STRIKE Heat Flux Reconstruction by Using Neural Networks: Application to the Experimental Results

Abstract

To characterize the SPIDER negative ion beam in terms of beam uniformity and divergence during short pulse operations, an instrumented calorimeter named short-time retractable instrumented kalorimeter experiment (STRIKE) has been designed and operated. STRIKE is made of 16 1D carbon fiber-carbon composite (CFC) tiles, intercepting the whole beam and observed on the rear side by two infrared (IR) cameras. To know the energy flux profile hitting the front surface and then the beam parameters, it is necessary to solve an inverse non-linear problem, mathematically ill-posed, upon knowing the non-linear characteristics of the tiles and the 2-D temperature pattern measured on the rear side of the tiles themselves. Most of the conventional methods used to solve this inverse problem are unbearably time-consuming; when fully operative STRIKE receives 1280 beamlets, each one characterized by at least five features, a ready-to-go tool to determine the beam condition is highly recommended. In this work, the inverse problem in stationary conditions is faced by using a neural network (NN) model, pursuing different training approaches. The NN is trained by associating features extracted from the 2-D temperature profile, obtained by a fitting process to the heat flux profile parameters. The proposed method is then applied to experimental STRIKE data from the beam campaigns.