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Design of diagnosis integrated management
and control system and application of AI
technology in laser experiment Wang Feng
Laser Fusion Research Center, CAEP
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Target diagnostics of ICF
Framework of the ICCS
Applications of AI
Conclusion
Items
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Varied ways to the controlled fusion
ICF
MCF
Enhance ρR by
ablated mass
t~ns
R~μm
Enhance t by
the strong
magnetic field
t~s
R~m
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High Pressure and Astrophysics researches on laser facilities
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1960s,the idea was given by three scientists individually
1980s,a professional team in ICF research was founded in China
Inertial Confinement Fusion(ICF)
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ICF observations
sp
ecie
s
optics
UV
X-ray
proton
neutron
gamma
dim
en
sio
ns
Spatial distribution
~μm
Temporal scale ~ps-ns
flux
spectrum
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Different design, different setup
Shift frequently experiment by experiment
ICF diagnostics
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Diagnostic instrument manipulator
Installed and aligned through the DIM
Face the risk of interference near the center part Chamber
valve
Inner vessel
diagnosis
cart crux
crux bellows linear actuator
NIF Target Chamber
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ICCS on NIF
Integrated Computer Control System(ICCS)
Framework of ICCS system
NIF control room
The main part of ICCS is the facility control subsystem
The control system is comprised of two principle layers—supervisory controls and FEP
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DCS is the subsystem of ICCS
minimal application code, easy testing, and high reliability
Target Diagnostic Control System Implementation
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The micro-Services framework Disperse the apps. in different micro-services, decouple the complicated system
Framework of diagnostic control system(DCS)
Speed up
Flexible
Extensible
Deployable
1st generation: Monolith 2nd generation: SOA 3rd generation: Microservices
• Tight coupled
• Complicated, Intricate
• Repeat : OS,DB, Middleware
• Completely enclosed
• loose coupled
• System integration by ESB
• Stated
• Big Team: 100-200
• TTM: 1year, half year, months
• Centralized, planned downtime
maintenance
• decoupled
• Small Team: 2 Pizza Team
• TTM: Days, weeks
• DevOps: CI, CD, Automatic
• High available: update,
extension without
shutdown
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DCS Architecture
ind
ivid
ua
l
Each service is developed by different team
Efficiency is ensured by target decomposition
relia
ble
The app. and unit element are decoupled
Would not collapse due to single failure service
rep
air
ab
le
Every service is deployed dispersedly
Every services is maintenanced dispersedly
ext
en
sib
le
Arranged in different servers and frameworks
No limitation in language & technology
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DCS has six basic-layers
The layers of DCS
• GUI for the integrated control and
experimental data display;
• DCS controls the instruments through the
distributed communication middleware
• The TANGO agreement is employed
• To lower the difficulties of joining up, both
the controller and instruments are
implemented an object oriented design,
and packaged the communication
protocol of infrastructure
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DCS consists of five individual functions
Functions of the DCS
Designed by Procedure driven
Target diagnostics control
system
System
manager
Instruments
manager
Procedure
manager
Experiment
manager Journalizing
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The controller unit is formed from the basic functions of the instrument, and programming by the operation procedures
To implement the sub-procedures following the experiment control point
Integrated control realized by procedure driven
Experiment setup
diagnosis preparation
diagnosis alignment
Pre-shot Main shot Post
treatment 诊
断系
统瞄靶
DIM连通 DIM预位
光阑开 DIM伸缩
DIM指向
CCD开
内筒照明开
视场切换
瞄准完成确认
光阑关
CCD关
内筒照明关
运动电机关
预设位置更新
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Still many processes need be participated by experimenters
AI technique is the primary candidate transforming to automatic control
Manual operation still needed in the DCS 发
次任
务配
置
激光参数配置
诊断系统参数
配置
发次配置确认
靶参数配置
诊断参数配置
驱动器置靶
示波器开机
诊断系统准备
发次流程配置
状态DIM开机 DIM连通
诊断
系统
复位
靶室占用状态
查询
靶室占
用就位状态确认否
是 DIM零位
DIM
伸缩
插板阀 位置参数配置 方向 DIM径向运动开 缩 位置状态更新
靶室占
用
伸
否
DIM运动异常关 是
指向调节
上运动
位置状态更新
下运动
左运动
右运动
Instrument setup
Instrument aligning
Warning for instrument collision
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Tr is the basic metric of the holhraum energetics physics
Tr is related to the albedo, asymmetry, etc., which will affect the convert efficiency
Holhraum temperature measurement
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Based on the simulation or the historical set
Affect the measurements precision, even damage the OSC
The setup of the oscilloscope
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0D:has approximated, un-precise
2D:depend on parameters,large amount of calculation
Measurements: with uncertainties, many influencing factors
Estimation of the Tr
Calculation based on the power balance(0D)
Simulation with the radiation-hydrodynamics codes(2D)
View factor
Based on the historical measurements
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Based on the PyTorch framework
Training data produced by 0D calculation Varies the laser power, holhraum dimension, LEH diameter etc.
Full connect CNN
Estimation of the peak Tr
Training database-1million
Adjust the parameters based on the CNN performances
After 965 epochs with 2 layers CNN, lose is convergent to 0.183
After 965 epochs with 4 layers CNN, lose is convergent to 0.160
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The relative error is about 4.76% for 4 layers, only 1.24% testing data bias larger than 20%
The relative error is about 11.39 % for 2 layers, 17.67% testing data will bias larger than 20%
Testing of the CNN model
Bias statics of 430 thousand testing data for 4 layers Bias statics of 430 thousand testing data for 2 layers
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Add 0-5% rand noise to the training data
Generalization and Robustness of the model
Robustness to the statics noise
An enhance learning were
performed by adding the
experimental data and 2D
simulation data
Begin to test the RNN model to
estimate the temporal profile
The precise has satisfied the requirement of control system by a simple 4-layers model
More computing resource, more data and RNN model will use for scaling research
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Diagnostics inserted into the cart of DIM to keep the chamber vacuum
Aligned to the target with the binocular visual system
Alignment of the DIM based instrument
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Two sets of binocular lens Binocular set with large VOF,to find the target
Binocular set with high resolution,to aim the target
Alignment procedures
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Different platform, different design and dimension of the targets
Poor lighting, large chamber, low quality of images
Autonomous target detection with machine vision
Traditional machine vision
Less robustness
Easy affected by quality of the
images
Need calibration
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Autonomous target detection with large FOV images by ROI label
Target detection with deep learning
ImageNet for
transfer learning
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Based on the Mask-CNN, aiming the target precisely by semantic segmentation
Target recognition
Produce the training data with 3D
target model and labelled
automatically
Enhanced training with real images
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Usability Simple background
Easy detected
Accuracy 3D model
Difficulties Low light level
Part of the target due to the FOV
High precision requirement
Characters of the auto-alignment
Improved the image quality through image fusion
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Primary problems of the auto-aiming Low quality images due to low light level
Image distortion due to the depth of focus
To improve the aiming precision by improving the image quality with image fusion
Image Fusion by deep learning
redundant information
Complementary information
Sensor A Sensor B
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Convert 2D images to 3D models through images fusion and joint
The instrument location real-time display can displayed through the 3D model and video monitors images arranged in different ports
Warning of the instrument interference
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CR39 data recognized
X-ray spectrum fitting
Scaling law of the implosion performance
Applications of AI in data analysis
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Diagnostics play an great role in ICF experiment researches
The DCS framework based on the micro-services and procedure driven
design has the advantages in easy coding, extension, and reliable
operation etc.
AI is significant for the autonomous control of the DCS, and will
be used in system setup, operation control and data regression
The applications in signal analysis, super-resolution imaging,
instrument health control are under developing by CNN, GAN, RL
Conclusion
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