Publications

Results 26–50 of 185
Skip to search filters

Towards an integrated and efficient framework for leveraging reduced order models for multifidelity uncertainty quantification

AIAA Scitech 2020 Forum

Blonigan, Patrick J.; Geraci, Gianluca G.; Rizzi, Francesco N.; Eldred, Michael S.

Truly predictive numerical simulations can only be obtained by performing Uncertainty Quantification. However, many realistic engineering applications require extremely complex and computationally expensive high-fidelity numerical simulations for their accurate performance characterization. Very often the combination of complex physical models and extreme operative conditions can easily lead to hundreds of uncertain parameters that need to be propagated through high-fidelity codes. Under these circumstances, a single fidelity uncertainty quantification approach, i.e. a workflow that only uses high-fidelity simulations, is unfeasible due to its prohibitive overall computational cost. To overcome this difficulty, in recent years multifidelity strategies emerged and gained popularity. Their core idea is to combine simulations with varying levels of fidelity/accuracy in order to obtain estimators or surrogates that can yield the same accuracy of their single fidelity counterparts at a much lower computational cost. This goal is usually accomplished by defining a priori a sequence of discretization levels or physical modeling assumptions that can be used to decrease the complexity of a numerical model realization and thus its computational cost. Less attention has been dedicated to low-fidelity models that can be built directly from a small number of available high-fidelity simulations. In this work we focus our attention on reduced order models (ROMs). Our main goal in this work is to investigate the combination of multifidelity uncertainty quantification and ROMs in order to evaluate the possibility to obtain an efficient framework for propagating uncertainties through expensive numerical codes. We focus our attention on sampling-based multifidelity approaches, like the multifidelity control variate, and we consider several scenarios for a numerical test problem, namely the Kuramoto-Sivashinsky equation, for which the efficiency of the multifidelity-ROM estimator is compared to the standard (single-fidelity) Monte Carlo approach.

More Details
TYPE Conference Poster YEAR 2020
ScopusOSTIDOI

srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications

Proceedings of the ASME Design Engineering Technical Conference

Tran, Anh; Eldred, Michael S.; McCann, Scott; Wang, Yan

Bayesian optimization (BO) is an efficient and flexible global optimization framework that is applicable to a very wide range of engineering applications. To leverage the capability of the classical BO, many extensions, including multi-objective, multi-fidelity, parallelization, and latent-variable modeling, have been proposed to address the limitations of the classical BO framework. In this work, we propose a novel multi-objective (MO) extension, called srMOBO-3GP, to solve the MO optimization problems in a sequential setting. Three different Gaussian processes (GPs) are stacked together, where each of the GP is assigned with a different task: the first GP is used to approximate a single-objective computed from the MO definition, the second GP is used to learn the unknown constraints, and the third GP is used to learn the uncertain Pareto frontier. At each iteration, a MO augmented Tchebycheff function converting MO to single-objective is adopted and extended with a regularized ridge term, where the regularization is introduced to smooth the single-objective function. Finally, we couple the third GP along with the classical BO framework to explore the richness and diversity of the Pareto frontier by the exploitation and exploration acquisition function. The proposed framework is demonstrated using several numerical benchmark functions, as well as a thermomechanical finite element model for flip-chip package design optimization.

More Details
TYPE Conference Poster YEAR 2020
ScopusOSTI

Multifideliy optimization under uncertainty for a scramjet-inspired problem

Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018

Menhorn, Friedrich M.; Geraci, Gianluca G.; Eldred, Michael S.; Marzouk, Youssef M.

SNOWPAC (Stochastic Nonlinear Optimization With Path-Augmented Constraints) is a method for stochastic nonlinear constrained derivative-free optimization. For such problems, it extends the path-augmented constraints framework introduced by the deterministic optimization method NOWPAC and uses a noise-adapted trust region approach and Gaussian processes for noise reduction. In recent developments, SNOWPAC is available in the DAKOTA framework which offers a highly flexible interface to couple the optimizer with different sampling strategies or surrogate models. In this paper we discuss details of SNOWPAC and demonstrate the coupling with DAKOTA. We showcase the approach by presenting design optimization results of a shape in a 2D supersonic duct. This simulation is supposed to imitate the behavior of the flow in a SCRAMJET simulation but at a much lower computational cost. Additionally different mesh or model fidelities can be tested. Thus, it serves as a convenient test case before moving to costly SCRAMJET computations. Here, we study deterministic results and results obtained by introducing uncertainty on inflow parameters. As sampling strategies we compare classical Monte Carlo sampling with multilevel Monte Carlo approaches for which we developed new error estimators. All approaches show a reasonable optimization of the design over the objective while maintaining or seeking feasibility. Furthermore, we achieve significant reductions in computational cost by using multilevel approaches that combine solutions from different grid resolutions.

More Details
TYPE Conference Poster YEAR 2020
ScopusOSTI

Multilevel uncertainty quantification of a wind turbine large eddy simulation model

Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018

Maniaci, David C.; Frankel, Ari L.; Geraci, Gianluca G.; Blaylock, Myra L.; Eldred, Michael S.

Wind energy is stochastic in nature; the prediction of aerodynamic quantities and loads relevant to wind energy applications involves modeling the interaction of a range of physics over many scales for many different cases. These predictions require a range of model fidelity, as predictive models that include the interaction of atmospheric and wind turbine wake physics can take weeks to solve on institutional high performance computing systems. In order to quantify the uncertainty in predictions of wind energy quantities with multiple models, researchers at Sandia National Laboratories have applied Multilevel-Multifidelity methods. A demonstration study was completed using simulations of a NREL 5MW rotor in an atmospheric boundary layer with wake interaction. The flow was simulated with two models of disparate fidelity; an actuator line wind plant large-eddy scale model, Nalu, using several mesh resolutions in combination with a lower fidelity model, OpenFAST. Uncertainties in the flow conditions and actuator forces were propagated through the model using Monte Carlo sampling to estimate the velocity defect in the wake and forces on the rotor. Coarse-mesh simulations were leveraged along with the lower-fidelity flow model to reduce the variance of the estimator, and the resulting Multilevel-Multifidelity strategy demonstrated a substantial improvement in estimator efficiency compared to the standard Monte Carlo method.

More Details
TYPE Conference Poster YEAR 2020
ScopusOSTI

Mfnets: Multi-fidelity data-driven networks for bayesian learning and prediction

International Journal for Uncertainty Quantification

Gorodetsky, Alex A.; Jakeman, John D.; Geraci, Gianluca G.; Eldred, Michael S.

This paper presents a Bayesian multifidelity uncertainty quantification framework, called MFNets, which can be used to overcome three of the major challenges that arise when data from different sources are used to enhance statistical estimation and prediction with quantified uncertainty. Specifically, we demonstrate that MFNets can (1) fuse heterogeneous data sources arising from simulations with different parameterizations, e.g., simulation models with different uncertain parameters or data sets collected under different environmental conditions; (2) encode known relationships among data sources to reduce data requirements; and (3) improve the robustness of existing multifidelity approaches to corrupted data. In this paper we use MFNets to construct linear-subspace surrogates and estimate statistics using Monte Carlo sampling. In addition to numerical examples highlighting the efficacy of MFNets we also provide a number of theoretical results. Firstly we provide a mechanism to assess the quality of the posterior mean of a MFNets Monte Carlo estimator as a frequentist estimator. We then use this result to compare MFNets estimators to existing single fidelity, multilevel, and control variate Monte Carlo estimators. In this context, we show that the Monte Carlo-based control variate estimator can be derived entirely from the use of Bayes rule and linear-Gaussian models—to our knowledge the first such derivation. Finally, we demonstrate the ability to work with different uncertain parameters across different models.

More Details
TYPE Journal Article YEAR 2020
ScopusOSTIDOI
Results 26–50 of 185
Results 26–50 of 185