Refine
Year of publication
- 2022 (86) (remove)
Document Type
- Master's Thesis (31)
- Conference Proceeding (30)
- Article (17)
- Part of a Book (4)
- Preprint (2)
- Book (1)
- Working Paper (1)
Institute
- Forschungszentrum Mikrotechnik (24)
- Forschungszentrum Energie (12)
- Forschungszentrum Business Informatics (9)
- Forschungszentrum Digital Factory Vorarlberg (8)
- Wirtschaft (6)
- Forschung (4)
- Josef Ressel Zentrum für Robuste Entscheidungen (4)
- Josef Ressel Zentrum für Intelligente Thermische Energiesysteme (2)
- Soziales & Gesundheit (2)
- Forschungsgruppe Empirische Sozialwissenschaften (1)
Language
- English (86) (remove)
Keywords
- photonics (4)
- arrayed waveguide gratings (3)
- 3D MMI splitter (2)
- AWG (2)
- Global optimization (2)
- PV (2)
- Rastrigin function (2)
- Value co-creation (2)
- diamond (2)
- polymers (2)
Flexibility estimation is the first step necessary to incorporate building energy systems into demand side management programs. We extend a known method for temporal flexibility estimation from literature to a real-world residential heat pump system, solely based on historical cloud data. The method proposed relies on robust simplifications and estimates employing process knowledge, energy balances and manufacturer's information. Resulting forced and delayed temporal flexibility, covering both domestic hot water and space heating demands as constraints, allows to derive a flexibility range for the heat pump system. The resulting temporal flexibility lay within the range of 24 minutes and 6 hours for forced and delayed flexibility, respectively. This range provides new insights into the system's behaviour and is the basis for estimating power and energy flexibility - the first step necessary to incorporate building energy systems into demand side management programs.
Startups usually have high growth ambitions but only limited resources. Therefore, they are looking for efficient and effective methods to grow their business. However, if they go interna-tional, challenging changes will likely be made if the company is not focused on the global market. The aim of this work is therefore to support startups in the internationalization of their business and to provide guidance from the beginning by using strategic marketing elements to facilitate this process. To achieve this, a qualitative method of analysis was chosen. First, a literature review was conducted on the relevant topics of a startup, strategic marketing, and internationalization. Five success factors emerged from the literature: commitment, strategy, research, marketing mix adaptation, and organization and network. These were then ana-lyzed in more detail in expert interviews. The analysis of the interview results shows that for the internationalization of startups, the commitment to internationalization and a global mindset is of great importance from the very beginning. This is because they influence all supporting and strategic marketing elements.
The paper deals with designing and numerical modelling a 2 x 2 optical switch for photonic integrated circuits based on 2 x 2 MMI elements and phase modulators. The 2 x 2 optical switch was modelled in the RsoftCAD with the simulation tool BeamPROP. The 2 x 2 optical switch is a common element for creating more complex 1 x N or N x N optical switches in all-optical signal processing.
The paper deals with the optimization of 2x2 optical switch for photonic integrated circuits based on two 2x2 MMI splitters and two phase-modulators. The optical switch was modelled in the RSoftCAD with the simulation tool BeamPROP. The optimization was done to minimise the insertion losses and broaden the spectral band at 1550 nm by using linear tapers in a 2x2 MMI splitter topology. The 2x2 optical switch is a common element for creating more complex 1xN or NxN optical switches in all-optical signal processing.
The design and development of smart products and services with data science enabled solutions forms a core topic of the current trend of digitalisation in industry. Enabling skilled staff, employees, and students to use data science in their daily work routine of designing such products and services is a key concern of higher education institutions, including universities, company workshop providers and in further education. The scope and usage scenario of this paper is to assess software modules (‘tools’) for integrated data and analytics as service (DAaaS). The tools are usually driven by machine learning, may be deployed in cloud infrastructures, and are specifically targeted at particular needs of the industrial manufacturing, production, or supply chain sector.
The paper describes existing theories and previous work, namely methods used in didactics, work done for visually designing and using machine learning algorithms (no-code / low- code tools), as well as combinations of these two topics. For tools available on the market, an extended assessment of their suitability for a set of learning scenarios and personas is discussed.
A trend from centralized to decentralized production is emerging in the manufacturing domain leading to new and innovative approaches for long-established production methods. A technology supporting this trend is Cloud Manufacturing, which adapts technologies and concepts known from cloud computing to the manufacturing domain. A core aspect of Cloud Manufacturing is representing knowledge about manufacturing, e.g., machine capabilities, in a suitable form. This knowledge representation should be flexible and adaptable so that it fits across various manufacturing domains, but, at the same time, should also be specific and exhaustive. We identify three core capabilities that such a platform has to support, i.e., the product, the process and the production.We propose representing this knowledge in semantically specified knowledge graphs, essentially creating three through features interconnected ontologies each representing a facet of manufacturing. Finally, we present an exemplary implementation of a Cloud Manufacturing platform using this representation and its advantages.
Bubble columns are recently used for the humidification of air in water treatment systems and fuel cells. They are well applicable due to their excellent heat and mass transfer and their low technical complexity. To design and operate such devices with high efficiency, the humidification process and the impact of the operating parameters need to be understood to a sufficient degree. To extend this knowledge, we use a refined and novel method to determine the volumetric air–liquid heat and mass transfer coefficients and the humidifier efficiency for various parametric settings. The volumetric transfer coefficients increase with both of the superficial air velocity and the liquid temperature. It is further shown that the decrease of vapor pressure with an increase of the salinity results in a corresponding decrease in the outlet humidity ratio. In contrast to previous studies, liquid heights smaller than 0.1 m are investigated and significant changes in the humidifier efficiency are seen in this range. We present the expected humidifier efficiency with respect to the superficial air velocity and the liquid height in an efficiency chart, such that optimal operating conditions can be determined. Based on this efficiency chart, recommendations for industrial applications as well as future scientific challenges are drawn.
Bubble column humidifiers (BCHs) are frequently used for the humidification of air in various water treatment applications. A potential but not yet profoundly investigated application of such devices is the treatment of oily wastewater. To evaluate this application, the accumulation of an oil-water emulsion using a BCH is experimentally analyzed. The amount of evaporating water vapor can be evaluated by measuring the humidity ratio of the outlet air. However, humidity measurements are difficult in close to saturated conditions, as the formation of liquid droplets on the sensor impacts the measurement accuracy. We use a heating section after the humidifier, such that no liquid droplets are formed on the sensor. This enables us a more accurate humidity measurement. Two batch measurement runs are conducted with (1) tap water and (2) an oil-water emulsion as the respective liquid phase. The humidity measurement in high humidity conditions is highly accurate with an error margin of below 3 % and can be used to predict the oil concentration of the remaining liquid during operation. The measured humidity ratio corresponds with the removed amount of water vapor for both tap water and the accumulation of an oil-water emulsion. Our measurements show that the residual water content
in the oil-water emulsion is below 4 %.