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TY - CONF AU - Donegani, E.M. ED - Kim, Changbum ED - Schaa, Volker R. W. ED - Kim, Dong-Eon ED - Lee, Jaeyu TI - Machine-Learning Based Temperature Prediction for Beam-Interceptive Devices in the ESS Linac J2 - Proc. of IBIC2021, Pohang, Rep. of Korea, 24-28 May 2021 CY - Pohang, Rep. of Korea T2 - International Beam Instrumentation Conference T3 - 10 LA - english AB - ’Where there is great power [density], there is great responsibility*.’ The concept holds true especially for beam-intercepting devices for the ESS linac commissioning. In particular, beam-intercepting devices will be subject to challenging beam power densities, stemming from proton energies up to 2 GeV, beam currents up to 62.5 mA, pulses up to few milliseconds long, and repetition rates up to 14 Hz. Dedicated Monte Carlo simulations and thermo-mechanical calculations are necessarily part of the design workflow, but they are too time-consuming when in need of rapid estimates of temperature trends. In this contribution, the usefulness of a Recurrent Neural Network (RNN) was explored in order to forecast (in few minutes) the bulk temperature of beam-interceptive devices. The RNN was trained with the already existing database of MCNPX/ANSYS results from design studies. The feasibility of the method will be exemplified in the case of the Insertable Beam Stop within the Spoke section of the ESS linac. PB - JACoW Publishing CP - Geneva, Switzerland SP - 306 EP - 308 KW - proton KW - linac KW - diagnostics KW - simulation KW - database DA - 2021/10 PY - 2021 SN - 2673-5350 SN - 978-3-95450-230-1 DO - doi:10.18429/JACoW-IBIC2021-TUPP37 UR - https://jacow.org/ibic2021/papers/tupp37.pdf ER -