Uncertainty Quantification for a Turbulent Round Jet Using Multifidelity Karhunen-Loeve Expansions

Jivani, Aniket; Huan, Xun; Safta, Cosmin; Zhou, Beckett Yx; Gauger, Nicolas R.

doi:10.2514/6.2021-1367

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…In the present work, the decay rate λ K+1 /λ 1 ≤ 0.02 was applied. The mutually uncorrelated random variables ζ k were approximated with PCE [13,14], to establish the relationship between Q i and ζ k . This eventually yields a response approximation based on PCE in conjunction with KLE with field dependent expansion coefficients:…”

Section: Stochastic Spectral Methodsmentioning

confidence: 99%

“…The objective of this research was to contribute to the development of scramjet engines, with a focus on ensuring efficient and stable propulsion during hypersonic flight conditions. A further advanced UQ methodology, which involves the combination of PCE and KLE, was applied for the analysis of uncertainties of a turbulent round jet by Jivani et al [14]. One promising metamodeling approach was also developed by Yue et al [15] and involves the approximation of a response function with PCE-HDMR.…”

Section: Introductionmentioning

confidence: 99%

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CFD Uncertainty Quantification using PCE-HDMR – Exemplary Application to a Buoyancy-Driven Mixing Process

Wenig

Kelm

Klein

2023

Preprint

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For the investigation of uncertainties in high dimensional spaces of computationally expensive engineering applications, reliable Uncertainty Quantification (UQ) methods are needed. These methods should provide accurate and efficient High-Dimensional Model Representations (HDMR) of stochastic results using a reasonable number of calculations. Therefore, the PCE-HDMR approach is utilized to qualify appropriate UQ methods for large-scale computations in the field of Computational Fluid Dynamics (CFD). This technique is a combination of Cut-HDMR, a hierarchical decomposition modeling approach, with Polynomial Chaos Expansion (PCE). To demonstrate its effectiveness, the PCE-HDMR methodology in conjunction with complementary modeling techniques is applied for the UQ analysis of a buoyancy-driven mixing process between two miscible fluids within the Differentially Heated Cavity (DHC) of aspect ratio 4. The results include a thorough probabilistic representation of time-dependent response quantities that comprehensively describe the mixing process. The stochastic models are derived from Large Eddy Simulations (LES) using PCE HDMR and the Sparse Grid Method (SGM), which serves as a reference for the results from PCE-HDMR. The results show that PCE-HDMR provides accurate statistics of the modeled time-dependent stochastic processes and shows good agreement with the SGM reference. Thus, PCE-HDMR indicates great potential for UQ of technical-scale computations due to its efficiency and flexibility in the construction of stochastic models.

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Section: Stochastic Spectral Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CFD Uncertainty Quantification using PCE-HDMR – Exemplary Application to a Buoyancy-Driven Mixing Process

Wenig

Kelm

Klein

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In the present work, the decay rate λ K+1 /λ 1 ≤ 0.02 was applied. The mutually uncorrelated random variables ζ k were approximated with PCE [21,22], to establish the relationship between Q i and ζ k :…”

Section: Karhunen-loève Expansionmentioning

confidence: 99%

Uncertainty Quantification of a Industrial Scale CFD Application Using Stochastic Spectral Methods

Wenig,

Kelm,

Klein

2023

5th International Conference on Uncertainty Quantification in Computational Sciences and Engineering

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Accurate prediction of flow phenomena is crucial for a wide range of industrial and scientific applications. However, due to the complexity of many fluid systems, even state-of-theart Computational Fluid Dynamics (CFD) simulations involve various sources of uncertainty, which need to be quantified. Therefore, this work focuses on the Uncertainty Quantification (UQ) of a CFD simulation of a technical scale experiment, which investigates buoyancy-driven mixing processes between two miscible fluids. The CFD model is subject to uncertainties in initial and boundary conditions, as well as in thermo-physical properties. Since computational runs of the CFD model are computationally expensive, the uncertainty analysis requires the use of efficient methods. For this reason, stochastic spectral methods, such as Polynomial Chaos Expansion (PCE) and Karhunen-Loève Expansion (KLE), are applied along with novel approaches like Stochastic Model Composition (SMC) for the uncertainty quantification of responses. Fluctuations due to turbulence are identified as considerable source of uncertainty and are modeled with tailored stochastic models. Results include a comprehensive probabilistic representation of response quantities with probability density functions (PDFs), statistical moments, variance-based decomposition and supplementary error estimates of the underlying stochastic models. This enables well-founded result interpretation and informed decision making. The UQ results show that the uncertain input parameters considerably affect the duration of the mixing process. Through the investigations, the applied UQ methods were tested on an engineering application and demonstrate great potential as a viable technique for uncertainty quantification in technical-scale computations.

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Uncertainty Quantification of LES for Buoyancy-Driven Mixing Processes Using PCE-HDMR

Wenig,

Kelm,

Klein

2023

ERCOFTAC Series

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Uncertainty Quantification for a Turbulent Round Jet Using Multifidelity Karhunen-Loeve Expansions

Cited by 5 publications

References 29 publications

CFD Uncertainty Quantification using PCE-HDMR – Exemplary Application to a Buoyancy-Driven Mixing Process

CFD Uncertainty Quantification using PCE-HDMR – Exemplary Application to a Buoyancy-Driven Mixing Process

Uncertainty Quantification of a Industrial Scale CFD Application Using Stochastic Spectral Methods

Uncertainty Quantification of LES for Buoyancy-Driven Mixing Processes Using PCE-HDMR

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