Energie und Mobilität
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The combination of indentation testing and acoustic emission (AE) is widely used to analyze the fracture toughness of test substrates. In the manufacturing of semiconductor devices this material parameter also plays an important role. During the so-called wafer testing inside a wafer prober small probe tips are pressed onto the chip surface to check its performance. To prevent damaging the chip by that, the fracture toughness and load limit of it has to be defined in a prequalification step. This paper presents a test system that imitates the wafer testing process as close as possible and uses acoustic emission to detect appearing cracks and thereby also the load limit. The frontend of the test setup consists of a modular single needle probe card which can be placed in a wafer prober. Also, the indenter properties (tip diameter, stiffness, etc.) can be adjusted to the probe used later on during productive wafer testing to ensure realistic probing conditions. A piezoelectric sensor and a full strain gauge Wheatstone bridge are implemented close to the single indenter to measure the AEs and the applied contact force respectively. The signal-to-noise-ratios (SNRs) of both sensors are improved with an own analog amplifier module before recording them with an USB oscilloscope. A developed measurement software (LabVIEW) is used to adjust measurement parameters (trigger limit, record length, conversion factors, etc.) and automatically store and separate measurement data from different acoustic events and imprints. To evaluate the result files a second software tool (MATLAB) reads the files out and clusters the recorded events to separate crack signals from electrical and mechanical disturbances. With a prototype first test results are generated and the functionality and accuracy of the measurement concept is proven.
As global demand for green hydrogen rises, potential hydrogen exporters move into the spotlight. While exports can bring countries revenue, large-scale on-grid hydrogen electrolysis for export can profoundly impact domestic energy prices and energy-related emissions. Our investigation explores the interplay of hydrogen exports, domestic energy transition and temporal hydrogen regulation, employing a sector-coupled energy model in Morocco. We find substantial co-benefits of domestic carbon dioxide mitigation and hydrogen exports, whereby exports can reduce market-based costs for domestic electricity consumers while mitigation reduces costs for hydrogen exporters. However, increasing hydrogen exports in a fossil-dominated system can substantially raise market-based costs for domestic electricity consumers, but surprisingly, temporal matching of hydrogen production can lower these costs by up to 31% with minimal impact on exporters. Here, we show that this policy instrument can steer the welfare (re-)distribution between hydrogen exporting firms, hydrogen importers, and domestic electricity consumers and hereby increases acceptance among actors.
Global Navigation Satellite Systems (GNSSs) are essential for modern technology, enabling precise geographic positioning in aviation, maritime shipping, and automotive systems. In the future, their role will be even more critical for autonomous vehicles, which rely on accurate localization for navigation and decision-making. However, the increasing connectivity of autonomous vehicles exposes them to cyber threats, including GNSS spoofing attacks, which manipulate location data to mislead onboard systems. As reliance on GNSS grows, so does the risk posed by spoofing attacks, making it a critical security concern. This paper describes GNSS spoofing attacks on autonomous vehicles, focusing on their detection both during and after an attack. Furthermore, we analyze data storage strategies to facilitate effective forensic analysis. We highlight the importance of position, signal, and camera data, which should be preserved to ensure a comprehensive forensic investigation. Finally, we suggest a simulation setup that enables studying which data could be used for a forensic investigation. Additionally, we examine established data frameworks and decide whether they are suitable for detecting GNSS spoofing attacks.
Global warming is already causing global destabilization. Geopolitical challenges fuel this instability and highlight the need for trusted energy partnerships to ensure energy security. To limit global warming and increase energy security, it is essential to take joint action on an international and global scale. The European Green Deal contributes to this. However, specific national actions are still needed. This research presents a way forward for a Franco-German energy transition and unlocks the potential of energy partnerships within Europe, using a cost-based, sector-coupled optimization model. Both countries are coupled via an isolated country optimization approach. Thus, the focus is on the development of each national energy system. The results highlight the increase of energy security in line with the European Green Deal. The main pillars of the energy transition are solar energy (32- 33%), wind energy (25-38%) and biogenic energy sources (13-27%). Nuclear power is being phased out in Germany and France. The results show that a Franco-German energy partnership has great potential. Overall, France can cost-effectively cover 25% of Germany’s hydrogen import needs (122 TWh) in 2050. At the same time, France can also benefit from the energy partnership in terms of economic growth and joint action to mitigate climate crisis.
Bifacial solar photovoltaic (PV) technology is currently taking over the solar PV module market, exceeding a 90% share in 2025. This important technology must be included in energy system modelling. This study provides a method for calculating the yield of monofacial and bifacial power plants in fixed-tilted, single-axis tracking, and east-west facing vertical setup. A novel method is introduced to maximise the capacity density of solar PV power plants without the need for detailed land cost for the most efficient use of the occupied area. The results indicate a 15-20% yield gain from single-axis tracking compared to fixed-tilted power plants, and a limited bifacial gain of up to 10% for most areas of the world. Higher bifacial gains are sporadically possible in specific conditions. Fixed-tilted systems show higher bifacial gains. Optimising tilt angles and row pitch would allow for 147 MW/ km2 capacity density today, though on average 70-110 MW/km2 can be achieved for 20.2% module efficiency. The impact on the power system, studied in a free cost optimisation scenario and forcing vertical bifacial PV scenario, implying agrivoltaics, is not significant with a +/- 10% change in total solar PV capacity, change in installed wind power of on average-10%, increase of installed battery capacity of on average 5%, and an on average changed levelised cost of electricity of-2% globally. Bifacial solar PV technology has been found to be beneficial but no game changer for future power systems; system improvements are widely possible underlining the important role of this technology.
Assessing the techno-economic benefits of LEMs for different grid topologies and prosumer shares
(2025)
The shift toward decentralized and renewable energy sources has introduced significant challenges to traditional power systems, necessitating innovative market designs. Local energy markets present a viable solution for integrating distributed energy resources such as photovoltaic systems, electric vehicles, and heat pumps. This study investigates the techno-economic implications of local energy markets compared to conventional market designs, focusing on their impact on average energy prices and operational peak power. Through comprehensive simulations across various grid topologies with varying penetration levels of the distributed energy resources, totaling 400 simulation setups, we demonstrate that local energy markets can enhance economic efficiency and grid stability with 99% of the scenarios boasting lower average energy prices and 80% lower operational peak power levels. The findings suggest that local energy markets can play a role in the future energy system, provided that additional infrastructure, management costs, and bureaucratic complexity are kept to a minimum.
Dieser Bericht entstand ursprünglich auf Initiative der Studierenden des damals neuen Masterstudiengangs „Elektro- und Informationstechnik (MEI)“ an der OTH Regensburg, die an der Pflichtlehrveranstaltung „Forschungsmethoden und Seminar (FMS)“ im Wintersemester 2020/21 teilnahmen.
Diese Lehrveranstaltung hat das Ziel, systematisch an das wissenschaftliche Arbeiten, speziell die Wissenschaftskommunikation, heranzuführen. Daher ist im Laufe der Veranstaltung geeignete Literatur zu einem individuellen Thema zu recherchieren, sind Veröffentlichungen auf ihre Relevanz hin zu beurteilen und letztendlich gilt es eine eigene Ausarbeitung basierend auf der Literaturrecherche zu erstellen und diese in einem Vortrag zu präsentieren.
Parallel dazu wird im Theorieteil die entsprechende Hinführung zu den verschiedenen Elementen der Wissenschaftskommunikation vermittelt:
• Bedeutung der Wissenschaftskommunikation für die Arbeit der Ingenieure in Forschung und Entwicklung
• Literaturrecherche, Suchmaschinen, Sichtung und Analyse vorhandener Publikationen, Bewertung der Qualität aufgefundener Fachliteratur, Auswahl geeigneter Materialien für die eigene Arbeit
• Aufbereitung und Darstellung der recherchierten technischer Inhalte in Form einer seitenanzahlbegrenzten wissenschaftlichen Ausarbeitung
• Einhalten formaler Randbedingungen bzgl. Strukturierung, einschl. Bildnachweise und Zitationsstile
• Peer-review-Prozess bei wertschätzender Beurteilung der Leistung anderer
• Publikumsangepasstes Aufbereiten komplexer fachlicher Inhalte mit hochschulöffentlicher Präsentation der Ergebnisse
• Führen mündlicher wissenschaftlicher Diskurse
Die präsentierten Themen beruhen nicht auf eigener Forschungstätigkeit, sondern sind als einführende Reviewartikel zum jeweiligen vorgegebenen technischen oder gesellschaftspolitischen Forschungsthema zu verstehen.
This study explores the relationship between travel stress, infrastructure quality, and cycling mode share. Utilizing multimodal trajectory data, survey responses on travel stress, and detailed road network information with geological and bicycle facility data, the study analyzes correlations between actual and perceived infrastructure quality, travel stress, and cycling mode share, as well as the impact of travel time and distance on cycling mode share. The results indicate significant correlations between perceived infrastructure quality and travel stress levels, and between travel stress levels and cycling mode share, particularly among regular bike users. However, clear correlations between actual and perceived infrastructure quality, and between perceived infrastructure quality and cycling mode share, were not observed. Additionally, direct effects of travel time and distance on cycling mode share were not evident. From these findings, three main insights emerge. Firstly, accurately estimating cycling mode share requires considering multiple parameters beyond travel time and distance. Secondly, to implement effective measures for bicycle facilities, a detailed analysis of perceived infrastructure quality, incorporating actual infrastructure design, is crucial. Lastly, travel stress, when carefully assessed with consideration of various factors beyond infrastructure design, emerges as a key determinant of travel mode choice. These insights have significant implications for future cycling research, including the development of travel mode choice models and estimating cycling demand. The study's valuable contributions advance efforts to promote cycling as a sustainable and preferred mode of transportation.
Die Modellierung der Verkehrsmittelwahl ist für die Vorhersage und das Verständnis des Mobilitätsverhaltens unerlässlich. Hierbei wurden in den letzten Jahren mit maschinellem Lernen vielversprechende Ergebnisse erzielt, insbesondere für XGBoost- und Random-Forest-Modelle.
Aufgrund der zunehmenden Verwendung von Tracking-basierten Smartphone-Apps zur Aufzeichnung des Mobilitätsverhaltens, wenden wir diese Modelle auf einen solchen Datensatz
an. Zudem analysieren wir eingehend deren Interpretierbarkeit. Wir kommen zu dem Schluss,
dass das XGBoost-Modell am leistungsstärksten und dennoch erklärbar ist. Die von solchen
Modellen gewonnenen Erkenntnisse können beispielsweise genutzt werden, um die Verkehrsmittelwahl für beliebige Quelle-Ziel-Paare vorherzusagen.