Performance analysis and multi-objective optimization of spiral fin heater based on CFD-DEM

Degassing is crucial in oil drilling, the drilling fluid needs to be heated through the heater in order to improve the efficiency of degassing. This study examines the flow characteristics of hard particles in refluxing drilling fluid in the heater spiral channel and their impact on the thermal efficiency of the heater. The effects of particle flow and size on the thermal performance of spiral heater fins were studied by numerical simulation. In addition, this research explores the use of neural networks and genetic algorithms in multi-objective optimization of spiral fin heaters. a numerical simulation using the CFD-DEM approach was performed to examine the impact of heater fin structural characteristics and drilling fluid inlet flow rate on heating performance. Secondly, to improve heat transfer and minimize pressure drop, a neural network prediction model was developed using simulation results of various parameters, followed by multi-objective optimization using evolutionary algorithms. The numerical simulation was validated by comparing the results of the spiral heater experimentally and numerically. The accuracy of the numerical simulation is confirmed by comparing the experimental results of the spiral heater with those obtained from the simulation.