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In this paper, the performance evaluation of Frequency Modulated Chaotic On-Off Keying (FM-COOK) in AWGN, Rayleigh and Rician fading channels is given. The simulation results show that an improvement in BER can be gained by incorporating the FM modulation with COOK for SNR values less than 10dB in AWGN case and less than 6dB for Rayleigh and Rician fading channels.
Trueness and precision of milled and 3D printed root-analogue implants: A comparative in vitro study
(2023)
The need for innovation around the control functions of inverters is great. PV inverters were initially expected to be passive followers of the grid and to disconnect as soon as abnormal conditions happened. Since future power systems will be dominated by generation and storage resources interfaced through inverters these converters must move from following to forming and sustaining the grid. As “digital natives” PV inverters can also play an important role in the digitalisation of distribution networks. In this short review we identified a large potential to make the PV inverter the smart local hub in a distributed energy system. At the micro level, costs and coordination can be improved with bidirectional inverters between the AC grid and PV production, stationary storage, car chargers and DC loads. At the macro level the distributed nature of PV generation means that the same devices will support both to the local distribution network and to the global stability of the grid. Much success has been obtained in the former. The later remains a challenge, in particular in terms of scaling. Yet there is some urgency in researching and demonstrating such solutions. And while digitalisation offers promise in all control aspects it also raises significant cybersecurity concerns.
The transport of carbon dioxide through pipelines is one of the important components of Carbon dioxide Capture and Storage (CCS) systems that are currently being developed. If high flow rates are desired a transportation in the liquid or supercritical phase is to be preferred. For technical reasons, the transport must stay in that phase, without transitioning to the gaseous state. In this paper, a numerical simulation of the stationary process of carbon dioxide transport with impurities and phase transitions is considered. We use the Homogeneous Equilibrium Model (HEM) and the GERG-2008 thermodynamic equation of state to describe the transport parameters. The algorithms used allow to solve scenarios of carbon dioxide transport in the liquid or supercritical phase, with the detection of approaching the phase transition region. Convergence of the solution algorithms is analyzed in connection with fast and abrupt changes of the equation of state and the enthalpy function in the region of phase transitions.
Pipeline transport is an efficient method for transporting fluids in energy supply and other technical applications. While natural gas is the classical example, the transport of hydrogen is becoming more and more important; both are transmitted under high pressure in a gaseous state. Also relevant is the transport of carbon dioxide, captured in the places of formation, transferred under high pressure in a liquid or supercritical state and pumped into underground reservoirs for storage. The transport of other fluids is also required in technical applications. Meanwhile, the transport equations for different fluids are essentially the same, and the simulation can be performed using the same methods. In this paper, the effect of control elements such as compressors, regulators and flaptraps on the stability of fluid transport simulations is studied. It is shown that modeling of these elements can lead to instabilities, both in stationary and dynamic simulations. Special regularization methods were developed to overcome these problems. Their functionality also for dynamic simulations is demonstrated for a number of numerical experiments.
AErOmAt Abschlussbericht
(2020)
Das Projekt AErOmAt hatte zum Ziel, neue Methoden zu entwickeln, um einen erheblichen Teil aerodynamischer Simulationen bei rechenaufwändigen Optimierungsdomänen einzusparen. Die Hochschule Bonn-Rhein-Sieg (H-BRS) hat auf diesem Weg einen gesellschaftlich relevanten und gleichzeitig wirtschaftlich verwertbaren Beitrag zur Energieeffizienzforschung geleistet. Das Projekt führte außerdem zu einer schnelleren Integration der neuberufenen Antragsteller in die vorhandenen Forschungsstrukturen.
In this paper, modeling of piston and generic type gas compressors for a globally convergent algorithm for solving stationary gas transport problems is carried out. A theoretical analysis of the simulation stability, its practical implementation and verification of convergence on a realistic gas network have been carried out. The relevance of the paper for the topics of the conference is defined by a significance of gas transport networks as an advanced application of simulation and modeling, including the development of novel mathematical and numerical algorithms and methods.
Solving transport network problems can be complicated by non-linear effects. In the particular case of gas transport networks, the most complex non-linear elements are compressors and their drives. They are described by a system of equations, composed of a piecewise linear ‘free’ model for the control logic and a non-linear ‘advanced’ model for calibrated characteristics of the compressor. For all element equations, certain stability criteria must be fulfilled, providing the absence of folds in associated system mapping. In this paper, we consider a transformation (warping) of a system from the space of calibration parameters to the space of transport variables, satisfying these criteria. The algorithm drastically improves stability of the network solver. Numerous tests on realistic networks show that nearly 100% convergence rate of the solver is achieved with this approach.
In this paper, an analysis of the error ellipsoid in the space of solutions of stationary gas transport problems is carried out. For this purpose, a Principal Component Analysis of the solution set has been performed. The presence of unstable directions is shown associated with the marginal fulfillment of the resistivity conditions for the equations of compressors and other control elements in gas networks. Practically, the instabilities occur when multiple compressors or regulators try to control pressures or flows in the same part of the network. Such problems can occur, in particular, when the compressors or regulators reach their working limits. Possible ways of resolving instabilities are considered.
The paper presents the topological reduction method applied to gas transport networks, using contraction of series, parallel and tree-like subgraphs. The contraction operations are implemented for pipe elements, described by quadratic friction law. This allows significant reduction of the graphs and acceleration of solution procedure for stationary network problems. The algorithm has been tested on several realistic network examples. The possible extensions of the method to different friction laws and other elements are discussed.
The general method of topological reduction for the network problems is presented on example of gas transport networks. The method is based on a contraction of series, parallel and tree-like subgraphs for the element equations of quadratic, power law and general monotone dependencies. The method allows to reduce significantly the complexity of the graph and to accelerate the solution procedure for stationary network problems. The method has been tested on a large set of realistic network scenarios. Possible extensions of the method have been described, including triangulated element equations, continuation of the equations at infinity, providing uniqueness of solution, a choice of Newtonian stabilizer for nearly degenerated systems. The method is applicable for various sectors in the field of energetics, including gas networks, water networks, electric networks, as well as for coupling of different sectors.
Photovoltaic (PV) power data are a valuable but as yet under-utilised resource that could be used to characterise global irradiance with unprecedented spatio-temporal resolution. The resulting knowledge of atmospheric conditions can then be fed back into weather models and will ultimately serve to improve forecasts of PV power itself. This provides a data-driven alternative to statistical methods that use post-processing to overcome inconsistencies between ground-based irradiance measurements and the corresponding predictions of regional weather models (see for instance Frank et al., 2018). This work reports first results from an algorithm developed to infer global horizontal irradiance as well as atmospheric optical properties such as aerosol or cloud optical depth from PV power measurements.
The temperature of photovoltaic modules is modelled as a dynamic function of ambient temperature, shortwave and longwave irradiance and wind speed, in order to allow for a more accurate characterisation of their efficiency. A simple dynamic thermal model is developed by extending an existing parametric steady-state model using an exponential smoothing kernel to include the effect of the heat capacity of the system. The four parameters of the model are fitted to measured data from three photovoltaic systems in the Allgäu region in Germany using non-linear optimisation. The dynamic model reduces the root-mean-square error between measured and modelled module temperature to 1.58 K on average, compared to 3.03 K for the steady-state model, whereas the maximum instantaneous error is reduced from 20.02 to 6.58 K.
This dataset contains data from two measurement campaigns in autumn 2018 and summer 2019 that were part of the BMWi project "MetPVNet", and serve as a supplement to the paper "Dynamic model of photovoltaic module temperature as a function of atmospheric conditions", published in the special edition of "Advances in Science and Research", the proceedings of the 19th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2019.
Data are resampled to one minute, and include:
PV module temperature
Ambient temperature
Plane-of-array irradiance
Windspeed
Atmospheric thermal emission
The data were used for the dynamic temperature model, as presented in the paper
Incoming solar radiation is an important driver of our climate and weather. Several studies (see for instance Frank et al. 2018) have revealed discrepancies between ground-based irradiance measurements and the predictions of regional weather models. In the realm of electricity generation, accurate forecasts of solar photovoltaic (PV)energy yield are becoming indispensable for cost-effective grid operation: in Germany there are 1.6 million PVsystems installed, with a nominal power of 46 GW (Bundesverband Solarwirtschaft 2019). The proliferation of PV systems provides a unique opportunity to characterise global irradiance with unprecedented spatiotemporalresolution, which in turn will allow for highly resolved PV power forecasts.
In view of the rapid growth of solar power installations worldwide, accurate forecasts of photovoltaic (PV) power generation are becoming increasingly indispensable for the overall stability of the electricity grid. In the context of household energy storage systems, PV power forecasts contribute towards intelligent energy management and control of PV-battery systems, in particular so that self-sufficiency and battery lifetime are maximised. Typical battery control algorithms require day-ahead forecasts of PV power generation, and in most cases a combination of statistical methods and numerical weather prediction (NWP) models are employed. The latter are however often inaccurate, both due to deficiencies in model physics as well as an insufficient description of irradiance variability.
The electricity grid of the future will be built on renewable energy sources, which are highly variable and dependent on atmospheric conditions. In power grids with an increasingly high penetration of solar photovoltaics (PV), an accurate knowledge of the incoming solar irradiance is indispensable for grid operation and planning, and reliable irradiance forecasts are thus invaluable for energy system operators. In order to better characterise shortwave solar radiation in time and space, data from PV systems themselves can be used, since the measured power provides information about both irradiance and the optical properties of the atmosphere, in particular the cloud optical depth (COD). Indeed, in the European context with highly variable cloud cover, the cloud fraction and COD are important parameters in determining the irradiance, whereas aerosol effects are only of secondary importance.
Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. The method is tested on data from two measurement campaigns that took place in the Allgäu region in Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 min resolution along with a non-linear photovoltaic module temperature model, global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 5.79 W m−2 (7.35 W m−2) under clear (cloudy) skies, averaged over the two campaigns, whereas for the retrieval using coarser 15 min power data with a linear temperature model the mean bias error is 5.88 and 41.87 W m−2 under clear and cloudy skies, respectively.
During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a 1D radiative transfer simulation, and the results are compared to both satellite retrievals and data from the Consortium for Small-scale Modelling (COSMO) weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken-cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work.
Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. Specifically, the aerosol (cloud) optical depth is inferred during clear sky (completely overcast) conditions. The method is tested on data from two measurement campaigns that took place in Allgäu, Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 minute resolution, the hourly global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 11.45 W m−2, averaged over the two campaigns, whereas for the retrieval using coarser 15 minute power data the mean bias error is 16.39 W m−2.
During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a one-dimensional radiative transfer simulation, and the results are compared to both satellite retrievals as well as data from the COSMO weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and are properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work.
Kinetic Inductance Detectors with Integrated Antennas for Ground and Space-Based Sub-mm Astronomy
(2009)
Very large arrays of Microwave Kinetic Inductance Detectors (MKIDs) have the potential to revolutionize ground and space based astronomy. They can offer in excess of 10.000 pixels with large dynamic range and very high sensitivity in combination with very efficient frequency division multiplexing at GHz frequencies. In this paper we present the development of a 400 pixel MKID demonstration array, including optical coupling, sensitivity measurements, beam pattern measurements and readout. The design presented can be scaled to any frequency between 80 GHz and >5 THz because there is no need for superconducting structures that become lossy at frequencies above the gap frequency of the materials used. The latter would limit the frequency coverage to below 1 THz for relatively high gap materials such as NbTiN. An individual pixels of the array consist of a distributed Aluminium CPW MKID with an integrated twin slot antenna at its end. The antenna is placed in the in the second focus of an elliptical high purity Si lens. The lens-antenna coupling design allows room for the MKID resonator outside of the focal point of the lens. The best dark noise equivalent power of these devices is measured to be NEP = 7×10-19 W/[square root]Hz and the optical coupling efficiency is around 30%, in which no antireflection coating was used on the Si lens. For the readout we use a commercial arbitrary waveform generator and a 1.5 GHz FFTS. We show that using this concept it is possible to read out in excess of 400 pixels with 1 board and 1 pair of coaxial cables.
Salts and proteins comprise two of the basic molecular components of biological materials. Kosmotropic/chaotropic co-solvation and matching ion water affinities explain basic ionic effects on protein aggregation observed in simple solutions. However, it is unclear how these theories apply to proteins in complex biological environments and what the underlying ionic binding patterns are. Using the positive ion Ca2+ and the negatively charged membrane protein SNAP25, we studied ion effects on protein oligomerization in solution, in native membranes and in molecular dynamics (MD) simulations. We find that concentration-dependent ion-induced protein oligomerization is a fundamental chemico-physical principle applying not only to soluble but also to membrane-anchored proteins in their native environment. Oligomerization is driven by the interaction of Ca2+ ions with the carboxylate groups of aspartate and glutamate. From low up to middle concentrations, salt bridges between Ca2+ ions and two or more protein residues lead to increasingly larger oligomers, while at high concentrations oligomers disperse due to overcharging effects. The insights provide a conceptual framework at the interface of physics, chemistry and biology to explain binding of ions to charged protein surfaces on an atomistic scale, as occurring during protein solubilisation, aggregation and oligomerization both in simple solutions and membrane systems.
Umwelt: Technologien, um die Folgen des Klimawandels zu begrenzen, gibt es. Die soziale und politische Dimension ist der Hemmschuh, so dass sie teils nur zögernd zum Einsatz kommen.
Die Redaktion der VDI nachrichten betreute drei Studierende, die aktuell von jedem Vortrag einen Artikel für unser Ingenieurportal www.ingenieur.de schrieben sowie eine Meldung für die VDI-nachrichten-E-Paper-App. Dieser Artikel ist das Resümee der drei zur Ringvorlesung, aber keine inhaltlich komplette Zusammenfassung. Alle Artikel lesen Sie online unter: www.ingenieur.de/Ringvorlesung2018
In diesem Paper wird ein Modell eines Photovoltaik(PV)-Diesel-Hybrid-Systems aufgebaut. Dieses System besitzt neben einer PV-Anlage einen Batteriespeicher und ist an das öffentliche Stromnetz angeschlossen. Bei einem Ausfall aller drei Energiequellen stellt ein Dieselgenerator die Stromversorgung sicher. Mit Hilfe des erstellten Modells wird der Einfluss der unterschiedlichen Jahreszeiten und Wetterbedingungen auf den PV-Ertrag und das gesamte System im Zeitraum von Februar 2016 bis Februar 2017 untersucht. Die Messdaten dafür stammen von einem Krankenhaus in Akwatia, Ghana. Das Krankenhaus besitzt bereits eine PV-Anlage und einen Dieselgenerator als Backup.
Ein weiterer Aspekt der Untersuchung ist der Einfluss der Stromausfälle, die in dieser Region häufig vorkommen, auf den Einsatz des Generators.
Resultat der Untersuchung ist die Relevanz saisonaler und infrastruktureller Einflüsse auf die Betriebsweise des Systems. Mit Hilfe des erstellten Modells wurde analysiert, dass besonders während der Regenzeit im August die PV-Leistung sinkt und folglich viel Energie durch das öffentliche Stromnetz und den Generator bereitgestellt werden muss. Ein weiterer signifikanter Einbruch im PV-Ertrag ist zur Zeit des Harmattans im Januar zu verzeichnen.
Die Norm EN ISO 13849-1 stellt explizite Anforderungen an sicherheitsgerichtete SPS-Software. Wie lassen sich diese im Maschinenbau praxisgerecht umsetzen? Mit dieser Frage hat sich ein von der DGUV gefördertes und an der Hochschule Bonn-Rhein-Sieg durchgeführtes Projekt beschäftigt. Der Beitrag skizziert die Vorgehensweise zur möglichen Umsetzung der normativen Anforderungen. Diese Vorgehensweise ist unabhängig von der verwendeten Sicherheits-SPS und daher allgemein anwendbar. Es wird auf insgesamt 10 dokumentierte Beispiele und einen ausführlichen Forschungsbericht verwiesen, die downloadbar sind.
Automatisierungstechnik
(2006)
Für die Entwicklung steuerungstechnischer Sicherheitsfunktionen muss ab 2012 die Normen EN ISO 13849-1 oder EN 62061 befolgt werden, die sowohl Anforderungen an die Hardware als auch Anforderungen an die Software beschreibt. Die Anforderungen an die Software spielten bis vor einigen Jahren kaum eine Rolle, da Sicherheitsfunktionen vorzugsweise in Hardware realisiert wurden. Heutzutage ist es jedoch sehr häufig üblich, Sicherheitsfunktionen mit einer dafür geeigneten programmierbaren SPS zu realisieren. Die neuen Normen bzgl. der sicheren Steuerung von Maschinen verlangen neben der Quantifizierung der Hardware-Ausfallraten von Sicherheitsfunktionen noch ein Management der Sicherheitsfunktionen. Dazu gehört auch ein Management der Softwareentwicklung für Sicherheitsfunktionen, um systematische Fehler zu minimieren. Dieses Management der Softwareentwicklung wird im Wesentlichen durch das V-Modell repräsentiert. Für die Maschinebauindustrie darf dieser Managementprozess nicht zu aufwendig sein, ansonsten wird dieser in der Praxis nur schwer angenommen. Eine Möglichkeit der Abarbeitung des V-Modells wird vorgestellt. Wahrscheinlich ist diese aufgezeigte Möglichkeit für die Industrie noch zu aufwendig.
Automatisierungstechnik
(2014)
Die Automatisierungstechnik wird aus praktischer Sicht mit vielen Beispielen und zahlreichen Bildern veranschaulicht. Studenten ingenieurwissenschaftlicher Fachrichtungen sowie Ingenieure und Techniker in der Ausbildung und der beruflichen Praxis können sich auf dem Stand der Technik selbständig einarbeiten.
DeltaV Neural ist eine Softwareapplikation innerhalb des Prozessautomatisierungssystems DeltaV, die es dem Anwender ermöglicht, auf einfache Art und Weise Softsensoren zu konfigurieren. Softsensoren besitzen die Aufgabe, schwer messbare oder nur in großen Zeitabständen ermittelbare Prozessausgangsgrößen mittels einfacher und schneller messbarer Ersatzmessgrößen zu schätzen bzw. vorherzusagen.
Dem RTPM (Real-Time Performance Monitoring) wurde in den letzten Jahren in der Automatisierungstechnik immer mehr Beachtung geschenkt. Drei ausgewählte Aspekte des RPTM werden behandelt: Alarmanalyse, Reglerperformance und Stelleinrichtungen. Die Reduktion von Alarmmeldungen mit Hilfe einer Alarmanalyse wird mit Hilfe von Beispielen aus der Industrie veranschaulicht. Ziel einer Analyse ist die Identifikation von (1) falschen Alarmgrenzen, (2) Reglern, bei denen Störungen im Handbetrieb ausgeregelt werden, (3) Reglern, bei denen Betriebspunktänderungen im Handbetrieb ausgeführt werden, (4) Reglern mit Stellgrößen bei 0% oder 100%, (5) falschen Reglerparametern sowie (6) Fehlern in der Messtechnik, Antrieben, Klappen oder Ventilen. Die industrielle Anwendung der Überwachung der Reglerperformance wird anhand des in das Prozessautomatisierungssystem DeltaV von Emerson Process Management integrierten Softwareproduktes DeltaV Inspect erläutert. DeltaV überwacht und bewertet (1) die Bereichsüberschreitungen der Regelgrößen und der Stellsignale, (2) die Betriebsarten (Hand oder Automatik) und (3) die Regelungsgüte. Die Regelungsgüte wird bei einem konstanten Sollwert und stochastischen Störungen aus dem Unterschied zwischen der tatsächlichen und der theoretisch erreichbaren Varianz des Regelfehlers berechnet. Anstelle einer Korrelations- bzw. Regressionsanalyse wird die theoretisch erreichbare minimale Varianz aus der aktuellen Varianz des Regelfehlers und der Varianz der Abweichung der aufeinander folgenden Regelfehlerabtastwerte berechnet.
In this contribution, we perform computer simulations to expedite the development of hydrogen storages based on metal hydride. These simulations enable in-depth analysis of the processes within the systems which otherwise could not be achieved. That is, because the determination of crucial process properties require measurement instruments in the setup which are currently not available. Therefore, we investigate the reliability of reaction values that are determined by a design of experiments.
Specifically, we first explain our model setup in detail. We define the mathematical terms to obtain insights into the thermal processes and reaction kinetics. We then compare the simulated results to measurements of a 5-gram sample consisting of iron-titanium-manganese (FeTiMn) to obtain the values with the highest agreement with the experimental data. In addition, we improve the model by replacing the commonly used Van’t-Hoff equation by a mathematical expression of the pressure-composition-isotherms (PCI) to calculate the equilibrium pressure.
Finally, the parameters’ accuracy is checked in yet another with an existing metal hydride system. The simulated results demonstrate high concordance with experimental data, which advocate the usage of approximated kinetic reaction properties by a design of experiments for further design studies. Furthermore, we are able to determine process parameters like the entropy and enthalpy.
The lattice Boltzmann method (LBM) is an efficient simulation technique for computational fluid mechanics and beyond. It is based on a simple stream-and-collide algorithm on Cartesian grids, which is easily compatible with modern machine learning architectures. While it is becoming increasingly clear that deep learning can provide a decisive stimulus for classical simulation techniques, recent studies have not addressed possible connections between machine learning and LBM. Here, we introduce Lettuce, a PyTorch-based LBM code with a threefold aim. Lettuce enables GPU accelerated calculations with minimal source code, facilitates rapid prototyping of LBM models, and enables integrating LBM simulations with PyTorch's deep learning and automatic differentiation facility. As a proof of concept for combining machine learning with the LBM, a neural collision model is developed, trained on a doubly periodic shear layer and then transferred to a different flow, a decaying turbulence. We also exemplify the added benefit of PyTorch's automatic differentiation framework in flow control and optimization. To this end, the spectrum of a forced isotropic turbulence is maintained without further constraining the velocity field.
Herein we report an update to ACPYPE, a Python3 tool that now properly converts AMBER to GROMACS topologies for force fields that utilize nondefault and nonuniform 1–4 electrostatic and nonbonded scaling factors or negative dihedral force constants. Prior to this work, ACPYPE only converted AMBER topologies that used uniform, default 1–4 scaling factors and positive dihedral force constants. We demonstrate that the updated ACPYPE accurately transfers the GLYCAM06 force field from AMBER to GROMACS topology files, which employs non-uniform 1–4 scaling factors as well as negative dihedral force constants. Validation was performed using β-d-GlcNAc through gas-phase analysis of dihedral energy curves and probability density functions. The updated ACPYPE retains all of its original functionality, but now allows the simulation of complex glycomolecular systems in GROMACS using AMBER-originated force fields. ACPYPE is available for download at https://github.com/alanwilter/acpype.
Abschlussbericht zum BMBF-Fördervorhaben Enabling Infrastructure for HPC-Applications (EI-HPC)
(2020)
Ein Projektvertrag bietet den Geschäftspartnern rechtliche Sicherheit. Häufig wird er von Anwälten aufgesetzt. Insbesondere in kleinen und mittelständischen Unternehmen ist es aber auch oft der Projektleiter, der den Vertrag formuliert. Prof. Dr. Uwe Braehmer erklärt, unter welchen Bedingungen man einen Vertrag ohne juristische Beratung erstellen kann und liefert eine Checkliste mit den Regelungen, die ein Vertrag enthalten sollte.
Fallstrick Arbeitsrecht
(2005)
Technische Innovationen verändern den Alltag enorm – auch den von Journalisten. Technik-Fachjournalisten sind daher gefragt wie nie. Was sie können müssen, wie in den Medien über Digitalisierung berichtet wird und inwiefern künstliche Intelligenz (KI) Journalisten in Zukunft überflüssig machen wird, beantwortet der Medienwissenschaftler Andreas Schümchen im Gespräch mit dem "Fachjournalist".
Reliable and regional differentiated power forecasts are required to guarantee an efficient and economic energy transition towards renewable energies. Amongst other renewable energy technologies, e.g. wind mills, photovoltaic (PV) systems are an essential component of this transition being cost-efficient and simply to install. Reliable power forecasts are however required for a grid integration of photovoltaic systems, which among other data requires high-resolution spatio-temporal global irradiance data.
Reliable and regional differentiated power forecasts are required to guarantee an efficient and economic energy transition towards renewable energies. Amongst other renewable energy technologies, e.g. wind mills, photovoltaic systems are an essential component of this transition being cost-efficient and simply to install. Reliable power forecasts are however required for a grid integration of photovoltaic systems, which among other data requires high-resolution spatio-temporal global irradiance data. Hence the generation of robust reviewed global irradiance data is an essential contribution for the energy transition.
Vorwort
(2022)
Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) <0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMA algorithm and the linear regression model show extreme error outliers of nRMSE >1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector.
This work proposes a novel approach for probabilistic end-to-end all-sky imager-based nowcasting with horizons of up to 30 min using an ImageNet pre-trained deep neural network. The method involves a two-stage approach. First, a backbone model is trained to estimate the irradiance from all-sky imager (ASI) images. The model is then extended and retrained on image and parameter sequences for forecasting. An open access data set is used for training and evaluation. We investigated the impact of simultaneously considering global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI) on training time and forecast performance as well as the effect of adding parameters describing the irradiance variability proposed in the literature. The backbone model estimates current GHI with an RMSE and MAE of 58.06 and 29.33 W m−2, respectively. When extended for forecasting, the model achieves an overall positive skill score reaching 18.6 % compared to a smart persistence forecast. Minor modifications to the deterministic backbone and forecasting models enables the architecture to output an asymmetrical probability distribution and reduces training time while leading to similar errors for the backbone models. Investigating the impact of variability parameters shows that they reduce training time but have no significant impact on the GHI forecasting performance for both deterministic and probabilistic forecasting while simultaneously forecasting GHI, DNI, and DHI reduces the forecast performance.
The introduction of new steering conceptsSteer-by-Wire (SBW) gives possibility to replace theconventional steering wheel by an alternative userinterface such as a sidestick. In SBW system the sidestickcan be used as user input element instead of a steeringwheel. The implementation of sidestick in the Human-Machine-Interface (HMI) allows combiningthe conventional steering consisting of a steeringwheel, an accelerator and a brake pedal into a singleelement. Also the implementation of the sidestickcreates new, interesting and flexible design optionswhich can be used to transform the driver’s spatialenvironment. This article describes an active sidestickfor a vehicle which has been developed, integrated andtested in accordance of haptic, ergonomic and safetyrelevant requirements. The control strategies used forthe active attenuators of the sidestick have beeninvestigated and optimised using a Simulink model.
The small and remote households in Northern regions demand thermal energy rather than electricity. Wind turbine in such places can be used to convert wind energy into thermal energy directly using a heat generator based on the principle of the Joule machine. The heat generator driven by a wind turbine can reduce the cost of energy for heating system. However the optimal performance of the system depends on the torque-speed characteristics of the wind turbine and the heat generator. To achieve maximum efficiency of operation both characteristics should be matched. In the article the condition of optimal performance is developed and an example of the system operating at maximum efficiency is simulated.
The article presents a solution to detect rotor position at stand still condition for all types of permanent magnet brushless dc motors. The solution provides both secure and fast method for starting of the brushless motor, that is independent on the sensorless control scheme used. Nonlinearities found in standard three phase permanent magnet dc motor are used to derive the rotor position at stand still. The described solution assumes that there is availability of the neutral point of the three phase star motor windings.
Novel methods for contingency analysis of gas transport networks are presented. They are motivated by the transition of our energy system where hydrogen plays a growing role. The novel methods are based on a specific method for topological reduction and so-called supernodes. Stationary Euler equations with advanced compressor thermodynamics and a gas law allowing for gas compositions with up to 100% hydrogen are used. Several measures and plots support an intuitive comparison and analysis of the results. In particular, it is shown that the newly developed methods can estimate locations and magnitudes of additional capacities (injection, buffering, storage etc.) with a reasonable performance for networks of relevant composition and size.
In this paper, the electrochemical alkaline methanol oxidation process, which is relevant for the design of efficient fuel cells, is considered. An algorithm for reconstructing the reaction constants for this process from the experimentally measured polarization curve is presented. The approach combines statistical and principal component analysis and determination of the trust region for a linearized model. It is shown that this experiment does not allow one to determine accurately the reaction constants, but only some of their linear combinations. The possibilities of extending the method to additional experiments, including dynamic cyclic voltammetry and variations in the concentration of the main reagents, are discussed.
It is shown that the electrochemical kinetics of alkaline methanol oxidation can be reduced by setting certain fast reactions contained in it to a steady state. As a result, the underlying system of Ordinary Differential Equations (ODE) is transformed to a system of Differential-Algebraic Equations (DAE). We measure the precision characteristics of such transformation and discuss the consequences of the obtained model reduction.
Alkaline methanol oxidation is an important electrochemical process in the design of efficient fuel cells. Typically, a system of ordinary differential equations is used to model the kinetics of this process. The fitting of the parameters of the underlying mathematical model is performed on the basis of different types of experiments, characterizing the fuel cell. In this paper, we describe generic methods for creation of a mathematical model of electrochemical kinetics from a given reaction network, as well as for identification of parameters of this model. We also describe methods for model reduction, based on a combination of steady-state and dynamical descriptions of the process. The methods are tested on a range of experiments, including different concentrations of the reagents and different voltage range.
An Universitäten und Fachhochschulen ist die Mathematik-Ausbildung eines der Nadelöhre für angehende Ingenieurinnen und Ingenieure. Viele Studierende der Ingenieurwissenschaften scheitern in den ersten Studiensemestern an den Anforderungen der Mathematik. Lehrende, Fach- und Hochschuldidaktiker/innen und zunehmend auch Fachvertretungen und Verbände stellen sich die Frage, was an den Fakultäten und Fachbereichen getan werden kann, damit Studierende ihre mathematischen Fähigkeiten vergrößern und den anspruchsvollen Studienweg zur Ingenieurin oder zum Ingenieur meistern können.
Fundamental hydrogen storage properties of TiFe-alloy with partial substitution of Fe by Ti and Mn
(2020)
TiFe intermetallic compound has been extensively studied, owing to its low cost, good volumetric hydrogen density, and easy tailoring of hydrogenation thermodynamics by elemental substitution. All these positive aspects make this material promising for large-scale applications of solid-state hydrogen storage. On the other hand, activation and kinetic issues should be amended and the role of elemental substitution should be further understood. This work investigates the thermodynamic changes induced by the variation of Ti content along the homogeneity range of the TiFe phase (Ti:Fe ratio from 1:1 to 1:0.9) and of the substitution of Mn for Fe between 0 and 5 at.%. In all considered alloys, the major phase is TiFe-type together with minor amounts of TiFe2 or \b{eta}-Ti-type and Ti4Fe2O-type at the Ti-poor and rich side of the TiFe phase domain, respectively. Thermodynamic data agree with the available literature but offer here a comprehensive picture of hydrogenation properties over an extended Ti and Mn compositional range. Moreover, it is demonstrated that Ti-rich alloys display enhanced storage capacities, as long as a limited amount of \b{eta}-Ti is formed. Both Mn and Ti substitutions increase the cell parameter by possibly substituting Fe, lowering the plateau pressures and decreasing the hysteresis of the isotherms. A full picture of the dependence of hydrogen storage properties as a function of the composition will be discussed, together with some observed correlations.
Naturwissenschaft und Technik können sich nur unvollkommen einer breiteren Öffentlichkeit darstellen. Eine Umfrage des Bundesforschungsministeriums hat bewiesen, dass die Voraussetzungen eigentlich günstig sind: Drei Viertel der Deutschen beurteilen die Technik positiv. Das Defizit liegt in der journalistischen Vermittlung wissenschaftlicher Erkenntnisse und technischer Fakten an ein Massenpublikum. Dieses Versagen des Journalismus wird unter anderem durch einen Strukturfehler in der Journalistenausbildung an den Hochschulen verursacht. Denn journalistische Ausbildungsgänge werden fast ausschließlich an geisteswissenschaftlichen Fakultäten unserer Hochschulen gelehrt. Infolgedessen begreifen diese vorwiegend soziologisch ausgebildeten Journalisten „Wissenschaftspublizistik“ manchmal einseitig als „Risiko- und Katastrophenjournalismus“. Meist wird nicht die Leistung, sondern das Versagen herausgestellt.
Der vorliegende Beitrag setzt sich mit der Bedeutung von Lernorten der beruflichen Bildung im Zuge einer BBNE sowie der diesbezüglichen Kompetenzentwicklung auseinander. Dabei wird entlang des BIBB-Modellversuchs „NAUZUBI“ ein möglicher Ansatz skizziert, der darauf ausgerichtet ist, eine integrative Kompetenzentwicklung in Nachhaltigkeitsthemen zu ermöglichen. Ausgangspunkt sind dabei betriebliche Nachhaltigkeitsaudits, die im vorliegenden Ansatz als kontextualisierte Zugänge für berufliche Lernanlässe dienten. Diese wurden in aufeinander abgestimmten Schritten im betrieblichen und schulischen Lernen reflektiert. Im Beitrag werden das Grundkonzept sowie die entsprechenden Umsetzungserfahrungen beschrieben. Es werden ferner Herausforderungen und Potenziale für das betriebliche, berufsschulische und das lernortkooperative Lernen und damit die integrative Kompetenzentwicklung dargestellt.
Das Thema Prozessorganisation hat die gfo in den letzten Jahren intensiv begleitet und auf mehreren Tagungen eingehend diskutiert. Um den aktuellen Umsetzungsstand der Prozessorganisation in Deutschland zu untersuchen wurde im Jahr 2014 eine empirische Studie durchgeführt. Neben der Ist-Situation liefert die Studie Einsichten in Erwartungen über zukünftige Entwicklungen, Hindernisse und Erfolgsfaktoren der Einführung einer Prozessorganisation sowie zur Zielerreichung durch prozessorientierte Organisationen.