Collaborative Research: Multi-Accuracy Bayesian Models for Improving Property Prediction of Nanotube Buckypaper Composites
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abstract
This collaborative research between Florida State University and Texas A&M University is to develop multi-accuracy predictive models that can enhance the prediction capability for bukypaper-based composite properties. The team will investigate proper modeling strategies to integrate the multi-accuracy information as well as the solution techniques that address the associated computational and design issues in order to guarantee the method''s efficiency and practicality. Currently, a few mechanics models are available for making property predictions for bukypaper-based composites but most of them suffer from having low accuracy due to model inadequacy and uncertainty. The outputs from the mechanics models and actual physical experiments constitute a set of multi-accuracy information sources, reflecting the same physical properties from different perspectives. Our conjecture is that combining the multi-accuracy outputs could help enhance the desired property predication for bukypaper-based composites.The successful development of this new methodology will potentially enable stable, repeatable, and scalable production processes for bukypaper-based composites, which are one of the most sought-after nano-materials, due to its properties unfound in traditional materials and applicability to a broad array of applications. The High-Performance Material Institute (HPMI) at Florida State University, with which the lead PI is affiliated, is one of the best research facilities in the nation in terms of buckypaper R&D and prototype production capabilities. Predictive models are the cornerstones for enabling any attempts of process and quality control in nano-manufacturing because only with these models can people identify the critical process variables for taking in-process measurements, or making adjustments, in order to yield expected outcomes.
