A multi-physics framework for optimizing FBG sensor thermal response via nanoparticle-engineered polymer coatings

Abstract Fiber Bragg grating (FBG) sensors are widely used for precise temperature monitoring, but standard polymer coatings often introduce thermal lag during rapid temperature fluctuations. While incorporating thermally conductive nanoparticles can accelerate heat transfer, high filler loadings typically cause agglomeration, increase interfacial (Kapitza) resistance, and compromise mechanical integrity. This study introduces a unified multi-physics framework to optimize nanoparticle–polymer coatings for FBG sensors without relying on high filler concentrations. The proposed model couples transient heat conduction, effective medium homogenization, and thermo-mechanical strain transfer, explicitly accounting for interfacial resistance, percolation-driven conductivity, fabrication-induced porosity, and strain-transfer efficiency. For randomly dispersed fillers, effective properties are predicted using coupled homogenization and percolation models. For engineered or oriented architectures, the framework directly integrates experimentally validated thermal and mechanical properties. Numerical results predict that at 5.8 vol%, graphene nanoplatelets can reduce the thermal response time by approximately 80% compared to a pure polymer coating, while silica nanoparticles are projected to extend the maximum operational temperature by approximately 27 K. Metallic fillers (Ag, Au) improve heat transfer but show limited high-temperature stability due to oxidation and interfacial resistance. The framework identifies optimal loading windows that balance thermal response time, mechanical integrity, and thermal durability. These findings provide practical, physics-based design guidelines for next-generation FBG sensors and establish the mechanistic foundation for adaptive, physics-informed Digital Twins tailored to aerospace, industrial, and biomedical applications.