首页|Findings from SRM Institute of Science and Technology Has Provided New Data on Machine Learning (Employing Ensemble Machine Learning Techniques for Predicting the Thermohydraulic Performance of Double Pipe Heat Exchanger With and Without ...)
Findings from SRM Institute of Science and Technology Has Provided New Data on Machine Learning (Employing Ensemble Machine Learning Techniques for Predicting the Thermohydraulic Performance of Double Pipe Heat Exchanger With and Without ...)
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NETL
NSTL
Elsevier
Fresh data on Machine Learning are presented in a new report. According to news reporting from Tiruchirappalli, India, by NewsRx journalists, research stated, “In this study, advanced machine learning techniques were utilized to forecast the thermohydraulic performance of a double pipe heat exchanger (DPHE). Key variables, including friction factor (f), Nusselt number (Nu), effectiveness (epsilon), and number transfer units (NTU), were modeled as functions of Reynolds number (Re), the number of turbulators (N), and the length of turbulators (L).” The news correspondents obtained a quote from the research from the SRM Institute of Science and Technology, “To predict the DPHE’s thermohydraulic performance, datasets were gathered from realtime experiments (case-1 and case-2) and literature (case-3). For clarity and analytical purposes, case-1 and case-2 were dimensioned to match the specifications outlined in literature (case-3). 1 represented a conventional DPHE without turbulators, while case-2 involved a DPHE with a gear disc turbulator. Additionally, datasets for DPHE with dolphin ring turbulators (case-3) were obtained for validation from literature. Subsequently, two ensemble boosting algorithms (extreme gradient and categorical) and one bagging algorithm (random forest) were applied to the dataset. The results highlighted that the categorical boosting model exhibited the highest accuracy, achieving a coefficient of determination (R2) of 0.9987 and an average absolute percent relative error (AAPRE) of 0.1837%. Furthermore, a sensitivity analysis was conducted for the best model (Random Forest), revealing relationships between input and output parameters.”
TiruchirappalliIndiaAsiaCyborgsEmerging TechnologiesMachine LearningSRM Institute of Science and Technology
NETL
NSTL
Elsevier
