Hybrid Modeling for Data-enhanced Multiobjective Optimization of Multibody Systems


There is hardly ever a situation where only one goal is of interest at a time. When carrying out a purchase, for example, we want to pay a low price for a high quality product. In the same manner, multiple objectives are present in the design of essentially all technical systems such as fast and energy efficient electric vehicles, light yet stable constructions, or more generally the simultaneous achievement of technical, economic and ecological objectives. This dilemma leads to the field of multiobjective optimization, where the aim is to optimize all relevant criteria simultaneously. While one optimal solution is usually sufficient in the single-objective setting, there exists an infinite number of optimal compromises in the presence of multiple, contradictory objectives. Knowledge of the associated Pareto set allows for the well-informed selection of trade-off solutions and for the flexible adaptation of designs to changing prioritization or external influence factors.Conflicting criteria also occur in the design of complex multibody systems such as vehicle suspension systems, where the minimization of production costs and tire wear as well as the maximization of driving comfort and safety are highly desirable. The complexity of such multibody dynamics has been constantly increasing over the past years, the key enabler being the rapidly growing computational capabilities which allow for the simulation of increasingly detailed, large scale models. Despite the available computing resources, the multicriteria design of systems with such a high degree of complexity is still beyond today's computing powers.To address these challenges, the central goal of this research project is the development of a highly flexible and adaptive data-enhanced framework for the multicriteria design of complex multibody systems. The central components are the online collection of data from various sources, the data-driven and hybrid modeling (i.e., using equations and data at the same time) of individual components with varying degrees of accuracy, and the interactive multiobjective optimization of the resulting hybrid model. The intention behind the hybrid modeling is to exploit the best of both worlds, i.e., to use equations governing the physics where possible, and on the other hand to further enhance the performance and accuracy using data-driven models whenever the physics-based models are inaccurate, too expensive to evaluate, or even unknown.The developed methodology will be highly useful in the design of a large number of multibody systems, and will thus be of great use to other projects within the Priority Programme while at the same time contributing to the overall program goals.

DFG Programme Priority Programmes

Subproject of SPP 2353: Daring More Intelligence - Design Assistants in Mechanics and Dynamics      

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Project duration:
10/2022 - 09/2025
Funded by:
DFG-Datenbank gepris
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Principal Investigators

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Prof. Dr.-Ing. habil. Walter Sextro

Dynamics and Mechatronics (LDM)

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Jun.-Prof. Dr. Sebastian Peitz

Data Science for Engineering

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