Doctoral Candidate:

Heinrich Lenk, DLR Institut für Elektrifizierte Luftfahrtantriebe (Heinrich.Lenk(at)dlr.de) 

Supervisors:

• Dr. Stefan Kazula, DLR Institut für Elektrifizierte Luftfahrtantriebe
• Dr. Markus Kober, DLR Institut für Elektrifizierte Luftfahrtantriebe

Project Description: 

The thermal management system will be a key component of future electrified aircraft propulsion systems. The heat exchanger, in particular, plays a crucial role in this context. The aim of this project is to improve, optimise and develop new highly efficient heat exchanger structures for the aviation sector. These must meet specific requirements regarding power density, durability and reliability. To meet these requirements, detailed numerical and experimental investigations are necessary. TPMS heat exchanger structures offer a promising approach in this regard, which will be investigated in depth in this project. Due to the complex geometries, additive manufacturing processes are particularly suitable for production; however, these result in a certain degree of surface roughness, which in turn brings both advantages and potential disadvantages. On the one hand, as surface roughness increases, the surface area increases; on the other hand, a rougher surface also exerts a stronger influence on the flow, which affects the degree of turbulence in the boundary layer. The turbulence of the flow naturally depends on other factors as well, such as the flow velocity (Reynolds number) and the channel geometry. The complex interplay of the phenomena mentioned requires a detailed investigation, which must be tailored to the specific problem of heat exchangers in fuel cell systems for the aviation sector.

Doctoral Candidate:

Josué Dávila, Bundesanstalt für Materialforschung und -prüfung (josue.davila(at)bam.de)

Supervisors:

• Dr.-Ing. Gunther Mohr, Bundesanstalt für Materialforschung und -prüfung (BAM)
• Prof. Dr.-Ing. Sebastian Härtel, BTU Cottbus-Senftenberg

Project Description:

The high design freedom of the PBF-LB/M process enables the efficient manufacturing of lightweight structures and complex functional components, making the process particularly suitable for aerospace applications. Another advantage is the high material efficiency, since unmelted powder can be reused after sieving. However, repeated powder reuse leads to changes in powder properties such as morphology and particle size distribution. This so-called powder degradation can particularly affect powder flowability and process stability. In addition, as the degree of reuse increases, the oxygen content also increases, which can be identified a major influencing factor on the resulting microstructure and part quality.

The aim of Project P2 is to develop a science-based recycling strategy for PBF-LB/M powders to enable sustainable and quality-assured additive manufacturing. The project focuses on the investigation of powder degradation during repeated reuse cycles as well as the analysis of powder batches with different oxidation levels. In particular, the influence of powder oxidation on mechanical properties and possible critical oxygen threshold values will be systematically investigated.

Based on these investigations, material-specific reuse limits and powder refresh strategies will be derived. Another objective is the systematic analysis of the relationship between thermal process loading, powder condition, and resulting part properties in order to qualify the influence of part geometry and build volume utilization on powder degradation.

Doctoral Candidate: 

Prathmesh Choudhary, BTU Cottbus-Senftenberg (choudhar(at)b-tu.de)

Supervisors:

• Prof. Dr.-Ing. Sebastian Härtel, BTU Cottbus-Senftenberg
• Dr.-Ing. Daniela Schob, Bundesanstalt für Materialforschung und -prüfung (BAM)

Project Description:

Development of ML-based models that couple LPBF process data with continuum-mechanical behavior to design AM components specifically for local loads and functional requirements.

Doctoral Candidate:

Amirhossein Mohammadkhani, Bundesanstalt für Materialforschung und -prüfung (amirhossein.mohammadkhani(at)bam.de)

Supervisors:

• Dr.-Ing. Jens Prager, Bundesanstalt für Materialforschung und -prüfung (BAM)
• Dr.-Ing. Friedrich Bake, Bundesanstalt für Materialforschung und -prüfung (BAM)
• Dr.-Ing. Stefan Kazula, DLR Institut für Elektrifizierte Luftfahrtantriebe

Project Description:

Structural Health Monitoring (SHM) using Guided Ultrasonic Waves (GUW) has been investigated for simple geometries such as straight pipes and constant cross-section plates under controlled laboratory conditions for decades. However, the application of GUW-SHM systems in practice remains rare.

In the context of Electrified Aero Engines (EAE), the available design space is severely constrained, and components of electric motors are subject to significant heat transfer. Consequently, the effects of bends, curvature, and temperature gradients in waveguides on UGW propagation, as well as on damage detection and localisation, are not well understood and require systematic experimental and numerical investigation within this project. This is particularly critical given the stringent safety requirements of EAEs. However, their early stage of development presents an opportunity to incorporate GUW-SHM approaches directly into the design process, thereby facilitating future implementation.

A substantial gap exists between the performance of GUW-SHM systems under laboratory conditions and their behaviour in field conditions, which is especially relevant for EAEs operating under demanding environmental and operational loads. In addition to conventional signal processing, the project facilitates data-driven and machine learning methods to develop effective GUW-SHM methodologies suited to the described challenges.

Doctoral Candidate:

Frederik Heise, DLR Institut für Elektrifizierte Luftfahrtantriebe (frederik.heise(at)dlr.de)

Supervisors:

• Dr.-Ing. Stefan Kazula, DLR Institut für Elektrifizierte Luftfahrtantriebe
• Dr.-Ing. Ilja Koch, DLR Institut für Elektrifizierte Luftfahrtantriebe 

Project Description:

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Doctoral Candidate:

Anantha Krishnan Rajagopalan, DLR Institut für Elektrifizierte Luftfahrtantriebe (anantha.rajagopalan(at)dlr.de)

Supervisors:

• Dr.-Ing. habil. Thomas Geyer, DLR Institut für Elektrifizierte Luftfahrtantriebe
• Dr.-Ing. Ralf Burgmayer, DLR Institut für Elektrifizierte Luftfahrtantriebe

Project Description:

In the future, electric motors will be a core component of aviation powertrains. These motors can exhibit various defects, which can be classified as electrical or mechanical faults. Their operational capability must be ensured at all times. Therefore, condition monitoring and fault detection will play a crucial role in ensuring aviation safety and extending machine service life. Various methods for condition monitoring of electric motors already exist, ranging from simple sensor-data monitoring to machine learning (ML) techniques. Most ML-based approaches have focused on data from a single sensor type. However, advances in data processing enable the synchronous acquisition of multiple sensor signals and the implementation of multi-source data fusion techniques. By combining information from different sensors, these methods can improve the accuracy and robustness of diagnostic systems.

Most existing approaches, however, are not tailored to the specific requirements of electric motors used in aviation. Although condition monitoring is well established for conventional propulsion systems, this knowledge cannot be directly transferred to electrified aircraft engines. The objective of this work is to implement and validate a fault detection system for electric motors in electrified aviation. Particular emphasis will be placed on integrating sensor technology into the motor using additive manufacturing processes.

Doctoral Candidate:

Eren Utku, Bundesanstalt für Materialforschung und -prüfung (eren.utku(at)bam.de)

Supervisors:

• Dr. Anna Skłodowska, Bundesanstalt für Materialforschung und -prüfung (BAM)
• Dr.-Ing. Mate Gaal, Bundesanstalt für Materialforschung und -prüfung (BAM)

Project Description:

The main goal of the project is to develop a novel structural health monitoring (SHM) concept for additively manufactured (AM) components used in electric motors for electrified aircraft engines. The scientific objective is to develop a reliable, data-driven method for non-contact SHM using air-coupled acoustic emission (AE) and ultrasonic sensors. The early-stage damage will be detected, localised, and classified based on the collected acoustic signatures of AM components, both after production and during operation.

By recording and analysing the acoustic behaviour of AM parts after their production and under operating conditions, the project will identify how typical damage alters their acoustic signatures. It will evaluate the feasibility of contactless sensing technologies, such as air-coupled ultrasonic sensors, and develop robust signal processing strategies to detect, localize, and classify early-stage structural faults. 

The project will further innovate by integrating advanced sensing technologies with sophisticated data analysis, addressing the specific challenges posed by the complex geometries and safety-critical applications of intricate AM components of electric motors. Unlike conventional quality assurance approaches, which often face limitations in non-destructive inspection of AM structures, this project proposes non-contact, data-driven monitoring solutions from manufacturing to operation.

Doctoral Candidate:

Johann Albers, BTU Cottbus-Senftenberg (johann.albers(at)b-tu.de)

Supervisors:

• Prof. Dr.-Ing. Sebastian Härtel, BTU Cottbus-Senftenberg
• Dr.-Ing. Thomas Geyer, DLR Institut für Elektrifizierte Luftfahrtantriebe

Project Description:

This project aims to develop a simulation- and sensor-based process control system for the production of additive manufactured metal structures with locally adjustable porosity. By combining numerical flow simulation (CFD) and Machine Learning (ML), it becomes possible to specifically control the formation of porous and dense regions within a component. Potential applications include lightweight components for the aerospace industry featuring bird-bone-like internal structures.

The manufacturing process utilizes Directed Energy Deposition (DED), where a metal powder and foaming agent mixture is selectively melted. Upon heating, the foaming agent releases gas, leaving behind a porous microstructure during solidification. A central challenge is the precise control of melt pool dynamics and gas bubble distribution to ensure the production of reproducible and mechanically resilient structures. The scientific innovation lies in linking CFD models with real-time sensor data for active process regulation. This approach overcomes the limitations of conventional methods, which cannot produce locally adapted pore structures, offering significant lightweight potential for the aerospace and energy sectors

Doctoral Candidate:

Peter Schiffke, BTU Cottbus-Senftenberg (Peter.Schiffke(at)b-tu.de)

Supervisors:

• Prof. Dr.-Ing. Lars Enghardt, BTU Cottbus-Senftenberg
• Prof. Dr. Peter Langendörfer, BTU Cottbus-Senftenberg
• Dr.-Ing. Thomas Geyer, DLR Institut für Elektrifizierte Luftfahrtantriebe
• Dr. Angie Burtchen, DLR Institut für Elektrifizierte Luftfahrtantriebe

Project Description:

In order to increase the energy efficiency and performance of electrified aircraft engines, innovative lightweight construction concepts are taking centre stage. Additive manufacturing offers a number of advantages for the implementation of these lightweight construction concepts, enabling the production of highly complex and structurally optimised components for electrified aviation. A promising and novel approach is the development of additively manufactured metal foam structures with graded properties that offer an optimal balance between weight reduction and mechanical stability. Reliable production is essential for the quality and performance of the components produced in this way, which is why monitoring the entire manufacturing process plays a central role. In order to guarantee this, sensor technology and suitable evaluation algorithms are to be developed as part of this doctoral project. Monitoring critical operating parameters not only contributes to quality assurance, but also helps to identify potential sources of error at an early stage and initiate the necessary measures to adapt the process. The prerequisite for this is the continuous recording of process variables as well as the precise processing, analysis and interpretation of sensor data. Machine learning and AI-supported methods are to be used to realise this requirement.