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Fusion Science and Technology
Latest News
A more open future for nuclear research
A growing number of institutional, national, and funder mandates are requiring researchers to make their published work immediately publicly accessible, through either open repositories or open access (OA) publications. In addition, both private and public funders are developing policies, such as those from the Office of Science and Technology Policy and the European Commission, that ask researchers to make publicly available at the time of publication as much of their underlying data and other materials as possible. These, combined with movement in the scientific community toward embracing open science principles (seen, for example, in the dramatic rise of preprint servers like arXiv), demonstrate a need for a different kind of publishing outlet.
A. Sengupta, P. Ranjan
Fusion Science and Technology | Volume 39 | Number 1 | January 2001 | Pages 1-17
Technical Paper | doi.org/10.13182/FST01-A146
Articles are hosted by Taylor and Francis Online.
In this paper, we examine the possibility of using a multilayered feedforward neural network to extract tokamak plasma parameters from magnetic measurements as an improvement over the traditional methodology of function parametrization. It is also used to optimize the number and locations of the magnetic diagnostics designed for the tokamak. This work has been undertaken with the specific purpose of application of the neural network technique to the newly designed (and currently under fabrication) Superconducting Steady-State Tokamak-1 (SST-1). The magnetic measurements will be utilized to achieve real-time control of plasma shape, position, and some global profiles. A trained neural network is tested, and the results of parameter identification are compared with function parametrization. Both techniques appear well suited for the purpose, but a definite improvement with neural networks is observed. Although simulated measurements are used in this work, confidence regarding the network performance with actual experimental data is ensured by testing the network's noise tolerance with Gaussian noise of up to 10%. Finally, three possible methods of ranking the diagnostics in decreasing order of importance are suggested, and the neural network is used to optimize the number and locations of the magnetic sensors designed for SST-1. The results from the three methods are compared with one another and also with function parametrization. Magnetic probes within the plasma-facing side of the outboard limiter have been ranked high. Function parametrization and one of the neural network methods show a distinct tendency to favor the probes in the remote regions of the vacuum vessel, proving the importance of redundancy. Fault tolerance of the optimized network is tested. The results obtained should, in the long run, help in the decision regarding the final effective set of magnetic diagnostics to be used in SST-1 for reconstruction of the control parameters.
