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In this paper, we provide an analytical study of the transmission eigenvalue problem with two conductivity parameters. We will assume that the underlying physical model is given by the scattering of a plane wave for an isotropic scatterer. In previous studies, this eigenvalue problem was analyzed with one conductive boundary parameter whereas we will consider the case of two parameters. We prove the existence and discreteness of the transmission eigenvalues as well as study the dependence on the physical parameters. We are able to prove monotonicity of the first transmission eigenvalue with respect to the parameters and consider the limiting procedure as the second boundary parameter vanishes. Lastly, we provide extensive numerical experiments to validate the theoretical work.
By developing innovative solutions to social and environmental problems, sustainable ventures carry greatpotential. Entrepreneurship which focuses especially on new venture creation can be developed through education anduniversities, in particular, are called upon to provide an impetus for social change. But social innovations are associatedwith certain hurdles, which are related to the multi-dimensionality, i.e. the tension between creating social,environmental and economic value and dealing with a multiplicity of stakeholders. The already complex field ofentrepreneurship education has to face these challenges. This paper, therefore, aims to identify starting points for theintegration of sustainability into entrepreneurship education. To pursue this goal experiences from three differentproject initiatives between the partner universities: Lapland University of Applied Sciences, FH Aachen University ofApplied Sciences and Turiba University are reflected and findings are systematically condensed into recommendationsfor education on sustainable entrepreneurship.
This study describes the development of a new combined polysaccharide-matrix-based technology for the immobilization of Lactobacillus rhamnosus GG (LGG) bacteria in biofilm form. The new composition allows for delivering the bacteria to the digestive tract in a manner that improves their robustness compared with planktonic cells and released biofilm cells. Granules consisting of a polysaccharide matrix with probiotic biofilms (PMPB) with high cell density (>9 log CFU/g) were obtained by immobilization in the optimized nutrient medium. Successful probiotic loading was confirmed by fluorescence microscopy and scanning electron microscopy. The developed prebiotic polysaccharide matrix significantly enhanced LGG viability under acidic (pH 2.0) and bile salt (0.3%) stress conditions. Enzymatic extract of feces, mimicking colon fluid in terms of cellulase activity, was used to evaluate the intestinal release of probiotics. PMPB granules showed the ability to gradually release a large number of viable LGG cells in the model colon fluid. In vivo, the oral administration of PMPB granules in rats resulted in the successful release of probiotics in the colon environment. The biofilm-forming incubation method of immobilization on a complex polysaccharide matrix tested in this study has shown high efficacy and promising potential for the development of innovative biotechnologies.
Market abstraction of energy markets and policies - application in an agent-based modeling toolbox
(2023)
In light of emerging challenges in energy systems, markets are prone to changing dynamics and market design. Simulation models are commonly used to understand the changing dynamics of future electricity markets. However, existing market models were often created with specific use cases in mind, which limits their flexibility and usability. This can impose challenges for using a single model to compare different market designs. This paper introduces a new method of defining market designs for energy market simulations. The proposed concept makes it easy to incorporate different market designs into electricity market models by using relevant parameters derived from analyzing existing simulation tools, morphological categorization and ontologies. These parameters are then used to derive a market abstraction and integrate it into an agent-based simulation framework, allowing for a unified analysis of diverse market designs. Furthermore, we showcase the usability of integrating new types of long-term contracts and over-the-counter trading. To validate this approach, two case studies are demonstrated: a pay-as-clear market and a pay-as-bid long-term market. These examples demonstrate the capabilities of the proposed framework.
The feasibility study presents results of a hydrogen combustor integration for a Medium-Range aircraft engine using the Dry-Low-NOₓ Micromix combustion principle. Based on a simplified Airbus A320-type flight mission, a thermodynamic performance model of a kerosene and a hydrogen-powered V2530-A5 engine is used to derive the thermodynamic combustor boundary conditions. A new combustor design using the Dry-Low NOx Micromix principle is investigated by slice model CFD simulations of a single Micromix injector for design and off-design operation of the engine. Combustion characteristics show typical Micromix flame shapes and good combustion efficiencies for all flight mission operating points. Nitric oxide emissions are significant below ICAO CAEP/8 limits. For comparison of the Emission Index (EI) for NOₓ emissions between kerosene and hydrogen operation, an energy (kerosene) equivalent Emission Index is used.
A full 15° sector model CFD simulation of the combustion chamber with multiple Micromix injectors including inflow homogenization and dilution and cooling air flows investigates the combustor integration effects, resulting NOₓ emission and radial temperature distributions at the combustor outlet. The results show that the integration of a Micromix hydrogen combustor in actual aircraft engines is feasible and offers, besides CO₂ free combustion, a significant reduction of NOₓ emissions compared to kerosene operation.
In order to reduce energy consumption of homes, it is important to make transparent which devices consume how much energy. However, power consumption is often only monitored aggregated at the house energy meter. Disaggregating this power consumption into the contributions of individual devices can be achieved using Machine Learning. Our work aims at making state of the art disaggregation algorithms accessibe for users of the open source home automation platform Home Assistant.
Residential and commercial buildings account for more than one-third of global energy-related greenhouse gas emissions. Integrated multi-energy systems at the district level are a promising way to reduce greenhouse gas emissions by exploiting economies of scale and synergies between energy sources. Planning district energy systems comes with many challenges in an ever-changing environment. Computational modelling established itself as the state-of-the-art method for district energy system planning. Unfortunately, it is still cumbersome to combine standalone models to generate insights that surpass their original purpose. Ideally, planning processes could be solved by using modular tools that easily incorporate the variety of competing and complementing computational models. Our contribution is a vision for a collaborative development and application platform for multi-energy system planning tools at the district level. We present challenges of district energy system planning identified in the literature and evaluate whether this platform can help to overcome these challenges. Further, we propose a toolkit that represents the core technical elements of the platform. Lastly, we discuss community management and its relevance for the success of projects with collaboration and knowledge sharing at their core.
The integration of high temperature thermal energy storages into existing conventional power plants can help to reduce the CO2 emissions of those plants and lead to lower capital expenditures for building energy storage systems, due to the use of synergy effects [1]. One possibility to implement that, is a molten salt storage system with a powerful power-to-heat unit. This paper presents two possible control concepts for the startup of the charging system of such a facility. The procedures are implemented in a detailed dynamic process model. The performance and safety regarding the film temperatures at heat transmitting surfaces are investigated in the process simulations. To improve the accuracy in predicting the film temperatures, CFD simulations of the electrical heater are carried out and the results are merged with the dynamic model. The results show that both investigated control concepts are safe regarding the temperature limits. The gradient controlled startup performed better than the temperature-controlled startup. Nevertheless, there are several uncertainties that need to be investigated further.
Despite the challenges of pioneering molten salt towers (MST), it remains the leading technology in central receiver power plants today, thanks to cost effective storage integration and high cost reduction potential. The limited controllability in volatile solar conditions can cause significant losses, which are difficult to estimate without comprehensive modeling [1]. This paper presents a Methodology to generate predictions of the dynamic behavior of the receiver system as part of an operating assistance system (OAS). Based on this, it delivers proposals if and when to drain and refill the receiver during a cloudy period in order maximize the net yield and quantifies the amount of net electricity gained by this. After prior analysis with a detailed dynamic two-phase model of the entire receiver system, two different reduced modeling approaches where developed and implemented in the OAS. A tailored decision algorithm utilizes both models to deliver the desired predictions efficiently and with appropriate accuracy.
This work presents the Multi-Bees-Tracker (MBT3D) algorithm, a Python framework implementing a deep association tracker for Tracking-By-Detection, to address the challenging task of tracking flight paths of bumblebees in a social group. While tracking algorithms for bumblebees exist, they often come with intensive restrictions, such as the need for sufficient lighting, high contrast between the animal and background, absence of occlusion, significant user input, etc. Tracking flight paths of bumblebees in a social group is challenging. They suddenly adjust movements and change their appearance during different wing beat states while exhibiting significant similarities in their individual appearance. The MBT3D tracker, developed in this research, is an adaptation of an existing ant tracking algorithm for bumblebee tracking. It incorporates an offline trained appearance descriptor along with a Kalman Filter for appearance and motion matching. Different detector architectures for upstream detections (You Only Look Once (YOLOv5), Faster Region Proposal Convolutional Neural Network (Faster R-CNN), and RetinaNet) are investigated in a comparative study to optimize performance. The detection models were trained on a dataset containing 11359 labeled bumblebee images. YOLOv5 reaches an Average Precision of AP = 53, 8%, Faster R-CNN achieves AP = 45, 3% and RetinaNet AP = 38, 4% on the bumblebee validation dataset, which consists of 1323 labeled bumblebee images. The tracker’s appearance model is trained on 144 samples. The tracker (with Faster R-CNN detections) reaches a Multiple Object Tracking Accuracy MOTA = 93, 5% and a Multiple Object Tracking Precision MOTP = 75, 6% on a validation dataset containing 2000 images, competing with state-of-the-art computer vision methods. The framework allows reliable tracking of different bumblebees in the same video stream with rarely occurring identity switches (IDS). MBT3D has much lower IDS than other commonly used algorithms, with one of the lowest false positive rates, competing with state-of-the-art animal tracking algorithms. The developed framework reconstructs the 3-dimensional (3D) flight paths of the bumblebees by triangulation. It also handles and compares two alternative stereo camera pairs if desired.
Today’s society is undergoing a paradigm shift driven by the megatrend of sustainability. This undeniably affects all areas of Western life. This paper aims to find out how the luxury industry is dealing with this change and what adjustments are made by the companies. For this purpose, interviews were conducted with managers from the luxury industry, in which they were asked about specific measures taken by their companies as well as trends in the industry. In a subsequent evaluation, the trends in the luxury industry were summarized for the areas of ecological, social, and economic sustainability. It was found that the area of environmental sustainability is significantly more focused than the other sub-areas. Furthermore, the need for a customer survey to validate the industry-based measures was identified.
The management of knowledge in organizations considers both established long-term processes and cooperation in agile project teams. Since knowledge can be both tacit and explicit, its transfer from the individual to the organizational knowledge base poses a challenge in organizations. This challenge increases when the fluctuation of knowledge carriers is exceptionally high. Especially in large projects in which external consultants are involved, there is a risk that critical, company-relevant knowledge generated in the project will leave the company with the external knowledge carrier and thus be lost. In this paper, we show the advantages of an early warning system for knowledge management to avoid this loss. In particular, the potential of visual analytics in the context of knowledge management systems is presented and discussed. We present a project for the development of a business-critical software system and discuss the first implementations and results.
Reducing poverty, protecting the planet, and improving life on earth for everyone are the essential goals of the "2030 Agenda for Sustainable Development"committed by the United Nations (UN). Achieving those goals will require technological innovation as well as their implementation in almost all areas of our business and day-to-day life. This paper proposes a high-level framework that collects and structures different uses cases addressing the goals defined by the UN. Hence, it contributes to the discussion by proposing technical innovations that can be used to achieve those goals. As an example, the goal "Climate Actionïs discussed in detail by describing use cases related to tackling biodiversity loss in order to conservate ecosystems.
This thesis aims at the presentation and discussion of well-accepted and new
imaging techniques applied to different types of flow in common hydraulic
engineering environments. All studies are conducted in laboratory conditions and
focus on flow depth and velocity measurements. Investigated flows cover a wide
range of complexity, e.g. propagation of waves, dam-break flows, slightly and fully
aerated spillway flows as well as highly turbulent hydraulic jumps.
Newimagingmethods are compared to different types of sensorswhich are frequently
employed in contemporary laboratory studies. This classical instrumentation as well
as the general concept of hydraulic modeling is introduced to give an overview on
experimental methods.
Flow depths are commonly measured by means of ultrasonic sensors, also known as
acoustic displacement sensors. These sensors may provide accurate data with high
sample rates in case of simple flow conditions, e.g. low-turbulent clear water flows.
However, with increasing turbulence, higher uncertainty must be considered.
Moreover, ultrasonic sensors can provide point data only, while the relatively large
acoustic beam footprint may lead to another source of uncertainty in case of
relatively short, highly turbulent surface fluctuations (ripples) or free-surface
air-water flows. Analysis of turbulent length and time scales of surface fluctuations
from point measurements is also difficult. Imaging techniques with different
dimensionality, however, may close this gap. It is shown in this thesis that edge
detection methods (known from computer vision) may be used for two-dimensional
free-surface extraction (i.e. from images taken through transparant sidewalls in
laboratory flumes). Another opportunity in hydraulic laboratory studies comes with
the application of stereo vision. Low-cost RGB-D sensors can be used to gather
instantaneous, three-dimensional free-surface elevations, even in flows with very
high complexity (e.g. aerated hydraulic jumps). It will be shown that the uncertainty
of these methods is of similar order as for classical instruments.
Particle Image Velocimetry (PIV) is a well-accepted and widespread imaging
technique for velocity determination in laboratory conditions. In combination with
high-speed cameras, PIV can give time-resolved velocity fields in 2D/3D or even as
volumetric flow fields. PIV is based on a cross-correlation technique applied to small
subimages of seeded flows. The minimum size of these subimages defines the
maximum spatial resolution of resulting velocity fields. A derivative of PIV for
aerated flows is also available, i.e. the so-called Bubble Image Velocimetry (BIV). This
thesis emphasizes the capacities and limitations of both methods, using relatively
simple setups with halogen and LED illuminations. It will be demonstrated that
PIV/BIV images may also be processed by means of Optical Flow (OF) techniques.
OF is another method originating from the computer vision discipline, based on the
assumption of image brightness conservation within a sequence of images. The
Horn-Schunck approach, which has been first employed to hydraulic engineering
problems in the studies presented herein, yields dense velocity fields, i.e. pixelwise
velocity data. As discussed hereinafter, the accuracy of OF competes well with PIV
for clear-water flows and even improves results (compared to BIV) for aerated flow
conditions. In order to independently benchmark the OF approach, synthetic images
with defined turbulence intensitiy are used.
Computer vision offers new opportunities that may help to improve the
understanding of fluid mechanics and fluid-structure interactions in laboratory
investigations. In prototype environments, it can be employed for obstacle detection
(e.g. identification of potential fish migration corridors) and recognition (e.g. fish
species for monitoring in a fishway) or surface reconstruction (e.g. inspection of
hydraulic structures). It can thus be expected that applications to hydraulic
engineering problems will develop rapidly in near future. Current methods have not
been developed for fluids in motion. Systematic future developments are needed to
improve the results in such difficult conditions.
We present the production of 58mCo on a small, 13 MeV medical cyclotron utilizing a siphon style liquid target system. Different concentrated iron(III)-nitrate solutions of natural isotopic distribution were irradiated at varying initial pressures and subsequently separated by solid phase extraction chromatography. The radio cobalt (58m/gCo and 56Co) was successfully produced with saturation activities of (0.35 ± 0.03) MBq μA−1 for 58mCo with a separation recovery of (75 ± 2) % of cobalt after one separation step utilizing LN-resin.
Density reduction effects on the production of [11C]CO2 in Nb-body targets on a medical cyclotron
(2023)
Medical isotope production of 11C is commonly performed in gaseous targets. The power deposition of the proton beam during the irradiation decreases the target density due to thermodynamic mixing and can cause an increase of penetration depth and divergence of the proton beam. In order to investigate the difference how the target-body length influences the operation conditions and the production yield, a 12 cm and a 22 cm Nb-target body containing N2/O2 gas were irradiated using a 13 MeV proton cyclotron. It was found that the density reduction has a large influence on the pressure rise during irradiation and the achievable radioactive yield. The saturation activity of [11C]CO2 for the long target (0.083 Ci/μA) is about 10% higher than in the short target geometry (0.075 Ci/μA).
Meitner-Auger-electron emitters have a promising potential for targeted radionuclide therapy of cancer because of their short range and the high linear energy transfer of Meitner-Auger-electrons (MAE). One promising MAE candidate is 197m/gHg with its half-life of 23.8 h and 64.1 h, respectively, and high MAE yield. Gold nanoparticles (AuNPs) that are labelled with 197m/gHg could be a helpful tool for radiation treatment of glioblastoma multiforme when infused into the surgical cavity after resection to prevent recurrence. To produce such AuNPs, 197m/gHg was embedded into pristine AuNPs. Two different syntheses were tested starting from irradiated gold containing trace amounts of 197m/gHg. When sodium citrate was used as reducing agent, no 197m/gHg labelled AuNPs were formed, but with tannic acid, 197m/gHg labeled AuNPs were produced. The method was optimized by neutralizing the pH (pH = 7) of the Au/197m/gHg solution, which led to labelled AuNPs with a size of 12.3 ± 2.0 nm as measured by transmission electron microscopy. The labelled AuNPs had a concentration of 50 μg (gold)/mL with an activity of 151 ± 93 kBq/mL (197gHg, time corrected to the end of bombardment).
The major advantage of labyrinth weirs over linear weirs is hydraulic efficiency. In hydraulic modeling efforts, this strength contrasts with limited pump capacity as well as limited computational power for CFD simulations. For the latter, reducing the number of investigated cycles can significantly reduce necessary computational time. In this study, a labyrinth weir with different cycle numbers was investigated. The simulations were conducted in FLOW-3D HYDRO as a Large Eddy Simulation. With a mean deviation of 1.75 % between simulated discharge coefficients and literature design equations, a reasonable agreement was found. For downstream conditions, overall consistent results were observed as well. However, the orientation of labyrinth weirs with a single cycle should be chosen carefully under consideration of the individual research purpose.
Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers’ cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines.
The concept of energy conversion into platform chemicals using bioelectrochemical systems (BES) has gained increasing attention in recent years, as the technology simultaneously provides an opportunity for sustainable chemical production and tackles the challenge of Power-to-X technologies. There are many approaches to realize the industrial scale of BES. One concept is to equip standard bioreactors with static electrodes. However, large installations resulted in a negative influence on various reactor parameters. In this study, we present a new single-chamber BES based on a stirred tank reactor in which the stirrer was replaced by a carbon fiber brush, performing the functions of the working electrode and the stirrer. The reactor is characterized in abiotic studies and electro-fermentations with Clostridium acetobutylicum. Compared to standard reactors an increase in butanol production of 20.14±3.66 % shows that the new BES can be efficiently used for bioelectrochemical processes.