Student Automotive Engineering, Mathematics, Computational Science or similar (f/m/x)

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Our Institute of Vehicle Concepts in Stuttgart is looking for a Student Automotive Engineering, Mathematics, Computational Science or similar (f/m/x), Bachelor's / Master's thesis: Structural optimization for the efficient design of dynamically loaded crash components

Das erwartet Sie:

The Institute of Vehicle Concepts researches, develops and evaluates new vehicle concepts and technologies in light of future demands on the transportation system.

The Urban Modular Vehicle is being developed at DLR as part of the Next Generation Car project. A key challenge in modern vehicle development is creating a fuel-efficient and safe vehicle that meets increasingly stringent requirements. Structural optimization plays a crucial role in this process. These optimization methods enable development engineers to design vehicle structures that are both optimal and lightweight. In the context of multi-material design, body structure concepts for alternatively powered vehicles are being developed using a wide range of materials. Ensuring crash safety is a primary goal in today's vehicle development. However, the current design process for crash-loaded structures is highly time-consuming due to the complexity involved. Given this, new strategies have been developed at the Institute of Vehicle Concepts to derive crash safety under simplified boundary conditions early in the development phase. The objective of this thesis is to develop a process chain to support these new strategies, focusing on crash structures such as battery boxes and similar components. Finally, the crash structure has to be verified using a complete vehicle.

Content of your work:

• Conduct research on existing crash optimization process chains
• Investigate recycled materials and their mechanical properties used in automotive design, particularly for crash structures
• Develop a new process chain for optimizing crash structures using the Python
• Perform optimization on the crash structures
• Verify the optimised result model with FEA global car model
• Document the entire process and results

Das erwarten wir von Ihnen:

• Students from the field related to automotive engineering, mathematics, computational science or a comparable engineering
• Ability to work effectively as part of a team
• An interest in automotive engineering, machine learning, and sustainability design
• Previous experience with of a scripting language (e.g., Python) and familiarity with the software such as LS-Dyna, Primer
• Ability to work thoroughly, reliably, and independently
• Capability to derive and verify design methods for estimating the crash safety of vehicle structures
• Knowledge and experience in sustainable design and Life Cycle Assessment (LCA) are advantageous
• An aptitude for quickly developing and applying practical solutions is an asset

Unser Angebot:

Das DLR steht für Vielfalt, Wertschätzung und Gleichstellung aller Menschen. Wir fördern eigenverantwortliches Arbeiten und die individuelle Weiterentwicklung unserer Mitarbeitenden im persönlichen und beruflichen Umfeld. Dafür stehen Ihnen unsere zahlreichen Fort- und Weiterbildungsmöglichkeiten zur Verfügung. Chancengerechtigkeit ist uns ein besonderes Anliegen, wir möchten daher insbesondere den Anteil von Frauen in der Wissenschaft und Führung erhöhen. Bewerbungen schwerbehinderter Menschen bevorzugen wir bei fachlicher Eignung.

Weitere Angaben:

• Eintrittsdatum: sofort
• Dauer: 6 months
• Vergütung: without remuneration
• Kennziffer: 97666