The present study elaborates the suitability of the artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) to predict the thermal performances of the thermoelectric generator system for waste heat recovery. Six ANN models and seven ANFIS models are formulated by considering hot gas temperatures and voltage load conditions as the inputs to predict current, power, and thermal efficiency of the thermoelectric generator system for waste heat recovery. The ANN model with the back-propagation algorithm, the Levenberg–Marquardt variant, Tan-Sigmoidal transfer function and 25 number of hidden neurons is found to be an optimum model to accurately predict current, power and thermal efficiency. For current, power and thermal efficiency, the ANFIS model with pi-5 or gauss-5-membership function is recommended as the optimum model when the prediction accuracy is important while the ANFIS model with gbell-3-membership function is suggested as the optimum model when the prediction cost plays a crucial role along with the prediction accuracy. The proposed optimal ANN and ANFIS models present higher prediction accuracy than the coupled numerical approach.

Nanofluids are suspended nano-sized particles in a base fluid. With increasing demand for more high efficiency thermal systems, nanofluids seem to be a promising option for researchers. As a result, numerous investigations have been undertaken to understand the behaviors of nanofluids. Since their discovery, the thermo-physical properties of nanofluids have been under intense research. Inadequate understanding of the mechanisms involved in the heat transfer of nanofluids has been the major obstacle for the development of sophisticated nanofluids with the desired properties. In this comprehensive review paper, investigations on synthesis, thermo-physical properties, and heat transfer mechanisms of nanofluids have been reviewed and presented. Results show that the thermal conductivity of nanofluids increases with the increase of the operating temperature. This can potentially be used for the efficiency enhancement of thermal systems under higher operating temperatures. In addition, this paper also provides details concerning dependency of the thermo-physical properties as well as synthesis and the heat transfer mechanism of the nanofluids.

The objective of this study was to investigate the power generation, efficiency, and thermal stress of a thermoelectric module with leg geometry, material, segmentation, and two-stage arrangement. The effects of leg geometry, material, segmentation, and two-stage arrangement on maximum power, maximum efficiency, and maximum stress under various temperature differences and voltage load conditions were investigated. The performance parameters of the thermoelectric module were evaluated based on a numerical approach using ANSYS 19.1 commercial software. An analytical approach based on theoretical equations of the thermoelectric module was used to verify the accuracy and reliability of the numerical approach. The numerically predicted values for maximum power and maximum efficiency of the thermoelectric module were validated as ±5% and those for the maximum thermal stress of the thermoelectric module as ±7% with the corresponding calculated theoretical values. In addition, the predicted values of maximum power and maximum stress of the thermoelectric module were validated as ±2% and ±5%, respectively, with studies reported by Ma et al. and Al-Merbati et al. Of all the combinations, the single stage segmented arrangement with cylindrical leg geometry and SiGe+Bi₂Te₃ material was suggested as the best combination with maximum power of 0.73 W, maximum efficiency of 13.2%, and maximum thermal stress of 0.694 GPa.

Nanofluids are considered a promising choice for several heat transfer applications. With the increasing awareness for energy saving and efficiency improvement in various thermal systems, including refrigeration systems, there is a growing interest in the refrigerant-based nanofluids owing to their superior thermo-physical properties. Nanorefrigerants are a class of nanofluid, which consist of suspended nanoparticles in a base refrigerant. In this paper, it is intended to include many articles on refrigeration systems that use nanorefrigerants, published in the period from 2005 to 2015. Although this is an extensive review, it could not include all the papers, and only some major research works were selected. It is believed that the dependency of thermal conductivity and other properties on temperature will make the thermal systems more efficient while operating at a high temperature. The literature reviews associated with the performance characteristics of nanorefrigerants in refrigeration systems for the last 10 years have been compiled and presented in this paper. Furthermore, recent studies related to thermo-physical properties of nanorefrigerants and nanolubricants have also been summarized and reviewed in this paper.