Thermoelectric generation from vented cavities with a rotating conic object and highly conductive CNT nanofluids for renewable energy systems

In the present work, thermoelectric power generation from cavities with ventilation ports is considered by using a rotating conic object and carbon-nanotube particles in the base fluid. Effects of different pertinent parameters such as Reynolds numbers of hot and cold fluid streams (between 200 and 1000), rotational Reynolds number of the conic object (between -400 and 400), size (between 0.05H and 0.25H) and horizontal location (between 0.2H and 0.6H) of the object and nanoparticle volume fractions of nanoparticles (between 0 and 0.02) on fluid flow, interface temperature and generated thermoelectric output power characteristics were studied. It was observed that exit port location of the hot cavity, fluid stream Reynolds number and rotational Reynolds number of the object have significant impacts on the fluid flow, interface temperatures and output power. When lowest and highest fluid stream Reynolds number is compared, 43.75% variations in the output power is obtained. The clockwise rotation of the conic object results in higher thermoelectric power generations as compared to a stationary cone while up to 49.20% increments of the power is attained at the highest rotational speed. The size of the rotating object is influential on the power generation while its horizontal location has slight effects. When nanofluid at the highest solid volume fraction configuration is compared with pure water case, 20% increment in the thermoelectric power is obtained. A predictive model for power estimations with adaptive network-based fuzzy inference system is proposed for input parameters of hot and cold fluid stream Reynolds number and rotational Reynolds number of the conic object which delivers accurate and fast prediction results.

Automated summary

This study investigates thermoelectric power generation using a rotating conic object and carbon-nanotube particles in a cavity with ventilation ports. Factors such as rotational speed, object size, and nanoparticle volume fraction significantly affect power generation. A predictive model based on fluid flow Reynolds numbers and rotational Reynolds number delivers accurate predictions for power output.