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TOWARDS FULL ELECTRICAL CERTIFICATION OF WIND TURBINES ON TEST BENCHES - EXPERIENCES GAINED FROM THE HIL-GRIDCOP PROJECT
No full text122129In recent years, more and more wind turbine manufacturers have been using test benches, such as the Dynamic Nacelle Testing Laboratory at Fraunhofer IWES in Bremerhaven, to test and optimize their turbines. To further accelerate the test phases, Fraunhofer IWES, together with the project partners Nordex Energy SE & Co. KG and Vestas Wind Systems A/S, has built a new test bench for grid compliance testing of reduced systems, consisting of the generator, the converter, and the transformer plus control as well as protection system. The HiL-GridCoP project demonstrated the comparability of test bench measurements on a wind turbine equipped with a doubly-fed induction generator with field measurements performed using a fault ride-through container. The aim of this paper is to present some of the key findings related to the electrical commissioning and test execution. The focus is on the grid simulator and on its impedance simulation functionality. It is discussed which limitations may be caused by specific test bench components and which issues might affect the test results. Finally, some measurements of voltage dips are presented to verify the test bench's comparability with field tests so that the full electrical certification can be carried out next
Concept About Shared Digital Twin of EV Batteries to Improve the Data Exchange in the Context of Battery Transport
No full textThe present work is based on a concept to improve the information exchange during off-site transport of batteries in the context of electromobility with different actors of the value chain using descriptive research design. The challenges regarding the exchange of information with corresponding actors such as the responsible authorities or safety institutions form the main research subject of this thesis. A measure of the European Commission for the electronic provision of transport information combined with the Shared Digital Twin of a battery by means of Battery Passport makes it possible to improve the error-prone current process, especially in the B2A sector. Hereby, synergy effects arise for different actors in terms of data availability, - integrity and - sovereignty through cloud-based provision of the transport information of batteries
Developing a Smart Economy Using Statistical Framework-Based Business Models in Smart Cities
No full text194205A smart city's smart economy thrives in various areas, including political strategy, operational efficiency, and innovation management. Business models in smart urban must be based on a new sustainable development strategy, one that conserves natural resources while safeguarding the environment. Therefore, this paper proposes Statistical Business Models (SBM) to enhance the business strategies for developing the economy in smart cities. Economic status in smart cities and changes in business models are part of SBM, a set of design concepts. Smart Business Models (SBM) are business strategies that take advantage of current economic situations by leveraging the power of influential smart communities. The implementation of data systems and business models is the foundation for a systematic study of managing the economy in a smart city. There are several connections between SDM's critical assessments of business models and the global economy and the business models. The experimental findings suggest that the proposed SBM achieves the highest statistical rate with sales revenue up to 95.23 %, gross margin ratio of 80.5%, consumer satisfaction ratio of 96.34%, efficiency ratio of 93.82%, and maintenance cost ratio of 15.08% compared to another existing method.9
Comparative Analysis of Modern, AI-based Data Compression on Power Quality Disturbance Data
No full textCurrent day power grids are continuously evolving away from large, synchronously-coupled, centralized generation units towards more decentralized structures. This change has a huge impact on grid inertia, stability, and through that power quality. Monitoring the quality of our power supply systems therefore becomes increasingly important in order to analyze and predict critical grid conditions. However, such monitoring systems can create huge amounts of data, which at some point become infeasible to transmit and store. Therefore, this contribution contains the analysis of two modern, AI-based data compression methods: recurrent autoencoders and convolutional autoencoders. They are compared with the more conventional technique of Compressed Sensing. All methods are applied to a diverse set of mathematically-modeled power quality disturbance data samples. Hyperparameter tuning is performed for the AI-based approaches, which shows the impact of different parameters on the networks' performance and identifies optimal choices for this power quality use case. It becomes clear, that the recurrent autoencoder's performance in not en-par with the other two approaches. The Compressed Sensing method introduces only very little compression error on harmonic disturbances, especially for lower compression rates. However, the convolutional autoencoder turns out to deliver a better overall performance
Dynamic Mechanical Properties of HTPB-IPDI Binders of Four PBX with Different HMX Contents and Energetic Particles Augmented Binder
No full text68373
Investigation of Aluminum and Gold Flip-Chip Bonding for Quantum Device Integration
No full text6165The majority of qubit chip integration is realized in a two-dimensional (2D) architecture. Whereas 3D architecture enables more advantages like efficient interconnect routing, allowing for more compact qubit coupling geometries, reducing form factor, and increased connectivity beyond nearest-neighbor interactions. Flip-chip (FC) assembly has been demonstrated to enable 3D architecture connecting qubit chip, interposer, and readout in a sandwich-like structure. Moreover, 3D integration allows the fabrication of hosting chip circuitry without degrading the qubit performance [1–4]. Different material considerations have to be taken into account since qubit operational frequency is in the gigahertz range and operating temperature is in the mK range to avoid thermal excitation. Materials that possess superconducting characteristics like Indium (In), Titanium Nitride (TiN), Tantalum Nitride (TaN), and Niobium (Nb) have been discussed as potential interconnect between building blocks [3, 5]. The In bumping on Aluminum (Al) redistribution layers require under-bump metallization (UBM) layers thus introducing multiple fabrication steps before the bonding process. An alternative approach would be to use the existing Al surface to electrochemically grow Al bumps and bond the chip using thermosonic bonding (TSB) at below 150°C to form a homogeneous metal-metal interface [6, 7]
Non-Volatile Inverter With 3D Cylindrical Metal-Ferroelectric-Metal Capacitor Realizing Digitized Voltage Output for Computing-In-Memory
No full text6164A non-volatile Hf0.5Zr0.5O2(HZO)-based Metal-Ferroelectric-Metal (MFM) inverter enabling AND/XNOR operations for Computation-in-Memory CiM) is proposed. Owing to the symmetric threshold voltage Vth) shift in the nFET and pFET of the MFM inverter, a digitized output after the multiply operation is successfully demonstrated for the first time with non-volatile memory, enabling the high precision CiM. In addition, the MFM inverter has the advantage of a large memory window (MW) because a large partial programming voltage is structurally applied to the MFM capacitor. Moreover, the MW and the endurance were intensively studied to clarify the key factors for reliable operation. Together with the process compatible FeRAM as a working memory, the MFM inverter has the potential to achieve low-power, high-density, and high precision non-volatile CiM systems
Wrapping it up: managing the successful hygrothermal supply and storage of timber boards
No full text752764For the last three decades Architects and Engineers have used one and two dimensional hygrothermal simulation tools to better understand the flow of heat and moisture through building envelopes. These tools have provided significant guidance regarding risks of moisture accumulation and mould growth. Many of the algorithms used in these tools have been established around the physical properties of solid wood products. Recent research has identified significant concerns from the design and construction professions regarding the moisture content of kiln dried solid wood products at construction sites. Changes in timber moisture content are inevitable as it progresses from end of production, through to storage, transportation and installation. Using WUFI® 2D modelling, this research utilises recorded under-wrap temperature and humidity data to investigate the likely change in timber moisture content that occurs due to the use of impermeable membranes for timber packaging. Packaging used for timber is typically impermeable and comes in a variety of colours and translucency. Previous research has proven that timber protected from the elements by impermeable plastic wrap can experience significant moisture content change from 11% to >30%, dependent on climate and storage practices. Such changes in moisture content are likely to promote mould growth and early stages of decay, rendering the timber unsatisfactory for building applications. This research seeks to prove that digital modelling can be used to reliably predict changes in timber moisture content prior to dispatch and improve management practices including the wrap membrane (thickness, colour and translucency) to mitigate supply claims and material loss
Dendritic plateau potentials can process spike sequences across multiple time-scales
No full textThe brain constantly processes information encoded in temporal sequences of spiking activity. This sequential activity emerges from sensory inputs as well as from the brain's own recurrent connectivity and spans multiple dynamically changing timescales. Decoding the temporal order of spiking activity across these varying timescales is a critical function of the brain, but we do not yet understand its neural implementation. The problem is, that the passive dynamics of neural membrane potentials occur on a short millisecond timescale, whereas many cognitive tasks require the integration of information across much slower behavioral timescales. However, actively generated dendritic plateau potentials do occur on such longer timescales, and their essential role for many aspects of cognition has been firmly established by recent experiments. Here, we build on these discoveries and propose a new model of neural computation that emerges from the interaction of localized plateau potentials across a functionally compartmentalized dendritic tree. We show how this interaction offers a robust solution to the timing invariant detection and processing of sequential spike patterns in single neurons. Stochastic synaptic transmission complements the deterministic all-or-none plateau process and improves information transmission by allowing ensembles of neurons to produce graded responses to continuous combinations of features. We found that networks of such neurons can solve highly complex sequence detection tasks by breaking down long inputs into sequences of shorter, random features that can be classified reliably. These results suggest that active dendritic processes are fundamental to neural computation.
Comparing Performance of Variational Quantum Algorithm Simulations on HPC Systems
No full text2127Variational quantum algorithms are of special importance in the research on quantum computing applications because of their applicability to current Noisy Intermediate-Scale Quantum (NISQ) devices. The main building blocks of these algorithms define a relatively large parameter space, making the comparison of results and performance between different approaches and software simulators cumbersome and prone to errors. In this paper, we employ a generic description of the problem, in terms of both Hamiltonian and ansatz, to consistently port a problem definition across different simulators. A use case relevant to current quantum hardware has been run on a set of HPC systems and software simulators to study the dependence of performance on the runtime environment, the scalability of the simulation codes, and the agreement between the physical results, respectively. The results show that our toolchain can successfully translate a problem definition between different simulators. On the other hand, variational algorithms are limited in their scaling by the long runtimes with respect to their memory footprint, so they expose limited parallelism to computation. This shortcoming is partially mitigated by using techniques like job arrays. The potential of the parser tool within the toolchain for exploring HPC performance and comparisons of results of variational algorithm simulations is highlighted