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The non-renewable energy sources coal, oil and natural gas that contribute the major share of the world's energy, will be running out in the next 40-80 years. With the growing energy demands especially in developing countries, which is likely to surpass that of the developed countries in next 50 years, an alternate energy source is the need to the hour. The nuclear fusion energy is foreseen as one of the potential candidates to solve the current global energy crisis. One of the major challenges faced by the fusion community is the problem of power exhaust. With the larger fusion devices to be built in the future, the heat load on the plasma facing components are expected to grow higher. The present work explores two numerical studies performed on the Wendelstein 7-X, the world's largest stellarator type fusion device, to cope with this problem.
The first project on `'Numerical Studies on the impact of Connection Length in Wendelstein 7-X'' identifies magnetic configuration with long connection lengths, which could bring down the peak heat fluxes onto the divertor to manageable levels, by greater role of cross-field transport which may assist to get a wider heat deposition profile. The second project on `'Development of Heating Scenario to Reduce the Impact of Bootstrap Currents in Wendelstein 7-X'' advocates a novel self-consistent approach to reach high plasma density at full heating power without overloading the divertor during the transient phase of the evolution of the toroidal plasma current, by controlling two parameters; density and power. The aim of both the projects is to contribute to tackling the challenge of the tremendous power exhaust from fusion plasma which, if solved, will be a large step closer to a fusion power plant.
Detecting changes in plasmas is compulsory for control and the detection of novelties.
Moreover, automated novelty detection allows one to investigate large data sets to substantially
enhance the efficiency of data mining approaches. To this end we introduce permutation entropy
(PE) for the detection of changes in plasmas. PE is an information-theoretic complexity measure
based in fluctuation analysis that quantifies the degree of randomness (resp. disorder,
unpredictability) of the ordering of time series data. This method is computationally fast and
robust against noise, which allows the evaluation of large data sets in an automated procedure.
PE is applied on electron cyclotron emission and soft x-ray measurements in different
Wendelstein 7-X low-iota configuration plasmas. A spontaneous transition to high core-electron
temperature (Te) was detected, as well as a localized low-coherent intermittent oscillation which
ceased when Te increased in the transition. The results are validated with spectrogram analysis
and provide evidence that a complexity measure such as PE is a method to support in-situ
monitoring of plasma parameters and for novelty detection in plasma data. Moreover, the
acceleration in processing time offers implementations of plasma-state-detection that provides
results fast enough to induce control actions even during the experiment.