function; otherwise, there would be no exploration of regions of larger uncertainty. Other acquisition functions available include the knowledge gradient (Ghoreishi and Allaire, 2018), expected quantile improvement (He et al, 2017;Picheny et al, 2013), improved expected improvement (Qin et al, 2017), entropy search (Hennig and Schuler, 2012), and minimization of the predictor (Andersson and Imsland, 2020).…”

Section: Single-fidelity Approachmentioning

confidence: 99%

Multifidelity multiobjective optimization for wake-steering strategies

Quick

King²,

Barter³

et al. 2022

Wind Energ. Sci.

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Abstract. Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of perpendicular alignment with the incoming wind, thereby “steering” wakes away from downstream turbines. However, trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present a multifidelity multiobjective optimization approach for exploring the Pareto front of trade-offs between power and loading during wake steering. A large eddy simulation is used as the high-fidelity model, where an actuator line representation is used to model wind turbine blades and a rainflow-counting algorithm is used to compute damage equivalent loads. A coarser simulation with a simpler loads model is employed as a supplementary low-fidelity model. Multifidelity Bayesian optimization is performed to iteratively learn both a surrogate of the low-fidelity model and an additive discrepancy function, which maps the low-fidelity model to the high-fidelity model. Each optimization uses the expected hypervolume improvement acquisition function, weighted by the total cost of a proposed model evaluation in the multifidelity case. The multifidelity approach is able to capture the logit function shape of the Pareto frontier at a computational cost only 30 % that of the single-fidelity approach. Additionally, we provide physical insights into the vortical structures in the wake that contribute to the Pareto front shape.

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Section: Single-fidelity Approachmentioning

confidence: 99%

Multifidelity multiobjective optimization for wake-steering strategies

Quick

King²,

Barter³

et al. 2022

Wind Energ. Sci.

View full text Add to dashboard Cite

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“…BMA is a model fusion technique that has some benefits in robust design. Other available techniques are fusion under known correlation [40][41][42][43], and the covariance intersection method [44]. The key distinction of BMA over other model fusion approaches is the assumption of statistical independence among models, which may be incorrect in some cases and can lead to potentially serious misconceptions regarding confidence in quantity of interest estimates.…”

Section: Applied Error Correlation-based Modelmentioning

confidence: 99%

Bayesian uncertainty quantification and information fusion in CALPHAD-based thermodynamic modeling

et al. 2019

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Calculation of phase diagrams is one of the fundamental tools in alloy design-more specifically under the framework of Integrated Computational Materials Engineering. Uncertainty quantification of phase diagrams is the first step required to provide confidence for decision making in property-or performance-based design. As a manner of illustration, a thorough probabilistic assessment of the CALPHAD model parameters is performed against the available data for a Hf-Si binary case study using a Markov Chain Monte Carlo sampling approach. The plausible optimum values and uncertainties of the parameters are thus obtained, which can be propagated to the resulting phase diagram. Using the parameter values obtained from deterministic optimization in a computational thermodynamic assessment tool (in this case Thermo-Calc) as the prior information for the parameter values and ranges in the sampling process is often necessary to achieve a reasonable cost for uncertainty quantification. This brings up the problem of finding an appropriate CALPHAD model with high-level of confidence which is a very hard and costly task that requires considerable expert skill. A Bayesian hypothesis testing based on Bayes' factors is proposed to fulfill the need of model selection in this case, which is applied to compare four recommended models for the Hf-Si system. However, it is demonstrated that information fusion approaches, i.e., Bayesian model averaging and an error correlation-based model fusion, can be used to combine the useful information existing in all the given models rather than just using the best selected model, which may lack some information about the system being modelled.

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“…An improvement to the Bayesian optimization paradigm is to employ multiple models representing the same quantity of interest. This is known as multi-fidelity BO and has been shown to effectively increase the robustness and efficiency of engineering design schemes [12][13][14][15][16] . These models are built upon different assumptions and/or simplifications and vary in fidelity and cost of the evaluation.…”

Section: Introductionmentioning

confidence: 99%

“…In the earlier works of refs. [12][13][14][15][16] , a multi-fidelity approach has been employed to optimize a single quantity of interest (single-objective optimization). Recently, this multi-fidelity setting has been expanded to multi-objective design problems as well 17 .…”

Section: Introductionmentioning

confidence: 99%

Bayesian optimization with active learning of design constraints using an entropy-based approach

et al. 2023

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The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures, as well as possess low density, high thermal conductivity, narrow solidification range, high solidus temperature, and a small linear thermal expansion coefficient. Traditional Integrated Computational Materials Engineering (ICME) methods are not sufficient for exploring combinatorially-vast alloy design spaces, optimizing for multiple objectives, nor ensuring that multiple constraints are met. In this work, we propose an approach for solving a constrained multi-objective materials design problem over a large composition space, specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy (MPEA) for potential use in next-generation gas turbine blades. Our approach is able to learn and adapt to unknown constraints in the design space, making decisions about the best course of action at each stage of the process. As a result, we identify 21 Pareto-optimal alloys that satisfy all constraints. Our proposed framework is significantly more efficient and faster than a brute force approach.

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A Fusion-Based Multi-Information Source Optimization Approach using Knowledge Gradient Policies

Cited by 19 publications

References 44 publications

Multifidelity multiobjective optimization for wake-steering strategies

Multifidelity multiobjective optimization for wake-steering strategies

Bayesian uncertainty quantification and information fusion in CALPHAD-based thermodynamic modeling

Bayesian optimization with active learning of design constraints using an entropy-based approach

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