CDS&E: Optimal control of material microstructure evolution via massively parallel computing

该奖项支持跨学科的计算研究和教育，旨在促进对材料微观结构的理解和控制。材料的一些重要性质，如机械强度或腐蚀敏感性，在很大程度上是微观结构的函数：材料成分的尺寸，形状和排列，例如多晶体中的晶粒，在微米的距离上。尽管微观结构的重要性，但用于合成和加工材料以获得所需微观结构并因此获得所需材料性质的技术仍然有限。理解材料制造过程中微观结构如何随时间演变仍然是一个根本性的挑战，微观结构演变对外部控制的敏感性几乎未被探索。该项目研究了使微观结构演变服从外部控制的基本物理特性，并探索了通过将材料建模，实时反馈和控制纳入材料加工来创建设计师微观结构的新途径。这项工作将关闭一个深层次的知识差距有关的基本限制的微观结构的可控性，从而奠定了基础的系统控制的微观结构的演变。“可控性”本身将是一个基本的物理性质出现的机制，管理微观结构的演变。该项目提供的计算工具和基本见解将指导未来开发用于特定材料加工应用的微结构控制器。该项目的计算方面要求极高，需要艾级计算机的全部能力：每秒执行10亿亿次浮点运算的计算机。因此，该项目的一个主要部分是有效地并行化几乎每个计算阶段。技术总结该奖项支持跨学科的计算研究和教育，旨在促进对材料微观结构的理解和控制。虽然已经投入了大量的努力来设计，发现和优化新材料，材料加工的最佳控制是相对较少的理解。特别是，基本的物理特性的一个不断发展的微观结构，决定对外部控制的敏感性程度仍然没有探索。本项目将通过以下方式为材料微结构控制奠定基础：a）设计微结构敏感的反馈控制器，B）开发用于有效实施反馈控制的计算工具，以及c）系统地探索反馈控制器在目标保真度、能效和成本最优性等指标方面的性能。这项工作位于控制理论，材料建模和高性能计算的交叉点。最终，研究人员的目标是通过利用大规模并行，高性能计算架构来探索材料加工中可控性的基本问题，为设计材料的合成创建最佳控制器，例如：如何最佳地控制材料加工参数以制造设计微结构？使用给定的加工方法可以制造的微结构类型的限制是什么？对于能够合成给定类型的微结构的加工方法，最低要求是什么？这项工作将探索材料的微观结构可控性的基本限制，使用控制理论的工具：工程的分支，为车辆，飞机和机器人的自主操作创建最佳算法。为了能够快速调查这一新领域，该项目将使用计算材料模型作为物理材料的替代品。目标是开发可在不同模型之间转移的模型无关控制工具。然而，努力将首先集中在一个特定的模型问题，即：相场模型的微观结构演变所管辖的艾伦-卡恩方程。由于要开发的控制方法足够通用，可以与其他材料模型一起使用。该奖项由数学和物理科学理事会材料研究部凝聚态物质和材料理论项目、土木工程理事会机械和制造创新部共同支持，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持和更广泛的影响审查标准。

NONTECHNICAL SUMMARYThis award supports interdisciplinary computational research and education aimed to advance understanding and control of the microstructure of materials. Some important properties of materials, such as mechanical strength or corrosion susceptibility, are largely a function of microstructure: the sizes, shapes, and arrangement of material constituents, for example grains in polycrystals, over distances of micrometers. Despite the importance of microstructure, techniques for synthesizing and processing materials to obtain desired microstructures, and therefore desired materials properties, remain limited. Understanding how microstructure evolves in time as a material is made remains a fundamental challenge and the susceptibility of microstructure evolution to external control is nearly unexplored. This project investigates the fundamental physical characteristics that make microstructure evolution amenable to external control and explores novel pathways for creating designer microstructures through incorporation of materials modeling, real-time feedback, and control into material processing. This work will close a deep knowledge gap concerning the fundamental limits of microstructure controllability, thereby laying the foundation for the systematic control of microstructure evolution. "Controllability" itself will be a fundamental physical property emerging from the mechanisms that govern microstructure evolution. The computational tools and fundamental insights provided by this project will guide future development of microstructure controllers to be used in specific materials processing applications. The computational aspects of this project are extremely demanding and will require the full power of exascale computers: ones that perform 1 billion billion floating point operations per second. A major part of this project is therefore to efficiently parallelize nearly every stage of computation.TECHNICAL SUMMARYThis award supports interdisciplinary computational research and education aimed to advance understanding and control of the microstructure of materials. While much effort has been invested into the design, discovery, and optimization of new materials, optimal control of materials processing is relatively less well understood. In particular, the fundamental physical characteristics of an evolving microstructure that determine the degree of susceptibility to external control remain unexplored. This project will lay the foundations for material microstructure control by: a) designing microstructure-sensitive feedback controllers, b) developing computational tools for efficient implementation of feedback control, and c) systematically exploring feedback controller performance with respect to metrics such as objective fidelity, energy-efficiency, and cost-optimality. This work lies at the intersection of control theory, materials modeling, and high-performance computing. Ultimately, the researchers aim to create optimal controllers for the synthesis of designer materials by exploiting massively parallel, high performance computing architectures to explore fundamental questions of controllability in materials processing, such as: How does one optimally control materials processing parameters to make designer microstructures? What are the limits to the types of microstructures that may be made using a given processing method? What are the minimal requirements for a processing method to be able to synthesize a given type of microstructure?This work will explore the fundamental limits of materials microstructure controllability using the tools of control theory: the branch of engineering that creates optimal algorithms for the autonomous operation of vehicles, aircraft, and robots. To enable rapid investigation of this new field, the project will use computational materials models as surrogates for physical materials. The goal is to develop model-agnostic control tools that are transferrable between different models. However, effort will initially focus on one specific model problem, namely: phase field models of microstructure evolution as governed by the Allen-Cahn equation. Since the control methods to be developed will be sufficiently general that they can be used with other materials models.This award is jointly supported by the Condensed Matter and Materials Theory Program in the Division of Materials Research in the Directorate for Mathematical and Physical Sciences, the Civil, Mechanical and Manufacturing Innovation Division in the Engineering Directorate, and the Software and Hardware Foundations Program in the Division of Computing and Communications Foundations in the Directorate for Computer and Information Science and Engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.