Collaborative Research: Multi-Accuracy Bayesian Models for Improving Property Prediction of Nanotube Buckypaper Composites

这项佛罗里达州立大学和德克萨斯A M大学之间的合作研究是开发多精度预测模型，可以提高布基纸基复合材料性能的预测能力。该团队将研究适当的建模策略，以整合多精度信息以及解决相关计算和设计问题的解决方案技术，以保证该方法的效率和实用性。目前，一些力学模型可用于对布基纸基复合材料进行性能预测，但由于模型的不充分性和不确定性，大多数模型的准确性较低。力学模型和实际物理实验的输出构成了一组多精度的信息源，从不同的角度反映了相同的物理性质。我们的猜想是，结合多精度的输出可以帮助提高所需的性能predictationforbukyper-based复合材料，这一新方法的成功开发将有可能实现稳定的，可重复的，和可扩展的生产过程中bukyper-based复合材料，这是最抢手的纳米材料之一，由于其性能在传统材料中找不到，并适用于广泛的应用。高性能材料研究所（HPMI）在佛罗里达州立大学，其中铅PI是附属机构，是最好的研究设施在巴克纸研发和原型生产能力方面的国家之一。预测模型是在纳米制造中实现任何过程和质量控制尝试的基石，因为只有使用这些模型，人们才能识别关键的过程变量，以便进行过程测量或进行调整，以产生预期的结果。

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.