Static and dynamic performance characteristics of thin-film thermocouples under diverse thermal excitations

Thin-film thermocouples (TFTCs), owing to their miniaturized structure, high sensitivity, and nanosecond-scale thermal response, have been widely applied to temperature monitoring in complex thermal environments. However, their performance evaluation still depends largely on post-processing tests, where unstable excitation and measurement noise often reduce accuracy and repeatability, slowing device optimization. To address these challenges, this study develops a multiphysics simulation based on COMSOL to predict the response performance of K-type TFTC under two representative testing scenarios: static calibration and dynamic calibration. For static calibration, a temperature-control function T w ( t ) was established by solving the heat transfer equation of furnace air, enabling stepwise heating and stable temperature holding. This approach provides precise multi-stage temperature regulation in high-temperature furnaces, improves the fidelity and uniformity of heat transfer, and reduces cumulative errors caused by localized non-uniform heating. For dynamic calibration, a laser heat-flux model was constructed by coupling a two-dimensional Gaussian spatial distribution with a rectangular temporal function. The model effectively captures the spatiotemporal characteristics of laser-pulse energy deposition, enabling high-resolution analysis of transient heat diffusion and the corresponding thermoelectric response. The static simulation yields a thermoelectric sensitivity of , which closely matches the experiment. The dynamic simulation predicts a response time of , differing by only 5% from the measured value of . This method significantly enhances the efficiency of evaluating TFTC performance, offering a versatile and reliable technical pathway for virtual development and performance verification. • Furnace-air heat-transfer modeling enables uniform heating and reliable calibration. • Gaussian-pulse heat-flux modeling captures transient diffusion and response. • Simulations match experiment, verifying model accuracy and feasibility.