Rapid Prediction of Aero-optical Effects of Laser Turret Based on Residual Neural Networks

doi:10.19596/j.cnki.1001-246x.8853

Abstract

Abstract:

The residual neural network is used to carry out machine learning on the steady-state flow field of the hemisphere-on-cylinder laser turret model in the range of Ma=0.3~0.8, and the subsonic/transonic flow field under any incoming flow conditions in this range is established. The prediction accuracy of this model is evaluated for beam wavefront distortion under different view-of-field angles. The learning model reproduces flow characteristics such as boundary layers, flow separation, and separated shear layers in turret flows, including in particular unanchored shock discontinuities in transonic flow. The wavefront distribution based on the predicted flow field under different viewing angles is basically consistent with that calculated based on the flow field of CFD. This machine learning method provides a strategy for adaptive correction of laser turret aero-optical effects in the engineering field.

Key words: laser turret, aero-optic effect, transonic flow, machine learning, residual neural network

Zhouweiyu CHEN, Xiang REN, Feizhou ZHANG, Tongxiang GU. Rapid Prediction of Aero-optical Effects of Laser Turret Based on Residual Neural Networks[J]. Chinese Journal of Computational Physics, 2025, 42(2): 160-170.

Figures/Tables 12

Fig.1 Turret model and direction of laser beam emission

Fig.2 Mach number and density distribution on center section of turret under partial conditions with Ma∞=0.3~0.8

Fig.3 Basic structure of residual neural network

Table 1 Parameters of three neural networks

网络	NN-1	NN-2	NN-3
输入	(x, y, z, Ma_∞, ρ_∞)	(x, y, z, Ma_∞, ρ_∞, SDF)	(x, y, z, Ma_∞, ρ_∞, SDF)
网络结构	FC(5, 64)	FC(6, 64)	FC(6, 64)
	ResBlock(32, 64)×4	ResBlock(32, 64)×4	ResBlock(32, 64)×6
	FC(64, 2)
输出	(Ma, ρ)

Fig.4 Predicted flow fields for freestream Ma∞=0.42 by residuals neural network without field-specific and CFD (a) residuals neural network; (b) CFD

Fig.5 Training results of three neural networks on Mach number field of CFD

Fig.6 Training results of three neural networks on density field of CFD

Fig.7 Flow field distribution near turret predicted by three neural networks at the freestream flow of Ma∞=0.42

Fig.8 Flow field distribution near turret predicted by three neural networks at the freestream flow of Ma∞=0.76

Fig.9 Wavefront distribution predicted by the third neural network at different α (a) 30°; (b) 60° and (c) 90° with Ma∞=0.42

Fig.10 Wavefront distribution of α=30°, 60° and 90° predicted by the third neural networks with freestream flow Ma∞=0.76

Fig.11 Wavefront distortion OPDrms predicted by the third neural networks at different viewing window angles (α=30°-100°) with different freestream flows (a) Ma∞=0.42 and (b) Ma∞=0.76

References 34

1	GORDEYEV S , JUMPER E . Fluid dynamics and aero-optics of turrets[J]. Progress in Aerospace Sciences, 2010, 46 (8): 388- 400. DOI
2	WANG Meng , MANI A , GORDEYEV S . Physics and computation of aero-optics[J]. Annual Review of Fluid Mechanics, 2012, 44, 299- 321. DOI
3	JUMPER E J , GORDEYEV S . Physics and measurement of aero-optical effects: Past and present[J]. Annual Review of Fluid Mechanics, 2017, 49 (1): 419- 441. DOI
4	MOLER J L, LAMBERSON S E. Airborne laser (ABL): A legacy and a future for high-energy lasers[C]//Gas and Chemical Lasers and Intense Beam Applications. Bellingham: SPIE, 1998: 99-105.
5	PERRAM G P, MARCINIAK M A, GODA M. High energy laser weapons: Technology overview[C]//Laser Technologies for Defense and Security, Bellingham: SPIE, 2004: 1-25.
6	JUMPER E J , ZENK M , GORDEYEV S , et al. Airborne aero-optics laboratory[J]. Optical Engineering, 2013, 52, 071408. DOI
7	JUMPER E J, GORDEYEV S, CAVALIERI D, et al. Airborne aero-optics laboratory-transonic(AAOL-T)[C]//53rd AIAA Aerospace Sciences Meeting. Kissimmee: American Institute of Aeronautics and Astronautics, 2015: 0675.
8	GORDEYEV S, VOROBIEV A, JUMPER E J, et al. Studies of flow topology around hemisphere at transonic speeds using time-resolved oil flow visualization[C]//54th AIAA Aerospace Sciences Meeting. San Diego: American Institute of Aeronautics and Astronautics, 2016: 1459.
9	NATARAJAN K , KUMAR R . Experimental characterization of supersonic flow over a wall-mounted hemisphere[J]. AIAA Journal, 2021, 59 (11): 4486- 4500. DOI
10	ROEDER A L , GORDEYEV S . Wake response downstream of a spanwise-oscillating hemispherical turret[J]. Journal of Fluids and Structures, 2022, 109, 103470. DOI
11	KIRIAKOS R M , RIVERO M J , KHAMSEH A P , et al. Unsteady motion in the supersonic flow over a wall-mounted hemisphere[J]. AIAA Journal, 2022, 60 (2): 688- 698. DOI
12	POND J E , SUTTON G W . Aero-optic performance of an aircraft forward-facing optical turret[J]. Journal of Aircraft, 2006, 43 (3): 600- 607. DOI
13	LADD J, MANI M, BOWER W. Validation of aerodynamic and optical computations for the unsteady flow field about a hemisphere-on-cylinder turret[C]//27th AIAA Applied Aerodynamics Conference. San Antonio: American Institute of Aeronautics and Astronautics, 2009: 4118.
14	MORGAN P E , VISBAL M R . Hybrid reynolds-averaged Navier-Stokes/large-eddy simulation investigating control of flow over a turret[J]. Journal of Aircraft, 2012, 49 (6): 1700- 1717. DOI
15	JELIC R , SHERER S , GREENDYKE R . Simulation of various turrets at subsonic and transonic flight conditions using OVERFLOW[J]. Journal of Aircraft, 2013, 50 (2): 398- 409. DOI
16	COIRIER W J, WHITELEY M, GOORSKEY D J, et al. Aero-optical evaluation of notional turrets in subsonic, transonic and supersonic regimes[C]//45th AIAA Plasma dynamics and Lasers Conference. Atlanta: American Institute of Aeronautics and Astronautics, 2014: 2355.
17	REN Xiang , YU Huahua , YAO Xianghong , et al. Shock boundary layer interaction and aero-optical effects in a transonic flow over hemisphere-on-cylinder turrets[J]. International Journal of Aerospace Engineering, 2022, 2022, 3397763.
18	LUCCA N D, GORDEYEV S, MORRIDA J J, et al. Modal analysis of the surface pressure field around a hemispherical turret using pressure sensitive paint[C]//2018 AIAA Aerospace Sciences Meeting. Kissimmee: American Institute of Aeronautics and Astronautics, 2018: 0932.
19	SAHBA S , SASHIDHAR D , WILCOX C C , et al. Dynamic mode decomposition for aero-optic wavefront characterization[J]. Optical Engineering, 2022, 61, 013105.
20	WERBOS P J . Backpropagation through time: What it does and how to do it[J]. Proceedings of the IEEE, 1990, 78 (10): 1550- 1560. DOI
21	YNAG Yuting, JIAO Licheng, LIU Xu, et al. Transformers meet visual learning understanding: A comprehensive review[EB/OL]. [2022-03-04]. https://arxiv.org/abs/2203.12944.
22	WONG Manfai , GUO Shangxin , HANG Chingnam , et al. Natural language generation and understanding of big code for AI-Assisted programming: A review[J]. Entropy, 2023, 25 (6): 888. DOI
23	张庆福, 姚军, 黄朝琴, 等. 裂缝性介质多尺度深度学习模型[J]. 计算物理, 2019, 36 (6): 665- 672. DOI
24	郑书昱, 彭佳臻, 张先梅, 等. 基于神经网络的托卡马克能量约束时间预测[J]. 计算物理, 2021, 38 (4): 423- 430. DOI
25	吴国正, 王发杰, 程隋福, 等. 基于物理信息神经网络的内部声场正反问题数值计算[J]. 计算物理, 2022, 39 (6): 687- 698. DOI
26	梁炜光, 桑建兵, 田红艳, 等. 基于数据驱动管道流体湍流模型的系数修正及流动特性分析[J]. 计算物理, 2023, 40 (1): 57- 66. DOI
27	SEKAR V , JIANG Qinghua , SHU Chang , et al. Fast flow field prediction over airfoils using deep learning approach[J]. The Physics of Fluids, 2019, 31 (5): 057103. DOI
28	DURU C , ALEMDAR H , BARAN O U . A deep learning approach for the transonic flow field predictions around airfoils[J]. Computers & Fluids, 2022, 236, 105312.
29	LI Jichao , BOUHLEL M A , MARTINS J R R A . Data-based approach for fast airfoil analysis and optimization[J]. AIAA Journal, 2019, 57 (2): 581- 596. DOI
30	CAI Guobiao , ZHANG Baiyi , LIU Lihui , et al. Fast vacuum plume prediction using a convolutional neural networks-based direct simulation Monte Carlo method[J]. Aerospace Science and Technology, 2022, 129, 107852. DOI
31	LECUN Y , BENGIO Y , HINTON G . Deep learning[J]. Nature, 2015, 521 (7553): 436- 444. DOI
32	GOODEFLLOW I , BENGIO Y , COURVILLE A . Deep learning[M]. Cambridge: MIT Press, 2016.
33	HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
34	WOLFE W L , ZISSIS G J . The infrared handbook[M]. Arlington: Office of Naval Research, 1978.

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[4]	WANG Guangxue, DENG Xiaogang, WANG Yuntao, LIU Huayong. High-Order Numerical Simulation of Vortex Breakdown on Delta Wing [J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 2012, 29(4): 489-494.
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[6]	GUO Jia-hong, LIU Gao-lian. VARIABLE DOMAIN VARIATIONAL FINITE ELEMENT APPROACH TO I_A INVERSE PROBLEM OF UNSTEADY TRANSONIC FLOW AROUND OSCILLATING AIRFOILS [J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 2000, 17(5): 518-524.
[7]	Yang Guowei, Li Fengwei, Zhuang Lixiang. A COMPARISON OF EXPLICIT AND IMPLICIT NUMERICAL METHODS FOR UNSTEADY TRANSONIC FLOW [J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 1998, 15(1): 121-127.
[8]	E Qin, Li Jie, Li Fengwei, Liu Jilun, Yu Jianzhong. NUMERICAL SIMULATION OF TRANSONIC FLOW OVER AN ADVANCED TWIN ENGINE TRANSPORT AIRCRAFT [J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 1997, 14(S1): 493-495.
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[10]	Li Fengwei, E Qin. TWO-DIMENSIONAL MESH GENERATION TECHNIQUESAND APPLICATION [J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 1993, 10(2): 155-162.
[11]	Huang Dongtao, Shen Mengyu, Chen Yongliang. A NUMERICAL SIMULATION OF THREE DIMENSIONAL TRANSONIC CASCADE IN VISCID FLOW [J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 1992, 9(2): 111-118.
[12]	Li Feng, Wang Yiyun. THE NUMERICAL SIMULATION FOR TURBULENT TRANSONIC FLOW OVER AIRFOILS [J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 1990, 7(4): 396-402.