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The worldwide Corona pandemic has severely restricted student projects in the higher semesters of engineering courses. In order not to delay the graduation, a new concept had to be developed for projects under lockdown conditions. Therefore, unused rooms at the university should be digitally recorded in order to develop a new usage concept as laboratory rooms. An inventory of the actual state of the rooms was done first by taking photos and listing up all flaws and peculiarities. After that, a digital site measuring was done with a 360° laser scanner and these recorded scans were linked to a coherent point cloud and transferred to a software for planning technical building services and supporting Building Information Modelling (BIM). In order to better illustrate the difference between the actual and target state, two virtual reality models were created for realistic demonstration. During the project, the students had to go through the entire digital planning phases. Technical specifications had to be complied with, as well as documentation, time planning and cost estimate. This project turned out to be an excellent alternative to on-site practical training under lockdown conditions and increased the students’ motivation to deal with complex technical questions.
In the context of the Corona pandemic and its impact on teaching like digital lectures and exercises a new concept especially for freshmen in demanding courses of Smart Building Engineering became necessary. As there were hardly any face-to-face events at the university, the new teaching concept should enable a good start into engineering studies under pandemic conditions anyway and should also replace the written exam at the end. The students should become active themselves in small teams instead of listening passively to a lecture broadcast online with almost no personal contact. For this purpose, a role play was developed in which the freshmen had to work out a complete solution to the realistic problem of designing, construction planning and implementing a small guesthouse. Each student of the team had to take a certain role like architect, site manager, BIM-manager, electrician and the technitian for HVAC installations. Technical specifications must be complied with, as well as documentation, time planning and cost estimate. The final project folder had to contain technical documents like circuit diagrams for electrical components, circuit diagrams for water and heating, design calculations and components lists. On the other hand construction schedule, construction implementation plan, documentation of the construction progress and minutes of meetings between the various trades had to be submitted as well. In addition to the project folder, a model of the construction project must also be created either as a handmade model or as a digital 3D-model using Computer-aided design (CAD) software. The first steps in the field of Building information modelling (BIM) had also been taken by creating a digital model of the building showing the current planning status in real time as a digital twin. This project turned out to be an excellent training of important student competencies like teamwork, communication skills, and self -organisation and also increased motivation to work on complex technical questions. The aim of giving the student a first impression on the challenges and solutions in building projects with many different technical trades and their points of view was very well achieved and should be continued in the future.
Urban farming is an innovative and sustainable way of food production and is becoming more and more important in smart city and quarter concepts. It also enables the production of certain foods in places where they usually dare not produced, such as production of fish or shrimps in large cities far away from the coast. Unfortunately, it is not always possible to show students such concepts and systems in real life as part of courses: visits of such industry plants are sometimes not possible because of distance or are permitted by the operator for hygienic reasons. In order to give the students the opportunity of getting into contact with such an urban farming system and its complex operation, an industrial urban farming plant was set up on a significantly smaller scale. Therefore, all needed technical components like water aeriation, biological and mechanical filtration or water circulation have been replaced either by aquarium components or by self-designed parts also using a 3D-printer. Students from different courses like mechanical engineering, smart building engineering, biology, electrical engineering, automation technology and civil engineering were involved in this project. This “miniature industrial plant” was also able to start operation and has now been running for two years successfully. Due to Corona pandemic, home office and remote online lectures, the automation of this miniature plant should be brought to a higher level in future for providing a good control over the system and water quality remotely. The aim of giving the student a chance to get to know the operation of an urban farming plant was very well achieved and the students had lots of fun in “playing” and learning with it in a realistic way.
Bitcoin is a cryptocurrency and is considered a high-risk asset class whose price changes are difficult to predict. Current research focusses on daily price movements with a limited number of predictors. The paper at hand aims at identifying measurable indicators for Bitcoin price movements and the development of a suitable forecasting model for hourly changes. The paper provides three research contributions. First, a set of significant indicators for predicting the Bitcoin price is identified. Second, the results of a trained Long Short-term Memory (LSTM) neural network that predicts price changes on an hourly basis is presented and compared with other algorithms. Third, the results foster discussions of the applicability of neural nets for stock price predictions. In total, 47 input features for a period of over 10 months could be retrieved to train a neural net that predicts the Bitcoin price movements with an error rate of 3.52 %.
With the increased interest for interstellar exploration after the discovery of exoplanets and the proposal by Breakthrough Starshot, this paper investigates the optimisation of photon-sail trajectories in Alpha Centauri. The prime objective is to find the optimal steering strategy for a photonic sail to get captured around one of the stars after a minimum-time transfer from Earth. By extending the idea of the Breakthrough Starshot project with a deceleration phase upon arrival, the mission’s scientific yield will be increased. As a secondary objective, transfer trajectories between the stars and orbit-raising manoeuvres to explore the habitable zones of the stars are investigated. All trajectories are optimised for minimum time of flight using the trajectory optimisation software InTrance. Depending on the sail technology, interstellar travel times of 77.6-18,790 years can be achieved, which presents an average improvement of 30% with respect to previous work. Still, significant technological development is required to reach and be captured in the Alpha-Centauri system in less than a century. Therefore, a fly-through mission arguably remains the only option for a first exploratory mission to Alpha Centauri, but the enticing results obtained in this work provide perspective for future long-residence missions to our closest neighbouring star system.
In addition to electromobility and alternative drive systems, a focus is set on electrically driven compressors (EDC), with a high potential for increasing the efficiency of internal combustion engines (ICE) and fuel cells [01]. The primary objective is to increase the ICE torque, provided independently of the ICE speed by compressing the intake air and consequently the ICE filling level supported by the compressor. For operation independent from the ICE speed, the EDC compressor is decoupled from the turbine by using an electric compressor motor (CM) instead of the turbine. ICE performances can be increased by the use of EDC where individual compressor parameters are adapted to the respective application area [02] [03]. This task contains great challenges, increased by demands with regard to pollutant reduction while maintaining constant performance and reduced fuel consumption. The FH-Aachen is equipped with an EDC test bench which enables EDC-investigations in various configurations and operating modes. Characteristic properties of different compressors can be determined, which build the basis for a comparison methodology. Subject of this project is the development of a comparison methodology for EDC with an associated evaluation method and a defined overall evaluation method. For the application of this comparison methodology, corresponding series of measurements are carried out on the EDC test bench using an appropriate test device.
The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
This paper presents the laser-based powder bed fusion (L-PBF) using various glass powders (borosilicate and quartz glass). Compared to metals, these require adapted process strategies. First, the glass powders were characterized with regard to their material properties and their processability in the powder bed. This was followed by investigations of the melting behavior of the glass powders with different laser wavelengths (10.6 µm, 1070 nm). In particular, the experimental setup of a CO2 laser was adapted for the processing of glass powder. An experimental setup with integrated coaxial temperature measurement/control and an inductively heatable build platform was created. This allowed the L-PBF process to be carried out at the transformation temperature of the glasses. Furthermore, the component’s material quality was analyzed on three-dimensional test specimen with regard to porosity, roughness, density and geometrical accuracy in order to evaluate the developed L-PBF parameters and to open up possible applications.