Collaborative Research: GOALI: Experimentally-Validated Computational Approach to Developing and Predicting Kinetics in Anisotropic Systems

合作研究：GOALI：经过实验验证的计算方法，用于开发和预测各向异性系统中的动力学

基本信息

批准号：
1411106
负责人：
Dallas Trinkle
金额：
$ 19.96万
依托单位：
University of Illinois at Urbana-Champaign
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-01 至 2018-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1411106&HistoricalAwards=false
关键词：
Collaborative Research GOALI Experimentally Validated

项目摘要

Non-technical summary:Reducing the weight of vehicles can increase fuel efficiency, which counters the rising economic and social costs of energy extraction and use. Magnesium is attractive as a potential alternative to steel and aluminum because of its low density, and high stiffness/weight and strength/weight ratios. However, alloy development can be a slow and costly process. To expedite the design of new casting alloys, this research uses experimentally validated computational methods, with particular emphasis on enabling a kinetically-informed approach. In the spirit of the Materials Genome Initiative, this project brings together computation and theory (Univ. Illinois, Urbana-Champaign), experimentation (Univ. Florida), and industrial modeling (ThermoCalc, LLC) in a joint effort to develop the science and data to predict relationships between processing, material structure, and eventually properties. The science and engineering advances will be applicable to other alloys as new methods are developed to study solidification and predict how atoms move at the atomic scale. This project takes advantage of new advances in computing power, new theoretical developments, and new high-throughput experimental methods. The program will support two PhD students, and the faculty are involved both in undergraduate research and education of high-school science teachers.Technical summary:To combat emissions and increase the fuel economy in transportation vehicles, considerable effort has been made to understand and enhance the structure-property relationships in magnesium (Mg) alloys due to their low density. This collaborative project unites computation (Univ. Illinois, Urbana-Champaign) and experimentation (Univ. Florida) in an integrated fashion to significantly advance the knowledge of transport in cast Mg alloys. In the spirit of the Materials Genome Initiative, the PIs proceed in an integrated and self-consistent manner to understand and predict transport phenomena during solidification. The approach integrates computation and experiment throughout, providing validation and connecting fundamental mechanisms to mesoscale transport. In collaboration with ThermoCalc, LLC, they will create new kinetic databases to enable material development.The goal of this approach is to produce a new fundamental understanding of solute and vacancy transport in Mg in a framework to predict phase transformation phenomena for new Mg alloys. A unified understanding of multiscale behavior provides a science-driven approach to future alloy development in a materials-genome framework. The data from this program will be disseminated via public databases (NIST MatDL) and the development of databases for mesoscale phase-field and continuum-level simulations. By accounting for temperature, time and compositional effects, the predictions from this work allow for the complete merger of processing-structure and structure-property predictions.The project has four tasks that build upon each other to predict the mesoscale microstructure for cast Mg: 1) Determine mobilities in binary and higher order systems for HCP and liquid Mg. Density functional theory (DFT) will lead this approach by calculating activation energies and mobilities. 2) Determine solid-liquid surface energies and molar volumes in all relevant systems via experiments and molecular dynamics simulations in DFT. 3) Assemble molar volumes, surface energies and mobilities in repositories and mobility databases. DICTRA simulations are compared to diffusion multiples, and segregation behavior and homogenization temperatures validated with experimental data. 4) Predict solidified microstructures through the integration of mobilities, molar volumes and surface energies. Simulations include the prediction of nuclei number density, size, and composition as a function of temperature and cooling rate, and are validated with experimental grain size distributions obtained from similar processing routes.The expected outcomes are a new fundamental understanding of atomic transport in HCP metals, with mobility, volumetric and surface energy databases that will serve both as a repository and a foundation for increasing system complexity (additional phases, solutes, etc.). The data will be supplied in a logical, manageable, and accessible fashion for transition into commercial practice. This platform may allow predictions of nucleation behavior in other relevant phases in the future.

非技术摘要：减轻车辆重量可以提高燃油效率，从而抵消能源开采和使用不断上升的经济和社会成本。镁由于密度低、刚度/重量比和强度/重量比高而作为钢和铝的潜在替代品很有吸引力。然而，合金开发可能是一个缓慢且昂贵的过程。为了加快新型铸造合金的设计，本研究使用经过实验验证的计算方法，特别强调实现动力学信息方法。本着材料基因组计划的精神，该项目将计算和理论（伊利诺伊大学厄巴纳-香槟分校）、实验（佛罗里达大学）和工业建模（ThermoCalc, LLC）结合在一起，共同努力发展科学和技术。用于预测加工、材料结构和最终性能之间关系的数据。随着研究凝固和预测原子在原子尺度上如何移动的新方法的开发，科学和工程的进步将适用于其他合金。该项目利用了计算能力的新进步、新的理论发展和新的高通量实验方法。该计划将支持两名博士生，教师们同时参与本科生研究和高中科学教师的教育。技术摘要：为了减少运输车辆的排放并提高燃油经济性，人们付出了相当大的努力来理解和加强镁（Mg）合金由于密度低而导致结构-性能关系。该合作项目以综合方式将计算（伊利诺伊大学厄巴纳-香槟分校）和实验（佛罗里达大学）结合起来，以显着推进铸造镁合金传输的知识。本着材料基因组计划的精神，PI 以综合且自洽的方式进行，以理解和预测凝固过程中的输运现象。该方法将计算和实验贯穿始终，提供验证并将基本机制与中尺度传输联系起来。他们将与 ThermoCalc, LLC 合作创建新的动力学数据库以实现材料开发。该方法的目标是在框架中对镁中的溶质和空位传输产生新的基本理解，以预测新型镁合金的相变现象。对多尺度行为的统一理解为材料基因组框架中的未来合金开发提供了科学驱动的方法。该计划的数据将通过公共数据库 (NIST MatDL) 以及中尺度相场和连续介质级模拟数据库的开发来传播。通过考虑温度、时间和成分影响，这项工作的预测可以将加工结构和结构性能预测完全合并。该项目有四项相互依赖的任务来预测铸造镁的介观微观结构：1 ) 确定 HCP 和液态镁在二元和高阶系统中的迁移率。密度泛函理论（DFT）将通过计算活化能和迁移率来引领这种方法。 2）通过DFT中的实验和分子动力学模拟确定所有相关系统中的固液表面能和摩尔体积。 3) 在存储库和迁移率数据库中汇集摩尔体积、表面能和迁移率。将 DICTRA 模拟与扩散倍数进行比较，并通过实验数据验证偏析行为和均质化温度。 4) 通过整合迁移率、摩尔体积和表面能来预测凝固微观结构。模拟包括预测核数密度、尺寸和成分随温度和冷却速率的变化，并通过从类似加工路线获得的实验晶粒尺寸分布进行验证。预期结果是对 HCP 金属中原子输运的新的基本了解，具有流动性、体积和表面能数据库，这些数据库将充当存储库和增加系统复杂性（附加相、溶质等）的基础。数据将以逻辑、可管理和可访问的方式提供，以便过渡到商业实践。该平台可以预测未来其他相关阶段的成核行为。