The knowledge of surface heat flux over aerodynamic surfaces is highly desirable for high-speed applications. Impulse test facilities like shock tubes and shock tunnels are invariantly employed for this where the aerodynamic test models experience step/ramp heat loads. Contrary to conventional methods, the usage of an advanced soft computing technique through an adaptive neuro-fuzzy inference system (ANFIS), for recovery of such surface heat loads, is theme of this paper. A coaxial thermal sensor is fabricated in house from chromel and constantan alloy. This E-type thermal probe is subjected to known heat flux (2–3.5 W) of laser light in an exclusive experimental setup, and the temperature responses are recorded. The simulations are also performed to get the temperature history for these heat loads. The experimental and computational results, either separately or together, are used to train the ANFIS network. The time-averaged values of heat flux obtained from ANFIS-based recovery shows excellent agreement in trend and magnitude (uncertainty band of $\pm 5\%$) with the applied heat load. The present studies demonstrate the possible use of a soft computing technique for heat flux recovery in short-duration experiments within a desired accuracy level by using training data obtained experimentally or computationally or both.

空气动力学表面上的表面热通量知识对于高速应用是非常需要的。在空气动力学测试模型经历阶跃/斜坡热载荷的情况下，始终采用冲击管和冲击隧道等脉冲测试设施。与传统方法相反，本文的主题是通过自适应神经模糊推理系统 (ANFIS) 使用先进的软计算技术来恢复这种表面热负荷。同轴热传感器由铬镍合金和康铜合金制成。这种 E 型热探头在专用实验装置中受到已知的激光热通量 (2-3.5 W) 的影响，并记录温度响应。还执行模拟以获得这些热负荷的温度历史。实验和计算结果，单独或一起用于训练 ANFIS 网络。从基于 ANFIS 的恢复中获得的热通量的时间平均值在趋势和幅度（不确定性带$\pm 5\%$) 施加的热负荷。目前的研究表明，通过使用通过实验或计算或两者获得的训练数据，在所需精度水平内，可以使用软计算技术在短期实验中进行热通量恢复。