4.2 million euros for interdisciplinary projects
The Bavarian Research Foundation added 11 projects to its funding program in 2018 - including seven involving the Technical University of Munich (TUM). Prof. Arndt Bode, President of the Bavarian Research Foundation, presented the funding notifications to the research groups in Garching today.
The Bavarian Research Foundation has been providing support to research groups since 1990 with the goal of promoting Bavaria’s international competitiveness in new technologies and creating jobs in the industries of the future. The funding applications must be submitted jointly by private-sector and academic research partners. The projects involving TUM researchers have a total of 22 companies participating. The research foundation will provide a total of 4.2 million euros in funding to the seven interdisciplinary research projects.
"The Bavarian Research Foundation is happy to support outstanding research and cooperation with industry and other research institutions. The funded projects display how Bavarian universities are able to combine science with entrepreneurial needs. This creates added value not only for the participants but for lots of others as well", said Prof. Arndt Bode, President of the Bavarian Research Foundation, who attended today’s official presentation of the funding notifications.
Prof. Wolfgang A. Herrmann, the president of TUM, welcomed the recognition earned by his colleagues: "In the 150th year of our history, our founding mission still applies: To communicate the spark of science to the world of commerce and industry. The results of our research have enormous potential for joint implementation with industrial and commercial partners. This is how scientific inventions are turned into key innovations. With its funding, the Bavarian Research Foundation enables our students to work with companies on the latest developments at a very early stage and gain insights into real-world applications."
Four departments of the Technical University of Munich are involved in the seven projects: the Department of Mechanical Engineering , the Department of Electrical and Computer Engineering , the TUM School of Governance , and the TUM School of Life Sciences Weihenstephan :
Prosthetics that are permanently implanted in bones, for example in artificial acetabular cups in hip replacement surgery, can become loose over time. The reason: Forces cannot be distributed evenly at the implant-bone interface because of differences in the rigidity of the metal used in the prosthetic and the bone.
The goal of the ASIMOV project (anatomy-specific implant anchoring using optimized deformation properties) is to develop a new method for making optimized, individually fitted implants. The prosthetics are modeled using simulations and then manufactured by means of 3D printing on the basis of those models. This manufacturing method permits greater flexibility in shaping the metal with the aid of internal structures, which in turn makes it possible to closely approximate the material properties of the bone.
The method was developed using the example of the artificial hip joint and is to be developed for use in other prosthetic types in the future. The project is headed by the Institute for Machine Tools and Industrial Management ,under the leadership of Prof. Michael Zäh.
FORTiGe - animal health through genomics
The goal of the research group FORTiGe is to improve the health of livestock. With the continuing expansion in animal-based food production to feed a growing global population, conflicts between the objectives of animal welfare, economics and ecology are intensifying. New genomic analysis methods permit a much more precise identification of the genome locations crucial to animal health. A detailed knowledge of such sites is a prerequisite for using genomic editing methods. The potential of these methods with regard to animal health are being explored in the hope that they can benefit animal breeding efforts organized by farmers. The research group will evaluate which genomic editing targets might make sense for cattle, pigs and chickens. At the same time - along with questions of safety and efficiency - they will study perceptions and opinions of the methods in the general public and among farmers.
The funding application for FORTiGe was submitted by the Chair of Animal Breeding under the leadership of Prof. Ruedi Fries. The project will bring together scientists at TUM and Ludwigs-Maximilians-Universität from the fields of animal breeding and genomics, reproduction technology, molecular biology, immunology, and science and technology policy. In addition, the group will cooperate with the Chair of Constitutional and Administrative Law at the University of Passau, which will explore the legal aspects of the research topic.
Optimal Parallel Battery - OparaBatt
Modern lithium-ion batteries often do not consist of a single battery cell, as in the past, and instead have a large number of smaller or larger cells connected in series or, in many cases, in parallel. Using a large number of smaller linked cells is not only more cost-effective as compared with large cells. It also makes the battery systems safer and creates additional degrees of freedom. Today’s electric cars, for example, contain up to 100 cells connected in series and the same number in parallel. The allocation of current to the parallel cells is not coordinated, as this is too complex. This can result in imbalances that negatively impact the usable energy, the performance and the operating lifetime of the energy storage.
Electric power distribution depends on many parameters such as the materials used, the load profile and the temperature. The OparaBatt project will investigate these relationships in detail to optimize the interdependencies between the cells and the connection layout. The research will focus on battery system technology and the cell configuration. The project will be headed by the Institute for Electrical Energy Storage Technology under the leadership of Prof. Andreas Jossen.
Polymer-based transmission fluid
The goal of this project is to develop environmentally friendly lubricants for transmissions based on water and regenerative raw materials. Lubricants are essential for extending the operating life and boosting the performance and efficiency of transmissions and must be taken into account when transmissions are developed. Transmissions for wind turbines need different lubricants than those in outboard motors, for example.
Until now, lubricants have almost always been based on mineral and synthetic oils. The project is intended to develop an environmentally friendly alternative based on water and regenerative raw materials and additives. The first experiments look very promising. The lubricant will be systematically studied to verify its properties and suitability for certain transmission applications. The funding application was submitted by the Institute for Machine Elements - Gear Research Center , headed by Prof. Karsten Stahl.
Project rAlcing (Deep Learning for automatic driving on the race track) aims to use machine learning processes to develop new software for autonomous electric-powered racing cars. The project will focus on two key issues: measuring the road friction potential and optimizing energy management. The road friction potential determines the maximum transferable acceleration, braking and lateral forces between the road surface and tires, and thus is a major factor for lap times of a racing car. It is therefore essential in motor racing to have the most detailed possible information on the friction potential and to make optimal use of it.
Optimized energy management seeks to ensure that the car, which is powered by four electric motors, can cover the race distance at the maximum possible speed combined with situation-aware adaptation of energy consumption. The software will be tested in a real vehicle. The project will be headed by the Institute of Automotive Technology under the leadership of Prof. Markus Lienkamp.
Additive manufacturing processes using 3D printing or laser melting offer enormous potential for industry. It makes it possible to integrate certain functions and save material while depositing layers to produce components. However, there are still problems to overcome in these processes. For example, deformations can occur, resulting in individual variations in the shape of components. This poses big challenges in series or mass production. In addition, the required surface finish tolerances cannot be met.
Due to these aspects, there is no other option but to rework the components. The ShapeAM research project ("enabling additive manufacturing technologies to manufacture functional components at high quality standards for industrial use") will explore new methods for optimizing hybrid manufacturing. This refers to the combination of additive manufacturing followed by rework through subtractive machining. The goal is to optimize the additive production of components so that the geometric and surface finish requirements are met. In addition, measures will be developed to improve the efficiency of follow-up machining. The project is headed by the Institute for Machine Tools and Industrial Management unter under the leadership of Prof. Michael F. Zäh.
Accurate assessment of aortic aneurysms
An abdominal aortic aneurysm is a pathological enlargement of the abdominal aorta which may lead to a rupture. It is among the 10 most common causes of death in the over-65 age group in Germany. Until now it has not been possible to determine the specific risk of aneurysms rupturing in individual patients. Consequently, the Bavarian Research Foundation is funding a project that will attempt to find answers. The participants plan to use patient-specific simulation models and the evaluation of individual parameters through machine learning to facilitate individual risk forecasting for small and medium-sized abdominal aortic aneurysms. In addition, the project will develop a graphic interface for use in clinics. This will introduce the results to the clinical environment in the form of a decision-making tool for doctors.
The TUM participants in the project are Michael W. Gee , head of the Mechanics & High Performance Computing Group in the Department of Mechanical Engineering, and Simon Hegelich , Professor of Political Data Science at the TUM School of Governance.