Award for Greta Ruppert


Scientist Greta Ruppert from the [1] Quince research group at the [2] TEMF Institute received the [3] Heinrich and Margarete Liebig Prize 2021 for her master’s thesis in the field of high-voltage equipment simulation.

The Heinrich and Margarete Liebig Prize is awarded annually for an outstanding master’s or diploma thesis in the fields of civil engineering, electrical engineering or mechanical engineering. Greta Ruppert received the €2,000 prize for her master’s thesis on “Adjoint Solution of a Nonlinear Transient Electroquasistatic Model for Cable Joints”. The master's thesis was supervised by Julian Buschbaum, Dr.-Ing. Yvonne Späck-Leigsnering, Prof. Dr. sc. Myriam Koch and Prof. Dr.-Ing. Herbert De Gersem.

Greta Ruppert studied Computational Engineering at the TU Darmstadt and graduated with a Master's degree in 2021. Since 2021 she has been working as a member of the research group “Quasistatics in Computational Engineering (QuinCE)” of Dr.-Ing. Yvonne Späck-Leigsnering at the Institute for Particle Acceleration and Electromagnetic Fields (TEMF) under Prof. Dr. Herbert De Gersem. Ms. Ruppert is researching simulation methods that contribute to a better understanding and further development of insulation systems.

Complex insulation systems are essential components of power engineering applications, such as in [4] high-voltage DC cables or [5] electrical machines. They separate different potentials, must dissipate heat losses to the environment and protect electrical equipment against external influences. However, a systematic design of the insulation material properties and the geometric arrangement is often impossible due to the very large number of design parameters. In her work, Greta Ruppert developed an efficient approach for the sensitivity calculation of transient nonlinear electroquasistatic problems with many design parameters. With the help of the adjoint method, she is able to calculate the sensitivities of complex application examples, such as a cable joint, with a computational effort that is almost independent of the number of design parameters. This is an important step towards gradient-based optimization of power engineering components.