Automotive industries are interested in material development with low weight and recycling. Grommet is made from EPDM at rubber and used as an automotive component. The nonlinear material properties of rubber are important to predict the behaviors of rubber product. This study concerns material property test to achieve stress-strain curve. Curve fitting is carried out to obtain the nonlinear material constant. The nonlinear material constants of rubber are used for the nonlinear finite element analysis. The results of finite element analysis is executed to predict the behavior property of grommet.
Progress made in renewable energy technologies (e.g. solar, wind and tidal energy) has fueled the research in energy storage technologies which are indispensible for the success of these intermittent sources of energy. Electro-chemical energy storage systems specially redox flow batteries (RFB) offer modular and scalable energy storage systems for large scale energy storage 1 . Research in different RFBs has been pursued to improve the efficiency, cost and safety aspects of these systems. While research on most aspects requires small scale (lab-scale) redox flow battery, research on some aspects can be performed and analyzed using a static cell set up (e.g. cross contamination, characterization of membrane and electrolyte etc.). These static cells (H cell) can be used to quickly estimate the performance of redox flow batteries (at least at the lab scale) including capacity loss due to cross contaminations 2 . Use of static cell with simplified modeling approach (including capacity loss) to validate experimental data is demonstrated by Tang et. al. 2 on vanadium redox flow battery. This approach can be made more robust and general to be useful for any redox couple by combining electrochemical models and robust optimization framework (parameter and state estimation techniques) to increase the confidence in predictions made by static cell. This presentation will focus on electrochemical models for static cell with parameters and state estimation framework to increase the fidelity of results obtained by experiments performed. Acknowledgements The authors acknowledge financial support from the U.S. Department of Energy’s Advanced Research Projects Agency- Energy (ARPA-E), and the Solar Energy Research Institute in India and the United States (SERIIUS), as well as, Washington University in St. Louis’ McDonnell Academy Global Energy and Environmental Partnership (MAGEEP) and SunEdison Grant. References 1. B. R. Chalamala, T. Soundappan, G. R. Fisher, M. R. Anstey, V. V. Viswanathan and M. L. Perry, Proceedings of the IEEE , 102 , 976 (2014). 2. A. Tang, J. Bao and M. Skyllas-Kazacos, J. Power Sources , 196 , 10737 (2011).
Data science, hailed as the fourth paradigm of science, is a rapidly growing field which has served to revolutionize the fields of bio-informatics and climate science and can provide significant speed improvements in the discovery of new materials, mechanisms, and simulations. Data science techniques are often used to analyze and predict experimental data, but they can also be used with simulated data to create surrogate models. Chief among the data science techniques in this application is machine learning (ML), which is an effective means for creating a predictive relationship between input and output vector pairs. Physics-based battery models, like the comprehensive pseudo-two-dimensional (P2D) model, offer increased physical insight, increased predictability, and an opportunity for optimization of battery performance which is not possible with equivalent circuit (EC) models. In this work, ML-based surrogate models are created and analyzed for accuracy and execution time. Decision trees (DTs), random forests (RFs), and gradient boosted machines (GBMs) are shown to offer trade-offs between training time, execution time, and accuracy. Their ability to predict the dynamic behavior of the physics-based model are examined and the corresponding execution times are extremely encouraging for use in time-critical applications while still maintaining very high (∼99%) accuracy.
Robust design uses the ordinary least squares method to obtain adequate response functions for the process mean and variance by assuming that experimental data are normally distributed and that there is no major contamination in the data set. Under these assumptions, the sample mean and variance are often used to estimate the process mean and variance. In practice, the above assumptions are not always satisfied. When these assumptions are violated, one can alternatively use the sample median and median absolute deviation to estimate the process mean and variance. However, the median and median absolute deviation both suffer from a lack of efficiency under the normal distribution, although they are fairly outlier-resistant. To remedy this problem, we propose new robust design methods based on a highly efficient and outlier-resistant estimator. Numerical studies substantiate the new methods developed and compare the performance of the proposed methods with the ordinary dual-response robust design.
An automotive transmission rubber mount is a device used in automotive systems to cushion the loads transmitted from the vehicle body structure. TM (transmission) rubber mount has been used to support engine in the vertical direction. In this study, the rubber specimens of the transmission mount are tested to obtain the hyperelastic and viscoelastic properties by the static and dynamic test, respectively. Uni-axial tension test, biaxial tension test, and pure shear test are carried out and Mooney-Rivlin constants are obtained from those static tests. Also, the viscoelastic properties such as storage and loss modulus are obtained from dynamic test. Using the static and dynamic test data, the dynamic stiffness of TM rubber mount subjected to static and dynamic load are predicted with finite element analysis. Solutions allow for comparison between FEA and experimental results. It is shown that the predictions of FEA are close to the experimental results.
Mathematical models of Redox Flow Batteries (RFBs) can be used to analyze cell performance, optimize battery operation, and control the energy storage system efficiently. Among many other models, physics-based electrochemical models are capable of predicting internal states of the battery, such as temperature, state-of-charge, and state-of-health. In the models, estimating parameters is an important step that can study, analyze, and validate the models using experimental data. A common practice is to determine these parameters either through conducting experiments or based on the information available in the literature. However, it is not easy to investigate all proper parameters for the models through this way, and there are occasions when important information, such as diffusion coefficients and rate constants of ions, has not been studied. Also, the parameters needed for modeling charge-discharge are not always available. In this paper, an efficient way to estimate parameters of physics-based redox battery models will be proposed. This paper also demonstrates that the proposed approach can study and analyze aspects of capacity loss/fade, kinetics, and transport phenomena of the RFB system.
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