Quantification of Modeling Uncertainty in Aeroelastic Analyses

Riley, Matthew E.; Grandhi, Ramana V.; Kolonay, Raymond M.

doi:10.2514/1.c031059

Cited by 34 publications

(10 citation statements)

References 13 publications

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“…There are many techniques in use for fusing information from multiple sources of information. Among these are approaches such as Bayesian modeling averaging [53,54,55,56,57,58], the use of adjustment factors [59,60,61,62], covariance intersection methods [63,64], and fusion under known correlation [65,66,67]. There are also techniques designed to value the improvement potential of a given information source through model refinement [68] and model refinement and selection [69].…”

Section: Correlation Exploiting Fusionmentioning

confidence: 99%

Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual-Phase Materials

Ghoreishi

Molkeri

Srivastava

et al. 2018

Journal of Mechanical Design

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Integrated Computational Materials Engineering (ICME) calls for the integration of computational tools into the materials and parts development cycle, while the Materials Genome Initiative (MGI) calls for the acceleration of the materials development cycle through the combination of experiments, simulation, and data. As they stand, both ICME and MGI do not prescribe how to achieve the necessary tool integration or how to efficiently exploit the computational tools, in combination with experiments, to accelerate the development of new materials and materials systems. This paper addresses the first issue by putting forward a framework for the fusion of information that exploits correlations among sources/models and between the sources and 'ground truth'. The second issue is addressed through a multi-information source optimization framework that identifies, given current knowledge, the next best information source to query and where in the input space to query it via a novel valuegradient policy. The querying decision takes into account the ability to learn correlations between information sources, the resource cost of querying an information source, and what a query is expected to provide in terms of improvement over * Address all correspondence to this author.the current state. The framework is demonstrated on the optimization of a dual-phase steel to maximize its strengthnormalized strain hardening rate. The ground truth is represented by a microstructure-based finite element model while three low fidelity information sources-i.e. reduced order models-based on different homogenization assumptionsisostrain, isostress and isowork-are used to efficiently and optimally query the materials design space. arXiv:1809.09686v1 [physics.app-ph]

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Section: Correlation Exploiting Fusionmentioning

confidence: 99%

Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual-Phase Materials

Ghoreishi

Molkeri

Srivastava

et al. 2018

Journal of Mechanical Design

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“…This phenomenon leads to difficulty in identifying the metamodels which best fits the set of data, referred to as model-form uncertainty. A common method used in the literature to quantify and reduce model-form uncertainty is to construct an aggregate model as the sum of the weighted individual models [10,11].…”

Section: Surrogate Modelingmentioning

confidence: 99%

“…Weights for these five models were then obtained from their respective residuals using Eqs. (10) and (11). Finally, the weighted response surface model was constructed using Eq.…”

Section: Benchmark Validation Casesmentioning

confidence: 99%

A Novel Surrogate Modeling Technique for Parametric Uncertainty Quantification in Simulation-Based Design

Riley¹,

Grandhi²

2012

53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference

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Due to the excessive computational cost associated with the subsequent function evaluations often utilized in parametric uncertainty quantification methods, metamodels are often utilized as a resource to alleviate the computational cost associated with repeated evaluations of full-scale simulations through lower-order representations of the design space. However, for these metamodels to be practical within the uncertainty quantification scheme, they must provide an accurate representation of the design space at a minimal computational cost. As such, much time has been invested at developing accurate surrogate modeling techniques to develop closed-form representations of complex problems using a minimal number of data points. This work details a novel surrogate modeling methodology design for application with the Fast Fourier Transform method for parametric uncertainty quantification. Utilizing k-fold sampling of the design space, the proposed methodology utilizes independent validation metrics from a limited sample to develop an efficient response surface model for application in the Fast Fourier Transform approach to parametric uncertainty quantification. The proposed surrogate modeling approach will first be validated on a benchmark surrogate modeling problems. Following, an application of the approach to the parametric uncertainty quantification of a high-complexity finite element simulation will be demonstrated. I. IntroductionMethods for the quantification of parametric uncertainties in the aeroelastic design and analysis of aircraft structures often follow the same general series of steps. The methods will first attempt to capture the uncertainty associated with the parameters in some form. Aleatory uncertainty quantification methods will do this by representing each variable through a probability density function of any ordinate form while epistemic uncertainty quantification methods will often use a more abstract data structure such as fuzzy logic, or basic belief assignments. Next, these methods will attempt to propagate the information regarding each of the variables through the simulation-based model through either exhaustive or "fast" reliability methods. The goal of this approach is normally one of two types of metrics of interest: developing a distribution of a single, or set of output parameters, or determining the reliability of the system of interest subject to a pre-defined set of performance margins.

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“…There are several techniques used in practice for combining information from multiple information sources. Among them are the adjustment factors approach [24][25][26], Bayesian model averaging [27][28][29][30][31], and fusion under known and unknown correlation [32,33]. Here, the fused Gaussian process is constructed based on the data and fidelity variances of the information sources which are queried.…”

Section: Introductionmentioning
confidence: 99%

A Fusion-Based Multi-Information Source Optimization Approach using Knowledge Gradient Policies
Ghoreishi
¹
,
Allaire
²

2018
2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
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4
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Optimization of complex systems often involves evaluation of a quantity several times, which is potentially computationally prohibitive. This can be alleviated by considering information sources representing the original model with lower fidelity and cost. This paper describes an optimization method for the case where the objective function is represented by different information sources with varying fidelities and computational costs. The proposed methodology creates a multi-information source value-of-information framework that defines optimal strategies for querying of information sources. The surrogate Gaussian process model is used for fusion of information sources. Then, the knowledge gradient policy is incorporated by considering the probability of violation of constraints for sequential decision making to identify the next design and information source to evaluate. The high performance of the developed methodology is demonstrated in terms of making balance between cost and information gain of various information sources of a one-dimensional example test problem and an aerodynamic design example.

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Quantification of Modeling Uncertainty in Aeroelastic Analyses

Cited by 34 publications

References 13 publications

Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual-Phase Materials

Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual-Phase Materials

A Novel Surrogate Modeling Technique for Parametric Uncertainty Quantification in Simulation-Based Design

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

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