Realizing perfect contact between any two surfaces is impossible due to the presence of microscopic irregularities in material surfaces. Therefore, for true thermal analysis of multiple components systems knowledge of thermal contact conductance (TCC) between two solid bodies is essential. Estimation of TCC for different applications has been carried out in this paper by inverse heat transfer technique. A novel stochastic Jaya algorithm has been developed for this purpose and its performance has been evaluated for different heat conduction problems like the estimation of TCC with two metals contact (case-1), periodically contacting surfaces (case-2), multilayer wall surfaces (case-3), and simultaneous estimation of TCC and boundary heat flux (case-4). The experiment has also been conducted to determine the efficacy of this new algorithm for real-life situations. Different parameters like population size, sensor location from the interface, and errors in measured temperature data are taken into consideration. The population size of 5 candidate solutions for case-1, 60 candidate solutions for case-2, and 50 candidate solutions for case-3 and case-4 is found to be suitable for estimation. The results are accurate with the root mean square (RMS) error of 12.62 W/m²K even when the sensors are located 50 mm far away from the interface. Although errors in measurements deteriorate the performance of the Jaya algorithm, it still gives stable results (RMS = 2.90 W/m²K at σ = 0.3). Jaya algorithm is found to be an accurate and promising algorithm for inverse problems.

由于材料表面存在微观不规则性，因此不可能实现任何两个表面之间的完美接触。因此，对于多组分系统的真正热分析，了解两个固体之间的热接触传导 (TCC) 是必不可少的。本文通过逆传热技术对不同应用的 TCC 进行了估算。为此目的开发了一种新的随机 Jaya 算法，并针对不同的热传导问题评估了其性能，例如估计具有两种金属接触的 TCC（案例 1）、周期性接触表面（案例 2）、多层壁面（案例 3），以及同时估算 TCC 和边界热通量（案例 4）。还进行了实验以确定这种新算法在现实生活中的有效性。考虑了不同的参数，如人口规模、来自界面的传感器位置以及测量温度数据中的误差。发现案例 1 的 5 个候选解决方案、案例 2 的 60 个候选解决方案以及案例 3 和案例 4 的 50 个候选解决方案的总体大小适合估计。结果准确，均方根 (RMS) 误差为 12.62 W/m 并且发现案例 3 和案例 4 的 50 个候选解适合估计。结果准确，均方根 (RMS) 误差为 12.62 W/m 并且发现案例 3 和案例 4 的 50 个候选解适合估计。结果准确，均方根 (RMS) 误差为 12.62 W/m即使传感器位于距接口 50 mm 远的地方，也为² K。尽管测量中的误差会降低 Jaya 算法的性能，但它仍能提供稳定的结果（σ = 0.3 时的RMS = 2.90 W/m ² K）。发现 Jaya 算法是一种准确且有前途的逆问题算法。