Data fusion for Uncertainty Quantification with Non-Intrusive Polynomial Chaos

Pepper, Nick; Montomoli, Francesco; Sharma, Sanjiv

doi:10.1016/j.cma.2020.113577

Cited by 7 publications

(2 citation statements)

References 64 publications

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“…Quantifying the effects of mixed uncertainty may require engineers to reconsider how uncertainty is visualised and communicated. Rather than specifying a single probability distribution for the QoI, a p-box approach that can be used to bound families of likely Cumulative Distribution Functions (CDFs) might be more effective [85,86]. A disadvantage of these methods is that p-boxes are an abstract concept, the meaning of which is difficult to convey to non-experts.…”

Section: Quantifying Mixed Uncertainty and Visualisationmentioning

confidence: 99%

Machine Learning Methods in CFD for Turbomachinery: A Review

Hammond

Pepper

Montomoli

et al. 2022

IJTPP

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Computational Fluid Dynamics is one of the most relied upon tools in the design and analysis of components in turbomachines. From the propulsion fan at the inlet, through the compressor and combustion sections, to the turbines at the outlet, CFD is used to perform fluid flow and heat transfer analyses to help designers extract the highest performance out of each component. In some cases, such as the design point performance of the axial compressor, current methods are capable of delivering good predictive accuracy. However, many areas require improved methods to give reliable predictions in order for the relevant design spaces to be further explored with confidence. This paper illustrates recent developments in CFD for turbomachinery which make use of machine learning techniques to augment prediction accuracy, speed up prediction times, analyse and manage uncertainty and reconcile simulations with available data. Such techniques facilitate faster and more robust searches of the design space, with or without the help of optimization methods, and enable innovative designs which keep pace with the demand for improved efficiency and sustainability as well as parts and asset operation cost reduction.

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Section: Quantifying Mixed Uncertainty and Visualisationmentioning

confidence: 99%

Machine Learning Methods in CFD for Turbomachinery: A Review

Hammond

Pepper

Montomoli

et al. 2022

IJTPP

Self Cite

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“…is a simpler mathematical representation of the simulation model built from a small set of model runs at parameter values determined by the specific design of experiments (DOE). A wide range of different metamodels have been explored in the literature, such as response surface, 14,15 radial basis function, 16,17 polynomial chaos expansion, 18,19 and Gaussian process model (GPM, also named Kriging). 20,21 Among them, the probabilistic and non-parametric GPM has attracted a lot of attention because of its modeling flexibility, computational tractability, and capability in prediction uncertainty estimation.…”

Section: Introductionmentioning

confidence: 99%

Analytical uncertainty quantification approach based on adaptive generalized co‐Gaussian process model

Wan

Zhang

Luo

et al. 2022

Numerical Meth Engineering

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Gaussian process modeling (GPM) is widely used to replace physical models for uncertainty quantification (UQ). The fidelity of training samples has a major impact on the prediction performance of the GPM, and the computational time of obtaining high‐fidelity (HF) samples from simulations may be very prohibitively expensive. Therefore, this study proposes the use of the multi‐fidelity GPM for UQ to make a trade‐off between the computational cost and accuracy by combining a few HF samples with many computationally‐cheaper low‐fidelity (LF) ones. Currently, the co‐Gaussian process model (co‐GPM, also known as multi‐fidelity GPM) requires that the LF and HF samples be nested. A generalized co‐Gaussian process model (GC‐GPM) is developed that can mix LF and HF samples under both nested and un‐nested conditions. In addition, to further alleviate the computational cost, an adaptive sampling strategy is introduced into GC‐GPM to reduce the number of training samples using a new infill criterion. Within the adaptive GC‐GPM framework, the high dimensional integrals associated with the statistical moments (mean and variance) of structural response can be decomposed to single dimensional ones that are analytically tractable, which finally allows for analytical calculation of the mean and variance. Three full‐scale structures are adopted to verify the reliability and efficiency of the proposed adaptive GC‐GPM method, and traditional GC‐GPM and Monte Carlo simulation (MCS) are used for comparison purpose. The comparison results show that the UQ results of the adaptive GC‐GPM match well with those of the MCS and compared to the traditional GC‐GPM, the adaptive GC‐GPM owns higher computational accuracy and efficiency.

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Sensitivity and uncertainty analysis of the nonlinear flight dynamics system of the flexible body

Qiao

2023

Aerospace Science and Technology

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Data fusion for Uncertainty Quantification with Non-Intrusive Polynomial Chaos

Cited by 7 publications

References 64 publications

Machine Learning Methods in CFD for Turbomachinery: A Review

Machine Learning Methods in CFD for Turbomachinery: A Review

Analytical uncertainty quantification approach based on adaptive generalized co‐Gaussian process model

Sensitivity and uncertainty analysis of the nonlinear flight dynamics system of the flexible body

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