Spatio-temporal behavior of laser induced plasma shock wave probed by optical beam deflection technique

Alnama, Koutayba; Sulaiman, Nawar; Ibrahim, Ihab

doi:10.1016/j.ijleo.2021.167832

Cited by 7 publications

(1 citation statement)

References 35 publications

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“…The velocity components are measured with an uncertainty of 4–6 m/s. The spatiotemporal evolution of shock waves produced by Nd:YAG lasers on copper targets at different pump laser power densities under atmospheric pressure circumstances was investigated by Alnama et al 12 . The scientists identified a supersonic shock wave that propagated and subsequently degraded into an acoustic wave using optical beam deflection.…”

Section: Related Workmentioning

confidence: 99%

Improving prediction accuracy of laser-induced shock wave velocity prediction using neural networks

Yang

2024

Sci Rep

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The velocity of laser-induced shock waves affects the efficiency and efficacy of laser-based processes. The ability to accurately estimate shock wave velocity is critical for optimizing experimental combinations, creating laser-based systems, and assuring desired results. Traditional approaches to predict shock wave velocity involve empirical equations and analytical models based on simplified assumptions. However, these methods often lack accuracy and fail to capture the complex dynamics of laser-matter interactions. To overcome these limitations, we used a combination of an artificial neural network and a genetic algorithm to predict shock wave velocity. In this method, the neural network structure is dynamically designed. The optimization method does this by modifying the neural network's weights and figuring out the network's structure on our behalf. Based on the findings, our suggested technique worked very well; it surpassed other comparison methods by achieving the lowest average errors in terms of RMSE and MAE, which are 4.38 and 3.74, respectively. Moreover, the analysis has shown that our proposed method has a high level of reliability in predicting impulsive wave velocity using a neural network.

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Section: Related Workmentioning

confidence: 99%

Improving prediction accuracy of laser-induced shock wave velocity prediction using neural networks

Yang

2024

Sci Rep

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Modeling temporal and spatial evolutions of laser-induced plasma characteristics by using machine learning algorithms

Bakhtiyari

2023

Optik

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Dynamics of laser-induced shock wave and cavitation during laser ablation of aliphatic nitroalkanes

Zhao

Yin

Shi

et al. 2023

Optics and Lasers in Engineering

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Spatio-temporal behavior of laser induced plasma shock wave probed by optical beam deflection technique

Cited by 7 publications

References 35 publications

Improving prediction accuracy of laser-induced shock wave velocity prediction using neural networks

Improving prediction accuracy of laser-induced shock wave velocity prediction using neural networks

Modeling temporal and spatial evolutions of laser-induced plasma characteristics by using machine learning algorithms

Dynamics of laser-induced shock wave and cavitation during laser ablation of aliphatic nitroalkanes

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