Two-component seedless velocimetry utilizing laser-induced shockwaves

Byun, Hosung; Do, Hyungrok; Kim, Kyeongsun; Kang, Kyung‐Won; Bae, Jiyeol

doi:10.1016/j.expthermflusci.2023.110924

Cited by 1 publication

(1 citation statement)

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“…According to this research, the collapsed bubble had a pressure of 13.5 GPa and included more vapor than gas. Byun et al 11 created a two-component seedless velocimetry technique that makes advantage of shockwaves produced in laser-induced plasmas (LIP) by concentrated nanosecond laser pulses. Two pairs of continuous-wave laser beams, each produced by a diode laser, are used in this procedure.…”

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

View full text Add to dashboard Cite

show abstract

Two-component seedless velocimetry utilizing laser-induced shockwaves

Cited by 1 publication

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

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