Diffusion, dynamics and solidification processes in Fe-Al-Si melts

Fe-Al-Si 熔体中的扩散、动力学和凝固过程

• 批准号: 505695238
• 负责人: Professor Dr. Jürgen Horbach
• 金额: --
• 依托单位: Grenoble INP - UGA Institut dingénierie et de management
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/505695238?language=en
• 关键词: Diffusion; dynamics; solidification; processes; Fe

The present proposal aims at investigating microscopic mechanisms that govern the diffusion dynamics in Fe-Al-Si melts as well as the early stages of solidification of these systems in their undercooled state. The German-French consortium set up a joint experimental and theoretical study for understanding these phenomena in Fe-Al-Si mixtures which is of fundamental and technological interest. State-of-the-art contact-free measurements on metallic liquids are performed, applying both electrostatic and electromagnetic levitation techniques, and using large-scale European facilities to measure high quality structural and dynamic properties in the liquid and undercooled alloys prior to solidification. The obtained experimental data will provide a wide spectrum of thermophysical properties of high-temperature Fe-Al-Si melts, namely density, thermal expansion, shear viscosity, static structure factors, self- and interdiffusion coefficients, and Soret coefficients. These data are associated to a data base from quantum calculations and enable us to construct interatomic force fields using machine learning methodologies based on Artificial Intelligence concepts. With these force fields, a realistic modelling in terms of large-scale molecular dynamics simulations shall be possible. From these simulations, we are aiming at a microscopic insight into kinetic as well as solidification processes. Moreover, for the example of the system Fe-Al-Si, the methodology shall be developed to obtain interaction models for ternary metallic systems from machine learning techniques. These interaction models shall provide a realistic description of materials properties of melts as well as of solidification processes and the properties of crystalline phases.

本建议的目的是调查的微观机制，支配在Fe-Al-Si熔体中的扩散动力学以及这些系统在其过冷状态下的凝固的早期阶段。德国-法国财团建立了一个联合实验和理论研究，以了解这些现象的Fe-Al-Si混合物，这是基本的和技术的利益。对金属液体进行最先进的非接触式测量，应用静电和电磁悬浮技术，并使用大型欧洲设施来测量凝固前液体和过冷合金的高质量结构和动态特性。所获得的实验数据将提供一个广泛的高温Fe-Al-Si熔体的热物理性质，即密度，热膨胀，剪切粘度，静态结构因子，自扩散和互扩散系数，和Soret系数。这些数据与量子计算的数据库相关联，使我们能够使用基于人工智能概念的机器学习方法来构建原子间力场。有了这些力场，就有可能进行大规模分子动力学模拟的逼真建模。从这些模拟，我们的目标是在微观上洞察动力学以及凝固过程。此外，对于系统Fe-Al-Si的例子，该方法将被开发以从机器学习技术获得三元金属系统的相互作用模型。这些相互作用模型应提供对熔体材料特性以及凝固过程和晶相特性的真实描述。