Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system

Ma, Ming; Lu, Jiacai; Tryggvason, Grétar

doi:10.1063/1.4930004

Cited by 125 publications

(72 citation statements)

References 38 publications

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“…Promising activities in LES include the use on neural networks to model subgrid-scale stress (Gamahara and Hattori 2017; Vollant et al 2014Vollant et al , 2017 and to represent the deconvolution of flow quantities from filtered flow fields (Maulik and San 2017). Ma et al (2015Ma et al ( , 2016 used machine learning to model the inter-phase mass and momentum fluxes in multiphase flow simulations.…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

Turbulence Modeling in the Age of Data

Duraisamy

Iaccarino

Xiao

2019

Annu. Rev. Fluid Mech.

972

499

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Data from experiments and direct simulations of turbulence have historically been used to calibrate simple engineering models such as those based on the Reynolds-averaged Navier-Stokes (RANS) equations. In the past few years, with the availability of large and diverse datasets, researchers have begun to explore methods to systematically inform turbulence models with data, with the goal of quantifying and reducing model uncertainties. This review surveys recent developments in bounding uncertainties in RANS models via physical constraints, in adopting statistical inference to characterize model coefficients and estimate discrepancy, and in using machine learning to improve turbulence models. Key principles, achievements and challenges are discussed. A central perspective advocated in this review is that by exploiting foundational knowledge in turbulence modeling and physical constraints, data-driven approaches can yield useful predictive models. arXiv:1804.00183v3 [physics.flu-dyn]

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Section: Challenges and Perspectivesmentioning

confidence: 99%

Turbulence Modeling in the Age of Data

Duraisamy

Iaccarino

Xiao

2019

Annu. Rev. Fluid Mech.

972

499

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“…In addition to RANS modeling as reviewed above, data and machine learning have been used to provide closures for (1) the subgrid-scale (SGS) fluxes for in LES, (2) the inter-phase momentum fluxes in multiphase flow simulations [25,26], and (3) the unresolved boundary layer physics in potential flow simulations [27]. Among these, researchers reported illconditioning issues in data-driven SGS models in LES that are similar to the ill-conditioning issue in the context of RANS modeling discussed above.…”

Section: Data-driven Closure Modeling Beyond Rans Simulationsmentioning

confidence: 99%

Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework

2018

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Reynolds-averaged Navier-Stokes (RANS) equations are widely used in engineering turbulent flow simulations. However, RANS predictions may have large discrepancies due to the uncertainties in modeled Reynolds stresses. Recently, Wang et al. demonstrated that machine learning can be used to improve the RANS modeled Reynolds stresses by leveraging data from high fidelity simulations (Physics informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data. Physical Review Fluids. 2, 034603, 2017). However, solving for mean flows from the improved Reynolds stresses still poses significant challenges due to potential ill-conditioning of RANS equations with Reynolds stress closures. Enabling improved predictions of mean velocities are of profound practical importance, because often the velocity and its derived quantities (QoIs, e.g., drag, lift, surface friction), and not the Reynolds stress itself, are of ultimate interest in RANS simulations. To this end, we present a comprehensive framework for augmenting turbulence models with physics-informed machine learning, illustrating a complete workflow from identification of input features to final prediction of mean velocities. This work has two innovations. First, we demonstrate a systematic procedure to generate mean flow features based on the integrity basis for mean flow tensors. Second, we propose using machine learning to predict linear and nonlinear parts of the Reynolds stress tensor separately. Inspired by the finite polynomial representation of tensors in classical turbulence modeling, such a decomposition is instrumental in overcoming the ill-conditioning of RANS equations.Numerical tests demonstrated merits of the proposed framework.

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“…This is a major advance in model uncertainty quantification in RANS simulations compared to the earlier framework of Kennedy and O'Hagan [19], where model inadequacy are introduced directly to the quantities of interest or the observed quantities and the numerical model Composite models are ubiquitous in various disciplines of science and engineering. For example, in multiphase flow simulations, models are used to describe interphase mass and momentum exchanges in averaged equations [175,176]; in climate and weather modeling, parameterization are used to account for unresolved or unknown physics including radiation, cloud, and boundary layer processes [177][178][179]. In all these examples, the conservation laws are all expressed in well-grounded PDEs, albeit containing unclosed terms.…”

Section: Appendix B Composite Model Theory and Openbox Treatment Of mentioning

confidence: 99%

Quantification of model uncertainty in RANS simulations: A review

Xiao

Cinnella

2019

Progress in Aerospace Sciences

272

131

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In computational fluid dynamics simulations of industrial flows, models based on the Reynoldsaveraged Navier-Stokes (RANS) equations are expected to play an important role in decades to come. However, model uncertainties are still a major obstacle for the predictive capability of RANS simulations. This review examines both the parametric and structural uncertainties in turbulence models. We review recent literature on data-free (uncertainty propagation) and data-driven (statistical inference) approaches for quantifying and reducing model uncertainties in RANS simulations. Moreover, the fundamentals of uncertainty propagation and Bayesian inference are introduced in the context of RANS model uncertainty quantification. Finally, the literature on uncertainties in scale-resolving simulations is briefly reviewed with particular emphasis on large eddy simulations.

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Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system

Cited by 125 publications

References 38 publications

Turbulence Modeling in the Age of Data

Turbulence Modeling in the Age of Data

Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework

Quantification of model uncertainty in RANS simulations: A review

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