IBIC2021 - List of Authors (Donegani, E.M.)

Paper	Title	Page
TUPP37	Machine-Learning Based Temperature Prediction for Beam-Interceptive Devices in the ESS Linac	306
	E.M. Donegani ESS, Lund, Sweden
	’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. Winston Churchill, 1906
	Poster TUPP37 [0.454 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2021-TUPP37
About •	paper received ※ 07 September 2021 paper accepted ※ 16 September 2021 issue date ※ 11 October 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Machine-Learning Based Temperature Prediction for Beam-Interceptive Devices in the ESS Linac

306

E.M. Donegani
ESS, Lund, Sweden

’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.
*Winston Churchill, 1906

Poster TUPP37 [0.454 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2021-TUPP37

About •

paper received ※ 07 September 2021 paper accepted ※ 16 September 2021 issue date ※ 11 October 2021

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)