ORCID Profile
0000-0003-2070-3654
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Publisher: American Physical Society (APS)
Date: 20-04-2018
Publisher: Elsevier BV
Date: 11-2019
Publisher: AIP Publishing
Date: 04-2021
DOI: 10.1063/5.0042564
Abstract: Two commonly used discrete velocity models [the linear discrete velocity model (LDVM) and the full discrete velocity model (FDVM)] are investigated using the two-relaxation time lattice Boltzmann method coupled to a fast Poisson solver in an electroconvection system. We derived analytically the LDVM, i.e., D2Q5 and D3Q7 and FDVM, i.e., D2Q9 and D3Q27, for the ion transport equation (convection–diffusion–drift equation) in both two- and three-dimensional systems. The analytical results indicate that the error terms in LDVM are higher orders and can be neglected in practical simulations. The numerical results of LDVM and FDVM are quantitatively compared, showing the differences between the models' prediction in charge density, velocity, and stability hysteresis loops. The numerical results are consistent with the theoretical analysis. We perform all the simulations using graphics processing units, and the computational efficiency is measured via the wall clock time. We find that the LDVM can substitute FDVM in certain conditions with a substantial saving in computational costs and a small sacrifice in accuracy.
Publisher: AIP Publishing
Date: 08-2018
DOI: 10.1063/1.5029403
Abstract: We present an analytical model for electro-hydrodynamic flow that describes the relationship between the corona voltage, electric field, and ion charge density. The interaction between the accelerated ions and the neutral gas molecules is modeled as an external body force in the Navier-Stokes equation. The gas flow characteristics are solved from conservation principles with spectral methods. This multiphysics model is shown to match experimental data for a point-to-ring corona configuration, shedding new insights into mass, charge, and momentum transport phenomena, and can be readily implemented in any numerical simulation.
Publisher: Elsevier BV
Date: 06-2020
Publisher: SAGE Publications
Date: 26-11-2023
DOI: 10.1177/09544062221136677
Abstract: In this work, the electro-thermo-hydrodynamic convection system induced by unipolar charge injection between two parallel electrodes is numerically investigated. A two-relaxation-time lattice Boltzmann method coupled with a fast Poisson solver is implemented to obtain the temporal and spatial distributions of the flow field, temperature field, electric field, and charge density of the system. Due to the electric force and destabilizing buoyancy force, the system exhibits electro-thermo-convective vortices and transitions to a chaotic flow field, where the flow fluctuates irregularly in time. The strong electric driving force is shown to double the heat transfer effects measured by the Nusselt number ( Nu). The size of the computational domain is found to influence the stability and flow analysis. Specifically, the system is chaotic in a large computational domain but the same set of parameters can lead to a steady-state condition in a small domain.
Publisher: AIP Publishing
Date: 04-2021
DOI: 10.1063/5.0044147
Abstract: The two-dimensional (2D) electro-convection (EC) flow of dielectric liquids between two concentric cylindrical electrodes driven by unipolar injection of ions is investigated numerically. The finite volume method is used to resolve the spatiotemporal distributions of the flow field, electric field, and charge density. The flow transition routes from steady laminar to chaotic flow states are studied in various scenarios where the mobility parameter M of the dielectric liquids varies from 5 to 200. The dynamic characteristics and bifurcation routes of the EC flow depend on the electric Rayleigh number T, a ratio of the electric force to viscous force, and the mobility parameter M. For increasing T, three different transition routes from a convective steady-state to chaos via different intermediate states are observed. The flow states have been quantified by the power spectral density distribution and phase space trajectory of the velocity. The fractal dimensions and Lyapunov exponents are calculated to identify the chaotic flow. The increase in the mobility parameter M leads to a shorter and more direct route with fewer intermediate states when bifurcating to chaos. In addition, the power scale of charge transport that is defined by the electric Nusselt number Ne and T is discussed when the EC flow develops into electro-turbulence.
Publisher: Authorea, Inc.
Date: 14-06-2023
DOI: 10.22541/ESSOAR.168677212.21341231/V1
Abstract: There is growing interest in discovering interpretable, closed-form equations for subgrid-scale (SGS) closures arameterizations of complex processes in Earth system. Here, we apply a common equation-discovery technique with expansive libraries to learn closures from filtered direct numerical simulations of 2D forced turbulence and Rayleigh-Benard convection (RBC). Across common filters, we robustly discover closures of the same form for momentum and heat fluxes. These closures depend on nonlinear combinations of gradients of filtered variables (velocity, temperature), with constants that are independent of the fluid/flow properties and only depend on filter type/size. We show that these closures are the nonlinear gradient model (NGM), which is derivable analytically using Taylor-series expansions. In fact, we suggest that with common (physics-free) equation-discovery algorithms, regardless of the system hysics, discovered closures are always consistent with the Taylor-series. Like previous studies, we find that large-eddy simulations with NGM closures are unstable, despite significant similarities between the true and NGM-predicted fluxes (pattern correlations 0.95). We identify two shortcomings as reasons for these instabilities: in 2D, NGM produces zero kinetic energy transfer between resolved and subgrid scales, lacking both diffusion and backscattering. In RBC, backscattering of potential energy is poorly predicted. Moreover, we show that SGS fluxes diagnosed from data, presumed the ‘truth’ for discovery, depend on filtering procedures and are not unique. Accordingly, to learn accurate, stable closures from high-fidelity data in future work, we propose several ideas around using physics-informed libraries, loss functions, and metrics. These findings are relevant beyond turbulence to closure modeling of any multi-scale system.
Publisher: American Physical Society (APS)
Date: 03-03-2020
Publisher: AIP Publishing
Date: 03-2021
DOI: 10.1063/5.0040286
Abstract: Developing data-driven subgrid-scale (SGS) models for large eddy simulations (LESs) has received substantial attention recently. Despite some success, particularly in a priori (offline) tests, challenges have been identified that include numerical instabilities in a posteriori (online) tests and generalization (i.e., extrapolation) of trained data-driven SGS models, for ex le, to higher Reynolds numbers. Here, using the stochastically forced Burgers turbulence as the test-bed, we show that deep neural networks trained using properly pre-conditioned (augmented) data yield stable and accurate a posteriori LES models. Furthermore, we show that transfer learning enables accurate/stable generalization to a flow with 10× higher Reynolds number.
Publisher: Elsevier BV
Date: 09-2021
Publisher: Springer Science and Business Media LLC
Date: 03-07-2017
Publisher: AIP Publishing
Date: 2021
DOI: 10.1063/5.0034889
Abstract: A numerical investigation of electrohydrodynamic flows of a dielectric liquid in a single wire–plate configuration with a cross Poiseuille flow has been presented. Unipolar charge injection takes place from a metallic wire electrode immersed in a dielectric liquid at the center of the channel. Although this configuration is frequently studied with gas as a working fluid in electrostatic precipitators, the flow of a dielectric liquid remains unexplored. Two-way coupled governing equations that include the Navier–Stokes equations for fluid flow, the charge transport equation, and the Poisson equation for electric potential are solved using a finite-volume method. A systematic analysis of flow characteristics with respect to the hydrodynamic Reynolds number (Re) and electric Reynolds number (RE) has been carried out. The transition process with four distinct flow patterns and two different flow mechanisms are discussed in detail. A comprehensive map of flow patterns with respect to various dimensionless parameters has been proposed. The results show that a higher Re can reduce the effect of electric field, and vice versa. The main flow pattern is found to be a strong function of the dimensionless external velocity.
Publisher: The Royal Society
Date: 08-2021
DOI: 10.1098/RSOS.202367
Abstract: Convection is a fundamental fluid transport phenomenon, where the large-scale motion of a fluid is driven, for ex le, by a thermal gradient or an electric potential. Modelling convection has given rise to the development of chaos theory and the reduced-order modelling of multiphysics systems however, these models have been limited to relatively simple thermal convection phenomena. In this work, we develop a reduced-order model for chaotic electroconvection at high electric Rayleigh number. The chaos in this system is related to the standard Lorenz model obtained from Rayleigh–Benard convection, although our system is driven by a more complex three-way coupling between the fluid, the charge density, and the electric field. Coherent structures are extracted from temporally and spatially resolved charge density fields via proper orthogonal decomposition (POD). A nonlinear model is then developed for the chaotic time evolution of these coherent structures using the sparse identification of nonlinear dynamics (SINDy) algorithm, constrained to preserve the symmetries observed in the original system. The resulting model exhibits the dominant chaotic dynamics of the original high-dimensional system, capturing the essential nonlinear interactions with a simple reduced-order model.
Publisher: Elsevier BV
Date: 09-2022
Publisher: Elsevier BV
Date: 10-2020
Publisher: The Royal Society
Date: 09-2020
Abstract: Electrohydrodynamic (EHD) thrust is produced when ionized fluid is accelerated in an electric field due to the momentum transfer between the charged species and neutral molecules. We extend the previously reported analytical model that couples space charge, electric field and momentum transfer to derive thrust force in one-dimensional planar coordinates. The electric current density in the model can be expressed in the form of Mott–Gurney law. After the correction for the drag force, the EHD thrust model yields good agreement with the experimental data from several independent studies. The EHD thrust expression derived from the first principles can be used in the design of propulsion systems and can be readily implemented in the numerical simulations.
Publisher: ASME International
Date: 25-06-2018
DOI: 10.1115/1.4040091
Abstract: Lean blowout (LBO) prediction based on the local parameters in the laboratory toroidal jet-stirred reactor (TJSR) is investigated. The reactor operated on methane is studied using three-dimensional computational fluid dynamics (CFD) the results are compared with the experimental data. Skeletal chemical kinetic mechanism with the eddy dissipation concept (EDC) model is used. Flow bifurcation in the radial (poloidal) plane due to the interaction between counter-rotating vortices creates one dominating poloidal recirculation zone (PRZ) and one weaker toroidal recirculation zone (TRZ). The Damkohler (Da) number in the reactor is the highest in the stabilization vortex it varies from about Da ∼ 2 at ϕ = 0.55 to Da ∼ 0.2–0.3 at LBO conditions. Due to the reduced turbulent dissipation rate in PRZ, the Da number is an order of magnitude higher than in TRZ. The global blowout event is predicted at the local Da = 0.2 in PRZ. Local blowout events in the regions of low Da can lead to flame instability and to a global flame blowout at a higher fuel–air ratio than predicted by the CFD. Local Da nonuniformity can be used for optimization and analysis of combustion system stability. Further research in the process parameterization and application to the practical combustion system is needed.
Publisher: Wiley
Date: 15-10-2021
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-402
Abstract: & & In large eddy simulations (LES), the subgrid-scale effects are modeled by physics-based or data-driven methods. This work develops a convolutional neural network (CNN) to model the subgrid-scale effects of a two-dimensional turbulent flow. The model is able to capture both the inter-scale forward energy transfer and backscatter in both a priori and a posteriori analyses. The LES-CNN model outperforms the physics-based eddy-viscosity models and the previous proposed local artificial neural network (ANN) models in both short-term prediction and long-term statistics. Transfer learning is implemented to generalize the method for turbulence modeling at higher Reynolds numbers. Encoder-decoder network architecture is proposed to generalize the model to a higher computational grid resolution.& &
Publisher: Elsevier BV
Date: 06-2022
Publisher: AIP Publishing
Date: 04-2021
DOI: 10.1063/5.0047181
Abstract: Electrokinetic flow in a microchannel driven by charged surface heterogeneity in the presence of an external electric field is investigated by three-dimensional simulations. A computational framework is developed coupling a two-relaxation-time lattice Boltzmann solver for the transport equations of fluids, charged species, and passive tracing scalars and a fast Poisson solver for the electric potential. The two-relaxation-time lattice Boltzmann method is used to resolve the spatiotemporal distribution of flow field, ion concentration, and two passive tracing scalars. The fast Poisson solver is used to solve the electric potential at every time step. Three charged surface patterns together with various external electric fields are investigated. The induced electrokinetic vortices contribute to the mixing and transport enhancement of the passive scalars, depending on the surface patterns and the external electric field. The transport enhancement is found to follow a power law with respect to the magnitude of the external electric field.
Publisher: Oxford University Press (OUP)
Date: 23-01-2023
DOI: 10.1093/PNASNEXUS/PGAD015
Abstract: Transfer learning (TL), which enables neural networks (NNs) to generalize out-of-distribution via targeted re-training, is becoming a powerful tool in scientific machine learning (ML) applications such as weather/climate prediction and turbulence modeling. Effective TL requires knowing (1) how to re-train NNs? and (2) what physics are learned during TL? Here, we present novel analyses and a framework addressing (1)–(2) for a broad range of multi-scale, nonlinear, dynamical systems. Our approach combines spectral (e.g. Fourier) analyses of such systems with spectral analyses of convolutional NNs, revealing physical connections between the systems and what the NN learns (a combination of low-, high-, band-pass filters and Gabor filters). Integrating these analyses, we introduce a general framework that identifies the best re-training procedure for a given problem based on physics and NN theory. As test case, we explain the physics of TL in subgrid-scale modeling of several setups of 2D turbulence. Furthermore, these analyses show that in these cases, the shallowest convolution layers are the best to re-train, which is consistent with our physics-guided framework but is against the common wisdom guiding TL in the ML literature. Our work provides a new avenue for optimal and explainable TL, and a step toward fully explainable NNs, for wide-ranging applications in science and engineering, such as climate change modeling.
Publisher: Springer Science and Business Media LLC
Date: 19-10-2017
Publisher: American Physical Society (APS)
Date: 10-2019
Publisher: American Physical Society (APS)
Date: 26-01-2021
Publisher: Elsevier BV
Date: 2023
No related grants have been discovered for Yifei Guan.