Bachelor- / Masterarbeit: Development of a Physically Based Model for the Oil Flow in Rolling Bearings Using Dimensional Analysis (Buckingham-n Theorem)

The Institute for Machine Elements and System Development researches the fundamental structural and tribological behavior of machine elements and maps this behavior through experimentally validated model descriptions. These model descriptions are used to analyze and design the functional performance, energy losses, and acoustic behavior of complete technical systems, with a focus on drivetrain technology. In addition, the developed models serve as a basis for researching and developing methods for Model-Based Systems Engineering (MBSE) as a central element in future industrial product development processes.

The oil flow in rolling bearings is influenced by numerous geometric, physical, and operational parameters. A full CFO simulation is highly computationally intensive and therefore unsuitable for system-level simulations. Consequently, there is a need for a simplified, yet physically sound surrogate model that can efficiently describe the input-output behavior of the oil flow.

In this work, a theoretical investigation is conducted to identify the key parameters that significantly affect the oil flow in rolling bearings and to determine how these can be combined into dimensionless numbers. Using the Buckingham n theorem, the relevant influencing factors are grouped into dimensionless parameters.

Based on this, physically motivated equations are derived to describe the inputoutput behavior. Finally, the study examines how this physical model structure can be complemented or trained using machine learning methods to develop a hybrid surrogate model.

Tasks:

• Literature review on oil flow in rolling bearings and dimensionless analysis (Buckingham TI theorem).
• Identification of relevant influencing parameters (e.g., rotational speed, oil viscosity, geometric parameters).
• Application of the Buckingham TI theorem to derive dimensionless groups.
• Development of physical equations describing the input-output behavior.
• Integration of the physical model with data-driven methods (e.g., POD, machine learning).

Requirements:

• Basic knowledge of fluid mechanics
• Mathematical fundamentals, especially dimensional analysis and the Buckingham TI theorem
• Understanding of flow characteristics in rolling bearings is desirable but not mandatory
• Initial knowledge of data-driven methods (e.g., machine learning) is desirable but not mandatory

We offer:

• Flexible focus areas within the thesis/project
• Possibility of rapid completion
• Intensive supervision
• Immediate start or by arrangement
• Excellent working atmosphere

We look forward to your application by email:

Amirreza Niazmehr, M. Sc. RWTH
Institut für Maschinenelemente und Systementwicklung

Schinkelstraße 10, 52062 Aachen
amirreza.niazmehr@imse.rwth-aachen.de