RWTH Aachen University

The transport sector faces two critical issues: a limited supply of fossil fuels and high greenhouse gas (GHG) emissions. Additionally, the demand for freight transport is steadily increasing, ultimately leading to higher GHG emissions. Since these emissions promote climate change, reducing the GHG emissions from the transport sector is necessary. Renewable drop-in fuels can play an essential role in this regard as those are CO2 neutral. Since these fuels come from so-called renewable sources, this represents a way to reduce carbon dioxide emissions from the current vehicle fleet to meet the EU’s Green Deal goals to become climate neutral by 2050. The drop-in fuels from renewable sources and later the purely renewable fuels serve as a bridging technology in this context. With this in mind, experiments were conducted with a Heavy-Duty Single Cylinder Engine (HD-SCE). The effects of four different renewable fuels or fuel blends – 93% RF/7% UCOME, 60% B0-Diesel/40% RF blend, 70% Diesel/30% Octanol blend and 100% Octanol – on engine performance and raw emissions were studied in comparison to fossil Diesel fuel. The investigations were conducted at three different load points — Rated Power (RP), best Brake Thermal Efficiency (BTE) and Cruise Point, covering all the relevant load points for HD engines. For all load points, the use of renewable fuels resulted in lower carbon dioxide (CO2), hydrocarbon (HC), carbon monoxide (CO) and FSN compared to fossil Diesel due to the fuel-borne oxygen and the lower C/H ratio of these alternative fuels. The blend of 60% B0-Diesel-40% RF shows the highest efficiency due to the paraffinic fuel structure, the fuel-borne oxygen, the higher calorific value, and the high cetane number. 100% Octanol resulted in a reduction in FSN by a factor of 3. All renewable fuels show a GHG emission reduction potential of around 2.5% to 5.5% in the Tank-to-Wheel (TtW) analysis.

Radio frequency fingerprint (RFF), which comes from the imperfect hardware, is a potential feature to ensure the security of communication. With the development of deep learning (DL), DL-based RFF identification methods have made excellent and promising achievements. However, on one hand, existing DL-based methods require a large amount of samples for model training. On the other hand, the RFF identification method is generally less effective with limited amount of samples, while the auxiliary dataset and the target dataset often needs to have similar data distribution. To address the data-hungry problems in the absence of auxiliary datasets, in this paper, we propose a supervised contrastive learning (SCL)-based RFF identification method using data augmentation and virtual adversarial training (VAT), which is called “SCACNN”. First, we analyze the causes of RFF, and model the RFF identification problem with augmented dataset. A non-auxiliary data augmentation method is proposed to acquire an extended dataset, which consists of rotation, flipping, adding Gaussian noise, and shifting. Second, a novel similarity radio frequency fingerprinting encoder (SimRFE) is used to map the RFF signal to the feature coding space, which is based on the convolution, long-short-term-memory, and a fully connected deep neural network (CLDNN). Finally, several secondary classifiers are employed to identify the RFF feature coding. The simulation results show that the proposed SCACNN has greater identification ratio than the other classical RFF identification methods. Moreover, the identification ratio of the proposed SCACNN achieves an accuracy of 92.68% with only 5% samples.

Wi-Fi based passive sensing is considered as one of the promising sensing techniques in advanced wireless communication systems due to its wide applications and low deployment cost. However, existing methods are faced with the challenges of low sensing accuracy, high computational complexity, and weak model robustness. To solve these problems, we firstly propose a robust channel state information (CSI)-based Wi-Fi passive sensing method using attention mechanism deep learning (DL). The proposed method is called as CNN-ABLSTM, a combination of convolutional neural networks (CNN) and attention-based bi-directional long short term memory (LSTM). Specifically, CSI-based Wi-Fi passive sensing is devised to achieve the high precision of human activity recognition (HAR) due to the fine-grained characteristics of CSI. Secondly, CNN is adopted to solve the problems of computational redundancy and high algorithm complexity which are often occurred by machine learning (ML) algorithms. Thirdly, we introduce attention mechanism to deal with the weak robustness of CNN models. Finally, simulation results are provided to confirm the proposed method in three aspects, high recognition performance, computational complexity, and robustness. Compared with CNN, LSTM and other networks, the proposed CNN-ABLSTM method improves the recognition accuracy by up to 4%, and significantly reduces the calculation rate. Moreover, it still retains 97% accuracy under the different scenes, reflecting a certain robustness.

Short-circuit faults in high-voltage DC (HVDC) grids must be timely isolated. Conventional fault isolation schemes for HVDC grids typically rely on numerical simulations or analytical calculations for threshold setting, which complicate engineering implementation. To solve this issue, this paper first introduces the convex point of backward traveling wave (BTW) voltage as the reference for the faulty line isolation in HVDC grids, which is interpreted as the singular feature in the second derivative (SD) of BTW voltage. The singularity in the SD of BTW voltage is independent of system or fault parameters, having good generality in HVDC grids. Through recognizing the unique pattern in the wavelet transform modulus maxima of the singular feature, the proposed protection method can identify faulty lines in HVDC grids in the initial phase of faults. Numerical studies carried out with PSCAD/EMTDC and MATLAB/Simulink demonstrate the effectiveness, robustness and applicability of the proposed method in detecting and isolating various single- and double-pole faults in HVDC grids.

The overall purpose of this work (including Part I in this issue) is to demonstrate the physical modeling of InP/InGaAs double heterojunction bipolar transistors (DHBTs) using a deterministic Boltzmann transport equation (BTE) solver and an augmented drift-diffusion (aDD) solver. In this Part II, the BTE and aDD solver are applied to a DHBT technology, leveraging a physics-based HICUM/L2 compact model for extracting experimental reference data. To account for uncertainty in the device profile and material properties, a calibration method is employed for matching the simulation results to measurable electrical quantities of DHBTs. Although the two solvers produce comparable results for the terminal characteristics of the DHBT, the BTE simulations expose significant physical phenomena that cannot be captured by transport formulations relying on moments of the BTE.

The overall purpose of this work (including Part II in this issue) is to demonstrate the physical modeling of InP/InGaAs double heterojunction bipolar transistors (DHBTs) using a deterministic Boltzmann transport equation (BTE) solver, an augmented drift-diffusion (aDD) solver, and the HICUM/L2 compact model. This Part I introduces all tools that are employed for the simulations of DHBT structures in Part II and applies them to a GaAs $\text{n}^{+}\text{nn}^{+}$ sample structure for illustrating the calibration of the aDD solver by BTE results and physical effects that occur in such devices. After the calibration, aDD and BTE solvers are shown to produce comparable results.

This contribution presents a multiscale approach for the analysis of shell structures using Reissner–Mindlin kinematics. A distinctive feature is that the thickness of the representative volume element (RVE) corresponds to the shell thickness. The main focus of this paper is on the choice of correct boundary conditions for the RVE. Three different types of boundary conditions, which fulfil the Hill–Mandel condition, are presented to bridge the two scales. A common feature is the application of zero-traction boundary conditions at the top and bottom surfaces of the RVE. Furthermore, an internal constraint is used to reduce the dependency of the stiffness components on the RVE size. The introduced boundary conditions differ mainly in the application of shear strains and their symmetry requirements on the RVE. The characteristic features are compared by means of linear-elastic benchmark tests. It is shown that the stress resultants and tangent stiffness components are obtained correctly. Moreover, the presented approach is verified using different macroscopic shell structures and different mesostructures. Both, linear and nonlinear small strain examples are compared to analytical values or full-scale solutions and demonstrate a wide applicability of the present formulation.

In this paper, we consider the weakly supervised multi-target regression problem where the observed data is partially or imprecisely labelled. The model of the multivariate normal distribution over the target vectors represents the uncertainty arising from the labelling process. The proposed solution is based on the combination of a manifold regularisation method, the use of the Wasserstein distance between multivariate distributions, and a cluster ensemble technique. The method uses a low-rank representation of the similarity matrix. An algorithm for constructing a co-association matrix with calculation of the optimal number of clusters in a partition is presented. To increase the stability and quality of the ensemble clustering, we use k-means with different distance metrics. The experimental part presents the results of numerical experiments with the proposed method on artificially generated data and real data sets. The results show the advantages of the proposed method over existing solutions.

The crude Monte Carlo method is computationally expensive. Hence, incorporating model order reduction methods enabling reliability analysis for high‐dimensional problems is necessary. However, this strategy may result in an inaccurate estimation of the probability of failure for rare events for two reasons. First, the model order reduction, represented by the proper orthogonal decomposition (POD) here, requires response information in the form of snapshots a priori. To capture the essential nonlinear response behavior, we propose to update the proper orthogonal modes using extreme events. Second, the crude Monte Carlo simulation requires many samples to estimate low failure probabilities reliably. To this end, subset simulation found wide application in reliability analysis to reduce computational effort. Following this strategy, the proposed samples gradually move toward the failure region. Thus, incorporating updates of the modes is particularly promising in evaluating samples from the current subset region. This contribution shows the computational efficiency of POD within subset simulations. We then propose to leverage the estimation of the probability of failure by updating the modes within each subset.

Development of a 3D sharp interface ghost node immersed boundary method (IBM) within a fluid solver based on the compressible Navier–Stokes equations is underway. The objective of the IBM is to accurately apply boundary conditions at fluid–solid interfaces. The Navier–Stokes solver is currently being verified using various test cases, including the classic cylinder in cross flow. As part of this verification process, particular attention was given to investigating the effects of different lateral boundary conditions. The results demonstrate that extrapolation boundary conditions exhibit better agreement with the literature compared to symmetry conditions, in cases with relatively narrow domains. These findings highlight the potential benefits of extrapolation boundary conditions in reducing confinement effects and removing nonphysical waves in external flow problems.

Objectives Focused ultrasound is mainly known for focal ablation and localized hyperthermia of tissue. During the last decade new treatment options were developed for neurological indications based on blood-brain-barrier opening or neuromodulation. Recently, the transcranial application of shock waves has been a subject of research. However, the mechanisms of action are not yet understood. Hence, it is necessary to know the energy that reaches the brain during the treatment and the focusing characteristics within the tissue. Methods The sound field of a therapeutic extracorporeal shock wave transducer was investigated after passing human skull bone (n=5) or skull bone with brain tissue (n=2) in this ex vivo study. The maximum and minimum pressure distribution and the focal pressure curves were measured at different intensity levels and penetration depths, and compared to measurements in water. Results Mean peak negative pressures of up to −4.97 MPa were reached behind the brain tissue. The positive peak pressure was attenuated by between 20.85 and 25.38 dB/cm by the skull bone. Additional damping by the brain tissue corresponded to between 0.29 and 0.83 dB/cm. Compared to the measurements in water, the pulse intensity integral in the focal spot was reduced by 84 % by the skull bone and by additional 2 % due to the brain tissue, resulting in a total damping of up to 86 %. The focal position was shifted up to 8 mm, whereas the basic shape of the pressure curves was preserved. Conclusions Positive effects may be stimulated by transcranial shock wave therapy but damage cannot be excluded.

Monoblock tubular shafts (MTS) are manufactured using seamless hollow tubes by radial and axial forming operations. The load path of such forming operations strongly influences the damage evolution in the produced parts. Heat treatment of cold formed parts has also shown to influence the damage level or the porosity in the metal. After cold forming operations, the shafts are solution annealed and then quenched in order to harden the MTS profiles. Metallographic investigations have shown the generation of newer voids along the external contour of the gears. The current work aims at clarifying the damage evolution mechanism in the cold forming and then quenching operation and its correlation with the temperature induced phase changes. Numerical simulations were used to study the triaxiality during the forming operation. The results show the presence of a positive triaxiality along the tooth flank surface and the inner contour of the hollow shafts. Metallographic investigations and electron backscatter diffraction (EBSD) are performed to determine the individual metallic phases before and after the quenching operation. Using simulation, the effect of such thermal shock on the residual stresses and the damage evolution are investigated. Characterizing the morphology of voids and non-metallic inclusions enhances the scope of the process design, which allows manufacturing of shafts with lower damage and an increased product life of the MTS profiles.

In bipolar plate production, extreme thin foil materials are becoming increasingly important due to the trend towards high-energy dense fuel cells. For a better of the material behavior and component failures, finite element simulations are used. In order to achieve an expressive numerical representation of the forming process, the behavior of material failure in sheet metal forming is described by forming limit curves (FLC). However, especially for thin metal foils, proven testing methods such as the Nakazima test are not applicable because the specimens start wrinkling or fail outside the defect zone specified in the norm. While there are alternative testing methods for the detection of the pure tension area of the FLC, there is no applicable testing method for the evaluation of the forming limit in the tensile-compression zone. Therefore, in this paper simulations as well as physical tests were carried out to define a suitable specimen geometry for the characterization of stainless steel foil (1.4404) with a thickness of 0.1 mm using a scaled Nakazima set up. The simulation results showed that by decreasing the parallel web length as well as the fillet radius the equivalent strain maximum is shifted towards the specimen center. This observation is supported by the physical tests where necking occurred in the specimen center. Additionally to the position of failure, first investigations in physical testing showed maximum strain ratios of \({\upvarepsilon }_{1}\) = 0.23 in major strain and \({\upvarepsilon }_{2}\) = −0.095 in minor strain. The strain ratio therefore represents the uniaxial tension area.

Friction is part of almost any forming process, thus, friction modelling is mandatory for process modelling. Since frictional stresses are difficult to measure, typically laboratory experiments are used, where a dimension sensitive to frictional stresses is measured instead. The conical tube-upsetting test, an advancement of ring compression test, is such a laboratory experiment. Here, the change in geometry is measured to inversely determine friction parameters. The process parameters, such as temperatures, strain rate and contact pressure should be as close as possible to the forming process that is modelled with the laboratory experiment. Especially in hot bulk metal forming, normal stresses greater than yield stress of the work piece can occur, which has significant influence on the frictional conditions. However, previous work has shown that normal stresses in conical tube-upsetting test are lesser than the yield stress of the workpiece material, which restricts the experiments application regarding hot bulk metal forming. Thus, in this work the specimen geometry of conical tube-upsetting test is investigated by means of FE-simulations with respect to the occurring normal stresses between workpiece and die. Additionally, the influence of altered geometries regarding the sensitivity towards occurring friction is taken into account. The results suggest that especially an increased thickness of the specimen in the bottom area leads to greater normal stresses. At the same time however, the sensitivity towards friction conditions decreases.

Zusammenfassung Die elektrische Maschine repräsentiert physikalisch einen Energiewandler. Dabei wird zwischen nicht rotierenden elektrischen Maschinen und rotierenden elektrischen Maschinen mit bewegenden Hauptelementen unterschieden. Allen elektrischen Maschinen ist gemeinsam, dass sie in ihrem Aufbau über einen magnetischen Kreis verfügen, der für die Funktionsweise wesentlich ist.