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The visualization of heart rhythm disturbance and atrial fibrillation therapy allows the optimization of new cardiac catheter ablations. With the simulation software CST (Computer Simulation Technology, Darmstadt) electromagnetic and thermal simulations can be carried out to analyze and optimize different heart rhythm disturbance and cardiac catheters for pulmonary vein isolation. Another form of visualization is provided by haptic, three-dimensional print models. These models can be produced using an additive manufacturing method, such as a 3d printer. The aim of the study was to produce a 3d print of the Offenburg heart rhythm model with a representation of an atrial fibrillation ablation procedure to improve the visualization of simulation of cardiac catheter ablation. The basis of 3d printing was the Offenburg heart rhythm model and the associated simulation of cryoablation of the pulmonary vein. The thermal simulation shows the pulmonary vein isolation of the left inferior pulmonary vein with the cryoballoon catheter Arctic Front Advance™ from Medtronic. After running through the simulation, the thermal propagation during the procedure was shown in the form of different colors. The three-dimensional print models were constructed on the base of the described simulation in a CAD program. Four different 3d printers are available for this purpose in a rapid prototyping laboratory at the University of Applied Science Offenburg. Two different printing processes were used and a final print model with additional representation of the esophagus and internal esophagus catheter was also prepared for printing. With the help of the thermal simulation results and the subsequent evaluation, it was possible to draw a conclusion about the propagation of the cold emanating from the catheter in the myocardium and the surrounding tissue. It was measured that just 3 mm from the balloon surface into the myocardium the temperature dropped to 25 °C. The simulation model was printed using two 3d printing methods. Both methods, as well as the different printing materials offer different advantages and disadvantages. All relevant parts, especially the balloon catheter and the conduction, are realistically represented. Only the thermal propagation in the form of different colors is not shown on this model. Three-dimensional heart rhythm models as well as virtual simulations allow very clear visualization of complex cardiac rhythm therapy and atrial fibrillation treatment methods. The printed models can be used for optimization and demonstration of cryoballoon catheter ablation in patients with atrial fibrillation.
High temperature components in internal combustion engines and exhaust systems must withstand severe mechanical and thermal cyclic loads throughout their lifetime. The combination of thermal transients and mechanical load cycling results in a complex evolution of damage, leading to thermomechanical fatigue (TMF) of the material. Analytical tools are increasingly employed by designers and engineers for component durability assessment well before any hardware testing. The DTMF model for TMF life prediction, which assumes that micro-crack growth is the dominant damage mechanism, is capable of providing reliable predictions for a wide range of high-temperature components and materials in internal combustion engines. Thus far, the DTMF model has employed a local approach where surface stresses, strains, and temperatures are used to compute damage for estimating the number of cycles for a small initial defect or micro-crack to reach a critical length. In the presence of significant gradients of stresses, strains, and temperatures, the use of surface field values could lead to very conservative estimates of TMF life when compared with reported lives from hardware testing. As an approximation of gradient effects, a non-local approach of the DTMF model is applied. This approach considers through-thickness fields where the micro-crack growth law is integrated through the thickness considering these variable fields. With the help of software tools, this method is automated and applied to components with complex geometries and fields. It is shown, for the TMF life prediction of a turbocharger housing, that the gradient correction using the non-local approach leads to more realistic life predictions and can distinguish between surface cracks that may arrest or propagate through the thickness and lead to component failure.
Many sectors, like finance, medicine, manufacturing, and education, use blockchain applications to profit from the unique bundle of characteristics of this technology. Blockchain technology (BT) promises benefits in trustability, collaboration, organization, identification, credibility, and transparency. In this paper, we conduct an analysis in which we show how open science can benefit from this technology and its properties. For this, we determined the requirements of an open science ecosystem and compared them with the characteristics of BT to prove that the technology suits as an infrastructure. We also review literature and promising blockchain-based projects for open science to describe the current research situation. To this end, we examine the projects in particular for their relevance and contribution to open science and categorize them afterwards according to their primary purpose. Several of them already provide functionalities that can have a positive impact on current research workflows. So, BT offers promising possibilities for its use in science, but why is it then not used on a large-scale in that area? To answer this question, we point out various shortcomings, challenges, unanswered questions, and research potentials that we found in the literature and identified during our analysis. These topics shall serve as starting points for future research to foster the BT for open science and beyond, especially in the long-term.
In many application domains, in particular automotives, guaranteeing a very low failure rate is crucial to meet functional and safety standards. Especially, reliable operation of memory components such as SRAM cells is of essential importance. Due to aggressive technology downscaling, process and runtime variations significantly impact manufacturing yield as well as functionality. For this reason, a thorough memory failure rate assessment is imperative for correct circuit operation and yield improvement. In this regard, Monte Carlo simulations have been used as the conventional method to estimate the variability induced failure rate of memory components. However, Monte Carlo methods become infeasible when estimating rare events such as high-sigma failure rates. To this end, Importance Sampling methods have been proposed which reduce the number of required simulations substantially. However, existing methods still suffer from inaccuracies and high computational efforts, in particular for high-sigma problems. In this paper, we fill this gap by presenting an efficient mixture Importance Sampling approach based on Bayesian optimization, which deploys a surface model of the objective function to find the most probable failure points. Its advantages include constant complexity independent of the dimensions of design space, the potential to find the global extrema, and higher trustworthiness of the estimated failure rate by accurately exploring the design space. The approach is evaluated on a 6T-SRAM cell as well as a master-slave latch based on a 28nm FDSOI process. The results show an improvement in accuracy, resulting in up to 63× better accuracy in estimating failure rates compared to the best state-of-the-art solutions on a 28nm technology node.
With the growing share of renewable energies in the electricity supply, transmission and distribution grids have to be adapted. A profound understanding of the structural characteristics of distribution grids is essential to define suitable strategies for grid expansion. Many countries have a large number of distribution system operators (DSOs) whose standards vary widely, which contributes to coordination problems during peak load hours. This study contributes to targeted distribution grid development by classifying DSOs according to their remuneration requirement. To examine the amendment potential, structural and grid development data from 109 distribution grids in South-Western Germany, are collected, referring to publications of the respective DSOs. The resulting data base is assessed statistically to identify clusters of DSOs according to the fit of demographic requirements and grid-construction status and thus identify development needs to enable a broader use of regenerative energy resources. Three alternative algorithms are explored to manage this task. The study finds the novel Gauss-Newton algorithm optimal to analyse the fit of grid conditions to regional requirements and successfully identifies grids with remuneration needs. It is superior to the so far used K-Means algorithm. The method developed here is transferable to other areas for grid analysis and targeted, cost-efficient development.
In this article the high-temperature behavior of a cylindrical lithium iron phosphate/graphite lithium-ion cell is investigated numerically and experimentally by means of differential scanning calorimetry (DSC), accelerating rate calorimetry (ARC), and external short circuit test (ESC). For the simulations a multi-physics multi-scale (1D+1D+1D) model is used. Assuming a two-step electro-/thermochemical SEI formation mechanism, the model is able to qualitatively reproduce experimental data at temperatures up to approx. 200 °C. Model assumptions and parameters could be evaluated via comparison to experimental results, where the three types of experiments (DSC, ARC, ESC) show complementary sensitivities towards model parameters. The results underline that elevated-temperature experiments can be used to identify parameters of the multi-physics model, which then can be used to understand and interpret high-temperature behavior. The resulting model is able to describe nominal charge/discharge operation behavior, long-term calendaric aging behavior, and short-term high-temperature behavior during extreme events, demonstrating the descriptive and predictive capabilities of physicochemical models.
Cast aluminum alloys are frequently used as materials for cylinder head applications in internal combustion gasoline engines. These components must withstand severe cyclic mechanical and thermal loads throughout their lifetime. Reliable computational methods allow for accurate estimation of stresses, strains, and temperature fields and lead to more realistic Thermomechanical Fatigue (TMF) lifetime predictions. With accurate numerical methods, the components could be optimized via computer simulations and the number of required bench tests could be reduced significantly. These types of alloys are normally optimized for peak hardness from a quenched state that maximizes the strength of the material. However due to high temperature exposure, in service or under test conditions, the material would experience an over-ageing effect that leads to a significant reduction in the strength of the material. To numerically account for ageing effects, the Shercliff & Ashby ageing model is combined with a Chaboche-type viscoplasticity model available in the finite-element program ABAQUS by defining field variables. The constitutive model with ageing effects is correlated with uniaxial cyclic isothermal tests in the T6 state, the overaged state, as well as thermomechanical tests. On the other hand, the mechanism-based TMF damage model (DTMF) is calibrated for both T6 and over-aged state. Both the constitutive and the damage model are applied to a cylinder head component simulating several cycles on an engine dynamometer test. The effects of including ageing for both models are shown.
A physical unclonable function (PUF) is a hardware circuit that produces a random sequence based on its manufacturing-induced intrinsic characteristics. In the past decade, silicon-based PUFs have been extensively studied as a security primitive for identification and authentication. The emerging field of printed electronics (PE) enables novel application fields in the scope of the Internet of Things (IoT) and smart sensors. In this paper, we design and evaluate a printed differential circuit PUF (DiffC-PUF). The simulation data are verified by Monte Carlo analysis. Our design is highly scalable while consisting of a low number of printed transistors. Furthermore, we investigate the best operating point by varying the PUF challenge configuration and analyzing the PUF security metrics in order to achieve high robustness. At the best operating point, the results show areliability of 98.37% and a uniqueness of 50.02%, respectively. This analysis also provides useful and comprehensive insights into the design of hybrid or fully printed PUF circuits. In addition, the proposed printed DiffC-PUF core has been fabricated with electrolyte-gated field-effect transistor technology to verify our design in hardware.
In this article we outline the model development planned within the joint projectModel-based city planningand application in climate change (MOSAIK). The MOSAIK project is funded by the German FederalMinistry of Education and Research (BMBF) within the frameworkUrban Climate Under Change ([UC]2)since 2016. The aim of MOSAIK is to develop a highly-efficient, modern, and high-resolution urban climatemodel that allows to be applied for building-resolving simulations of large cities such as Berlin (Germany).The new urban climate model will be based on the well-established large-eddy simulation code PALM, whichalready has numerous features related to this goal, such as an option for prescribing Cartesian obstacles. Inthis article we will outline those components that will be added or modified in the framework of MOSAIK.Moreover, we will discuss the everlasting issue of acquisition of suitable geographical information as inputdata and the underlying requirements from the model's perspective.
Development of Fully Printed Oxide Field-Effect Transistors using Graphene Passive Structures
(2019)
During the past decade to the present time, the topic of printed electronics has gained a lot of attention for their potential use in a number of practical applications, including biosensors, photovoltaic devices, RFIDs, flexible displays, large-area circuits, and so on. To fully realize printed electronic components and devices, effective techniques for the printing of passive structures and electrically and chemically compatible materials in the printed devices need to be developed first. The opportunity of using electrically conducting graphene inks will enable the integration of passive structures into active devices, as for example, printed electrolyte-gated transistors (EGTs). Accordingly, in this study, we present the parametric results obtained on fully printed electrolyte-gated transistors having graphene as the passive electrodes, an inorganic oxide semiconductor as the active channel, and a composite solid polymer electrolyte (CSPE) as the gate insulating material. This configuration offers high chemical and electrical stability while at the same time allowing EGT operation at low potentials, implying the distinct advantage of operation at low input voltages. The printed in-plane EGTs we developed exhibit excellent performance with device mobility up to 16 cm2 V–1 s–1, an ION/IOFF ratio of 105, and a subthreshold slope of 120 mV dec–1.