Data and decision science-driven assessment of sustainable materials for thermal protection in aerospace

Significant progress has been made in researching materials for thermal protection in aerospace vehicle technologies. The rapid development of aerospace technology has introduced new requirements for high-performance thermal protection systems, including but not limited to high-temperature resistance, lightweight characteristics, high transmission capabilities, and so on. Materials Informatics-enabled materials discovery and selection, popularly termed Materials 4.0, is enabled on the existing big data of synthesis, processing, modeling, characterization, and properties of diverse materials for thermal protection in aerospace. A novel methodology that combines multiple-attribute decision-making (MADM) methods, principal component analysis (PCA), and hierarchical cluster analysis (HCA) is applied. Weights of the properties (or attributes) were evaluated by objective (Shannon's entropy method) and subjective methods. The rank assigned by several MADMs, viz., Technique of order preference by similarity to ideal solution (TOPSIS), Grey Relational Analysis (GRA), Multi-criteria Q Analysis (MCQA), Operational competitive ratio (OCRA), and so forth, were consistent. PCA and HCA consolidated the MADM ranks and grouped similar alloys. The investigation highlights similarities across several materials for thermal protection, suggests potential replacement or substitute for existing materials for thermal protection, and provides directions for improvement and/or development of sustainable materials for thermal protection.