• Abstract and Figures
• Public Full-text

Pattern recognition in spaces of probability distributions for the analysis of edge-localized modes in tokamak plasmas Aqsa Shabbir M¨unchen 2016 Pattern recognition in spaces of probability distributions for the analysis of edge-localized modes in tokamak plasmas Aqsa Shabbir Doctoral thesis submitted in order to obtain the academic degrees of Doktors der Naturwissenschaften (Dr. rer. nat.) an der Fakult¨atf¨urPhysik der Ludwig-Maximilians-Universit¨atM¨unchen Doctor of Engineering Physics Faculty of Engineering and Architecture Ghent University vorgelegt von Aqsa Shabbir aus Lahore, Pakistan M¨unchen, den 11. May 2016 . Erstgutachter: Prof. Dr. Hartmut Zohm Zweitgutachter: Prof. Dr. Harald Lesch Tag der m¨undlichen Pr¨ufung:07.07.2016 This research work has been performed at: Ghent University Faculty of Engineering and Architecture Department of Applied Physics Sint-Pietersnieuwstraat 41 B-9000 Ghent, Belgium Max-Planck-Institut f¨urPlasmaphysik Boltzmannstraße 2 D-85748 Garching, Germany Joint European Torus Culham Centre for Fusion Energy (CCFE) Abingdon OX14 3DB, United Kingdom i ii Members of the examination committee Prof. Dr. Rik Van de Walle Chairman: Ghent University Prof. Dr. Jean-Marie Noterdaeme Secretary: Ghent University and Max-Planck-Institut f¨urPlasmaphysik Prof. Dr. Geert Verdoolaege Ghent University and Promoters: Royal Military Academy, Brussels Prof. Dr. Hartmut Zohm Max-Planck-Institut f¨urPlasmaphysik Prof. Dr. Ralf Bender Ludwig-Maximilians University of Munich Prof. Dr. Otmar Biebel Ludwig-Maximilians University of Munich Prof. Dr. Gert de Cooman Other members: Ghent University Prof. Dr. Harald Lesch Ludwig-Maximilians University of Munich Prof. Dr. Gregor Morfill Ludwig-Maximilians University of Munich and terraplasma GmbH Dr. Andrea Murari Culham Science Centre and Consorzio RFX (CNR, ENEA, INFN, Acciaierie Venete S.P.A., Universit`adi Padova) iii iv Doctoral guidance committee Prof. Dr. Geert Verdoolaege Ghent University and Promoters: Royal Military Academy, Brussels Prof. Dr. Hartmut Zohm Max-Planck-Institut f¨urPlasmaphysik Dr. Otto J.W.F Kardaun Supervisor: Max-Planck-Institut f¨urPlasmaphysik Prof. Dr. Jean-Marie Noterdaeme Ghent University and Max-Planck-Institut f¨urPlasmaphysik Dr. Andrea Murari Culham Science Centre and Other members: Consorzio RFX (CNR, ENEA, INFN, Acciaierie Venete S.P.A., Universit`adi Padova) v vi Like Attar, Rumi, Razi and Ghazali, One may be mystic, great or wise, But none can reach his goal without hard work and morning sighs Dr. Allama Muhammad Iqbal vii viii Dedicated to my parents Dr. Shahnaz Cheema & Dr. Shabbir Ahmed for being my inspiration, for teaching me the importance of being human, for their sacrificial unconditional love & for encouraging me at each step of life, especially this one ix x Acknowledgements This doctoral thesis would be devoid of its spirit, if it had not been for the invaluable academic, educational, psychological and emotional contribution of several people, whom I would like to thank most sincerely. However, first and foremost I humbly thank Allah Almighty for his countless blessings and for bestowing me with the strength and perseverance required for this endeavor. Next I wish to express heartfelt gratitude for my joint PhD promoters, Prof. Geert Verdoolaege and Prof. Hartmut Zohm. Geert has led by example. He was always available for my questions and gave generously of his time and vast knowledge. It is hard to overstate my appreciation for him, except that this journey would have been impossible without him. Prof. Zohm has been an inspiration and I thank him profoundly for his guidance, enthusiasm and encouragement. My sincere thanks also goes to Dr.Otto Kardaun for his immense knowledge, didactic guidance, patience and motivation. He inspired me with his tireless passion for the field and the stimulating and engaging discussions gave me the impetus to go further during the grueling work days. I would like to express my appreciation and gratitude for Prof. Jean-Marie Noterdaeme and Prof. Guido Van Oost for their support and advice. My special thanks go to Dr. Gregoire Hornung and Frank Jansens. Gregoire is an amazing colleague and I have benefited greatly from many of his formal as well as informal advice. Frank, with his vivaciousness, enabled me to truly appreciate Belgian warmth and hospitality. Special thanks also goes to Kathleen Van Oost for ever so meticulously manging the bureaucratic aspects of my PhD. I would also like to acknowledge my officemates, Patrick Vanraes, and Xiaolong Deng for the many laughters and the many ups and downs which we faced together. A special mention and gratitude is again due for Geert, Gregoire, Frank and Kathleen for the many xi lunches we shared, the fond memories of which will always remain with me. It is imperative to thank all colleagues at the Department of Applied Physics, Ghent University, Max-Planck Institute for Plasma Physics (IPP) and Culham Center for Fusion Energy (CCFE-JET) as well as all the fellow FUSION-DC PhD students, for contributing and becoming a unique part of my PhD journey. I seize this opportunity to also acknowledge and thank my colleagues and superiors at Lahore College for Women University, Lahore and my teachers and classmates at University of Engineering and Technology, Lahore for contributing to my growth, inspiring me and providing me with invaluable experiences. Finally, I cannot agree more with a wise man who once said: family is not an important thing, it is everything. I want to humbly thank my late maternal grandparents, Chahudhry Rehmatullah Cheema and Afzal Begum, who were true visionaries and who laid the foundation for a series of strong, empowered and educated women. No words can describe my gratitude for my parents Dr. Shahnaz Cheema and Dr. Shabbir Ahmed. Mama , Papa, I owe it all to you. Next, I want to thank my best friend (my jiggy) Yasmin Ansari for understanding me, accepting me, supporting me and for being my sister, that I never had. I am truly fortunate to have you in my life. Moving on to the most special corner of my heart, I want to thank my daughter, my princess and my little fairy, Fatima Ali, for being my strength and my biggest motivation. Next, I want to thank my son, Mustafa Ali, who accompanied me in my womb as I attended conferences, worked on manuscripts and conducted experiments. Thank you for being our little bundle of joy. And, finally I thank my husband, Ali Hussain Kazim, for his patience, love, friendship, humor and support. Having you as my husband, is a constant reminder that Allah is happy with me. Thanks for everything! Aqsa Shabbir En route Ghent to Munich 22 April 2016 xii Abstracts English summary Magnetically confined fusion plasmas provide several data analysis challenges due to the occurrence of massive data sets, substantial measurement uncertainty, stochasticity and data dimensionality, and often nonlinear interactions between measured quantities. Recently, methods from the fields of machine learning and probability theory|some standard, some more advanced|have come to play an increasingly important role in analyzing data from fusion experiments. The capabilities offered by such methods to efficiently extract, possibly in real time, additional information from the data that is not immediately apparent to human experts, has attracted attention from an increasing number of researchers. In addition, innovative methods for real-time data processing can play an important role in plasma control, in order to ensure safe and reliable operation of the machine. Pattern recognition is a discipline within the information sciences that concerns the exploration of structure in (multidimensional) data sets using computer-based methods and algorithms. In this doctoral work, pattern recognition techniques are developed and applied to data from tokamak plasmas, in order to contribute to a systematic analysis of edge-localized modes (ELMs). ELMs are magnetohydrodynamic (MHD) instabilities occurring in the edge region of high-confinement (H-mode) fusion plasmas. The type I ELMy H-mode is the reference scenario for operation of the next-step fusion device ITER. On the one hand, ELMs have a beneficial effect on plasma operation through their role in impurity control. On the other hand, ELMs eject energy and particles from the plasma and, in ITER, large unmitigated ELMs are expected to cause intolerable heat loads on the plasma-facing components (PFCs). In interpreting experiments focused on ELM understanding and control, a xiii significant challenge lies in handling the measurement uncertainties and the inherent stochasticity of ELM properties. In this work, we employ probabilistic models (distributions) for a quantitative data description geared towards an enhanced systematization of ELM phenomenology. Hence, we start from the point of view that the fundamental object resulting from the observation of a system is a probability distribution, with every single measurement providing a sample from this distribution. We argue that, particularly for richly stochastic phenomena like ELMs, the probability distribution of physical quantities contain significantly more information compared to mere averages. Consequently, in exploring the patterns emerging from the various ELM regimes and relations, we need methods that can handle the intrinsic probabilistic nature of the data. The original contributions of this work are twofold. First, several novel pattern recognition methods in non-Euclidean spaces of probability distribution functions (PDFs) are developed and validated. The second main contribution lies in the application of these and other techniques to a systematic analysis of ELMs in tokamak plasmas. In regard to the methodological aims of the work, we employ the framework of information geometry to develop pattern visualization and classification methods in spaces of probability distributions. In information geometry, a family of probability distributions is considered as a Riemannian manifold. Every point on the manifold represents a single PDF and the distribution parameters provide local coordinates on the manifold. The Fisher information plays the role of a Riemannian metric tensor, enabling calculation of geodesic curves on the surface.

Load more

  254

Copy Link