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Accurate measurement of pressure drop in energy sectors especially oil and gas exploration is a challenging and crucial parameter for optimization of the extraction process. Many empirical and analytical solutions have been developed to anticipate pressure loss for non-Newtonian fluids in concentric and eccentric pipes. Numerous attempts have been made to extend these models to forecast pressure loss in the annulus. However, there remains a void in the experimental and theoretical studies to establish a model capable of estimating it with higher accuracy and lower computation. Rheology of fluid and geometry of system cumulatively dominate the pressure gradient in an annulus. In the present research, the prediction for Herschel-Bulkley fluids is analyzed by Bayesian Neural Network (BNN), random forest (RF), artificial neural network (ANN), and support vector machines (SVM) for pressure loss in the concentric and eccentric annulus. This study emphasizes on the performance evaluation of given algorithms and their pitfalls in predicting accurate pressure drop. The predictions of BNN and RF exhibit the least mean absolute error of 3.2% and 2.57%, respectively, and both can generalize the pressure loss calculation. The impact of each input parameter affecting the pressure drop is quantified using the RF algorithm.

Recent advancements in thermo-fluid technology assisted with highly thermal conductive nanomaterials have shown assuring outcomes. It is also proven that thermal conductivity alone cannot define the overall heat transfer characteristics, and the viscous properties are equally significant towards thermal management. Therefore, this research involves investigating the rheological behavior of hybrid nanosuspensions containing high thermally conductive diamond and graphene nanoplatelets (1:1). These nanomaterials are dispersed in mineral oil using a two-step technique. Hybrid nanofluids' stability is achieved using a non-ionic stabilizer Span85, exhibiting no sedimentation for a minimum of five months. Nanomaterial characterizations are performed to study morphology, purity, and chemical analysis. The flow behavior of hybrid nanosuspensions is investigated at varying nanomaterial mass concentrations (0-2 %), temperatures (298.15-338.15 K), and shear rates (1-2000 s -1 ). Hybrid nanofluids exhibit shear-thinning behavior, which is also correlated with the Ostwald-de-Waele model. The temperature-viscosity relationship is well predicted using the Vogel-Fulcher-Tammann model. Hybrid nanofluids show a maximum enhancement of 35% viscosity at 2% concentration. A generalized twovariable correlation is used to express viscosity as a function of temperature and nanofluid concentration with an excellent agreement. Three different machine learning methods, i.e., Artificial Neural Network (ANN), Gradient Boosting Machine (GBM), and Random Forest (RF) algorithms are also introduced to predict the viscosity of hybrid nanofluids based on the three input parameters (temperature, concentration, and shear rate). The parity plots conclude that all algorithms can predict big-data viscous behavior with high precision.

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Novel Coronavirus Disease (COVID-19) is a communicable disease that originated during December 2019, when China officially informed the World Health Organization (WHO) regarding the constellation of cases of the disease in the city of Wuhan. Subsequently, the disease started spreading to the rest of the world. Until this point in time, no specific vaccine or medicine is available for the prevention and cure of the disease. Several research works are being carried out in the fields of medicinal and pharmaceutical sciences aided by data analytics and machine learning in the direction of treatment and early detection of this viral disease. The present report describes the use of machine learning algorithms [Linear and Logistic Regression, Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and SVM with Grid Search] for the prediction and classification in relation to COVID-19. The data used for experimentation was the COVID-19 dataset acquired from the Center for Systems Science and Engineering (CSSE), Johns Hopkins University (JHU). The assimilated results indicated that the risk period for the patients is 12-14 days, beyond which the probability of survival of the patient may increase. In addition, it was also indicated that the probability of death in COVID cases increases with age. The death probability was found to be higher in males as compared to females. SVM with Grid search methods demonstrated the highest accuracy of approximately 95%, followed by the decision tree algorithm with an accuracy of approximately 94%. The present study and analysis pave a way in the direction of attribute correlation, estimation of survival days, and the prediction of death probability. The findings of the present study clearly indicate that machine learning algorithms have strong capabilities of prediction and classification in relation to COVID-19 as well.