Numerical modeling and simulation on nanosecond laser-target interactions

Abstract

Nanosecond lasers are widely used in industrial applications as they are relatively inexpensive, and their compactness and robustness are an advantage. Much experimental work has been carried out to understand deeper the interaction between the nanosecond laser pulses and the targets, as these are complex, transient processes with spatial inhomogeneities. Beside the experiments, the modeling and numerical simulation on the laser interaction with the target are also crucial for understanding the dynamics of laser-material interactions and for optimizing laser processing applications. In this review, the progress of numerical modeling and simulation on nanosecond laser-target interactions are summarized from the aspects of laser-target interactions and target-plasma interface, laser-plasma interactions and plasma radiation, and numerical models on different scales with artificial intelligence advancing. The laser ablation, mass and energy transfer, and mechanical coupling are discussed in the aspect of the nanosecond laser-target interactions and target-plasma interface. The plasma expansion, plasma ionization and recombination, and plasma radiation are discussed in the aspect of the nanosecond laser-plasma interactions and plasma radiation. Then the numerical advances, including microscopic approaches based on molecular dynamics, mesoscopic approaches based on kinetic and statistical physics, macroscopic approaches based on fluid dynamics, and numerical simulations with machine learning are discussed. Finally, the challenges currently being encountered by numerical modeling and simulation on nanosecond laser-target interactions and its potential development direction are considered.

Published in Journal of Physics D: Applied Physics. This is the authors' version. 58 pages, 10 figures. Corrected the author name in Ref. [255]

References in corpus (17)

• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• DGM: A deep learning algorithm for solving partial differential equations
• Inferring solutions of differential equations using noisy multi-fidelity data
• Physics-informed deep learning for incompressible laminar flows
• Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture
• Simulation of multi-species flow and heat transfer using physics-informed neural networks
• Optical diagnostics of laser-produced plasmas
• Jet formation from bubbles near a solid boundary in a compressible liquid. Numerical study of distance dependence
• The data-driven future of high energy density physics
• Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks
• Low-temperature plasma simulation based on physics-informed neural networks: frameworks and preliminary applications
• Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics
• Blast wave kinematics: theory, experiments, and applications
• Accelerating physics-informed neural network based 1D arc simulation by meta learning
• Prompt electrons driving ion acceleration and formation of a two temperatures plasma in nanosecond laser-ablation domain
• A Physics-Informed Deep Learning Model of the Hot Tail Runaway Electron Seed
• Spatio-temporal dynamics of anisotropic emission from nano second laser produced aluminium plasma

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