DMREF/Collaborative Research: Accelerated Soft Magnetic Alloy Design and Synthesis Guided by Theory and Simulation

DMREF/合作研究：理论和仿真引导的加速软磁合金设计与合成

• 批准号: 1629026
• 负责人: Cristian Ciobanu
• 金额: $ 72.42万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629026&HistoricalAwards=false
• 关键词: DMREF; Collaborative; Research; Accelerated; Soft

Soft magnetic materials have use in power conversion, conditioning, distribution, and generation technologies, including transportation (electric vehicles), renewable energy (solar inverters), and aerospace (power converters and inductors) sectors. The term "soft magnet" refers to a magnetic material that easily changes magnetic pole directions using small magnetic fields. With over 20 percent of all generated electricity in the US being consumed by industrial electric motor drives, a mere 1 percent improvement in energy efficiency would result in significant financial and environmental benefits. The magnetic components are a major source of energy loss in the above-mentioned applications, motivating the need for soft magnets with better energy efficiency. The design cycle for new soft magnetic materials has so far been informed mainly by direct human engineering intuition and historic knowledge and bias, with materials development occurring by trial-and-error approaches. This Designing Materials to Revolutionize and Engineer our Future (DMREF) award supports research to establish, demonstrate, and validate a computation-guided framework for accelerated discovery of new, better performing soft magnetic materials. This approach will use computational materials science tools to guide alloy design, with the synthesis and experimental validation of properties performed for down-selected new alloys.Recently, new alloys with microstructures comprised of an amorphous matrix and nanocrystalline grains have revolutionized advanced soft magnetic materials by enabling smaller hysteresis than has been achieved in traditional magnetic materials. This award supports research on the design of new alloys of this type using hierarchical, multi-scale, magneto-structural modeling with input from density functional theory calculations of structural and magnetic properties for single-crystals. Micromagnetic theory will provide the constitutive law for the continuum-level model for optimization of realistic microstructures consisting of an amorphous matrix surrounding nanocrystals. The continuum-level modeling represents a fundamental advancement that will provide much-needed insight into the interplay between the microstructure effects and the magnetic properties of the crystalline phase in determining small hysteresis, as well as an operational understanding of the applicability limits of the currently-prevalent random anisotropy model for coercivity. Structural considerations will be evaluated by continuum thermodynamics modeling and resulting magnetic performance characteristics will be evaluated by micromagnetics modeling. Down-selected alloy compositions - as optimized by these computational approaches - will be synthesized using rapid solidification with subsequent annealing and characterized using state-of-the-art structural and magnetic characterization tools.

软磁料在电力转换，调理，分布和发电技术中都使用，包括运输（电动汽车），可再生能源（太阳能逆变器）和航空航天（电力转换器和电感器）扇区。术语“软磁铁”是指轻松使用小磁场更改磁极方向的磁性材料。由于工业电动机驱动器消耗的所有发电量超过20％，因此能源效率的提高只会提高1％，这将带来巨大的财务和环境利益。磁性组件是上述应用中的能源损失的主要来源，激发了对能量效率提高的软磁铁的需求。迄今为止，新的软磁性材料的设计周期主要是由直接人类工程直觉以及历史知识和偏见来告知的，并且通过试用方法进行了材料的开发。这种设计材料彻底改变和设计我们的未来（DMREF）奖，支持研究，证明和验证一个计算引导的框架，以加速发现新的，更好地性能的软磁性材料。这种方法将使用计算材料科学工具来指导合金设计，并通过对下选择的新合金进行的属性进行合成和实验验证。实际上，具有微观结构的新合金由无定形基质和纳米晶体晶粒组成的微观结构与传统的磁性材料相比，通过实现较小的近米的高级磁性彻底改变了先进的软磁材料。该奖项支持使用分层，多尺度的磁结构建模对这种类型的新合金设计进行研究，并通过从密度功能理论计算单晶的结构和磁性计算的输入。微磁理论将为连续级模型提供本构定律，以优化由纳米晶体周围的无定形基质组成的现实微观结构。连续级建模代表了一个基本进步，它将提供急需的见解，以了解微观结构效应与晶相之间的相互作用，并在确定小滞后时的磁性特性，以及对当前持续的随机反应模型的适用性限制的操作理解。将通过连续热力学建模来评估结构考虑因素，并通过微磁模型评估产生的磁性能特征。这些计算方法优化的下选择的合金组成将使用快速固化和随后的退火进行合成，并使用最先进的结构和磁性特征工具进行表征。