Experimental Studies on Pressure Drop Characterization of Curved Tube Sections in Laminar Flow Regime

Abstract

In fluid metering technology, Coriolis Mass Flow Meter (CMFM) is the most prominent mass flow measuring instrument owing to its high accuracy, repeatability, and fidelity. The meter works on the principle of Coriolis force generated by the change in momentum of fluid, flowing through a vibrating tube. In CMFM, a straight or curved tube section is used to measure the mass flow rate of flowing fluid. The performance of such devices is found to vary with the shape and size of the curved tube and also with the flow regimes. Moreover, it has been reported to underpredict the flow rate than actual values in the laminar flow regime. In the recent past, few researchers had attempted to explain the phenomenon underlying the error in measurement with the help of secondary flow development in the laminar regime. The present investigations attempt to highlight the phenomenon with the help of pressure drops occurring in different sections of the tube. Characterization of hydraulic resistance of the system with respect to flow rate is thought to be a fundamental aspect to enlighten the issues. In this regard, an extensive experimental investigation has been undertaken on pressure drop characteristics in four tube shapes widely used in commercial CMFM such as U (Basic U and Narrow U), Omega, Delta and Diamond shape tubes in laminar flow regime. The results are expressed in terms of non-dimensional numbers for comparison.

Epilepsy Identification using Hybrid CoPrO-DCNN Classifier

“…To avoid searching for prey in specific areas, [61] the hybrid coyote optimization's decision-making and adaptability traits are combined with characteristics of long jump of the predator, [62] as a result, shows faster convergence and less time spent is attained looking for prey. The growing prevalence of these conditions indeed necessitates novel methodologies to ensure timely and [63], [64] accurate diagnoses. Hybrid algorithm shown in Table 1 typically combine elements from multiple algorithms or techniques to leverage their respective strengths and [65] mitigate weaknesses.…”