Installed Jet Noise Analysis Using a Coupled LES/APE High-Order Method

Moratilla-Vega, Miguel A.; Xia, Hao; Page, Gary J.

doi:10.1007/s10494-023-00468-5

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…Fluid dynamics is a scientific discipline of critical importance in several areas of science and engineering, such as aeronautics [16,17], civil engineering [18], biology and medicine [19,20], environmental science [21] and computer-generated imagery (CGI) [22]. The availability of fluiddynamics data has been increasing over the last decades owing to improved measurement devices and highly parallel computational simulations, which have enabled the recent application of DL techniques to fluid dynamics with different goals.…”

Section: Introductionmentioning

confidence: 99%

Current and emerging deep-learning methods for the simulation of fluid dynamics

et al. 2023

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Over the last decade, deep learning (DL), a branch of machine learning, has experienced rapid progress. Powerful tools for tasks that have been traditionally complex to automate have been developed, such as image synthesis and natural language processing. In the context of simulating fluid dynamics, this has led to a series of novel DL methods for replacing or augmenting conventional numerical solvers. We broadly classify these methods into physics- and data-driven methods. Physics-driven methods, generally, tune a DL model to provide an analytical and differentiable solution to a given fluid dynamics problem by minimizing the residuals of the governing partial differential equations. Data-driven methods provide a fast and approximate solution to any fluid dynamics problem that shares some physical properties with the observations used when tuning the DL model’s parameters. Meanwhile, the symbiosis of numerical solvers and DL has led to promising results in turbulence modelling and accelerating iterative solvers. However, these methods present some challenges. Exclusively data-driven flow simulators often suffer from poor extrapolation, error accumulation in time-dependent simulations, as well as difficulties in training against turbulent flows. Substantial effort is, therefore, being invested into approaches that may improve the current state of the art.

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Section: Introductionmentioning

confidence: 99%

Current and emerging deep-learning methods for the simulation of fluid dynamics

et al. 2023

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A comparison of Helmholtz oscillators with differently shaped petal nozzles

Li,

Cheng,

Hou

et al. 2024

Physics of Fluids

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This study analyzes the effect of nozzle geometry on the dynamic characteristics of water jets. The flow characteristics of Helmholtz oscillators with three different nozzle shapes (four, six, and eight petals) are investigated using large eddy simulations. The relationship between coherent structures and cavitation is illustrated using the vortex transport equation. The results show that the evolution of cavitation clouds in a Helmholtz cavity results in a periodic change in the jet pressure and the formation of pulsed water jets from petal-shaped nozzles. In addition, the cavitation bubbles inhibit the stretching and expansion of the vortex structure, and the convergence of the turbulent kinetic energy is conducive to maintaining its stability. The four-petal nozzle has the highest velocity at the central axis of the jet at 1.76% higher than that of the six-petal nozzle. When the number of petals in the nozzle is increased from six to eight, the velocity decreases by 7.96%. The streamwise vortex strength of the jet with six petals is enhanced by 61.31% compared to the four-petal case, while the eight-petal nozzle jet is only 11.75% higher than that of the six-petal nozzle. The six-petal nozzle significantly improves the mixing characteristics by slightly reducing the velocity. This study provides guidance for reducing the stagnation pressure loss when using nozzles with special shapes to enhance jet mixing.

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Installed Jet Noise Analysis Using a Coupled LES/APE High-Order Method

Cited by 2 publications

References 40 publications

Current and emerging deep-learning methods for the simulation of fluid dynamics

Current and emerging deep-learning methods for the simulation of fluid dynamics

A comparison of Helmholtz oscillators with differently shaped petal nozzles

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