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相关论文: Toward a chaotic adjoint for LES

200 篇论文

Most sub-grid scale (SGS) models employed in LES (large eddy simulation) formulations were originally developed for incompressible, single phase, inert flows and assume transfer of energy based on the classical energy cascade mechanism.…

流体动力学 · 物理学 2023-09-13 Jhon Cordova , Cesar Celis , Andres Mendiburu , Luis Bravo , Prashant Khare

In this paper, we consider a least-squares (LS)-based distributed algorithm build on a sensor network to estimate an unknown parameter vector of a dynamical system, where each sensor in the network has partial information only but is…

系统与控制 · 电气工程与系统科学 2022-12-19 Siyu Xie , Yaqi Zhang , Lei Guo

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

Extremum seeking (ES) optimization approach has been very popular due to its non-model based analysis and implementation. This approach has been mostly used with gradient based search algorithms. Since least squares (LS) algorithms are…

系统与控制 · 电气工程与系统科学 2020-03-10 Nursefa Zengin , Baris Fidan

This work presents an optimization framework for tailoring the nonlinear dynamic response of lightly damped mechanical systems using Spectral Submanifold (SSM) reduction. We derive the SSM-based backbone curve and its sensitivity with…

最优化与控制 · 数学 2025-12-23 Matteo Pozzi , Jacopo Marconi , Shobhit Jain , Mingwu Li , Francesco Braghin

Adjoint methods have been the pillar of gradient-based optimization for decades. They enable the accurate computation of a gradient (sensitivity) of a quantity of interest with respect to all system's parameters in one calculation. When the…

流体动力学 · 物理学 2024-11-12 Defne E. Ozan , Luca Magri

We consider time series data modeled by ordinary differential equations (ODEs), widespread models in physics, chemistry, biology and science in general. The sensitivity analysis of such dynamical systems usually requires calculation of…

统计方法学 · 统计学 2017-09-20 Valdemar Melicher , Tom Haber , Wim Vanroose

Simulations of turbulent fluid flow around long cylindrical structures are computationally expensive because of the vast range of length scales, requiring simplifications such as dimensional reduction. Current dimensionality reduction…

流体动力学 · 物理学 2021-02-25 Bernat Font , Gabriel D. Weymouth , Vinh-Tan Nguyen , Owen R. Tutty

We give an algorithmic introduction to Lagrangian coherent structures (LCSs) using a newly developed computational engine, LCS Tool. LCSs are most repelling, attracting and shearing material lines that form the centerpieces of observed…

混沌动力学 · 物理学 2016-04-12 K. Onu , F. Huhn , G. Haller

We devise the fast adjoint response algorithm for the gradient of physical measures (long-time-average statistics) of discrete-time hyperbolic chaos with respect to many system parameters. Its cost is independent of the number of…

动力系统 · 数学 2022-09-13 Angxiu Ni

In the dynamical systems approach to describing turbulent or otherwise chaotic flows, an important quantity is the Lyapunov exponents and vectors that characterize the strange attractor of the flow. In particular, knowledge of the Lyapunov…

流体动力学 · 物理学 2019-05-01 Malik Hassanaly , Venkat Raman

The need for accurate and fast scale-resolving simulations of fluid flows, where turbulent dispersion is a crucial physical feature, is evident. Large-eddy simulations (LES) are computationally more affordable than direct numerical…

流体动力学 · 物理学 2025-12-30 Justin Plogmann , Oliver Brenner , Patrick Jenny

The shape of the trailing edge of a gas turbine nozzle guide vane has a significant effect on the downstream stagnation pressure loss and heat transfer over the surface of the vane. Traditionally, adjoint-based design optimization methods…

流体动力学 · 物理学 2020-11-30 Chaitanya Talnikar , Qiqi Wang

Direct statistical simulation (DSS) of nonlinear dynamical systems bypasses the traditional route of accumulating statistics by lengthy direct numerical simulations (DNS) by solving the equations that govern the statistics themselves. DSS…

流体动力学 · 物理学 2026-01-21 Kuan Li , J. B. Marston , Steven M. Tobias

A previously developed modeling procedure for large eddy simulations (LESs) is extended to allow physical space implementations for inhomogeneous flows. The method is inspired by the well-established theoretical analyses and numerical…

流体动力学 · 物理学 2022-10-28 Guangrui Sun , J. Andrzej Domaradzki

For a parameterized hyperbolic system $\frac{du}{dt}=f(u,s)$ the derivative of the ergodic average $\langle J \rangle = \lim_{T \to \infty}\frac{1}{T}\int_0^T J(u(t),s)$ to the parameter $s$ can be computed via the Least Squares Shadowing…

动力系统 · 数学 2017-09-13 Mario Chater , Angxiu Ni , Patrick J. Blonigan , Qiqi Wang

Accurate subgrid-scale (SGS) modeling remains a major challenge in large eddy simulation (LES), particularly for wall-bounded turbulent flows with strong near-wall anisotropy. This study proposes a novel SGS model based on Liutex theory,…

流体动力学 · 物理学 2025-12-18 Jiawei Chen , Yifei Yu , Chaoqun Liu

The least-mean-squares (LMS) algorithm is the most popular algorithm in adaptive filtering. Several variable step-size strategies have been suggested to improve the performance of the LMS algorithm. These strategies enhance the performance…

数据结构与算法 · 计算机科学 2017-03-22 Muhammad Omer Bin Saeed

We establish the theoretical framework for adjoint-based phase reduction analysis for incompressible periodic flows. Through this adjoint-based method, we obtain spatiotemporal phase sensitivity fields through a single pair of forward and…

流体动力学 · 物理学 2022-10-11 Yoji Kawamura , Vedasri Godavarthi , Kunihiko Taira

The Large Eddy Simulation (LES) approach - solving numerically the large scales of a turbulent system and accounting for the small-scale influence through a model - is applied to nonlinear gyrokinetic systems that are driven by a number of…

等离子体物理 · 物理学 2015-06-18 A. Bañón Navarro , B. Teaca , F. Jenko , G. W. Hammett , T. Happel , the ASDEX Upgrade Team