About the uncertainty quantification of turbulence and cavitation models in cavitating flows simulations

Congedo, Pietro Marco; Goncalves, Éric

doi:10.1016/j.euromechflu.2015.05.005

Cited by 23 publications

(10 citation statements)

References 38 publications

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“…A cha llenge in model ing a nd quanLifying cloud cavitaL ion co llapse is t he dep endence of cr it ical quan t ities or in Lerest (Qols) , such as p eak press ure or co ll apse time, on a particu lar (random) cloud co nfi g uration [77] (see also Figure 2) . The systematic sLudy of such dependencies can be addressed t hrough a n un ce rLainty quantification (UQ ) fr a mework, recently a pplied in [17,6]. In [46 , 47, 48], a mathematical fr amewo rk was provided for uncerLain solu Lions of hyp erbolic equa tions.…”

Section: J3 J362mentioning

confidence: 99%

Multilevel Control Variates for Uncertainty Quantification in Simulations of Cloud Cavitation

Šukys¹,

Rasthofer²,

Wermelinger³

et al. 2018

SIAM J. Sci. Comput.

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\l\le quant ify uncertainti es in the location and magnitud e of extreme pressure spots revealed from la rge scale multiph ase flow s imul a tions of cloud cavitat ion coll apse. 'vVe examin e clouds containin g 500 cav iti es a nd qu antify uncerta inti es relaLed Lo th eir ini t ial spatial a rrangement. T he resulting 2,000-dimensional s pace is sampl ed using a noninLrusive and com putat ionally effici ent multi level Monte Carlo (JVILMC) methodology. We introduce novel empirically optimal control vari ate coeffici ents to enh ance th e variance reduction in MLMC. The proposed multil evel control vari ates l\tlonLe Carlo leads to more than two orders of magnitude sp eedup when compared Lo st a nd a rd Monte Carlo methods. We id entify large un certa inti es in the locat ion and magnitud e of the peak pressure pu lse a nd present iLs statistical correla tion s and joint proba bility density fun ct ion s wit h the geo metrical characteris tics of the cloud. Characteristic properties of s pati al cloud structure a re identified as potential causes of s ignificant un certa int ies in exe rted collapse pressures. Key words. compressible multiph ase flow , hi gh perform a nce com putin g, diffuse interface met hod, bubble collapse, cloud cav itation , uncerta inty qua ntifica tion , multilevel !\,Jonte Carlo, control va ri ates, fault tolerance

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Section: J3 J362mentioning

confidence: 99%

Multilevel Control Variates for Uncertainty Quantification in Simulations of Cloud Cavitation

Šukys¹,

Rasthofer²,

Wermelinger³

et al. 2018

SIAM J. Sci. Comput.

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“…In light of these limitations of the current RANS models, a data assimilation technique is conducted to improve the predictions. It has been previously demonstrated [6] that quantifying the parameter uncertainty related to the cavitation & turbulence models and the inlet conditions is feasible with the data assimilation technique. In this work, we apply an Ensemble-based variational data assimilation scheme (4DEnVar) [1] to unsteady cavitating flows.…”

Section: Data Assimilationmentioning

confidence: 99%

Proceedings of the 10th International Symposium on Cavitation (CAV2018)

Zhang¹,

Gomez²,

Xiao³

et al. 2018

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This paper investigates the application of data-driven approach to the optimization of cavitating flow simulations. An evaluation of the performance of commonly used RANS models (k-e, k-w and k-w SST) is presented by comparison with high fidelity data (DNS solution and X-ray experimental measurements). An ensemble based variational method is introduced and used to reconstruct the inlet velocity and calibrate the empirical parameters in the turbulence model and the cavitation model. Machine learning approach is discussed to construct a discrepancy function of the Reynolds stresses to address the RANS model-form uncertainty.

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“…Next, the cavitation nuclei grow, accumulate and collapse in the fluid. High speed, high pressure and high temperature are produced in collapse, which can generate noise and materials damage so as to achieve the cleaning of the attachments on the net [5][6][7]. Currently, scholars from all over the world mainly laid the research emphasis on the characteristics of the flow field in the nozzle.…”

Section: Introductionmentioning

confidence: 99%

Cavitation Simulation and Mechanism Analysis of Underwater Jet of Cleaning Equipment for Aquaculture Fishing Net

Yuan¹,

Xiao-hua²,

Hu³

et al. 2019

Preprint

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In order to reveal the mechanism and influencing factors of high pressure jet cavitation of nozzle in submerged environment, this study focused on the evolutionary process of cavitation bubbles and combined finite volume method and mixed multi-phase flow model to analyze the cavitation, velocity distribution and experimental cavitation intensity of fishing net cleaning equipment. Results show that the cavitation inception, growth and collapse mainly occurred in the peripheral region of the flow field. Ring-shaped cavitation erosion zone appeared on the test sample target. A lot of small dense erosion pits were densely distributed in the ring-shaped erosion zone, erosion marks were observed in the center. The cavitation erosion intensity was greatly affected by the nozzle structure. As the diameter of nozzle increased from 0.6 mm to 1 mm, the maximum gas volume fraction increased by 8.53%. The nozzle outlet enlargement angle greatly increased the cavitation intensity. The nozzle with an outlet angle of 30° exhibited the optimal cavitation erosion performance . The cavitation volume fraction of the nozzle with short necking structure was slightly larger than that of the nozzle with long necking structure at the same level in the necking length rang of 3mm to 7mm. In terms of the influence of nozzle structure on the cavitation erosion effect, the nozzle diameter D ranked the first, followed by the outlet angle α, and the necking length L was at the last.

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About the uncertainty quantification of turbulence and cavitation models in cavitating flows simulations

Cited by 23 publications

References 38 publications

Multilevel Control Variates for Uncertainty Quantification in Simulations of Cloud Cavitation

Multilevel Control Variates for Uncertainty Quantification in Simulations of Cloud Cavitation

Proceedings of the 10th International Symposium on Cavitation (CAV2018)

Cavitation Simulation and Mechanism Analysis of Underwater Jet of Cleaning Equipment for Aquaculture Fishing Net

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