# S. Russenschuck

- Institution: CERN
- Department: Italo Gorini PhD Schools
- Speaker: Stephan Russenschuck
- Year: 2018
- Language: English
- Source: [CERN](https://videos.cern.ch/record/3009353)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Teens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Mathematical foundations of field computation and magnetic measurements for accelerator magnets

Magnetic measurements are required in magnet projects for accelerators at three stages: To validate the design options and the used software packages (numerical field computation), to follow up the magnet production and to provide feedback for machine operation. Unfortunately no universal measurement method exists that can be traced to first principles (SI Base units). The reduction of measurement uncertainty therefore relies on stable mechanics and precise positioning, a low level environment, the reparameterization to arc-length, the compensation of the main signals (bucking) and oversampling, among others. Systematic errors must be reduced by calibration and cross-calibration.

Fortunately, the fields in accelerator magnets "live" on trivial domains, that is, the bore of the magnet (simply connected) is free of any current sources and magnetic material. Therefore, the regularity conditions of the magnetic fields can be used for post-processing the results and for developing application specific transducers for the tasks. These methods require a solid basis of the underlying mathematical foundations; these are the subject of the lecture..

0:00:00 Slide 1

0:01:54 Slide 2

0:02:54 Slide 3

0:03:53 Slide 4

0:05:12 Slide 5

0:06:02 Slide 6

0:09:38 Slide 7

0:12:21 Slide 8

0:13:47 Slide 9

0:16:52 Slide 10

0:17:29 Slide 11

0:18:18 Slide 12

0:18:53 Slide 13

0:20:59 Slide 14

0:22:45 Slide 15

0:23:30 Slide 16

0:25:17 Slide 17

0:26:37 Slide 18

0:27:35 Slide 19

0:28:36 Slide 20

0:29:20 Slide 21

0:30:34 Slide 22

0:31:48 Slide 23

0:32:56 Slide 24

0:34:04 Slide 25

0:34:57 Slide 26

0:35:52 Slide 27

0:37:13 Slide 28

0:37:57 Slide 29

0:40:00 Slide 30

0:42:48 Slide 31

0:42:55 Slide 32

0:43:08 Slide 33

0:43:41 Slide 34

0:45:26 Slide 35

0:46:48 Slide 36

0:47:18 Slide 37

0:48:05 Slide 38

0:53:48 Slide 39

0:53:52 Slide 40

0:54:15 Slide 41

0:56:06…

## Lectures

1. [S. Russenschuck](https://invisiblenetwork.ai/learn/cern/3009353/59ec636c12c8), 1 h 45 min (CERN's player)