Surface Heat Flux Recovery during Short Duration Experiments–Conceptual Demonstration from Probe Design to Soft computing Analysis

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Recovery of surface heat flux in short duration experiments is a challenging task due to the dominance of unsteadiness in the temperature signal. Therefore, the heat flux estimation is carried out using the transient temperatures through suitable sensor modeling. In most of the cases (e.g., in thin-film gauges and coaxial probes), the sensor is assumed as a semi-infinite body with one-dimensional heat conduction through the sensing surface and substrate. In some cases, the surface heat flux is computed through numerical simulation. Nevertheless, all these processes involve various assumptions, simplifications and mathematical complications. In recent years, the advanced data science and soft computing methods are considered as important techniques in various applications. Therefore, the theme of the thesis is to introduce soft computing approach as a benchmark tool for recovery of surface heat flux in short duration experiments. The foremost intention of the present study is to implement a soft computing technique; Adaptive neuro-fuzzy inference system (ANFIS), to recover heat flux from the temperature signal in case of short duration experiments. The ANFIS technique needs a training process through known data sets (temperature signals and their corresponding heat flux). Therefore, the training data sets (transient temperature and surface heat flux) have been generated through various heat transfer experiments involving step and impulsive loads, convective and radiative heat loads. Side by side, numerical modelling of sensors (with similar experimental environment) is also considered as training data generation for ANFIS methods. Subsequently, inverse approach is followed to recover unknown (surface heat flux) parameters through trained data sets of ANFIS
Supervisors: Sahoo, Niranjan and Kalita, Pankaj