The acoustic specificity of steel-railway bridges

Janas, L.

doi:10.35117/a_eng_17_09_05

TO - PK

2017

DOI: 10.35117/a_eng_17_09_05

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The acoustic specificity of steel-railway bridges

L. Janas

Abstract: In the paper, the acoustic effects in the vicinity of four railway bridges have been studied. One of the objects has a track fastened directly to the steel deck plate, another has a deck in the form of an open grillage. The other two objects have a track situated on the ballast and various types of girders. The influence of the bridge on the level of acoustic pressure in the neighborhood of railway roads has been analyzed. General recommendations for the design of silent bridges were discussed.

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2021

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Analysis and Interpretation of Data-Driven Closure Models for Large Eddy Simulation of Internal Combustion Engine

Mitra¹,

Haghshenas²,

Santo³

et al. 2021

SAE Int. J. Adv. & Curr. Prac. in Mobility

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<div class="section abstract"><div class="htmlview paragraph">We present an automatic data-driven machine learning (ML) approach for the development, evaluation and interpretation of deep neural networks (DNNs) for turbulence closures and demonstrate their usage in the context of cold-flow large-eddy simulation (LES) of the four-stroke Darmstadt engine using an open-source compressible multi-dimensional CFD solver OFICE, in a hybrid PDE-ML framework. Rather than explicitly using canonical formulations of closure terms, these DNNs robustly discover the functional relationships between the large-scale features of the resolved flow (cell Re, strain and rotation rate tensors etc.) obtained by solving the Navier Stokes to the small-scale unresolved terms. Experimentally validated high-fidelity LES solutions of the engine at different crank angles are utilized as the ground truth to train the DNN based closure models. Since optimizing these DNNs can be a laborious process for scientific datasets, and often require specialized expertise, we propose a Bayesian optimization framework that automatically determines the best set of network parameters, including the architecture and training hyperparameters - batch size, regularization etc. for optimum performance. We compare and contrast various networks for their effectiveness in an <i>a-priori</i> testing setting. Finally, the best ‘learnt’ network is integrated with the open-source CFD solver (OFICE), and solutions are obtained over several injection cycles. These experiments reveal that the DNN models temporally track resolved scalar variance with a good accuracy. Additionally, we interpret the artificial neural networks with sensitivity analysis to determine the relevant large-scale features for the learning process.</div></div>

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Analysis and Interpretation of Data-Driven Closure Models for Large Eddy Simulation of Internal Combustion Engine

Mitra¹,

Haghshenas²,

Santo³

et al. 2021

SAE Int. J. Adv. & Curr. Prac. in Mobility

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show abstract

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The acoustic specificity of steel-railway bridges

Cited by 1 publication

References 5 publications

Analysis and Interpretation of Data-Driven Closure Models for Large Eddy Simulation of Internal Combustion Engine

Analysis and Interpretation of Data-Driven Closure Models for Large Eddy Simulation of Internal Combustion Engine

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