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Current design constraints have encouraged the studies of aeroacoustics fields around compressible jet flows. The present work addresses the numerical study of unsteady turbulent jet flows for aeroacoustic analyses of main engine rocket…

流体动力学 · 物理学 2022-12-26 Carlos Junqueira-Junior , Sami Yamouni , Joao Luiz F. Azevedo , William Wolf

Current design constraints have encouraged the studies of aeroacoustics fields around compressible jet flows. The present work addresses the numerical study of subgrid scale modeling for unsteady turbulent jet flows as a preliminary step…

流体动力学 · 物理学 2023-01-03 Carlos Junqueira-Junior , Sami Yamouni , Joao Luiz F. Azevedo , William Wolf

New regulations are imposing noise emissions limitations for the aviation industry which are pushing researchers and engineers to invest efforts in studying the aeroacoustics phenomena. Following this trend, an in-house computational fluid…

分布式、并行与集群计算 · 计算机科学 2020-03-24 Carlos Junqueira-Junior , João Luiz F. Azevedo , Jairo Panetta , William R. Wolf , Sami Yamouni

Dynamic mode decomposition (DMD) has proven to be a valuable tool for the analysis of complex flow-fields but the application of this technique to flows with moving boundaries is not straightforward. This is due to the difficulty in…

流体动力学 · 物理学 2020-07-28 Karthik Menon , Rajat Mittal

Modal decomposition techniques are important tools for the analysis of unsteady flows and, in order to provide meaningful insights with respect to coherent structures and their characteristic frequencies, the modes must possess a robust…

流体动力学 · 物理学 2023-08-24 Lucas F. de Souza , Renato F. Miotto , William R. Wolf

The interaction of multiple fluids through a heterogeneous pore space leads to complex pore-scale flow dynamics, such as intermittent pathway flow. The non-local nature of these dynamics, and the size of the 4D datasets acquired to capture…

地球物理 · 物理学 2024-09-24 Aman Raizada , Steffen Berg , Sally M. Benson , Hamdi A. Tchelepi , Catherine Spurin

Dynamic mode decomposition (DMD) is a popular approach to analyzing and modeling fluid flows. In practice, datasets are almost always corrupted to some degree by noise. The vanilla DMD is highly noise-sensitive, which is why many…

流体动力学 · 物理学 2025-01-30 Andre Weiner , Janis Geise

Dynamic mode decomposition (DMD) provides a principled approach to extract physically interpretable spatial modes from time-resolved flow field data, along with a linear model for how the amplitudes of these modes evolve in time. Recently,…

流体动力学 · 物理学 2020-07-29 Aditya G. Nair , Benjamin Strom , Bingni W. Brunton , Steven L. Brunton

Self-sustained oscillation in Mach 3 supersonic cavity with a length-to-depth ratio of 3 is investigated using wall modeled Large Eddy Simulation (LES) methodology for ReD = 3.39*10^5. The unsteady data obtained through computation is…

流体动力学 · 物理学 2021-02-01 Rahul Kumar Soni , Nitish Arya , Ashoke De

Acoustics loads are rocket design constraints which push researches and engineers to invest efforts in the aeroacoustics phenomena which is present on launch vehicles. Therefore, an in-house computational fluid dynamics tool is developed in…

计算工程、金融与科学 · 计算机科学 2020-03-23 Carlos Junqueira-Junior , João Luiz F. Azevedo , Jairo Panetta , William R. Wolf , Sami Yamouni

Dynamic Mode Decomposition (DMD) is a data-driven and model-free decomposition technique. It is suitable for revealing spatio-temporal features of both numerically and experimentally acquired data. Conceptually, DMD performs a…

流体动力学 · 物理学 2020-12-18 Tim Krake , Stefan Reinhardt , Marcel Hlawatsch , Bernhard Eberhardt , Daniel Weiskopf

High-fidelity large-eddy simulations are suitable to obtain insight into the complex flow dynamics in extended wind farms. In order to better understand these flow dynamics, we use dynamic mode decomposition (DMD) to analyze and reconstruct…

流体动力学 · 物理学 2022-04-26 Xuan Dai , Da Xu , Mengqi Zhang , Richard J. A. M. Stevens

Measurement techniques such as Magnetic Resonance Velocimety (MRV) and Magnetic Resonance Concentration (MRC) are useful for obtaining 3D time-averaged flow quantities in complex turbulent flows, but cannot measure turbulent correlations or…

In the present work, large-eddy simulations of free supersonic jet flows are performed to investigate the influence of inflow conditions on the jet flow field and its turbulent properties. A high-order nodal discontinuous Galerkin method is…

流体动力学 · 物理学 2026-02-11 Diego F. Abreu , Joao Luiz F. Azevedo , Carlos Junqueira-Junior

The present work evaluates the effects of three inflow boundary conditions on large-eddy simulations of supersonic jet flows. The three inlet flow configurations considered are an inviscid profile, a stationary turbulent profile extracted…

The present work compares results for different numerical methods in search of alternatives to improve the quality of large-eddy simulations for the problem of supersonic turbulent jet flows. Previous work has analyzed supersonic jet flows…

流体动力学 · 物理学 2025-09-23 D. F. Abreu , C. Junqueira-Junior , E. T. V. Dauricio , J. L. F. Azevedo

Shallow flows are common in natural and human-made environments. Even for simple rectangular shallow reservoirs, recent laboratory experiments show that the developing flow fields are particularly complex, involving large-scale turbulent…

流体动力学 · 物理学 2023-07-13 Daniel Valero , Daniel B. Bung , Sebastien Erpicum , Yann Peltier , Benjamin Dewals

A data-driven framework using snapshots of an uncontrolled flow is proposed to identify, and subsequently demonstrate, effective control strategies for different objectives in supersonic impinging jets. The approach, based on a dynamic mode…

流体动力学 · 物理学 2023-09-12 Spencer L. Stahl , Datta V. Gaitonde

Numerical simulations of turbulent round jets, using explicit-filtered LES technique, are performed, for three different Reynolds numbers(Re = 3600, 88000, 400000), to understand the Reynolds number effect on subsonic jets with Mach number…

流体动力学 · 物理学 2017-11-22 Ramanathan Varadharajan

Streaming Dynamic Mode Decomposition (sDMD) (Hemati et al., Phys. Fluids 26(2014)) is a low-storage version of Dynamic Mode Decomposition (DMD) (Schmid, J. Fluid Mech. 656 (2010)), a data-driven method to extract spatio-temporal flow…

流体动力学 · 物理学 2022-06-16 Rui Yang , Xuan Zhang , Philipp Reiter , Moritz Linkmann , Detlef Lohse , Olga Shishkina
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