Uncertainty Quantification in Large Eddy Simulations of a Rich-Dome Aviation Gas Turbine

Masquelet, Matthieu; Yan, Jie; Dord, Anne; Laskowski, Gregory M.; Shunn, Lee; Jofre, L.; Iaccarino, Gianluca

doi:10.1115/gt2017-64835

Cited by 14 publications

(13 citation statements)

References 12 publications

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“…Papers II-IV in this thesis use an entropy stable flux scheme. The flux is computed with a modified Lax-Friedrich scheme (Masquelet et al, 2017), with minimal dissipation of kinetic energy.…”

Section: Entropy Stable Schemesmentioning

confidence: 99%

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Numerical simulation of particle jet formation induced by shock wave acceleration in a Hele-Shaw cell

2017

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First of all I would like to thank my main supervisor, Prof. Bjørn Anders Pettersson Reif for his guidance and support throughout my Ph.D. studies. His insight and intuition for flow physics is exceptional, and we have had countless interesting discussions over the last years. He always manages to keep focus on the important questions, and this ability has shaped this thesis. I would also like to thank my other supervisors Magnus Vartdal and Marianne Gjestvold Omang. There is never a problem too difficult for Magnus, which is always inspiring for everyone around him. His involvement in the details in the studies within this thesis has been invaluable. Marianne has also provided valuable advice throughout these years. My period at the Norwegian Defence Estates Agency was very enjoyable and taught me a lot. During these years I have been fortunate to be allowed to work with the fluid dynamics group at the Norwegian Defence Research Establishment. Special thanks to Espen Åkervik, who I have shared an office with and can always ask for advice. I would also like to thank

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Section: Entropy Stable Schemesmentioning

confidence: 99%

“…Steady straining flow. Journal of Fluid Mechanics, 284:97-135 Masquelet, M., Yan, J., Dord, A., Laskowski, G., Shunn, L., Jofre, L., and Iaccarino, G. (2017)…”

mentioning

confidence: 99%

Numerical simulation of particle jet formation induced by shock wave acceleration in a Hele-Shaw cell

2017

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“…The performance of various candidate MF estimators constructed by means of CV and ML strategies was analyzed by utilizing a set of 32 pilot samples generated following a design of experiment (DoE) based on the KDOE approach [19,20]. KDOE is an iterative method that introduces stochasticity in the sampling process by means of a variable kernel density estimation to optimize the uniformity of the DoE.…”

Section: Resultsmentioning

confidence: 99%

Multifidelity Approaches for Uncertainty Estimation in Wildfire Spread Simulators

Valero¹,

Jofre²,

Torres³

2021

14th WCCM-ECCOMAS Congress

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A variety of wildfire models are currently used for prescribed fire management, fire behaviour studies and decision support during wildfire emergencies, among other applications. All these applications are based on predictive analysis, and therefore require careful estimation of aleatoric and epistemic uncertainties such as weather conditions, vegetation properties and model parameters. However, the large computational cost of high-fidelity computaional fluid dynamics models prohibits the straightforward utilization of traditional Monte Carlo methods. Conversely, low-fidelity fire models are several orders of magnitude faster but they typically do not provide enough accuracy and they do not resolve all relevant phenomena. Multifidelity frameworks offer a viable solution to this limitation through the efficient combination of high-and low-fidelity simulations. While high-fidelity models provide the required level of accuracy, low-fidelity simulations are used to economically improve the confidence on estimated uncertainty. In this work, we assessed the suitability of multifidelity methodologies to quantify uncertainty in wildfire simulations. A collection of different multifidelity strategies, including Multilevel and Control Variates Monte Carlo, were tested and their computational efficiency compared. Fire spread was predicted in a canonical scenario using popular simulators such as the Wildland-Urban Interface Fire Dynamics Simulator (WFDS) and FARSITE. Results show that multifidelity estimators allow speedups in the order of 100× to 1000× with respect to traditional Monte Carlo.

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“…In fact, the diversity of uncertainties on the CFD boundary conditions or initial conditions as well as on model parameters (input data, geometry, simplification of the model physics, etc) limits the validity of the simulations: the quantity of interest (QoI) can be easily affected and shadowed by the conjugation of all types of uncertainties. This assessment explains why uncertainty quantification (UQ) is now becoming a mandatory step in application‐oriented modeling for operational and industrial purposes . It not only provides insight into the level of uncertainty in the numerical simulation results but also gives access to the sensitivity analysis (SA), which aims at describing the respective influences of the input parameters on the QoI.…”

Section: Introductionmentioning

confidence: 99%

Resampling strategies to improve surrogate model‐based uncertainty quantification: Application to LES of LS89

Roy

Segui

Jouhaud

et al. 2018

Numerical Methods in Fluids

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Summary Uncertainty quantification (UQ) is receiving more and more attention for engineering applications particularly from robust optimization. Indeed, running a computer experiment only provides a limited knowledge in terms of uncertainty and variability of the input parameters. These experiments are often computationally expensive, and surrogate models can be constructed to address this issue. The outcome of an uncertainty quantification study is, in this case, directly correlated to the surrogate's quality. Thus, attention must be devoted to the design of experiments to retrieve as much information as possible. This work presents 2 new strategies for parameter space resampling to improve a Gaussian process surrogate model. These techniques indeed show an improvement of the predictive quality of the model with high‐dimensional analytical input functions. Finally, the methods are successfully applied to a turbine blade large‐eddy simulation application: the aerothermal flow around the LS89 blade cascade.

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Uncertainty Quantification in Large Eddy Simulations of a Rich-Dome Aviation Gas Turbine

Cited by 14 publications

References 12 publications

Numerical simulation of particle jet formation induced by shock wave acceleration in a Hele-Shaw cell

Numerical simulation of particle jet formation induced by shock wave acceleration in a Hele-Shaw cell

Multifidelity Approaches for Uncertainty Estimation in Wildfire Spread Simulators

Resampling strategies to improve surrogate model‐based uncertainty quantification: Application to LES of LS89

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