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In this paper a practical way for fatigue life prediction of rubber products under multiaxial loads is shown. This is done by means of fracture mechanical concepts and the energy release rate as the failure criterion. Due to a FEA post-processor the potential energy release rate might be calculated at every material point supposed there was a crack. And therefore the risk of failure and with the help of a strain number curve the time to fatigue is able to be calculated by FEA. This concept is applied for an estimation of the life time of a test specimen with tensile loading from fatigue data of a shear loaded specimen of different design. This rather more theoretical concept of the energy release rate is complemented by experimental crack growth data by a Tear Fatigue Analyzer with its great advantage of reduction of testing time and costs compared to those of fatigue tests. For some materials a thorough characterization of crack growth and fatigue behavior is presented and is applied to estimate the time to fatigue by FEA for a real component under multiaxial loads.
There are additional long-term effects which also change the micro-structure of the polymer network and consequently the effective number of polymer chains in the material. These effects are summarized by ageing processes and will be used in the following to explain the basic assumptions of the model which can be generalized to simulate the viscous behaviour of the material. An implementation of these concepts into FEM codes is straightforward and has been carried out to the solver ABAQUS, Baaser & Ziegler (2006), Baaser et al. (2009).
A system for the on-line/in-line measurement of soot particle sizes and concentrations in the undiluted exhaust gas of diesel engines was developed and successfully tested. The unit uses the individual attenuations of three different laser wavelengths and is combined with an optical cell (white principle) with adjustable path lengths from 2.5 to 15 meters.
The purpose of this study was to describe the effects of running speed and slope on metatarsophalangeal (MTP) joint kinematics. 22 male and female runners underwent 3D motion analysis on an instrumented treadmill at three different speeds (2.5 m/s, 3.0 m/s, 3.5 m/s). At each speed, participants ran at seven slope conditions (downhill: -15%, -10%, -5%, level, and uphill: +5%, +10%, +15%). We found a significant main effect (p < 0.001) of running speed and slope on peak MTP dorsiflexion and a running speed by slope interaction effect (p < 0.001) for peak MTP dorsiflexion velocity. These findings highlight the need to consider running intensity and environmental factors like running surface inclination when considering MTP joint mechanics and technological aids to support runners.
Short-term load forecasting (STLF) has been playing a key role in the electricity sector for several decades, due to the need for aligning energy generation with the demand and the financial risk connected with forecasting errors. Following the top-down approach, forecasts are calculated for aggregated load profiles, meaning the sum of singular loads from consumers belonging to a balancing group. Due to the emerging flexible loads, there is an increasing relevance for STLF of individual factories. These load profiles are typically more stochastic compared to aggregated ones, which imposes new requirements to forecasting methods and tools with a bottom-up approach. The increasing digitalization in industry with enhanced data availability as well as smart metering are enablers for improved load forecasts. There is a need for STLF tools processing live data with a high temporal resolution in the minute range. Furthermore, behin-the-meter (BTM) data from various sources like submetering and production planning data should be integrated in the models. In this case, STLF is becoming a big data problem so that machine learning (ML) methods are required. The research project “GaIN” investigates the improvement of the STLF quality of an energy utility using BTM data and innovative ML models. This paper describes the project scope, proposes a detailed definition for a benchmark and evaluates the readiness of existing STLF methods to fulfil the described requirements as a reviewing paper.
The review highlights that recent STLF investigations focus on ML methods. Especially hybrid models gain more and more importance. ML can outperform classical methods in terms of automation degree and forecasting accuracy. Nevertheless, the potential for improving forecasting accuracy by the use of ML models depends on the underlying data and the types of input variables. The described methods in the analyzed publications only partially fulfil the tool requirements for STLF on company level. There is still a need to develop suitable ML methods to integrate the expanded data base in order to improve load forecasts on company level.
Colored glass products with various printing technologies are becoming more important in industry. The aim is to achieve individual solution in a very short delivery time. Conventional thermal treatment of burning glasses in oven for tempered color printing has predominant issues with high time consumption, energy consumption and manufacturing cost. It requires alternative process development.
This paper proposes laser process to overcome issues in conventional treatment with the latest results of tempering colored glass. Samples have been analyzed with the scanning electron microscope (SEM). Two different laser systems have been applied and the glass has been printed with black paste.
Combined heat and power production (CHP) based on solid oxide fuel cells (SOFC) is a very promising technology to achieve high electrical efficiency to cover power demand by decentralized production. This paper presents a dynamic quasi 2D model of an SOFC system which consists of stack and balance of plant and includes thermal coupling between the single components. The model is implemented in Modelica® and validated with experimental data for the stack UI-characteristic and the thermal behavior. The good agreement between experimental and simulation results demonstrates the validity of the model. Different operating conditions and system configurations are tested, increasing the net electrical efficiency to 57% by implementing an anode offgas recycle rate of 65%. A sensitivity analysis of characteristic values of the system like fuel utilization, oxygen-to-carbon ratio and electrical efficiency for different natural gas compositions is carried out. The result shows that a control strategy adapted to variable natural gas composition and its energy content should be developed in order to optimize the operation of the system.