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We present a simple modification of the direct-forcing immersed boundary method (IBM) proposed by Uhlmann [J. Comput. Phys, 2005] in order to enable it to be applied to particulate flows with solid-to-fluid density ratios around unity. The…

流体动力学 · 物理学 2023-06-21 Manuel Garcia-Villalba , Blanca Fuentes , Jan Dusek , Manuel Moriche , Markus Uhlmann

Data-driven turbulence modeling studies have reached such a stage that the fundamental framework is basically settled, but several essential issues remain that strongly affect the performance, including accuracy, smoothness, and…

流体动力学 · 物理学 2022-09-21 Yuhui Yin , Yufei Zhang , Haixin Chen , Song Fu

FEARLESS (Fluid mEchanics with Adaptively Refined Large Eddy SimulationS) is a new numerical scheme arising from the combined use of subgrid scale (SGS) model for turbulence at the unresolved length scales and adaptive mesh refinement (AMR)…

宇宙学与河外天体物理 · 物理学 2014-11-20 L. Iapichino , A. Maier , W. Schmidt , J. C. Niemeyer

The immersed boundary (IB) method has been used as a means to simulate fluid-membrane interactions in a wide variety of biological and engineering applications. Although the numerical convergence of the method has been empirically verified,…

数值分析 · 数学 2025-10-09 Alexandre X. Milewski , Charles S. Peskin

We consider non-spherical rigid body particles in an incompressible fluid in the regime where the particles are too large to assume that they are simply transported with the fluid without back-coupling and where the particles are also too…

流体动力学 · 物理学 2021-09-07 Henry von Wahl , Thomas Richter

Wall-bounded turbulence is characterized by coherent, worm-like structures such as hairpin vortices. The attached-eddy model provides a successful statistical framework for the log-law region, yet the complex geometry and multiscale nature…

流体动力学 · 物理学 2026-04-15 Weiyu Shen , Yuchen Ge , Zishuo Han , Yaomin Zhao , Yue Yang

Wall-bounded turbulent flows are chaotic and multiscale, rendering real-time prediction at high Reynolds numbers computationally prohibitive in applications such as wind farms. Classical data assimilation methods are based on repeated…

流体动力学 · 物理学 2026-05-25 Fabian Steinbrenner , Baris Turan , Hao Teng , Heng Xiao

Our work is motivated by the analysis of ash plume dynamics, arising in the study of volcanic eruptions. Such phenomena are characterized by large Reynolds number (exceeding $10^7$) and a large number of polydispersed particles~[1]. Thus,…

High-fidelity modeling of turbulent flows is one of the major challenges in computational physics, with diverse applications in engineering, earth sciences and astrophysics, among many others. The rising popularity of high-fidelity…

流体动力学 · 物理学 2019-03-06 Arvind Mohan , Don Daniel , Michael Chertkov , Daniel Livescu

Strongly coupled immersed boundary (IB) methods solve the nonlinear fluid and structural equations of motion simultaneously for strongly enforcing the no-slip constraint on the body. Handling this constraint requires solving several large…

流体动力学 · 物理学 2021-03-12 Nirmal Jayaprasad Nair , Andres Goza

A realistic inflow boundary condition is essential for successful simulation of the developing turbulent boundary layer or channel flows. Recent advances in artificial intelligence (AI) have enabled the development of an inflow generator…

流体动力学 · 物理学 2020-02-19 Junhyuk Kim , Changhoon Lee

We propose an efficient algorithm for the immersed boundary method on distributed-memory architectures, with the computational complexity of a completely explicit method and excellent parallel scaling. The algorithm utilizes the…

分布式、并行与集群计算 · 计算机科学 2015-01-29 Jeffrey K. Wiens , John M. Stockie

In this investigation, a data-driven turbulence closure framework is introduced and deployed for the sub-grid modelling of Kraichnan turbulence. The novelty of the proposed method lies in the fact that snapshots from high-fidelity numerical…

流体动力学 · 物理学 2018-11-14 Romit Maulik , Omer San , Adil Rasheed , Prakash Vedula

Despite its great scientific and technological importance, wall-bounded turbulence is an unresolved problem in classical physics that requires new perspectives to be tackled. One of the key strategies has been to study interactions among…

There is wide agreement that the accuracy of turbulence models suffer from their sensitivity with respect to physical input data, the uncertainties of user-elected parameters, as well as the model inadequacy. However, the application of…

数值分析 · 数学 2015-08-07 Hoang A. Tran , Clayton G. Webster , Guannan Zhang

Porous media are ubiquitous in energy storage and conversion, catalysis, biomechanics, hydrogeology, as well as many other fields. These materials possess high surface-to-volume ratios and their complex channels can restrict and guide the…

流体动力学 · 物理学 2025-12-04 Olivier Guévremont , Lucka Barbeau , Vaiana Moreau , Federico Galli , Nick Virgilio , Bruno Blais

Simulations of complex turbulent flow are part and parcel of the engineering design process. Eddy viscosity based turbulence models represent the workhorse for these simulations. The underlying simplifications in eddy viscosity models make…

流体动力学 · 物理学 2024-05-15 Minghan Chu , Weicheng Qian

Bayesian calibration is widely used for inverse analysis and uncertainty analysis for complex systems in the presence of both computer models and observation data. In the present work, we focus on large-scale fluid-structure interaction…

数值分析 · 数学 2022-01-03 Shunxiang Cao , Daniel Zhengyu Huang

For a single timestep, a spectral solver is known to be more accurate than its finite-difference counterpart. However, as we show in this paper, turbulence simulations using the two methods have nearly the same accuracy. In this paper, we…

流体动力学 · 物理学 2025-08-15 Akash Rodhiya , Shashwat Bhattacharya , Mahendra K Verma

High-fidelity simulations of unsteady fluid flow are now possible with advancements in high-performance computing hardware and software frameworks. Since computational fluid dynamics (CFD) computations are dominated by linear algebraic…

分布式、并行与集群计算 · 计算机科学 2024-02-28 Rahul Sundar , Dipanjan Majumdar , Chhote Lal Shah , Sunetra Sarkar
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