Lehigh University

Cds&e: Development of Data-driven Physics-based Reduced-order Models for the Solidification Process of Binary Alloys -this Computation and Data-enabled Science and Engineering (cds&e) Project Seeks to Support Research the Explores Computationally Efficient Generation of the Process-structure-property (PSP) Map Linking Manufacturing Process Parameters to the Resulting Solidification Microstructure. This Understanding, Which Has a Strong Influence on the Properties of Additively Manufactured Materials, Is Critical to the Design of Metals With Superior Properties and Can Also Be Leveraged in an Inverse Problem Framework to Optimize Process Parameters for Desired Properties. However, the Vast Range of Length and Time Scales Inherent in the Manufacturing Process Makes Constructing PSP Maps Computationally Prohibitive. This Research Seeks to Address This Challenge by Developing a Computationally Efficient Surrogate Model for Solidification, Significantly Accelerating Both Forward and Inverse Problems on the Process-structure (PS) Linkage. by Enhancing the Computational Efficiency of Manufacturing Process Parameter Optimization, This Looks to Drive Technological Innovation, Strengthen the Us Economy, and Support Workforce Development by Engaging Graduate and Middle-school Students in Stem Learning, Cultivating the Next Generation of Engineers and Scientists. This Project Seeks to Develop a Physics-based, Data-driven Reduced-order Model (ROM) for Predicting Microstructures Evolution in Binary Alloy Solidification. the Proposed Non-intrusive Rom Reduces the Computational Cost of High-dimensional Models by Projection-based Model Reduction Utilizing Nonlinear Manifolds and Sparsity-inducing Operator Learning to Capture Transport- Dominated Phenomena Across the Solid-liquid Interface. the Research Will: (1) Develop a Model Reduction Framework Tailored for Nonlinear Transport-dominated Processes, (2) Construct a Parametric Rom That Maps Process Parameters to Microstructure Attributes, and (3) Establish an Inverse Problem Framework for Optimizing Process Parameters. by Accelerating the Process-structure Linkage in PSP Maps, This Work Looks to Advance the Design of High-performance Materials, and Has Broad Applicability to Other Engineering Problems, Including Crack Propagation, Solute Distribution and Wave Propagation. 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.- Subawards Are Not Planned for This Award.