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With recent developments in the Ukrainian-Russian conflict, many are discussing about Germany’s dependency on fossil fuel imports in its energy system, and how can the country proceed with reducing that dependency. With its wide-ranging consumption sectors, the electricity sector comes as the perfect choice to start with. Recent reports showed that the German federal government is already intending to have a fully renewable electricity by 2035 while exploiting all possible clean power options. This was published in the federal government’s climate emergency program (Easter Package) in early 2022. The aim of this package is to initiate a rapid transition and decarbonization of the electricity sector. The Easter Package expects an enormous growth of renewable energies to a completely new level, with already at least 80% renewable gross energy consumption, with extensive and broad deployment of different generation technologies on various scales. This paper will discuss this ambitious plan and outline some insights into this huge and rapidly increasing step, and show how much will Germany need in order to achieve this huge milestone towards a fully green supply of the electricity sector. Different scenarios and shares of renewables will be investigated in order to elaborate on preponed climate-neutral goal of the electricity sector by 2035. The results pointed out some promising aspects in achieving a 100% renewable power, with massive investments in both generation and storage technologies.
Die Positionierung mobiler Systeme mit hoher Genauigkeit ist eine Voraussetzung für intelligentes autonomes Verhalten, sowohl in der Feldrobotik als auch in industriellen Umgebungen. Dieser Beitrag beschreibt den Aufbau einer Roboterplattform und ihre Verwendung für den Test und die Bewertung von Kalman-Filter-Konfigurationen. Der Aufbau wurde mit einem mobilen Roboter Husky A200 und einem LiDAR-Sensor (Light Detection and Ranging) realisiert. Zur Verifizierung des vorgeschlagenen Aufbaus wurden fünf verschiedene Szenarien ausgearbeitet. Mit denen wurden die Filter auf ihre Leistungsfähigkeit hinsichtlich der Genauigkeit der Positionsbestimmung getestet.
Evaluierung von Kalman Filter Konfigurationen zur Roboterlokaliserung mittels Sensordatenfusion
(2023)
In dieser Arbeit werden drei verschiedene Konfigurationen der von Tom Moore, für das Robot Operating System, entwickelte Kalman-Filter vorgestellt. Diese bilden die Grundlage für eine Lokalisierung mittels Sensorfusion in dem verwendeten ROS-Framework. Ziel dieser Arbeit ist der Aufbau und die Verifikation einer Lokalisierung für ein mobiles Robotersystem Husky A200 der Firma Clearpath Robotics. Hierzu wurden die Möglichkeiten des bestehenden Systems untersucht und mehrere Versionen von Lokalisierungsfiltern konfiguriert. Am an Ende, wird eine Verifikation der Ergebnisse in verschiedenen Szenarien gegeneinandergestellt. Hierzu werden die Ergebnisse einer Variante des Extended Kalman-Filters in 2D (EKF2D), eine Variante des Unscented Kalman-Filter in 2D (UKF2D) und eine Variante des Extended Kalman-Filters in 3D (EKF3D) verifiziert und verglichen. Die Untersuchungen ergaben das der EKF2D die besten und robustesten Ergebnisse für eine Lokalisierung erbringt, trotz, im Vergleich zu der UKF2D Variante, 17,3 % höhere Endpositionsabweichung aufweist. Die in diesem Projekt gewählte EKF3D Konfigurationsvariante eignet sich, wegen seinen starken Ungenauigkeiten in der Höhenbestimmung nicht für eine aussagekräftige Positionsbestimmung.
A smart energy concept was designed and implemented for a cluster of 5 existing multi-family houses, which combines heat pumps, photovoltaic (PV) modules and combined heat and power units (CHP) to achieve energy- and cost-efficient operation. Measurement results of the first year of operation show that the local power generation by PV modules and CHP unit has a positive effect on the electrical self-sufficiency by reducing electricity import from the grid. In winter, when the CHP unit operates continuously for long periods, the entire electricity for the heat pump and 91 % of the total electricity demand of the neighborhood are supplied locally. In summer, only 53 % is generated within the neighborhood. The use of a specifically developed energy management system EMS is intended to further increase this share. CO2 emissions for heating and electricity of the neighborhood are evaluated and amount to 18.4 kg/(m2a). Compared to the previous energy system consisting of gas boilers (29.1 kg/(m2a)), savings of 37 % are achieved with electricity consumption from the grid being reduced by 65 %. In the second construction stage, an additional heat pump, CHP unit and PV modules will be added. The measurement results indicate that the final district energy system is likely to achieve the ambitious CO2 reduction goal of -50% and further increase the self-sufficiency of the district.
Hot forging dies are subjected to high cyclic thermo-mechanical loads. In critical areas, the occurring stresses can exceed the material’s yield limit. Additionally, loading at high temperatures leads to thermal softening of the used martensitic materials. These effects can result in an early crack initiation and unexpected failure of the dies, usually described as thermo-mechanical fatigue (TMF). In previous works, a temperature-dependent cyclic plasticity model for the martensitic hot forging tool steel 1.2367 (X38CrMoV5-3) was developed and implemented in the finite element (FE)-software Abaqus. However, in the forging industry, application-specific software is usually used to ensure cost-efficient numerical process design. Therefore, a new implementation for the FE-software Simufact Forming 16.0 is presented in this work. The results are compared and validated with the original implementation by means of a numerical compression test and a cyclic simulation is calculated with Simufact Forming.
The importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to healthcare support, almost every area of daily life and industry is coming into contact with machine learning. Besides all the benefits ML brings, the lack of transparency and difficulty in creating traceability pose major risks. While solutions exist to make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge, as unnoticed modification of a model is also a danger when using ML. This paper proposes to create an ML Birth Certificate and ML Family Tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model.
As industrial networks continue to expand and connect more devices and users, they face growing security challenges such as unauthorized access and data breaches. This paper delves into the crucial role of security and trust in industrial networks and how trust management systems (TMS) can mitigate malicious access to these networks.The TMS presented in this paper leverages distributed ledger technology (blockchain) to evaluate the trustworthiness of blockchain nodes, including devices and users, and make access decisions accordingly. While this approach is applicable to blockchain, it can also be extended to other areas. This approach can help prevent malicious actors from penetrating industrial networks and causing harm. The paper also presents the results of a simulation to demonstrate the behavior of the TMS and provide insights into its effectiveness.
Printed electronics can add value to existing products by providing new smart functionalities, such as sensing elements over large-areas on flexible or non-conformal surfaces. Here we present a hardware concept and prototype for a thinned ASIC integrated with an inkjet-printed temperature sensor alongside in-built additional security and unique identification features. The hybrid system exploits the advantages of inkjet-printable platinum-based sensors, physically unclonable function circuits and a fluorescent particle-based coating as a tamper protection layer.