4,691 research outputs found
Differential spectrum modeling and sensitivity for keV sterile neutrino search at KATRIN
Starting in 2026, the KATRIN experiment will conduct a high-statistics measurement of the differential tritium -spectrum to energies deep below the kinematic endpoint. This enables the search for keV sterile neutrinos with masses less than the kinematic endpoint energy , aiming for a statistical sensitivity of for the mixing amplitude. The differential spectrum is obtained by decreasing the retarding potential of KATRIN\u27s main spectrometer, and by determining the -electron energies by their energy deposition in the new TRISTAN SDD array. In this mode of operation, the existing integral model of the tritium spectrum is insufficient, and a novel differential model is developed in this work.
The new model (TRModel) convolves the differential tritium spectrum using responese matrices to predict the energy spectrum of registered events after data acquisition. Each response matrix encodes the spectral spectral distrortion from individual experimental effects, which depend on adjustable systematic parameters. This approach allows to efficiently assess the sensitivity impact of each systematics individually or in combination with others. The response matrices are obtained from monte carlo simulations, numerical convolution, and analytical computation.
In this work, the sensitivity impact of 20 systematic parameters is assessed for the TRISTAN Phase-1 measurement for which nine TRISTAN SDD modules are integrated into the KATRIN beamline. Furthermore, it is demonstrated that the sensitivity impact is significantly mitigated with several beamline field adjustments and minimal hardware modifications
Energy storage design and integration in power systems by system-value optimization
Energy storage can play a crucial role in decarbonising power systems by balancing
power and energy in time. Wider power system benefits that arise from these
balancing technologies include lower grid expansion, renewable curtailment, and
average electricity costs. However, with the proliferation of new energy storage
technologies, it becomes increasingly difficult to identify which technologies are
economically viable and how to design and integrate them effectively.
Using large-scale energy system models in Europe, the dissertation shows that solely
relying on Levelized Cost of Storage (LCOS) metrics for technology assessments can
mislead and that traditional system-value methods raise important questions about
how to assess multiple energy storage technologies. Further, the work introduces a
new complementary system-value assessment method called the market-potential
method, which provides a systematic deployment analysis for assessing multiple
storage technologies under competition. However, integrating energy storage in
system models can lead to the unintended storage cycling effect, which occurs in
approximately two-thirds of models and significantly distorts results. The thesis
finds that traditional approaches to deal with the issue, such as multi-stage optimization
or mixed integer linear programming approaches, are either ineffective
or computationally inefficient. A new approach is suggested that only requires
appropriate model parameterization with variable costs while keeping the model
convex to reduce the risk of misleading results.
In addition, to enable energy storage assessments and energy system research around
the world, the thesis extended the geographical scope of an existing European opensource
model to global coverage. The new build energy system model ‘PyPSA-Earth’
is thereby demonstrated and validated in Africa. Using PyPSA-Earth, the thesis
assesses for the first time the system value of 20 energy storage technologies across
multiple scenarios in a representative future power system in Africa. The results offer
insights into approaches for assessing multiple energy storage technologies under
competition in large-scale energy system models. In particular, the dissertation
addresses extreme cost uncertainty through a comprehensive scenario tree and finds
that, apart from lithium and hydrogen, only seven energy storage are optimizationrelevant
technologies. The work also discovers that a heterogeneous storage design
can increase power system benefits and that some energy storage are more important
than others. Finally, in contrast to traditional methods that only consider single
energy storage, the thesis finds that optimizing multiple energy storage options
tends to significantly reduce total system costs by up to 29%.
The presented research findings have the potential to inform decision-making processes
for the sizing, integration, and deployment of energy storage systems in
decarbonized power systems, contributing to a paradigm shift in scientific methodology
and advancing efforts towards a sustainable future
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments
In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident.
In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion.
This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture.
Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data.
As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis
Techno-Economic Assessment in Communications: New Challenges
This article shows a brief history of Techno-Economic Assessment (TEA) in
Communications, a proposed redefinition of TEA as well as the new challenges
derived from a dynamic context with cloud-native virtualized networks, the
Helium Network & alike blockchain-based decentralized networks, the new network
as a platform (NaaP) paradigm, carbon pricing, network sharing, and web3,
metaverse and blockchain technologies. The authors formulate the research
question and show the need to improve TEA models to integrate and manage all
this increasing complexity. This paper also proposes the characteristics TEA
models should have and their current degree of compliance for several use
cases: 5G and beyond, software-defined wide area network (SD-WAN), secure
access service edge (SASE), secure service edge (SSE), and cloud cybersecurity
risk assessment. The authors also present TEA extensibility to request for
proposals (RFP) processes and other industries, to conclude that there is an
urgent need for agile and effective TEA in Comms that allows industrialization
of agile decision-making for all market stakeholders to choose the optimal
solution for any technology, scenario and use case.Comment: 18 pages, 1 figure, 2 table
Serial-batch scheduling – the special case of laser-cutting machines
The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning
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