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The goal of this paper is twofold: first, it investigates the effect of low-pass spatial filters for approximate deconvolution large eddy simulation (AD-LES) of turbulent incompressible flows. Second, it proposes the hyper-differential…

流体动力学 · 物理学 2018-01-29 Omer San , Anne E. Staples , Traian Iliescu

This study proposes a novel method for developing discretization-consistent closure schemes for implicitly filtered Large Eddy Simulation (LES). Here, the induced filter kernel, and thus the closure terms, are determined by the properties…

流体动力学 · 物理学 2023-12-14 Andrea Beck , Marius Kurz

Sediment transport over wave-induced ripples is a very complex phenomenon where available models fail to provide accurate predictions. For coastal engineering applications, the 1-DV advection-diffusion equation could be used with an…

地球物理 · 物理学 2011-06-07 Rafik Absi , Hitoshi Tanaka

Objective functions that optimize deep neural networks play a vital role in creating an enhanced feature representation of the input data. Although cross-entropy-based loss formulations have been extensively used in a variety of supervised…

计算机视觉与模式识别 · 计算机科学 2023-12-19 Deen Dayal Mohan , Bhavin Jawade , Srirangaraj Setlur , Venu Govindaraj

The simulation of high Reynolds number (Re) separated turbulent flows faces significant problems for decades: large eddy simulation (LES) is computationally too expensive, and Reynolds-averaged Navier-Stokes (RANS) methods and hybrid…

流体动力学 · 物理学 2024-12-02 Stefan Heinz , Adeyemi Fagbade

This paper investigates the soft covering lemma under both the relative entropy and the total variation distance as the measures of deviation. The exact order of the expected deviation of the random i.i.d. code for the soft covering problem…

信息论 · 计算机科学 2019-02-22 Mohammad Hossein Yassaee

A wide range of natural and engineered fluid flows exhibit spatial or temporal viscosity variations, spanning scales from microbial locomotion to planetary mantle convection. These variations introduce qualitatively new physical mechanisms…

流体动力学 · 物理学 2025-08-01 Arjun Sharma , Ritabrata Thakur , Sharath Jose , Rama Govindarajan

Information theoretic quantities play a central role in machine learning. The recent surge in the complexity of data and models has increased the demand for accurate estimation of these quantities. However, as the dimension grows the…

机器学习 · 统计学 2024-05-21 Viktor Nilsson , Anirban Samaddar , Sandeep Madireddy , Pierre Nyquist

Velocity and temperature distributions are both crucial for modeling compressible wall-bounded turbulent flows. The compressible law of the wall for velocity has been extensively examined through velocity transformations. However, a…

流体动力学 · 物理学 2026-03-06 Youjie Xu , Steffen J. Schmidt , Nikolaus A. Adams

An eigenvalue equation, for linear instability modes involving large scales in a convective hydromagnetic system, is derived in the framework of multiscale analysis. We consider a horizontal layer with electrically conducting boundaries,…

混沌动力学 · 物理学 2009-11-11 M. Baptista , S. M. A. Gama , V. Zheligovsky

Computationally resolving turbulence remains a central challenge in fluid dynamics due to its multi-scale interactions. Fully resolving large-scale turbulence through direct numerical simulation (DNS) is computationally prohibitive,…

机器学习 · 计算机科学 2025-10-29 Yiheng Du , Aditi S. Krishnapriyan

Embedding-based representations in Euclidean space $\mathbb{R}^d$ are a cornerstone of modern machine learning, where a major goal is to use the \emph{smallest dimension} that faithfully captures data relations. In this work, we prove sharp…

数据结构与算法 · 计算机科学 2026-05-06 Dionysis Arvanitakis , Vaggos Chatziafratis , Yiyuan Luo

In supersonic and hypersonic flows, the near-wall density variation due to wall cooling poses a challenge for accurately predicting the near-wall velocity and temperature profiles using classical eddy viscosity turbulence models.…

流体动力学 · 物理学 2025-08-19 Zifei Yin

We explore a well-known integral representation of the logarithmic function, and demonstrate its usefulness in obtaining compact, easily-computable exact formulas for quantities that involve expectations and higher moments of the logarithm…

信息论 · 计算机科学 2020-02-19 Neri Merhav , Igal Sason

Linear stability analysis has proven to be a useful tool in the analysis of dominant coherent structures, such as the von K\'{a}rm\'{a}n vortex street and the global spiral mode associated with the vortex breakdown of swirling jets. In…

流体动力学 · 物理学 2016-08-24 Lothar Rukes , Christian Oliver Paschereit , Kilian Oberleithner

We present a new version of a dynamical spectral model for Large Eddy Simulation based on the Eddy Damped Quasi Normal Markovian approximation \cite{sao,chollet_lesieur}. Three distinct modifications are implemented and tested. On the one…

流体动力学 · 物理学 2009-11-13 Julien Baerenzung , Helene Politano , Yannick Ponty , Annick Pouquet

A transformation that relates a compressible wall-bounded turbulent flow with non-uniform fluid properties to an equivalent incompressible flow with uniform fluid properties is derived and validated. The transformation accounts for both…

流体动力学 · 物理学 2023-10-05 Asif Manzoor Hasan , Johan Larsson , Sergio Pirozzoli , Rene Pecnik

In this work we introduce a computational framework for determining optimal closures of the eddy-viscosity type for Large-Eddy Simulations (LES) of a broad class of PDE models, such as the Navier-Stokes equation. This problem is cast in…

流体动力学 · 物理学 2020-03-12 Pritpal Matharu , Bartosz Protas

This article presents a formulation that extends the variational multiscale modelling for compressible large-eddy simulation to a vast family of compact nodal numerical methods represented by the high-order flux reconstruction scheme. The…

计算物理 · 物理学 2020-02-19 Farshad Navah , Marta de la Llave Plata , Vincent Couaillier

We present an implementation in a linear-scaling density-functional theory code of an electronic enthalpy method, which has been found to be natural and efficient for the ab initio calculation of finite systems under hydrostatic pressure.…

介观与纳米尺度物理 · 物理学 2015-06-16 Niccolò R. C. Corsini , Andrea Greco , Nicholas D. M. Hine , Carla Molteni , Peter D. Haynes