Robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers

Numerical and experimental investigation of heat transfer enhancement in double tube heat exchanger using nail rod inserts

The Double-tube heat exchanger (DTHX) is widely favored across various industries due to its compact size, low maintenance requirements, and ability to operate effectively in high-pressure applications. This study explores methods to enhance heat transfer within a DTHX using both experimental and numerical approaches, specifically by integrating a nail rod insert (NRI). A steel nails rod insert, 1000 mm in length, is introduced into the DTHX, which is subjected to turbulent flows characterized by Reynolds numbers ranging from 3200 to 5700. Three different pitches of NRI (100 mm, 50 mm, and 25 mm) are investigated. The results indicate a significant increase in the Nusselt (Nu) number upon the insertion of nail rods, with further improvements achievable by reducing the pitch length. Particularly noteworthy is the Nu number enhancement ratio for the 25 mm pitch NRI, which is 1.81-1.9 times higher than that for the plain tube. However, it is observed that pressure drop increases in all configurations with NRI due to heightened turbulence and obstruction by the NRI. Among the various pitch lengths, the 25 mm pitch exhibits the highest pressure drop values. Moreover, exergy efficiency is found to improve across all cases with NRI, corresponding to increased heat transfer, with the 25 mm pitch length showing a remarkable 128% improvement. Numerical analysis reveals that the novel insert enhances flow turbulence through the generation of secondary flows, thereby enhancing heat transfer within the DTHX. This study provides a comprehensive analysis, including temperature, velocity, and pressure drop distributions derived from numerical simulations.

Intelligent modeling of hydrogen sulfide solubility in various types of single and multicomponent solvents

This study aims to study the solubility of acid gas, i.e., hydrogen sulfide (H₂S) in different solvents. Three intelligent approaches, including Multilayer Perceptron (MLP), Gaussian Process Regression (GPR) and Radial Basis Function (RBF) were used to construct reliable models based on an extensive databank comprising 5148 measured samples from 54 published sources. The analyzed data cover 95 single and multicomponent solvents such as amines, ionic liquids, electrolytes, organics, etc., in broad pressure and temperature ranges. The proposed models require just three simple input variables, i.e., pressure, temperature and the equivalent molecular weight of solvent to determine the solubility. A competitive examination of the novel models implied that the GPR-based one gives the most appropriate estimations with excellent AARE, R² and RRMSE values of 4.73%, 99.75% and 4.83%, respectively for the tested data. The mentioned intelligent model also performed well in describing the physical behaviors of H₂S solubility at various operating conditions. Furthermore, analyzing the William's plot for the GPR-based model affirmed the high reliability of the analyzed databank, as the outlying data points comprise just 2.04% of entire data. In contrast to the literature models, the newly presented approaches proved to be applicable for different types of single and multicomponent H₂S absorbers with AAREs less than 7%. Eventually, a sensitivity analysis based on the GPR model reflected the fact that the solvent equivalent molecular weight is the most influential factor in controlling H₂S solubility.