Spelling suggestions: "subject:"aerospace matematerials"" "subject:"aerospace datenmaterials""
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Assessing Viability of Open-Source Battery Cycling Data for Use in Data-Driven Battery Degradation ModelsRitesh Gautam (17582694) 08 December 2023 (has links)
<p dir="ltr">Lithium-ion batteries are being used increasingly more often to provide power for systems that range all the way from common cell-phones and laptops to advanced electric automotive and aircraft vehicles. However, as is the case for all battery types, lithium-ion batteries are prone to naturally occurring degradation phenomenon that limit their effective use in these systems to a finite amount of time. This degradation is caused by a plethora of variables and conditions including things like environmental conditions, physical stress/strain on the body of the battery cell, and charge/discharge parameters and cycling. Accurately and reliably being able to predict this degradation behavior in battery systems is crucial for any party looking to implement and use battery powered systems. However, due to the complicated non-linear multivariable processes that affect battery degradation, this can be difficult to achieve. Compared to traditional methods of battery degradation prediction and modeling like equivalent circuit models and physics-based electrochemical models, data-driven machine learning tools have been shown to be able to handle predicting and classifying the complex nature of battery degradation without requiring any prior knowledge of the physical systems they are describing.</p><p dir="ltr">One of the most critical steps in developing these data-driven neural network algorithms is data procurement and preprocessing. Without large amounts of high-quality data, no matter how advanced and accurate the architecture is designed, the neural network prediction tool will not be as effective as one trained on high quality, vast quantities of data. This work aims to gather battery degradation data from a wide variety of sources and studies, examine how the data was produced, test the effectiveness of the data in the Interfacial Multiphysics Laboratory’s autoencoder based neural network tool CD-Net, and analyze the results to determine factors that make battery degradation datasets perform better for use in machine learning/deep learning tools. This work also aims to relate this work to other data-driven models by comparing the CD-Net model’s performance with the publicly available BEEP’s (Battery Evaluation and Early Prediction) ElasticNet model. The reported accuracy and prediction models from the CD-Net and ElasticNet tools demonstrate that larger datasets with actively selected training/testing designations and less errors in the data produce much higher quality neural networks that are much more reliable in estimating the state-of-health of lithium-ion battery systems. The results also demonstrate that data-driven models are much less effective when trained using data from multiple different cell chemistries, form factors, and cycling conditions compared to more congruent datasets when attempting to create a generalized prediction model applicable to multiple forms of battery cells and applications.</p>
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Wind Turbine Airfoil Optimization by Particle Swarm MethodEndo, Makoto January 2011 (has links)
No description available.
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Three-Dimensional Model of Solid Ignition and Ignition Limit by a Non-Uniformly Distributed Radiant Heat SourceTseng, Ya-Ting 30 June 2011 (has links)
No description available.
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Implementing a Piezoelectric Transformer for a Ferroelectric Phase Shifter CircuitRoberts, Anthony M. 16 May 2012 (has links)
No description available.
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Wind Tunnel Blockage Corrections: An Application to Vertical-Axis Wind TurbinesRoss, Ian Jonathan 05 May 2010 (has links)
No description available.
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Characterization of a 3D Multi-Mechanism SMA Material Model for the Prediction of the Cyclic "Evolutionary" Response of NiTi for Use in ActuationsDhakal, Binod January 2013 (has links)
No description available.
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Material Health Monitoring of SIC/SIC Laminated Ceramic Matrix Composites With Acoustic Emission And Electrical ResistanceGordon, Neal A. January 2014 (has links)
No description available.
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A Cumulative Damage Approach to Modeling Atmospheric Corrosion of SteelRose, David Harry January 2014 (has links)
No description available.
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Investigation of Interfacial Bonding Between Shape Memory Alloys and Polymer Matrix CompositesQuade, Derek J. January 2017 (has links)
No description available.
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Role of Elasticity in Respiratory and Cardiovascular FlowSubramaniam, Dhananjay Radhakrishnan 23 July 2018 (has links)
No description available.
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