Fluid Dynamics · Cross-listed from cond-mat.mes-hall
Fast spectral solver for viscoelastic structures under oscillatory flow in free space or wall-bounded domains: applications to quartz crystal microbalance and force spectroscopy
Pablo Palacios Alonso, Raúl Pérez Peláez and Rafael Delgado-Buscalioni
We present a fast spectral solver for the linear response of viscoelastic structures under oscillatory flow either in free space or close to a flat moving wall. The scheme works in the frequency domain (using phasors) and couples the oscillatory Stokes equation with rigid or flexible structures, modeled by viscoelastic networks of immersed boundary kernels. The fluid-structure coupling can be solved by two routes. One route calculates the hydrodynamic mobility
matrix required to solve the equation for the structure deformation rate in
matrix form. The second route iteratively solves the coupled fluid-structure equations: fluid-induced forces on the structures create a tension field which is then transferred to the fluid, until convergence. The resulting fixed-point problem is solved iteratively using the Anderson acceleration method. The mobility route is optimal when dealing with one or few structures, while the iterative scheme is preferred for denser dispersions. In any case, the flow resulting from the body forces is solved by a recently developed scheme [J. Fluid. Mech. 1010 A57, 2025] which is spectral in space and time and deals with doubly periodic open domains (either free-space or wall-bounded) where meshing is restricted to the region of interest around the structures. We test the present scheme in two applied contexts: quartz-
crystal-microbalance (QCM) of spheres, suspended, adsorbed or tethered to viscoelastic linkers; and force spectroscopy (via atomic force microscopy) reproducing the power spectra of vibrating microparticles near a solid boundary. In all cases, comparisons with analytical, numerical and experimental results show excellent agreement. We conclude by discussing new routes the scheme opens in force spectroscopy and QCM analyses of soft objects.
Read the full abstract ▾
Computational Physics
H3PC: Hypersonic, High-Order, High-Performance Code with Adaptive Mesh Refinement and Real Chemistry
Ahmad Peyvan, Khemraj Shukla and George Em Karniadakis
We have developed a hypersonic high-order, high-performance code (H$^3$PC) utilizing the “Trixi.jl" framework in order to simulate both non-reactive and chemically reactive compressible Euler and Navier-Stokes equations for complex three-dimensional geometries. H$^3$PC is parallel on CPU platforms and can perform exascale parallel computations of hypersonic turbulent flows. The numerical approach is based on the discontinuous Galerkin spectral element method, satisfying the
entropy and energy stability conditions for the Euler equations. H$^3$PC can perform simulations of high-speed flows from subsonic to hypersonic speeds based on frozen,
equilibrium, and non-
equilibrium chemistry modeling of the gas mixture, using the Mutation.jl , which is a Julia package developed to wrap the C++-based Mutation++ library. H$^3$PC can also perform parallel adaptive mesh refinement for two- and three-dimensional Euler and Navier-Stokes discretizations with non-conforming elements. In this study, we first demonstrate the successful integration of Mutation++ into the H$^3$PC solver, and then verify its accuracy through simulations of Taylor-Green
vortex flow, supersonic flow past a square and circular cylinder, and hypersonic P8-inlet.
Read the full abstract ▾
General Physics
Classical gauge theories as systems with constraints: a geometric point of view
M. F. Araujo de Resende
In this paper, we briefly review the
Hamiltonian formulation of classical systems that are constrained to submanifolds so that, within this context, the true meaning of classical gauge theories becomes clear. Please note that this paper is nothing more than a near-literal translation of Ref. [1], which we originally published in Brazilian Portuguese in 2018. Therefore, if you, the reader, find this paper useful enough to cite it in any of your works, we kindly ask that you (also) cite Ref. [1].
Read the full abstract ▾
Fluid Dynamics
Mitigating numerical dissipation in simulations of subsonic turbulent flows
James Watt, Christoph Federrath, Claudius Birke et al. (+1 more)
Magnetohydrodynamic (MHD) simulations of subsonic (Mach number~$<1$)
turbulence are crucial to our understanding of several processes including oceanic and atmospheric flows, the amplification of
magnetic fields in the early universe,
accretion discs, and stratified flows in
stars. In this work, we demonstrate that conventional numerical schemes are excessively dissipative in this low-Mach regime. We demonstrate that a new numerical scheme (termed `USM-BK' and implemented in the FLASH MHD code) reduces the dissipation of kinetic and
magnetic energy, constrains the divergence of
magnetic field to zero close to machine precision, and resolves smaller-scale structure than other, more conventional schemes, and hence, is the most accurate for simulations of low-Mach turbulent flows among the schemes compared in this work. We first compare several numerical schemes/solvers, including Split-Roe, Split-Bouchut, USM-Roe, USM-HLLC, USM-HLLD, and the new USM-BK, on a simple
vortex problem. We then compare the schemes/solvers in simulations of the turbulent dynamo and show that the choice of scheme affects the growth rate, saturation level, and viscous and resistive dissipation scale of the dynamo. We also measure the numerical kinematic Reynolds number (Re) and
magnetic Reynolds number (Rm) of our otherwise ideal MHD flows, and show that the new USM-BK scheme provides the highest Re and comparable Rm amongst all the schemes compared.
Read the full abstract ▾
All authors James Watt, Christoph Federrath, Claudius Birke, Christian Klingenberg
Geophysics
Pervasive Label Errors in Seismological Machine Learning Datasets
Albert Leonardo Aguilar Suarez and Gregory Beroza
The recent boom in artificial intelligence and machine learning has been powered by large
datasets with accurate labels, combined with algorithmic advances and efficient computing. The quality of data can be a major factor in determining model performance. Here, we detail observations of commonly occurring errors in popular seismological machine learning
datasets. We used an ensemble of available deep learning models PhaseNet and EQTransformer to evaluate the
dataset labels and found four types of errors ranked from most prevalent to least prevalent: (1) unlabeled earthquakes; (2) noise samples that contain earthquakes; (3) inaccurately labeled arrival times, and (4) absent earthquake signals. We checked a total of 8.6 million examples from the following
datasets: Iquique, ETHZ, PNW, TXED, STEAD, INSTANCE, AQ2009, and CEED. The average error rate across all
datasets is 3.9 %, ranging from nearly zero to 8 % for individual
datasets. These faulty data and labels are likely to degrade model training and performance. By flagging these errors, we aim to increase the quality of the data used to train machine learning models, especially for the measurement of arrival times, and thereby to improve the reliability of the models. We present a companion list of examples that contain problems, aiming to integrate them into training routines so that only the reliable data is used for training.
Read the full abstract ▾
Physics and Society
Urban Density and Equity of Access to Social Services in Australian Urban Areas
Kerry A. Nice and Mark Stevenson
To measure access to social services (primary health care, early childhood care/education, and public transport), we created two social service access indexes (SSPT and SSI) for Australian capital cities. We show that only two cities, Melbourne and Sydney, have some limited characteristics of a compact or 15-minute city, but only in the city centres and inner city areas where population densities are highest and have less low density housing types. In the outer suburban and peri-urban areas, as well as across all of the remaining cities, proximity to social services is poor and residents suffer the consequences of spatial inequity.
Read the full abstract ▾
Plasma Physics
Molecular Dynamics Simulation of Hydrodynamic Transport Coefficients in Plasmas
Briggs Damman, Jarett LeVan and Scott Baalrud
Molecular dynamics (MD) simulations are used to calculate transport coefficients in a two-component
plasma interacting through a repulsive Coulomb potential. The thermal conductivity, electrical conductivity, electrothermal coefficient, thermoelectric coefficient, and shear viscosity are computed using the Green-Kubo formalism over a broad range of Coulomb coupling strength, $0.01 \leq \Gamma \leq 140$. Emphasis is placed on testing standard results of the Chapman-Enskog solution in the weakly coupled regime ($\Gamma \ll 1$) using these first-principles simulations. As expected, the results show good agreement for $\Gamma \lesssim 0.1$. However, this agreement is only possible if careful
attention is paid to the definitions of linear constitutive relations in each of the theoretical models, a point that is often overlooked. For example, the standard Green-Kubo expression for thermal conductivity is a linear combination of thermal conductivity, electrothermal and thermoelectric coefficients computed in the Chapman-Enskog formalism. Meaningful results for electrical conductivity are obtained over the full range of coupling strengths explored, but it is shown that potential and virial components of the other transport coefficients diverge in the strongly coupled regime ($\Gamma \gg 1$). In this regime, only the kinetic components of the transport coefficients are meaningful for a classical
plasma.
Read the full abstract ▾
Plasma Physics
Plasma hydrodynamics from mean force kinetic theory
Jarett LeVan and Scott D. Baalrud
Mean force kinetic theory is used to evaluate the electrical conductivity, thermal conductivity, electrothermal coefficient, thermoelectric coefficient, and shear viscosity of a two-component (ion-electron)
plasma. Results are compared with molecular dynamics simulations. These simulations are made possible by assuming a repulsive Coulomb force for all interactions. Good agreement is found for all coefficients up to a Coulomb coupling strength of $\Gamma \approx 20$. This is over 100-times larger than the coupling strength at which traditional theories break down. It is concluded that mean force kinetic theory provides a means to extend hydrodynamics to dense
plasmas.
Read the full abstract ▾
Chemical Physics
Solvaformer: an SE(3)-equivariant graph transformer for small molecule solubility prediction
Jonathan Broadbent, Michael Bailey, Mingxuan Li et al. (+6 more)
Accurate prediction of small molecule solubility using material-sparing approaches is critical for accelerating synthesis and process optimization, yet experimental measurement is costly and many learning approaches either depend on quantumderived descriptors or offer limited interpretability. We introduce Solvaformer, a geometry-aware graph
transformer that models solutions as multiple molecules with independent SE(3)
symmetries. The architecture combines intramolecular SE(3)-equivariant
attention with intermolecular scalar
attention, enabling cross-molecular communication without imposing spurious relative geometry. We train Solvaformer in a multi-task setting to predict both solubility (log S) and solvation free energy, using an alternating-batch regimen that trains on
quantum-mechanical data (CombiSolv-QM) and on experimental measurements (BigSolDB 2.0). Solvaformer attains the strongest overall performance among the learned models and approaches a DFT-assisted gradient-boosting baseline, while outperforming an EquiformerV2 ablation and sequence-based alternatives. In addition,
token-level
attention produces chemically coherent attributions: case studies recover known intra- vs. inter-molecular hydrogen-bonding patterns that govern solubility differences in positional isomers. Taken together, Solvaformer provides an accurate, scalable, and interpretable approach to solution-phase property prediction by uniting
geometric inductive bias with a mixed
dataset training strategy on complementary computational and experimental data.
Read the full abstract ▾
All authors Jonathan Broadbent, Michael Bailey, Mingxuan Li, Abhishek Paul, Louis De Lescure, Paul Chauvin, Lorenzo Kogler-Anele, Yasser Jangjou, Sven Jager
Fluid Dynamics
Structure tensor Reynolds-averaged Navier-Stokes turbulence models with equivariant neural networks
Aaron Miller, Sahil Kommalapati, Robert Moser et al. (+1 more)
Accurate and generalizable Reynolds-averaged Navier-Stokes (RANS) models for turbulent flows rely on effective closures, but currently available closures are notoriously unreliable. Kassinos et al. (J. Fluid Mechanics, 428, pp. 213-248, 2001) hypothesized that this unreliability of RANS models was due to an insufficient description of the statistical state of the
turbulence and proposed a set of structure tensors as a candidate for a sufficiently rich description. To test this hypothesis for the rapid pressure-strain term, we introduce tensor-based,
symmetry aware closures in terms of the structure tensors using equivariant neural networks (ENNs), and present an
algorithm for enforcing algebraic contraction relations among tensor components. Using data from rapid distortion theory, experiments show that such ENNs can effectively learn relationships involving high-order tensors. The resulting ENN structure tensor models are orders of magnitude more accurate than existing models for the rapid pressure-strain correlation, effectively validating the Kassinos et al. hypothesis for this term. Results show that ENNs provide a physically consistent alternative to classical tensor basis models, enabling end-to-end learning of unclosed terms in RANS and other tensor modeling domains, and rapid exploration of model dependencies.
Read the full abstract ▾
All authors Aaron Miller, Sahil Kommalapati, Robert Moser, Petros Koumoutsakos
Applied Physics · Cross-listed from cond-mat.mes-hall
Impact of Contact Gating on Scaling of Monolayer 2D Transistors Using a Symmetric Dual-Gate Structure
Victoria M. Ravel, Sarah R. Evans, Samantha K. Holmes et al. (+4 more)
The performance and scalability of two-dimensional (2D) field-effect transistors (FETs) are strongly influenced by geometry-defined electrostatics. In most 2D FET studies, the gate overlaps with the source and drain electrodes, allowing the gate potential to modulate the 2D
semiconductor underneath the electrodes and ultimately effect carrier transport at the metal-
semiconductor interface - a phenomenon known as contact gating. Here, a symmetric dual-gate structure with independently addressable back and top gates is employed to elucidate the impact of contact gating on a monolayer MoS2 channel. Unlike previous studies of contact gating, this symmetric structure enables quantification of the phenomena through a contact gating factor, revealing a 2x enhancement in on-state performance in long-channel devices. At scaled dimensions (50 nm channel and 30 nm contact length), the influence of contact gating becomes amplified, yielding a 5x increase in on-state performance and a 70% reduction in transfer length when contact gating is present. Since many reported record-performance 2D FETs employ back-gate geometries that inherently include contact gating, these results establish contact gating as a critical and previously underappreciated determinant of device performance in the 2D FET landscape.
Read the full abstract ▾
All authors Victoria M. Ravel, Sarah R. Evans, Samantha K. Holmes, James L. Doherty, Md Sazzadur Rahman, Tania Roy, Aaron D. Franklin
Atomic Physics · Cross-listed from hep-ph
Searching for screened scalar forces with long-baseline atom interferometers
Hannah Banks, John Carlton, Benjamin Elder et al. (+2 more)
Screened scalars are ubiquitous in many dark-sector models. They give rise to non-trivial fifth forces whilst evading experimental constraints through density-dependent screening mechanisms. We propose equipping a 10\,m-scale long-baseline atom interferometer with an annular planar source mass inside the
vacuum chamber to search for such screened fifth forces. Two key challenges arise: distinguishing the static fifth force from backgrounds, and isolating it from the plate's Newtonian
gravity. We introduce the `$Q$-flip protocol', which alternates between interferometry sequences to induce controllable time-dependence, aiding signal extraction and de-trending of transient noise. We further develop an in situ calibration procedure to characterise the plate's Newtonian
gravity and reach shot-noise-limited sensitivity. We show that our proposal could test theoretically motivated parameter space, advancing existing bounds in chameleon and symmetron screened scalar models by $1$ to $1.5$ orders of magnitude. Our proposal is directly applicable to forthcoming experiments, such as AION-10 or VLBAI, and is readily extensible to broader theoretical models and longer baselines.
Read the full abstract ▾
All authors Hannah Banks, John Carlton, Benjamin Elder, Thomas Hird, Christopher McCabe
Atmospheric and Oceanic Physics
The Role of Deep Mesoscale Eddies in Ensemble Forecast Performance
Justin Cooke, Kathleen Donohue, Clark D Rowley et al. (+2 more)
Present
forecasting efforts rely on assimilation techniques that adjust the model basic state, meaning that profiles of temperature and salinity are used as measured or converted to temperature and salinity through statistical relationships. This information influences the upper ocean ( $< 1000$ m depth), while minimally influencing the deep ocean. Nevertheless, development of the full water column circulation critically depends upon the dynamical interactions between upper and deep fields. A review of ensemble forecasts in the Gulf of Mexico demonstrates the importance of the initial deep ocean features in the
evolution of the surface field. Initial conditions throughout the full water column that agree with observations are needed to improve the forecast predictions. Here, best and worst ensemble members in two 92-day forecasts are identified and contrasted in order to determine how the deep ocean features differ between these groups. The forecasts cover the duration of the Loop Current Eddy Thor separation event, which coincides with available deep observations. Model member performance is assessed with a newly developed ranking method, demonstrated with surface variables against verifying analysis and satellite altimeter data during the forecast time-period. Deep cyclonic and anticyclonic features are reviewed, and compared against deep observations, indicating subtle differences in locations of deep eddies at relevant times. These results highlight both the importance of deep circulation dynamics of the Loop Current system and more broadly motivate efforts to assimilate deep observations to better constrain the deep initial fields and improve surface and sub-surface predictions.
Read the full abstract ▾
All authors Justin Cooke, Kathleen Donohue, Clark D Rowley, Prasad G Thoppil, D Randolph Watts
Optics
Temperature-dependent excitonic light manipulation with atomically-thin optical elements
Ludovica Guarneri, Qitong Li, Thomas Bauer et al. (+5 more)
Monolayer 2D
semiconductors, such as WS2, exhibit uniquely strong light-matter interactions due to
exciton resonances that enable atomically-thin optical elements. Similar to geometry-dependent plasmon and Mie resonances, these intrinsic material resonances offer coherent and tunable light scattering. Thus far, the impact of the
excitons temporal dynamics on the performance of such excitonic metasurfaces remains unexplored. Here, we show how the excitonic decay rates dictate the focusing efficiency of an atomically-thin lens carved directly out of exfoliated monolayer WS2. By isolating the coherent
exciton radiation from the incoherent background in the focus of the lens, we obtain a direct measure of the role of
exciton radiation in wavefront shaping. Furthermore, we investigate the influence of
exciton-
phonon scattering by characterizing the focusing efficiency as a function of temperature, demonstrating an increased optical efficiency at cryogenic temperatures. Our results provide valuable insights in the role of excitonic light scattering in 2D nanophotonic devices.
Read the full abstract ▾
All authors Ludovica Guarneri, Qitong Li, Thomas Bauer, Jung-Hwan Song, Ashley P. Saunders, Fang Liu, Mark L. Brongersma, Jorik van de Groep
Chemical Physics
Orbital-Optimized Unitary Coupled Cluster for Indirect Nuclear Spin-Spin Coupling Constants within a Quantum Linear Response Framework
Juliane H. Fuglsbjerg, Peter Reinholdt, Erik Kjellgren et al. (+4 more)
We present a
quantum linear response (qLR) approach within an active-space framework for computing indirect
nuclear spin-
spin coupling constants, a key ingredient in NMR spectra predictions. The method employs the
unitary coupled cluster (UCC) ansatz and its orbital-optimized variant (ooUCC), both suitable for
quantum computing implementations, to evaluate
spin-
spin coupling constants via qLR. Test calculations on five small molecules are compared with CASCI, CASSCF, and conventional CCSD results. qLR with UCC/ooUCC yields
spin-
spin coupling constants comparable to classical methods. We further examine the role of orbital optimization and find that ooUCC markedly affects the computed couplings; orbital-optimized results show better agreement with CCSD. These findings indicate that orbital optimization is important for accurate NMR coupling predictions within
quantum-computing-friendly correlated methods.
Read the full abstract ▾
All authors Juliane H. Fuglsbjerg, Peter Reinholdt, Erik Kjellgren, Phillip W. K. Jensen, Sonia Coriani, Jacob Kongsted, Stephan P. A. Sauer
Plasma Physics
The Data Fusion Labeler (dFL): Challenges and Solutions to Data Harmonization, Labeling, and Provenance in Fusion Energy
Craig Michoski, Matthew Waller, Brian Sammuli et al. (+14 more)
Fusion energy research increasingly depends on the ability to integrate heterogeneous,
multimodal datasets from high-resolution diagnostics, control systems, and multiscale simulations. The sheer volume and complexity of these
datasets demand the development of new tools capable of systematically harmonizing and extracting knowledge across diverse modalities. The Data Fusion Labeler (dFL) is introduced as a unified workflow instrument that performs uncertainty-aware data harmonization, schema-compliant data fusion, and provenance-rich manual and automated labeling at scale. By
embedding alignment, normalization, and labeling within a reproducible, operator-order-aware framework, dFL reduces time-to-analysis by greater than 50X (e.g., enabling >200 shots/hour to be consistently labeled rather than a handful per day), enhances label (and subsequently training) quality, and enables cross-device comparability. Case studies from DIII-D demonstrate its application to automated ELM detection and confinement regime classification, illustrating its potential as a core component of data-driven discovery, model validation, and real-time control in future burning
plasma devices.
Read the full abstract ▾
All authors Craig Michoski, Matthew Waller, Brian Sammuli, Zeyu Li, Tapan Ganatma Nakkina, Raffi Nazikian, Sterling Smith, David Orozco, Dongyang Kuang, Martin Foltin, Erik Olofsson, Mike Fredrickson, Jerry Louis-Jeune, David R. Hatch, Todd A. Oliver, Mitchell Clark, Steph-Yves Louis
Chemical Physics · Cross-listed from quant-ph
Constrained Shadow Tomography for Molecular Simulation on Quantum Devices
Irma Avdic, Yuchen Wang, Michael Rose et al. (+4 more)
Quantum state
tomography is a fundamental task in
quantum information science, enabling detailed characterization of correlations,
entanglement, and electronic structure in
quantum systems. However, its exponential measurement and computational demands limit scalability, motivating efficient alternatives such as classical shadows, which enable accurate prediction of many observables from randomized measurements. In this work, we introduce a bi-objective semidefinite programming approach for constrained shadow
tomography, designed to reconstruct the two-particle reduced density
matrix (2-RDM) from noisy or incomplete shadow data. By integrating $N$-representability constraints and
nuclear-norm regularization into the optimization, the method builds an $N$-representable 2-RDM that balances fidelity to the shadow measurements with energy minimization. This unified framework mitigates noise and sampling errors while enforcing physical consistency in the reconstructed states. Numerical and hardware results demonstrate that the approach significantly improves accuracy, noise resilience, and scalability, providing a robust foundation for physically consistent fermionic state reconstruction in realistic
quantum simulations.
Read the full abstract ▾
All authors Irma Avdic, Yuchen Wang, Michael Rose, Lillian I. Payne Torres, Anna O. Schouten, Kevin J. Sung, David A. Mazziotti
Space Physics · Cross-listed from astro-ph.SR
Simulated Operational Testing of the Prototype Implementation of the SOFIE Model: The 2025 Space Weather Prediction Testbed Exercise
Weihao Liu, Lulu Zhao, Igor V. Sokolov et al. (+29 more)
The CLEAR Space Weather Center of Excellence's
solar energetic particle (SEP) model,
SOlar wind with FIeld lines and Energetic particles (SOFIE), was run and evaluated on-site during the Space Weather Prediction Testbed (SWPT) exercise at the National Oceanic and Atmospheric Administration's Space Weather Prediction Center (NOAA/SWPC) in May 2025. As a physics-based SEP model, SOFIE simulates the acceleration and transport of energetic particles by the coronal mass ejection (CME)-driven shock in the
solar corona and inner heliosphere, and has been validated against historical events. However, questions remain regarding whether a physics-based model, traditionally considered computationally expensive, could meet operational needs. The SWPT exercise offered a valuable opportunity to evaluate SOFIE under simulated operational conditions. On-site interactive feedback from SWPC forecasters, Space Radiation Analysis Group (SRAG) console operators, Community Coordinated Modeling Center (CCMC) personnel, and Moon-to-Mars Space Weather Analysis Office (M2M SWAO) analysts led to significant strategic improvements in the model configuration. The simulation grid was optimized by combining a coarser background grid with higher-resolution regions along the CME path and toward Earth, reducing computational cost without compromising accuracy. In this work, we present the simulated operational performance of SOFIE and its capability to predict SEP fluxes significantly faster than real time. During the SWPT exercise, SOFIE completed a 4-day SEP simulation within 5 hours using 1,000 central processing unit cores, although the earliest SEP forecast was obtained a few hours after CME onset. This marks a milestone in demonstrating SOFIE's operational usefulness and robustness to support future human space exploration.
Read the full abstract ▾
All authors Weihao Liu, Lulu Zhao, Igor V. Sokolov, Kathryn Whitman, Tamas I. Gombosi, Nishtha Sachdeva, Eric T. Adamson, Hazel M. Bain, Claudio Corti, M. Leila Mays, Michelangelo Romano, Carina R. Alden, Madeleine M. Anastopulos, Mary E. Aronne, Janet E. Barzilla, Wesley T. Cook, Shawn D. Dahl, Hannah Hermann, Anthony J. Iampietro, A. Steve Johnson, Elizabeth A. Juelfs, Melissa R. Kane, Jonathan D. Lash, Kimberly Moreland, Briana K. Muhlestein, Teresa Nieves-Chinchilla, Edward Semones, James F. Spann, Earl M. Spencer, Luke A. Stegeman, Christopher J. Stubenrauch, Kenneth L. Tegnell
Fluid Dynamics
Novel mixed approximate deconvolution subgrid-scale models for large-eddy simulation
Ehsan Amani, Mohammad Bagher Molaei and Morteza Ghorbani
Approximate Deconvolution (AD) has emerged as a promising closure for Large-Eddy Simulation (LES) in complex multi-physics flows, where the conventional pure Dynamic Eddy-Viscosity (DEV) models experience issues. In this research, we propose novel improved mixed hard-deconvolution or secondary-regularization models and compare their performance with the existing standard mixed AD-DEV and penalty-term regularizations. For this aim, five consistency criteria, based on the properties of the modeled sub-filter-scale stress in limit conditions, are introduced for the first time. It is proved that the conventional hard-deconvolution models do not adhere to a couple of important primary criteria. Furthermore, through a priori and a posteriori analyses of Burgers
turbulence and turbulent channel flow, it is manifested that the inconsistency with the primary criteria can result in larger modeling errors, the over-prediction and pile-up of kinetic energy in eddies of a length scale between the explicit filter width and grid size, and even the solution instability. On the other hand, the favorable characteristics of the new mixed models, in terms of the consistency criteria, significantly improve the accuracy of the predictions, the solution stability, and even the computational cost, particularly for one of the new models called mixed Alternative-DEV (A-DEV).
Read the full abstract ▾