Application-Specific Computational Materials Design via Multiscale Modeling and the Inductive Design Exploration Method (IDEM)

The development of materials is a laborious, iterative, expensive, and intuitive process, often requiring decades to transition from early laboratory studies to commercial applications. This research seeks to address this issue by demonstrating a systematic process for linking process-structure-property-performance (PSPP) relations. We argue that the limitations on time for the material development process arise in large part due to lack of effective approaches for exploring the material design space that anticipates application requirements, objectives, and constraints. The material design process employed here utilizes hierarchical multiscale modeling, analytical models, and associated metamodels to construct a set of bottom-up deductive mappings, along with the inductive design exploration method (IDEM) to account for uncertainty in pursuing top-down inductive decision support problems that address application-specific design objectives. The demonstrated problem considers the simultaneous design of ultra-high-performance concrete material and a structural panel able to withstand a 1.5-MPa-ms reflected blast wave impulse. A set of PSPP mappings were constructed across micro-, meso-, and macro-length-scales using analytical expressions and a hierarchical multiscale finite element model at the single fiber, multiple fiber, and structural length scales. The set of PSPP deductive mappings considered seven design variables—panel thickness, fiber pitch, ratio of water to cementitious materials, curing temperature, and volume fractions of fibers, cement, and silica fume—across four hierarchical levels. After the set of deductive PSPP mappings were constructed and validated, ranged sets of feasible values for each design variable were determined via IDEM. Starting with the highest and next-to-higher hierarchical levels as the output and input spaces, respectively, IDEM was implemented via application of three steps—discretization of input variables, projection of discretized sets of input variables with account of uncertainty to a range in the output space, and determination of which sets of discrete input values satisfy the output space requirement(s). By recursively applying these three steps, the PSPP relations were robustly searched for properties, structures, and processes that satisfy the performance requirement(s). The advantages of this approach are the identification of ranged sets of values of design variables and the ability to account for propagated uncertainty. By defining additional mass and cost objectives, the feasible input space was then searched to find the preferred combination of values of design variables that minimized mass and minimized cost while maintaining a robust material and structural design.