Injection optimization - Delta - TU Dortmund

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Location & approach

By car

The campus of the Technical University of Dortmund is located near the freeway junction Dortmund West, where the Sauerland line A45 crosses the Ruhr expressway B1/A40. The Dortmund-Eichlinghofen exit on the A45 leads to the South Campus, the Dortmund-Dorstfeld exit on the A40 leads to the North Campus. The university is signposted at both exits.

By bus and train

The "Dortmund Universität" S-Bahn station is located directly on the North Campus. From there, the S-Bahn line S1 runs every 15 or 30 minutes to Dortmund main station and in the opposite direction to Düsseldorf main station via Bochum, Essen and Duisburg. In addition, the university can be reached by bus lines 445, 447 and 462. Timetable information can be found on the homepage of the Rhine-Ruhr Transport Association, and DSW21 also offer an interactive route network map.

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By H-Bahn

One of the landmarks of the TU Dortmund is the H-Bahn. Line 1 runs every 10 minutes between Dortmund Eichlinghofen and the Technology Center via Campus South and Dortmund University S, while Line 2 commutes every 5 minutes between Campus North and Campus South. It covers this distance in two minutes.

By plane

From Dortmund Airport, the AirportExpress takes just over 20 minutes to Dortmund Central Station and from there the S-Bahn (suburban train) takes you to the university. A wider range of international flight connections is offered by Düsseldorf Airport, about 60 kilometers away, which can be reached directly by S-Bahn from the university's train station.

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Cafeteria menus

To overview

Location & approach

By car

The campus of the Technical University of Dortmund is located near the freeway junction Dortmund West, where the Sauerland line A45 crosses the Ruhr expressway B1/A40. The Dortmund-Eichlinghofen exit on the A45 leads to the South Campus, the Dortmund-Dorstfeld exit on the A40 leads to the North Campus. The university is signposted at both exits.

By bus and train

The "Dortmund Universität" S-Bahn station is located directly on the North Campus. From there, the S-Bahn line S1 runs every 15 or 30 minutes to Dortmund main station and in the opposite direction to Düsseldorf main station via Bochum, Essen and Duisburg. In addition, the university can be reached by bus lines 445, 447 and 462. Timetable information can be found on the homepage of the Rhine-Ruhr Transport Association, and DSW21 also offer an interactive route network map.

Translated with www.DeepL.com/Translator (free version)

By H-Bahn

One of the landmarks of the TU Dortmund is the H-Bahn. Line 1 runs every 10 minutes between Dortmund Eichlinghofen and the Technology Center via Campus South and Dortmund University S, while Line 2 commutes every 5 minutes between Campus North and Campus South. It covers this distance in two minutes.

By plane

From Dortmund Airport, the AirportExpress takes just over 20 minutes to Dortmund Central Station and from there the S-Bahn (suburban train) takes you to the university. A wider range of international flight connections is offered by Düsseldorf Airport, about 60 kilometers away, which can be reached directly by S-Bahn from the university's train station.

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Optimization of the injection

At the DELTA storage ring facility, all magnets as well as the Radio Frequency (RF) system of the Booster Synchrotron (BoDo) are software-controlled to cyclically ramp up and down. Each of these energy ramp cycles takes about 7 seconds, whereby the electron energy is increased from 90 MeV to 1.5 GeV. Depending on the electron transfer rate (injection efficiency) and the stored beam current in BoDo, 150 to 200 ramp cycles are needed to reach the maximum beam current of 130 mA in the storage ring (see image on the right). To minimize injection times, optimizing the injection efficiency from the Booster Synchrotron to the storage ring is crucial.

During the injection process, a multitude of parameters (e.g., strengths of transfer channel magnets, trigger timings of all pulsed injection and extraction magnets) need to be manually adjusted. The efficiency of injection primarily depends on the settings of the Booster extraction elements, the magnets in the transfer line (T2), and the injection components in the storage ring. To automate the injection procedure and enhance electron transfer rates, innovative machine learning concepts have been tested.

The upper figure shows the measured injection efficiency per injection cycle and the corresponding BoDo beam current at the storage ring facility DELTA. The lower figure shows the related histogram for the injection efficiency. — Measured injection efficiency at the DELTA accelerator facility (exemplary).

The picture shows part of the transfer line magnets (T2) in the transition from the booster synchrotron (BoDo) to the storage ring. — Magnets in the transfer channel T2, the transition from the booster synchrotron (BoDo, left) to the storage ring (top right).

The figure shows the sequence of components in the second transfer line (T2) between the booster synchrotron (BoDo) and the storage ring. — Schematic layout of the second transfer line (T2).

The figure shows a step-by-step scan to optimise injection efficiency based on neural networks (NN) and Gaussian process models (GPR). — Optimisation of injection efficiency using neural networks (NN) and Gaussian process regressors (GPR) as prediction models.

In the course of several studies ([12], [13]), initially 13 and later up to 18 injection parameters were systematically and randomly altered, and the impact on injection efficiency was measured. These measurements served as training data for supervised learning of injection prediction models. In addition to neural networks (NN), decision trees (DT) and Gaussian Process Regressors (GPR) were employed as prediction models. Heuristic (e.g. simulated annealing) and statistical (e.g. Bayesian optimization) optimization algorithms utilized these ML-based surrogate models to improve injection settings between injection cycles (approximately 7 seconds). This process led to significant improvements in injection efficiency within a few iterations (injection cycles) for GPR- and NN-based models. Decision trees proved to be less successful. Further details are presented in studies [12], [13] and summarized in the IPAC-23 conference paper.