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Neural Networks Applications and Electronics Development for Nuclear Fusion Neutron Diagnostics
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy. (Plasma Physics and Nuclear Fusion)
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis describes the development of electronic modules for fusion neutron spectroscopy as well as several implementations of artificial neural networks (NN) for neutron diagnostics for the Joint European Torus (JET) experimental reactor in England.

The electronics projects include the development of two fast light pulser modules based on Light Emitting Diodes (LEDs) for the calibration and stability monitoring of two neutron spectrometers (MPRu and TOFOR) at JET. The particular electronic implementation of the pulsers allowed for operation of the LEDs in the nanosecond time scale, which is typically not well accessible with simpler circuits. Another electronic project consisted of the the development and implementation at JET of 32 high frequency analog signal amplifiers for MPRu. The circuit board layout adopted and the choice of components permitted to achieve bandwidth above 0.5 GHz and low distortion for a wide range of input signals. The successful and continued use of all electronic modules since 2005 until the present day is an indication of their good performance and reliability.

The NN applications include pulse shape discrimination (PSD), deconvolution of experimental data and tomographic reconstruction of neutron emissivity profiles for JET. The first study showed that NN can perform neutron/gamma PSD in liquid scintillators significantly better than other conventional techniques, especially for low deposited energy in the detector. The second study demonstrated that NN can be used for statistically efficient deconvolution of neutron energy spectra, with and without parametric neutron spectroscopic models, especially in the region of low counts in the data. The work on tomography provided a simple but effective parametric model for describing neutron emissivity at JET. This was then successfully implemented with NN for fast and automatic tomographic reconstruction of the JET camera data.

The fast execution time of NN, i.e. usually in the microsecond time scale, makes the NN applications presented here suitable for real-time data analysis and typically orders of magnitudes faster than other commonly used codes. The results and numerical methods described in this thesis can be applied to other diagnostic instruments and are of relevance for future fusion reactors such as ITER, currently under construction in Cadarache, France.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis , 2009. , p. 126
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 673
Keywords [en]
Neural networks, tomography, unfolding, real time, pulse shape discrimination, PSD, neutron spectroscopy, MPRu, TOFOR, KN3, neutron camera, LED, summing amplifiers, electronics, JET
National Category
Fusion, Plasma and Space Physics Other Physics Topics
Research subject
Applied Nuclear Physics; Electronics
Identifiers
URN: urn:nbn:se:uu:diva-108583ISBN: 978-91-554-7613-7 (print)OAI: oai:DiVA.org:uu-108583DiVA, id: diva2:236513
Public defence
2009-11-06, Häggsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1 Polacksbacken, Uppsala, 10:00 (English)
Opponent
Supervisors
Available from: 2009-11-02 Created: 2009-09-23 Last updated: 2013-08-01Bibliographically approved
List of papers
1. A bipolar LED drive technique for high performance, stability and power in the nanosecond time scale
Open this publication in new window or tab >>A bipolar LED drive technique for high performance, stability and power in the nanosecond time scale
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2009 (English)In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 599, no 2-3, p. 243-247Article in journal (Refereed) Published
Abstract [en]

Pulsed light sources are often used to monitor the stability of light detectors such as photomultiplier tubes. Light emitting diodes (LEDs) are suitable for this due to their high specific light yield. While pulsed operation in the region of [mu]s is generally accessible with most LEDs and drivers, the ns time scale often represents a technical challenge. This paper describes a technique of bipolar LED drive that can produce light pulses of a few ns at high stability, reliability and power. The driver also offers control over the properties of the light pulse produced such as shape, intensity and repetition rate. This approach has been studied in 2003 and implemented in 2004 for two fusion neutron spectrometers at the Joint European Torus (JET) namely the Magnetic Proton Recoil upgrade (MPRu) and the Time Of Flight Optimized for Rate (TOFOR). A driver has been manufactured and connected to the scintillation detectors of each spectrometer through an optical fiber distribution network. Both MPRu and TOFOR have been successfully relying on this system for calibration and performance monitoring for several years, confirming the long-term stability and reliability of this technique.

Keywords
LED, Driver, Nanosecond, Fast, Bipolar, High stability, High power, JET, MPRu, TOFOR
National Category
Physical Sciences
Identifiers
urn:nbn:se:uu:diva-129629 (URN)10.1016/j.nima.2008.11.001 (DOI)000263706500018 ()
Available from: 2010-08-19 Created: 2010-08-19 Last updated: 2017-12-12Bibliographically approved
2. Development and implementation of pulse summing amplifier modules for the MPRu fusion neutron spectrometer at JET
Open this publication in new window or tab >>Development and implementation of pulse summing amplifier modules for the MPRu fusion neutron spectrometer at JET
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(English)Manuscript (preprint) (Other academic)
National Category
Physical Sciences
Identifiers
urn:nbn:se:uu:diva-109477 (URN)
Available from: 2009-10-15 Created: 2009-10-15 Last updated: 2010-01-14Bibliographically approved
3. A neural network pulse shape discrimination and pile-up rejection framework for the BC501 neutron/gamma liquid scintillator
Open this publication in new window or tab >>A neural network pulse shape discrimination and pile-up rejection framework for the BC501 neutron/gamma liquid scintillator
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2009 (English)In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 610, no 2, p. 534-539Article in journal (Refereed) Published
Abstract [en]

BC-501 is a liquid scintillation detector sensitive to both neutrons and gamma rays. As these produce slightly different signals in the detector, they can be discriminated based on their pulse shape (Pulse Shape Discrimination, PSD). This paper reports on results obtained with several PSD techniques and compares them with a method based on artificial neural networks (NN) developed for this application. Results indicated a large performance advantage of NN especially in the region of small deposited energy which typically contains the majority of the events. NN were also applied for discrimination of pile-up events with good results. This framework can be implemented on some of the most recent programmable data acquisition cards and it is suitable for real-time application.

National Category
Natural Sciences
Identifiers
urn:nbn:se:uu:diva-109469 (URN)10.1016/j.nima.2009.08.064 (DOI)
Available from: 2009-10-15 Created: 2009-10-15 Last updated: 2017-12-12
4. Applications of neural networks for free unfolding of experimental data from fusion neutron spectrometers
Open this publication in new window or tab >>Applications of neural networks for free unfolding of experimental data from fusion neutron spectrometers
2008 (English)In: Computational Intelligence in Decision and Control: Proceedings of the 8th International FLINS Conference, Madrid, Spain, 21-24 September 2008, World Scientific Pub Co Inc , 2008Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
World Scientific Pub Co Inc, 2008
Series
World Scientific Proceedings Series on Computer Engineering and Information Science
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:uu:diva-109478 (URN)978-9812799463 (ISBN)
Conference
8th International FLINS Conference, Madrid, Spain, 21-24 September 2008
Available from: 2009-10-15 Created: 2009-10-15 Last updated: 2018-01-12Bibliographically approved
5. A Neural Networks Framework for Real-Time Unfolding of Neutron Spectroscopic Data at JET
Open this publication in new window or tab >>A Neural Networks Framework for Real-Time Unfolding of Neutron Spectroscopic Data at JET
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2008 (English)In: Review of Scientific Instruments, ISSN 0034-6748, E-ISSN 1089-7623, Vol. 79, no 10, p. 10E513-Article in journal (Refereed) Published
Abstract [en]

A determination of fast ion population parameters such as intensity and kinetic temperature is important for fusion reactors. This becomes more challenging with finer time resolution of the measurements, since the limited data in each time slice cause increasing statistical variations in the data. This paper describes a framework using Bayesian-regularized neural networks (NNs) designed for such a task. The method is applied to the TOFOR 2.5 MeV fusion neutron spectrometer at JET. NN training data are generated by random sampling of variables in neutron spectroscopy models. Ranges and probability distributions of the parameters are chosen to match the experimental data. Results have shown good performance both on synthetic and experimental data. The latter was assessed by statistical considerations and by examining the robustness and time consistency of the results. The regularization of the training algorithm allowed for higher time resolutions than simple forward methods. The fast execution time makes this approach suitable for real-time analysis with a time resolution limit in the microsecond time scale.

National Category
Physical Sciences
Identifiers
urn:nbn:se:uu:diva-16763 (URN)10.1063/1.2953492 (DOI)000260573500095 ()
Note
JET-EFDA Contributors. Conference information: 17th Topical Conference High Temperature Plasma Diagnostics, May 2008, Albaquerque, New Mexico, USAAvailable from: 2008-06-05 Created: 2008-06-05 Last updated: 2017-12-08Bibliographically approved
6. A parametric model for fusion neutron emissivity tomography for the KN3 neutron camera at JET
Open this publication in new window or tab >>A parametric model for fusion neutron emissivity tomography for the KN3 neutron camera at JET
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2010 (English)In: Nuclear Fusion, ISSN 0029-5515, E-ISSN 1741-4326, Vol. 50, no 3, p. 035008-Article in journal (Refereed) Published
Abstract [en]

A parametric model for fusion neutron emissivity is presented and applied to the KN3 neutron camera data collected during the trace tritium experiment at the Joint European Torus. This work is aimed at achieving a good compromise between accuracy of tomographic reconstruction and low model complexity. This means low numerical degeneracy and good time consistency of the results. The model is compared both with plasma simulation codes and other tomographic techniques, which use KN3 line integrated emissivity data, showing good agreement over the entire data set analysed approximate to‰ˆ 500 plasma discharges).

National Category
Physical Sciences
Identifiers
urn:nbn:se:uu:diva-129635 (URN)10.1088/0029-5515/50/3/035008 (DOI)000275619700022 ()
Available from: 2010-08-19 Created: 2010-08-19 Last updated: 2017-12-12Bibliographically approved
7. Neural networks based neutron emissivity tomography at JET with real-time capabilities
Open this publication in new window or tab >>Neural networks based neutron emissivity tomography at JET with real-time capabilities
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2010 (English)In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 613, no 2, p. 295-303Article in journal (Refereed) Published
Abstract [en]

Tomographic reconstruction techniques typically require computationally intensive algorithms which are not suitable for real-time application. This paper describes a framework to perform neutron emissivity tomography at the Joint European Torus (JET) using neural networks with successful results over a broad range of magnetic configurations, heating and fueling schemes. Application times in the [mu]s time scale allows for real-time applicability of the method.

Keywords
KN3, Neutron camera, Neutron profile monitor, Neutron emissivity, Neural networks, Real-time, Trace tritium experiment, TTE
National Category
Physical Sciences
Identifiers
urn:nbn:se:uu:diva-129627 (URN)10.1016/j.nima.2009.12.023 (DOI)000274882000018 ()
Available from: 2010-08-19 Created: 2010-08-19 Last updated: 2017-12-12Bibliographically approved

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