Numerical simulation on shock wave propagation for microscale laser shock processing

2008 ◽  
Author(s):  
Zhigang Che ◽  
Liangcai Xiong ◽  
Tielin Shi ◽  
Nan Tang
Keyword(s):  
Numerical Simulation ◽  
Shock Wave ◽  
Wave Propagation ◽  
Shock Wave Propagation ◽  
Laser Shock ◽  
Laser Shock Processing ◽  
Shock Processing

Influence of Light Leakage from Q-Switched Laser on Laser-Induced Shock Wave

2011 ◽  
Vol 314-316 ◽  
pp. 1895-1899
Author(s):  
Yun Xia Ye ◽  
Zhong Wu ◽  
Hai Bing Guan
Keyword(s):  
Shock Wave ◽  
Delay Time ◽  
Pressure Wave ◽  
Pressure Waveform ◽  
Laser Shock ◽  
Laser Shock Processing ◽  
Pressure Signal ◽  
Beneficial Effects ◽  
Laser Pumping ◽  
Shock Processing

Experiments were performed to study the influence of light leakage from Q-switched laser on laser-induced shock wave. Through changing the voltage applied on the Q switch crystal and the delay time between opening Q switch and triggering laser pumping source, different amount of light leakage can be obtained. It is ascertained that longer-pulse light leakage from Q-switched laser is responsible for the second weaker pressure signal appearing on the tested pressure waveform. The results show that the leakage energy not only has not beneficial effects on laser shock processing, but also will weaken the main pressure wave.

Predictive Modeling and Uncertainty Quantification of Laser Shock Processing by Bayesian Gaussian Processes With Multiple Outputs

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Yongxiang Hu ◽  
Zhi Li ◽  
Kangmei Li ◽  
Zhenqiang Yao
Keyword(s):  
Numerical Simulation ◽  
Gaussian Process ◽  
Uncertainty Quantification ◽  
Predictive Model ◽  
Process Model ◽  
Gaussian Model ◽  
Laser Shock ◽  
Laser Shock Processing ◽  
Input Parameters ◽  
Shock Processing

Accurate numerical modeling of laser shock processing, a typical complex physical process, is very difficult because several input parameters in the model are uncertain in a range. And numerical simulation of this high dynamic process is very computational expensive. The Bayesian Gaussian process method dealing with multivariate output is introduced to overcome these difficulties by constructing a predictive model. Experiments are performed to collect the physical data of shock indentation profiles by varying laser power densities and spot sizes. A two-dimensional finite element model combined with an analytical shock pressure model is constructed to obtain the data from numerical simulation. By combining observations from experiments and numerical simulation of laser shock process, Bayesian inference for the Gaussian model is completed by sampling from the posterior distribution using Morkov chain Monte Carlo. Sensitivities of input parameters are analyzed by the hyperparameters of Gaussian process model to understand their relative importance. The calibration of uncertain parameters is provided with posterior distributions to obtain concentration of values. The constructed predictive model can be computed efficiently to provide an accurate prediction with uncertainty quantification for indentation profile by comparing with experimental data.

Sign in / Sign up

Export Citation Format

Share Document