Numerical Research on Suppression of Thermally Induced Wavefront Distortion of Solid-state Laser Based on Neural Network

Hang Liu; Ping He; Juntao Wang; Dan Wang; Jianli Shang

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자료유형: 학술저널

저자정보: Hang Liu (China Academy of Engineering Physics) Ping He (China Academy of Engineering Physics) Juntao Wang (China Academy of Engineering Physics) Dan Wang (China Academy of Engineering Physics) Jianli Shang (China Academy of Engineering Physics)

저널정보: 한국광학회 Current Optics and Photonics Current Optics and Photonics Vol.6 No.5

발행연도: 2022.10

수록면: 479 - 488 (10page)

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To account for the internal thermal effects of solid-state lasers, a method using a back propagation (BP) neural network integrated with a particle swarm optimization (PSO) algorithm is developed, which is a new wavefront distortion correction technique. In particular, by using a slab laser model, a series of fiber pumped sources are employed to form a controlled array to pump the gain medium, allowing the internal temperature field of the gain medium to be designed by altering the power of each pump source. Furthermore, the BP artificial neural network is employed to construct a nonlinear mapping relationship between the power matrix of the pump array and the thermally induced wavefront aberration. Lastly, the suppression of thermally induced wavefront distortion can be achieved by changing the power matrix of the pump array and obtaining the optimal pump light intensity distribution combined using the PSO algorithm. The minimal beam quality β can be obtained by optimally distributing the pumping light. Compared with the method of designing uniform pumping light into the gain medium, the theoretically computed single pass beam quality β value is optimized from 5.34 to 1.28. In this numerical analysis, experiments are conducted to validate the relationship between the thermally generated wavefront and certain pumping light distributions.

#Back propagation neural network #Particle swarm optimization algorithm #Pump array #Wavefront distortion

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