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The building sector is one of the main consumers of energy. Therefore, heating and cooling concepts for renewable energy sources become increasingly important. For this purpose, low-temperature systems such as thermo-active building systems (TABS) are particularly suitable. This paper presents results of the use of a novel adaptive and predictive computation method, based on multiple linear regression (AMLR) for the control of TABS in a passive seminar building. Detailed comparisons are shown between the standard TABS and AMLR strategies over a period of nine months each. In addition to the reduction of thermal energy use by approx. 26% and a significant reduction of the TABS pump operation time, this paper focuses on investment savings in a passive seminar building through the use of the AMLR strategy. This includes the reduction of peak power of the chilled beams (auxiliary system) as well as a simplification of the TABS hydronic circuit and the saving of an external temperature sensor. The AMLR proves its practicality by learning from the historical building operation, by dealing with forecasting errors and it is easy to integrate into a building automation system.
Photovoltaics Energy Prediction Under Complex Conditions for a Predictive Energy Management System
(2015)
There is a growing trend for the use of thermo-active building systems (TABS) for the heating and cooling of buildings, because these systems are known to be very economical and efficient. However, their control is complicated due to the large thermal inertia, and their parameterization is time-consuming. With conventional TABS-control strategies, the required thermal comfort in buildings can often not be maintained, particularly if the internal heat sources are suddenly changed. This paper shows measurement results and evaluations of the operation of a novel adaptive and predictive calculation method, based on a multiple linear regression (AMLR) for the control of TABS. The measurement results are compared with the standard TABS strategy. The results show that the electrical pump energy could be reduced by more than 86%. Including the weather adjustment, it could be demonstrated that thermal energy savings of over 41% could be reached. In addition, the thermal comfort could be improved due to the possibility to specify mean room set-point temperatures. With the AMLR, comfort category I of the comfort norms ISO 7730 and DIN EN 15251 are observed in about 95% of occasions. With the standard TABS strategy, only about 24% are within category I.
Adaptive predictive control of thermo-active building systems (TABS) based on a multiple regression algorithm: First practical test. Available from: https://www.researchgate.net/publication/305903009_Adaptive_predictive_control_of_thermo-active_building_systems_TABS_based_on_a_multiple_regression_algorithm_First_practical_test [accessed Jul 7, 2017].
Demand Side Management for Thermally Activated Building Systems based on Multiple Linear Regression
(2015)
Gamification wird in vielen Bereichen, die auch den Bildungssektor einschließen, zur Motivations- und Leistungssteigerung eingesetzt. Dieser Beitrag beschreibt das Design, die Umsetzung und Evaluierung eines Gamification-Konzeptes für die Vorlesung „Software Engineering" an der Hochschule Offenburg. Gamification soll nach Intention der Lehrenden eine kontinuierliche und tiefergehende Auseinandersetzung mit den Themen der Vorlesung forcieren sowie einen positiven Einfluss auf die Motivation der Studierenden haben, um den Lernprozess zu unterstützen. Zentral für das Gamification-Design sind dabei eine freiwillige Teilnahme, die Wahrnehmung der Bedeutung der Lerninhalte und ein zielorientierter Einsatz von Gamification-Elementen. Das entwickelte Konzept wurde in der Lernplattform Moodle realisiert, über drei Semester eingesetzt und parallel evaluiert. Die Ergebnisse dieser Evaluierungen zeigen, dass die Studierenden den gamifizierten Kurs intensiv und oft über das gesamte Semester nutzten und aus eigenem Antrieb eine Vielzahl von Übungen absolvierten.
Digitale Lernszenarien in der Hochschullehre. Bedeutung und Funktion aus Sicht von Studierenden
(2021)
Bedingt durch die Coronapandemie wurde in den Informatikkursen Software Engineering und Computernetze an der Hochschule Offenburg ein Lernsetting entwickelt, das mehrere digitale Lernszenarien (Online-Sessions, Lernvideos, Wikis, Quiz, Foren und die selbst entwickelte Lernplattform MILearning) integriert. Im Wintersemester 2020/2021 fand eine Evaluierung statt, um den Einsatz der unterschiedlichen digitalen Lernszenarien in der aktuellen Situation zu bewerten und um zu entscheiden, welche Lernszenarien sinnvoll für einen Einsatz nach der Pandemie sind. Aus dem Blickwinkel des Didaktischen Designs spielen dabei die Eignung der Szenarien für die Wissensvermittlung, die Aktivierung der Studierenden und die Betreuung bei Fragen und Problemen eine wichtige Rolle. Die Ergebnisse zeigen, dass Studierende das Lernsetting intensiv nutzen und die angebotenen digitalen Lernszenarien lernförderlich kombinieren.
A Hybrid Optoelectronic Sensor Platform with an Integrated Solution‐Processed Organic Photodiode
(2021)
Hybrid systems, unifying printed electronics with silicon‐based technology, can be seen as a driving force for future sensor development. Especially interesting are sensing elements based on printed devices in combination with silicon‐based high‐performance electronics for data acquisition and communication. In this work, a hybrid system integrating a solution‐processed organic photodiode in a silicon‐based system environment, which enables flexible device measurement and application‐driven development, is presented. For performance evaluation of the integrated organic photodiode, the measurements are compared to a silicon‐based counterpart. Therefore, the steady state response of the hybrid system is presented. Promising application scenarios are described, where a solution‐processed organic photodiode is fully integrated in a silicon system.
Eine Regelung zur optimalen Kraftschlußausnutzung von Lokomotiven setzt das Erreichen folgender Ziele voraus: Frühzeitiges Erkennen der Schleudergrenze zur Vermeidung von Gleitvorgängen; Fahren eines optimalen Kraftschlusses vom Fahr- und Bremsbetrieb ohne Überschreitung des Kraftschlußmaximums und schnelle Anpassung an wechselnde Arbeitspunkte, zum Beispiel an wechselnde Schienenzustände. Die vorgestellte optimale Regelung der Kraftschlußausnutzung erfaßt Schleuder- und Gleitzustände, die mit bisher eingesetzten Verfahren nicht erkannt werden können und ist Basis für ein Konzept, das ein quasi permanentes Fahren in der Nähe des Kraftschlußmaximums ermöglicht.
Der Beitrag beschreibt wichtige Eckdaten und Ergebnisse der Kraftschlußregelung, die in der Lokomotive 12X auf internationalen Strecken erprobt wurde, und mit der auch zukünftige Projekte ausgestattet werden. Diese werden nicht nur von weiteren technischen Verbesserungen profitieren, sondern auch von geringerem Aufwand für die Inbetriebsetzung.
Purpose
This study aims to investigate a systematic approach to the production and use of additively manufactured injection mould inserts in product development (PD) processes. For this purpose, an evaluation of the additive tooling design method (ATDM) is performed.
Design/methodology/approach
The evaluation of the ATDM is conducted within student workshops, where students develop products and validate them using AT-prototypes. The evaluation process includes the analysis of work results as well as the use of questionnaires and participant observation.
Findings
This study shows that the ATDM can be successfully used to assist in producing and using AT mould inserts to produce valid AT prototypes. As a reference for the implementation of AT in industrial PD, extracts from the work of the student project groups and suitable process parameters for prototype production are presented.
Originality/value
This paper presents the application and evaluation of a method to support AT in PD that has not yet been scientifically evaluated.
Membrane distillation (MD) is a thermal separation process which possesses a hydrophobic, microporous
membrane as vapor space. A high potential application for MD is the concentration of hypersaline brines, such as
e.g. reverse osmosis retentate or other saline effluents to be concentrated to a near saturation level with a Zero
Liquid Discharge process chain. In order to further commercialize MD for these target applications, adapted MD
module designs are required along with strategies for the mitigation of membrane wetting phenomena. This
work presents the experimental results of pilot operation with an adapted Air Gap Membrane Distillation
(AGMD) module for hypersaline brine concentration within a range of 0–240 g NaCl /kg solution. Key performance
indicators such as flux, GOR and thermal efficiency are analyzed. A new strategy for wetting mitigation
by active draining of the air gap channel by low pressure air blowing is tested and analyzed. Only small reductions
in flux and GOR of 1.2% and 4.1% respectively, are caused by air sparging into the air gap channel.
Wetting phenomena are significantly reduced by avoiding stagnant distillate in the air gap making the air blower
a seemingly worth- while additional system component.
Ansatzpunkte zur Verknüpfung von Wertmanagement und Wertemanagement aus Sicht der Führungspraxis
(2014)
In the last decade, deep learning models for condition monitoring of mechanical systems increasingly gained importance. Most of the previous works use data of the same domain (e.g., bearing type) or of a large amount of (labeled) samples. This approach is not valid for many real-world scenarios from industrial use-cases where only a small amount of data, often unlabeled, is available.
In this paper, we propose, evaluate, and compare a novel technique based on an intermediate domain, which creates a new representation of the features in the data and abstracts the defects of rotating elements such as bearings. The results based on an intermediate domain related to characteristic frequencies show an improved accuracy of up to 32 % on small labeled datasets compared to the current state-of-the-art in the time-frequency domain.
Furthermore, a Convolutional Neural Network (CNN) architecture is proposed for transfer learning. We also propose and evaluate a new approach for transfer learning, which we call Layered Maximum Mean Discrepancy (LMMD). This approach is based on the Maximum Mean Discrepancy (MMD) but extends it by considering the special characteristics of the proposed intermediate domain. The presented approach outperforms the traditional combination of Hilbert–Huang Transform (HHT) and S-Transform with MMD on all datasets for unsupervised as well as for semi-supervised learning. In most of our test cases, it also outperforms other state-of-the-art techniques.
This approach is capable of using different types of bearings in the source and target domain under a wide variation of the rotation speed.
It is important to minimize the unscheduled downtime of machines caused by outages of machine components in highly automated production lines. Considering machine tools such as, grinding machines, the bearing inside of spindles is one of the most critical components. In the last decade, research has increasingly focused on fault detection of bearings. In addition, the rise of machine learning concepts has also intensified interest in this area. However, up to date, there is no single one-fits-all solution for predictive maintenance of bearings. Most research so far has only looked at individual bearing types at a time.
This paper gives an overview of the most important approaches for bearing-fault analysis in grinding machines. There are two main parts of the analysis presented in this paper. The first part presents the classification of bearing faults, which includes the detection of unhealthy conditions, the position of the error (e.g. at the inner or at the outer ring of the bearing) and the severity, which detects the size of the fault. The second part presents the prediction of remaining useful life, which is important for estimating the productive use of a component before a potential failure, optimizing the replacement costs and minimizing downtime.
Mit zunehmender Datenverfügbarkeit wird der Einsatz Maschinellen Lernens zur Steuerung und Optimierung von Supply Chains attraktiver, da die Qualität der Datenauswertung erhöht und gleichzeitig der Aufwand gesenkt werden kann. Anhand des SCOR-Modells werden exemplarische Ansätze als Orientierungshilfe eingeordnet und dazu passende Verfahren des Maschinellen Lernens vorgestellt.
Cast aluminum alloys are frequently used as materials for cylinder head applications in internal combustion gasoline engines. These components must withstand severe cyclic mechanical and thermal loads throughout their lifetime. Reliable computational methods allow for accurate estimation of stresses, strains, and temperature fields and lead to more realistic Thermomechanical Fatigue (TMF) lifetime predictions. With accurate numerical methods, the components could be optimized via computer simulations and the number of required bench tests could be reduced significantly. These types of alloys are normally optimized for peak hardness from a quenched state that maximizes the strength of the material. However due to high temperature exposure, in service or under test conditions, the material would experience an over-ageing effect that leads to a significant reduction in the strength of the material. To numerically account for ageing effects, the Shercliff & Ashby ageing model is combined with a Chaboche-type viscoplasticity model available in the finite-element program ABAQUS by defining field variables. The constitutive model with ageing effects is correlated with uniaxial cyclic isothermal tests in the T6 state, the overaged state, as well as thermomechanical tests. On the other hand, the mechanism-based TMF damage model (DTMF) is calibrated for both T6 and over-aged state. Both the constitutive and the damage model are applied to a cylinder head component simulating several cycles on an engine dynamometer test. The effects of including ageing for both models are shown.
Cast iron materials are used as materials for cylinder heads for heavy duty internal combustion engines. These components must withstand severe cyclic mechanical and thermal loads throughout their service life. While high-cycle fatigue (HCF) is dominant for the material in the water jacket region, the combination of thermal transients with mechanical load cycles results in thermomechanical fatigue (TMF) of the material in the fire deck region, even including superimposed TMF and HCF loads. Increasing the efficiency of the engines directly leads to increasing combustion pressure and temperature and, thus, lower safety margins for the currently used cast iron materials or alternatively the need for superior cast iron materials. In this paper (Part I), the TMF properties of the lamellar graphite cast iron GJL250 and the vermicular graphite cast iron GJV450 are characterized in uniaxial tests and a mechanism-based model for TMF life prediction is developed for both materials. The model can be used to estimate the fatigue life of components by means of finite-element calculations (Part II of the paper) and supports engineers in finding the appropriate material and design. Furthermore, the effect of the elastic, plastic and creep properties of the materials on the fatigue life can be evaluated with the model. However, for a material selection also the thermophysical properties, controlling to a high level the thermal stresses in the component, must be considered. Hence, the need for integral concepts for material characterization and selection from a multitude of existing and soon-to-be developed cast iron materials is discussed.
We present a video-densitometric quantification method in combination with diode-array quantification for the methyl-, ethyl-, propyl-, and butylparaben in cosmetics. These parabens were separated on cyanopropyl bonded plates using water-acetonitrile-dioxane-ethanol-NH3 (25%) (8:2:1:1:0.05, v/v) as mobile phase. The quantification is based on UV-measurements at 255 nm and a bioeffectively-linked analysis using Vibrio fischeri bacteria. Within 5 min, a Tidas S 700 diode-array scanner (J&M, Aalen, Germany) scans 8 tracks and thus measures in total 5600 spectra in the wavelengths range from 190 to 1000 nm. The quantification range for all these parabens is from 20 to 400 ng per band, measured at 255 nm. In the V. fischeri assay a CCD-camera registers the white light of the light-emitting bacteria within 10 min. All parabens effectively suppress the bacterial light emission which can be used for quantifying within a linear range from 100 to 400 ng. Measurements were carried out using a 16-bit MicroChemi chemiluminescence system (biostep GmbH, Jahnsdorf, Germany), using a CCD camera with 4.19 megapixels. The range of linearity is achieved because the extended Kubelka-Munk expression was used for data transformation. The separation method is inexpensive, fast, and reliable.