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Thermal interface resistance of Thermoelectric Generator (TEG) plays a vital role in power production. Improving surface finish of contact surfaces, applying pressure between the contact surfaces and use of Thermal Interface Material (TIM) are few methods of reducing thermal resistance and thereby improving the efficiency of TEG. There is a need to evaluate the influence of these methods and use them optimally for TEG system. Experiments were carried out to study the influence of parameters such as thermal conductivity of TIM, contact pressure, surface roughness and heat source temperature on the voltage and power outputs from TEG. Experimental results are validated with simulations using mathematical heat transfer model and COMSOLTM Multiphysics numerical model. Appreciable agreement is seen between the experimental observations and model outputs. Experimental and model results indicate 0.6 W/mK as optimum thermal conductivity for TIM material. Hence, use of costly TIMs like MWCNT (Multi Wall Carbon Nano Tube) and copper nanoparticles may not be required for the selected application. The contact pressure and surface roughness have appreciable influence when air is used as TIM. These factors have insignificant influence for TIMs with higher thermal conductivity. Increase in heat source temperature increases voltage and power output of TEG.

This paper presents a reliable thermal design for a Thermoelectric Generator (TEG) with a heat sink integrated with Thermal Energy Storage (TES) unit for solar reversible power generation of thermoelectric modules. The heat sink filled with Phase Change Material (PCM) stores thermal energy, which aids in producing continuous solar power generation with extended duration during the night. Thermoelectric Generator (TEG) modules are heated and cooled respectively with the open circuit and closed circuit conditions. The heat rejected by TEG is stored as thermal energy in PCM and this stored thermal energy is utilized as a heat source for power generation during cooling. The experimental setup for cooling comprises of a heat exchanger, which works on the principle of the thermosyphon effect. Thermal and electrical parameters of open-circuit voltage, closed-circuit voltage and electric power output were analyzed for different heat fluxes during heating and cooling. Differential Scanning Calorimeter (DSC) and Thermo Gravimetric Analysis (TGA) tests were conducted for PCM. However, PCM showed a supercooling property in the experimental study at a temperature of 156 °C. In the experimental section, the power generation was almost the same for the heating and cooling cycles at a heat flux of 5.5 kW/m2 - heating cycle produced a net power output of 0.39 W, whereas the cooling cycle produced a net power output of 0.31 W. Thus, experimental investigation signifies that the reversible operation of TEG modules is favourable for day and night cycle operations for solar power generation.

A primary challenge still exists in the field of thermoelectric generators (TEG) for practical applications in which a thermal system of the TEG is a crucial factor in TEG power generation. The material development for TEG has contributed significantly towards advancement in TEG applications over a decade, the need for a thermal system configuration is inevitable considering the applications. The thermal efficiency of TEG depends upon the temperature difference across its modules (between the hot and cold surfaces). Thermal design of the thermoelectric system is important to ensure that there exists a maximum temperature difference across the hot and cold surfaces of the TEG. Thermal Interface Material (TIM) in thermoelectric systems plays a main role in improving the efficiency of thermoelectric systems by reducing the temperature difference between the heat source and the hot surface of the TEG and similarly, the temperature difference between the cold surface of TEG and the heat sink. This review paper predominantly focuses on the thermal interfaces between the TEG modules which reduces the performance of a thermoelectric system. The characteristics of TIM in a TEG system (contact pressure, surface roughness and thermal conductivity) were analyzed with a mathematical model to emphasize the importance of TIM in a TEG system. This paper also highlights the existing challenges for Thermal Interface Materials in TEG applications and concludes with a brief discussion on future directions of TIM in TEG thermal systems.

High cost imported components of seamless steel tube manufacturing plants wear frequently and need replacement to ensure the quality of the product. Hard chrome plating, which is time consuming and hazardous, is conventionally used to restore the original dimension of the worn-out surface of the machine components. High Velocity Oxy-Fuel (HVOF) thermal spray coatings with NiCrBSi super alloy powder and Cr3C2 NiCr75/25 alloy powder applied on a 50CrMo4 (DIN-1.7228) chromium molybdenum alloy steel, the material of the wear prone machine component, were evaluated for use as an alternative for hard chrome plating in this present work. The coating characteristics are evaluated using abrasive wear test, sliding wear test and microscopic analysis, hardness test, etc. The study results revealed that the HVOF based NiCrBSi and Cr3C2NiCr75/25 coatings have hardness in the range of 800–900 HV0.3, sliding wear rate in the range of 50–60 µm and surface finish around 5 microns. Cr3C2 NiCr75/25 coating is observed to be a better option out of the two coatings evaluated for the selected application.

Prediction of wall temperature for the range of operating conditions and selecting appropriate material for water-wall tubes, cooled by turbulent water/steam with drastic changes in property, is important in boiler design. An analytical route of predicting the wall temperature for such flow conditions is not reliable. Empirical correlations of non-dimensional numbers, based on experimental data, are used for predicting wall temperatures of turbulent flow with abrupt changes in fluid properties. BHEL has conducted many experiments with supercritical water/steam and developed Artificial Neural Network (ANN) based wall temperature prediction model. This model predicts wall temperature using the given inputs of fluid pressure, fluid temperature, product of mass flux and diameter, and heat flux. The model has prediction accuracy of 100% for the experimental data and 81.94% for the literature data at a deviation level of ±7 °C. This ANN model is useful for predicting wall temperatures of supercritical boilers operating in the tested range of parameters.

In the present work, the heat transfer characteristics of supercritical pressure water are numerically investigated in an upward flow vertical smooth tube. The numerical simulations are carried out by using Ansys-Fluent solver. The objective of the present work is to investigate the effect of heat flux and mass flux on heat transfer characteristics in supercritical water. In order to perform numerical simulation, experimental data of Mokryet al.[2] is considered. Various simulations were carried out for the inlet parameters of temperature 350°C, pressure 240bar; heat flux values ranging from 190 to 884kW/m2and mass flux values ranging from 498 to 1499kg/m2s. Based on the available parameters of heat flux and mass flux, they are segregated as groups with heat flux to mass flux ratios of 0.39 and 0.67. According to computational data, the heat transfer enhancement and heat transfer deterioration phenomenon of supercritical water were analyzed and based on the comparison with experimental data; their occurrence and mechanism were addressed.

In the fossil fuel (coal) based power plants, for estimating the combustion air requirement and for ensuring effective combustion of coal, it is very essential to know the elemental composition of the coal that is fired. Ultimate analysis is the process to be performed to know elemental composition of the coal collected. The ultimate analysis is costly, time-taking and also cumbersome in nature and therefore at the power-plants only gross-level coal compositions are estimated which is called proximate analysis. Based on the gross-level compositions of the coal, the elemental compositions are estimated using standard empirical formulae. The relationship between the gross level composition (i.e. proximate analysis) and the elemental level composition (i.e. ultimate analysis) is nonlinear, whereas the empirical formulae are linear assumptions which may lead to erroneous estimations. The empirical formulae based erroneous estimations lead to variation in the combustion behavior and thereby leading to suboptimal performance of the boilers. To achieve better control on the boilers and thereby to achieve better performance, accurate computation of elemental composition is required. In this article, we suggest a method to compute ultimate analysis based on the proximate analysis information using Artificial Neural Network model (ANN). The predictions of ANN and empirical models have been compared. It is found that the ANN prediction is in very good agreement with lab data than the predictions of empirical model. General Terms Predicting Coal properties using Artificial Neural Networks Model

The thermochemistry of the combustion of a blend of coal and 5% petroleum coke was analyzed. Thermodynamic modeling and microscopic techniques were used to study the behavior of the inorganic constituents upon combustion of the blend of coal and petroleum coke. The chemical composition and phase constitution of the combustion products, as well as the deposits at several temperatures corresponding to those at the various parts of the boiler, were deduced by free-energy minimization. These results were compared with actual results obtained from a commercial pulverized fuel boiler fired with coal and petroleum coke blend. The deposits on the fireside surfaces of the boiler tubes in the various parts (water walls, platen superheater, final superheater, economizer, and electrostatic precipitator) of the commercial pulverized fuel boiler fired with coal and 5% petroleum coke were characterized by particle size analysis, chemical analysis, x-ray diffraction, optical microscopy, and scanning electron microscopy. ...

Artificial Neural Networks, as a paradigm, is extremely relevant in the present day context where data obtained from processes is plagued by uncertainty and insufficiency. Hybrid prediction techniques for process control systems are the order of the day, which involve a combination of data driven models and knowledge driven models. In this paper an Artificial Neural Network prediction tool has been generated with Visual Basic GUI to predict the spray values in a 500 MW boiler within permissible tolerances. The prediction of sprays is done using General Regression Neural Network (GRNN), smoothing factors of which have been generated using a Genetic Algorithm. The General Regression Neural Network predicts the ReHeater Spray and SuperHeater Spray from the input combination of Burner Tilt, Mill Combination, Excess Air Percentage and Load.