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Designing a CODAC for Compass. Presented by: André Sancho Duarte. Outline. Introduction to the CODAC concept Compass Tokamak CODAC in modern fusion experiments Issues Needs Solutions CODAC implementations Firesignal Other examples Application to Compass. CODAC System. - PowerPoint PPT Presentation
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Designing a CODAC for Compass
Presented by:André Sancho Duarte
Outline
• Introduction to the CODAC concept• Compass Tokamak• CODAC in modern fusion experiments
– Issues– Needs– Solutions
• CODAC implementations– Firesignal– Other examples
• Application to Compass
9 October 2008, European Doctorate on Fusion Science and Engineering2
CODAC System
Control, Data Access and CommunicationsSystem for:• Control
– Experiment configuration– Support systems configuration
• Data Acquisition and Retrieval• Communications
– Remote Participation
9 October 2008, European Doctorate on Fusion Science and Engineering3
CODAC Diagram for ITER
9 October 2008, European Doctorate on Fusion Science and Engineering4
Compass Tokamak
• Major radius 0.56 m
• Minor radius 0.18 – 0.23 m
• Plasma current < 350 kA
• Magnetic field 1.2 or 2.1 T
• Triangularity ~ 0.5 - 0.7
• Elongation ~ 1.8
• Pulse length < 1 s
• PLH, 1.3 GHz < 0.4 MW
• PNBI 2 0.3 MW
9 October 2008, European Doctorate on Fusion Science and Engineering
CODAC for Compass
• The development of a control and data acquisition system for Compass represents an opportunity to test ITER relevant solutions
• The following areas are planned to test in Compass
– Remote maintenance/upgrade of the control software and re-programmable hardware.
– Automatic/interactive installation and deployment of instrumentation hardware.
– Formal self-description of plant systems, including diagnostic systems, using the XML set of technologies.
– Fast, real-time multivariable (MIMO) plasma controllers.
– Online data reduction as an option or in parallel to raw data storage on large memories.
9 October 2008, European Doctorate on Fusion Science and Engineering6
Modern Fusion Experiments
• Pulse duration over 1 second– Expectation of human intervention
• Around 50 diagnostics, some very complex• Over 100 MB/s of data per diagnostic
– Example: Rogowsky coils in Compass can produce 256 MB/s (32 channels of 4 bytes @ 2 Msamples/s)
• Small number of pulses during a campaign• Constant monitoring of the machine and its
envolving
9 October 2008, European Doctorate on Fusion Science and Engineering7
Typical Experiment Flow Chart
9 October 2008, European Doctorate on Fusion Science and Engineering8
Desired Experimental Chart
9 October 2008, European Doctorate on Fusion Science and Engineering9
Issues- Collected Data (1/3)
• The size of the data collected can cause data transport and storage issues and increment of the operation cycle-time beyond the machine constrains– Implement faster data transport to comply with
machine cycle-time (use of new generation faster data transport networks)
– Higher-speed real-time pulse processing both during and after shot?
– Implement event-driven data acquisition operation– Data is acquired or actions performed (e.g. change
acquisition rate) only when relevant events occur– Provide data compression capability into the
diagnostics (less data to store and faster data transfer)
9 October 2008, European Doctorate on Fusion Science and Engineering10
Issues- Collected Data (2/3)
• Some diagnostics require high sampling frequencies; current technical capabilities may be exceeded when operating for large periods– Use of standards-based fast data transfer on the
data paths (e.g. PCIe)– Use of local fast memory with sizes of several GB
and bandwidth of GB/s– Use of data compression when bandwidth
bottlenecks still remain
9 October 2008, European Doctorate on Fusion Science and Engineering11
Issues- Collected Data (3/3)
Data reduction techniques:• Data Compression:
– Lossless algorithms• Keep all the data• Fast compression and decompression available• Typical data can be highly compressed
– Loss algorithms can provide extra compression• Can provide extra compression for specific data
• Variable acquisition rates– Good for events localized in time– Data loss for unexpected events
9 October 2008, European Doctorate on Fusion Science and Engineering12
Issues – RT Data Processing (1/2)
• Higher RTC processing power required for local data compression or reduction, monitoring of diagnostic output and generation of plasma control variables– Use of processors with parallel processing
capabilities, high-throughput and low latency (multi-core CPUs, FPGAs, DSP …)
– hardware processors included on the digitizers can process and manage RTC high throughput data flow and perform preliminary basic algorithms or data compression/reduction
– Use of data processing units where various boards are interconnected through a full-mesh topology network having low-latency and high bandwidth
9 October 2008, European Doctorate on Fusion Science and Engineering13
Issues – RT Data Processing (2/2)
• New diagnostics and plasma controllers may require an updated real-time control and monitoring infrastructure.– Higher algorithm complexity and higher number
of input signals– Lower loop delays for time-critical real-time
control and distribution of plasma variables and events (sometimes under 10 µs)
– Better timing, synchronization and RT messages networks.
9 October 2008, European Doctorate on Fusion Science and Engineering14
Issues – Digital Instrumentation
9 October 2008, European Doctorate on Fusion Science and Engineering
Issues Actions
Multitude of different hardware platforms to maintain on the sub-systems. A comprehensive platform easier to deploy and maintain is required.
Design of a ‘generic’ sub-system platform capable of operate as a local controller, a feedback controller and/or a data acquisition unit.
Improve systems availability and reliability
operation for long periods or in real-time
radiation environment
Standards based instrumentation with inherent redundancy and mechanical/thermal characteristics.
Implement electronics redundancy
Malfunction detection/correction.
Local and global management of hardware operation (on enclosures, boards and components) is required to improve maintainability. Required operations are:
- Measurement of temperatures
- Cooling control
- Energy management
- Bus management
Specification of a standards based improved hardware management interface (e.g. Inteligent Platform Management Interface (IPMI), Shelf (crate) management (ShM).
Implement a hardware management infrastructure testbench.
Easier installation/replacement of hardware modules Hardware interface designed for ‘Plug-and-Play’, and ‘Hot-swap’.
Implementation of a prototype of the hardware description using XML
Innovation on Instrumentation
• The referred requirements reveal the importance of a platform capable of providing:– High-throughput real-time hardware signal processors
at the acquisition level– Low-latency serial gigabit full-mesh interconnection
between cards– Integrated RTC event-based acquisition, operation
and storage– Integrated synchronism of all digitizer
• Presently the ATCA based instrumentation is a good candidate
• ATCA systems are expected to become the backbone of the CODAC in Compass
9 October 2008, European Doctorate on Fusion Science and Engineering16
Existing CODACs for Long Pulses (1/2)
• LHD (Japan)– Based on PC cluster– Communication through TCP/IP– VXI based systems– Data Streaming (10 s slices)– Lossless data compression (ZLIB and JPEG-LS)– Two stage backup– Web interface for data analysis
9 October 2008, European Doctorate on Fusion Science and Engineering17
Existing CODACs for Long Pulses (2/2)
• EAST (China)– Distributed data system– Communications via TCP/IP network– CAMAC and PCI based systems– Data streaming (5 s slices)– Data compression with LZO– Windows software for data analysis
9 October 2008, European Doctorate on Fusion Science and Engineering18
The Firesignal System
• Modular client/server approach with XML plant description/ systems integration.
• Standalone operation or interfaced with other CODACs.
• Event-driven/Steady State Operation on absolute time.
• User friendly interface with remote management and participation = control room spread over campus/web.
• Easy and universal integration (Matlab, IDL, SciLab, C, Java, Python...).
• Modules connected through CORBA run in various OS.
• Plug&Play and HotSwap of hardware
9 October 2008, European Doctorate on Fusion Science and Engineering19
Conclusions
• Modern fusion experiments share common needs and issues regarding control and data acquisition
• Technological developments in hardware and software allow us to address them efficiently
• Existing CODACs have implemented with success many of these technologies
• Compass provides an excellent platform for deploying and testing the ideas here presented.
• It is desirable for the new CODAC to be flexible, in order to accommodate new developments
9 October 2008, European Doctorate on Fusion Science and Engineering20
Improvements on Firesignal
Issues Improvements
Data transmission bottleneck Data transmitted through TCP/IP Support to data streamingDistributed serverVariable acquisition ratesData compression
Assumes all data with same size and equally spaced in time
Improved support for other types of data
Event support added later somewhat poor event management
Event support from start
Designed mainly for data acquisition More flexible support of mixed acquisitions and real-time control boards
Hardware clients need to be restarted after “hot-swap”
Intrinsic support to “hot-swap”
9 October 2008, European Doctorate on Fusion Science and Engineering21
SUPPORT SLIDES
9 October 2008, European Doctorate on Fusion Science and Engineering22
Data Compression
9 October 2008, European Doctorate on Fusion Science and Engineering
Diagnostic/ShotFile Size(MBytes)
Deltacompression
over time (%)
Deltacompression
overframes(rows)
Deltacompression
overframes(columns)
WinZipCompress
(Unix)
KL7/65140 66.7 92.78 92.05 92.04 82.21 78.30
KL7/70231 232.5 94.36 94 .65 85.39 92.09 91.50
KL8/69787 192.8 88.80 90.48 90.11 77.95 75.17
KL8/70231 69.3 94.90 95.31 95.28 96.99 97.40
KL8/70398 138.8 88.26 92.77 92.45 87.27 85.25
JET’s Fast Camera. Results provided by Jesús Vega (CIEMAT/ES)
L.Ying, L. Jiarong, L. Guiming, Z. Yingfei, L. Shia, The EAST Distributed Data System, Fusion Eng. Des. 82 (2007) 339 - 343