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The aim of current research on internal combustion engines is to further reduce exhaust gas pollutant emissions while simultaneously lowering carbon dioxide emissions in order to limit the greenhouse effect. Due to the restricted potential for reducing CO2 (carbon dioxide) emissions when using fossil fuels, an extensive defossilisation of the transport sector is necessary. Investigations of future propulsion systems should therefore not focus solely on further development of the prime mover, but also on the energy carrier which is used. In this context, fuels from renewable energy sources are of particular interest, e.g. paraffinic diesel fuels such as hydrogenated vegetable oil (HVO) or potentially entirely synthetic fuels like POMDME (polyoxymethylene dimethyl ether, short: OME) as well as blends of such fuels. If renewable energy is used for fuel production, the current disadvantage of fossil energy carriers regarding CO2 production is eliminated, while at the same time further advantages can be exploited through lower pollutant emissions compared to conventional fuels. As an example, soot emissions can be significantly reduced with both of the above-mentioned alternative fuels in comparison to diesel. When using OME without additional blend components, the soot-NOx (nitrogen oxides) trade-off is no longer relevant as combustion is almost soot free.
However, further research and development is required, particularly with regard to the identification of suitable fuels (e.g. concerning emission reduction potential, suitability as a fuel for mobile applications and availability) and with respect to the optimization of the combustion process for the corresponding fuels. Within the framework of a joint funded project, OME and blends of HVO and OME are investigated in a single-cylinder research engine. The different fuel blend combinations are systematically compared with respect to the experimental results, and the most promising combinations for an ultra-low emission concept based on such fuel blends will be determined.
New engine concepts such as Miller, HCCI or highly diluted combustion offer great potential for further optimization of ICEs in terms of fuel economy and pollutant emissions. However, the development of such concepts requires a high degree of variability in the control of gas exchange, characterized by variability in valve spread, maximum valve lift and – ideally independent of these two variables – in valve opening time. In current series variable valvetrains, valve lift and opening duration are usually directly dependent one from the other. In the ideal case, however, engine concepts such as Miller require a fully flexible variation of the closing time of the intake valve while still maintaining the same intake opening time. Here, a methodology for the geometric layout of fully variable valve trains with significantly extended functionalities is presented. In this concept, the control of the valve opening and closing events is distributed to two synchronously rotating cam disks. This geometric separation allows to vary the valve opening duration at constant maximum valve lift by varying the phase offset between the two disks. On the other hand, the geometric properties of the system can be used to vary the maximum valve lift at the constant valve opening and/or valve closing (depending on the layout), as well as for switching additional valve events on or off.
The methodology presented here includes the computer-aided and partially automated generation of the characteristic geometric features of the system and the kinematic simulation and evaluation of the concept. By kinematic simulation, various possible resulting valve lift curves can be evaluated and optimized by adapting the geometry and the motion rules. The subsequent investigations on a component test bench serve to assess the newly developed concept with respect to functionality, required drive torque, stiffness and speed capability, thus proving its technical feasibility.
The use of vegetable oil as a fuel for agricultural and forestry vehicles allows a CO2 reduction of up to 60 %. On the other hand, the availability of vegetable oil is limited, and price competitiveness depends heavily on the respective oil price. In order to reduce the dependence on the availability of specific fuels, the joint research project “MuSt5-Trak” (Multi-Fuel EU Stage 5 Tractor) aims at developing a prototype tractor capable of running on arbitrary mixtures of diesel and rapeseed oil.
Depending on the fuel mixture used, the engine parameters need to be adapted to the respective operating conditions. For this purpose, it is necessary to detect the composition of the fuel mixture and the fuel quality. Regardless of the available fuel mixture, all functions for regular engine operation must be maintained. A conventional active regeneration of the diesel particulate filter (DPF) cannot be carried out because rapeseed oil has a flash point of 230°C, compared to 80°C for diesel fuel. This leads to a condensation of rapeseed oil while using post-injection at low and medium part load operating points, which causes a dilution of the engine oil.
In this work, engine-internal measures for achieving DPF regeneration with rapeseed oil and mixtures of diesel fuel and rapeseed oil are investigated. In order to provide stationary operating conditions in real engine operation, a “high-idle” operating point is chosen. The fuel mixtures are examined with regard to compatibility concerning a reduction of the air-fuel ratio, late combustion phasing and multiple injections. The highest temperatures are expected from a combination of these control options. After the completion of a regeneration cycle, the fuel input into the engine oil is controlled. These investigations will serve as a basis for the subsequent development of more complex regeneration strategies for close-to-reality engine operating cycles with varying load conditions.
Due to the steadily increasing number of decentralized generation units, the upcoming smart meter rollout and the expected electrification of the transport sector (e-mobility), grid planning and grid operation at low-voltage (LV) level are facing major challenges. Therefore, many studies, research and demonstration projects on the above topics have been carried out in recent years, and the results and the methods developed have been published. However, the published methods usually cannot be replicated or validated, since the majority of the examination models or the scenarios used are incomprehensible to third parties. There is a lack of uniform grid models that map the German LV grids and can be used for comparative investigations, which are similar to the example of the North American distribution grid models of the IEEE. In contrast to the transmission grid, whose structure is known with high accuracy, suitable grid models for LV grids are difficult to map because of the high number of LV grids and distribution system operators. Furthermore, a detailed description of real LV grids is usually not available in scientific publications for data privacy
reasons. For investigations within a research project, the most characteristic synthetic LV grid models have been created, which are based on common settlement structures and usual grid planning principles in Germany. In this work, these LV grid models, and their development are explained in detail. For the first time, comprehensible LV grid models for the middle European area are available to the public, which can be used as a benchmark for further scientific research and method developments.
This document is an English version of the paper which was originally written in German1. In addition, this paper discusses a few more aspects especially on the planning process of distribution grids in Germany.
Regelkonzept für eine Niederspannungsnetzautomatisierung unter Verwendung des Merit-Order-Prinzips
(2022)
Durch die zunehmende Erzeugungsleistung auf Niederspannungsnetzebene (NS-Netzebene) durch Photovoltaikanlagen, sowie die Elektrifizierung des Wärme- und des Verkehrssektors sind Investitionen in die NS-Netze notwendig. Ein höherer Digitalisierungsgrad im NS-Netz birgt das Potential, die notwendigen Investitionen genauer zu identifizieren, und damit ggf. zu reduzieren oder zeitlich zu verschieben. Hierbei stellt die Markteinführung intelligenter Messsysteme, sog. Smart Meter, eine neue Möglichkeit dar, Messwerte aus dem NS-Netz zu erhalten und auf deren Grundlage die Stellgrößen verfügbarer Aktoren zu optimieren. Dazu stellt sich die Frage, wie Messdaten unterschiedlicher Messzyklen in einem Netzautomatisierungssystem genutzt werden können und wie sich das nicht-lineare ganzzahlige Optimierungsproblem der Stellgrößenoptimierung effizient lösen lässt. Diese Arbeit befasst sich mit der Lösung des Optimierungsproblems. Dazu kommt eine Stellgrößenoptimierung nach dem Merit-Order-Prinzip zur Anwendung.
Understanding the mechanisms and controlling
the possibilities of surface nanostructuring is of crucial interest
for both fundamental science and application perspectives.
Here, we report a direct experimental observation
of laser-induced periodic surface structures (LIPSS) formed
near a predesigned gold step edge following single-pulse
femtosecond laser irradiation. Simulation results based on a
hybrid atomistic-continuum model fully support the experimental
observations. We experimentally detect nanosized
surface features with a periodicity of ∼300 nm and heights of
a few tens of nanometers.We identify two key components of
single-pulse LIPSS formation: excitation of surface plasmon
polaritons and material reorganization. Our results lay a
solid foundation toward simple and efficient usage of light
for innovative material processing technologies.
We present an identification benchmark data set for a full robot movement with an KUKA KR300 R2500 ultra SE industrial robot. It is a robot with a nominal payload capacity of 300 kg, a weight of 1120 kg and a reach of 2500mm. It exhibits 12 states accounting for position and velocity for each of the 6 joints. The robot encounters backlash in all joints, pose-dependent inertia, pose-dependent gravitational loads, pose-dependent hydraulic forces, pose- and velocity dependent centripetal and Coriolis forces as well as a nonlinear friction, which is temperature dependent and therefore potentially time varying. We supply the prepared dataset for black-box identification of the forward or the inverse robot dynamics. Additional to the data for black-box modelling, we supply high-frequency raw data and videos of each experiment. A baseline and figures of merit are defined to make results compareable across different identification methods.
Grinding is one of the effective manufacturing processes with which to produce highly accurate parts with an ultra-fine surface finish. The tool used to remove materials in grinding is called the grinding wheel. Abrasive grains made of extremely hard materials (alumina, silica, cubic boron nitride, and diamond) having a definite grit size but a random shape are bonded on the circumferential surface of the grinding wheel. The fabrication process is controlled so that the wheel exhibits a prescribed structure (in the scale of soft to hard). At the same time, the distribution of grains must follow a prescribed grade (in the scale of dense to open). After the fabrication, the wheel is dressed to make sure of its material removal effectiveness, which itself depends on the surface topography. The topography is quantified by the distribution and density of active abrasive grains located on the circumferential surface, the grains’ protrusion heights, and their pore volume ratio. The prediction of the surface topography mentioned above requires a model that considers the entire manufacturing process and the influences on the grinding wheel properties. This study fills this gap in modelling the grinding wheel by presenting a surface topography model and simulation framework for the effect of the grinding wheel fabrication process on the surface topography. The simulation results have been verified by conducting experiments. This study will thus help grinding wheel manufacturers in developing more effective grinding wheels.
This paper aims to improve the traditional calibration method for reconfigurable self-X (self-calibration, self-healing, self-optimize, etc.) sensor interface readout circuit for industry 4.0. A cost-effective test stimulus is applied to the device under test, and the transient response of the system is analyzed to correlate the circuit's characteristics parameters. Due to complexity in the search and objective space of the smart sensory electronics, a novel experience replay particle swarm optimization (ERPSO) algorithm is being proposed and proved a better-searching capability than some currently well-known PSO algorithms. The newly proposed ERPSO expanded the selection producer of the classical PSO by introducing an experience replay buffer (ERB) intending to reduce the probability of trapping into the local minima. The ERB reflects the archive of previously visited global best particles, while its selection is based upon an adaptive epsilon greedy method in the velocity updating model. The performance of the proposed ERPSO algorithm is verified by using eight different popular benchmarking functions. Furthermore, an extrinsic evaluation of the ERPSO algorithm is also examined on a reconfigurable wide swing indirect current-feedback instrumentation amplifier (CFIA). For the later test, we proposed an efficient optimization procedure by using total harmonic distortion analyses of CFIA output to reduce the total number of measurements and save considerable optimization time and cost. The proposed optimization methodology is roughly 3 times faster than the classical optimization process. The circuit is implemented by using Cadence design tools and CMOS 0.35 µm technology from Austria Microsystems (AMS). The efficiency and robustness are the key features of the proposed methodology toward implementing reliable sensory electronic systems for industry 4.0 applications.