Search

This paper analyzes the impact of hybrid nanoparticles (Cu-TiO2) on a two-dimensional peristaltic blood flow pattern in a nonuniform cylindrical annulus in the presence of an external induced magnetic field with wall slip. Further, this study focuses on the flow dynamics of single and hybrid nanofluids through endoscopic or catheterized effects. The mathematical model consisting of continuity, linear momentum, thermal energy, and Maxwell's equations is simplified under the assumptions of long wavelength and negligible Reynolds number. The Homotopy perturbation method (HPM) is employed to get an approximate analytical solution of nonlinear dimensionless momentum equations. Based on the mathematical relationships and graphic visualization, the influence of the pertinent parameters described the velocity profile, temperature distribution, induced magnetic field, current density distribution, wall shear stress, and heat transfer coefficient. With the help of contours, the trapping phenomenon is also presented. The results reveal that the Lorentz force significantly reduces the Cu-TiO2blood nanofluid velocity, whereas the elevating Grashof number does the opposite. Compared with copper nanoparticles, hybrid nanoparticles have a higher wall shear stress. The increasing values of Reynolds numbers amplify the induced magnetic field on annular surfaces. In the axial direction, Lorentz force significantly decreases the current density distribution for hybrid nanofluid. Moreover, hybrid nanoparticles (Cu-TiO2) exhibit superior heat transfer than Copper (Cu) nanoparticles in the blood-based fluid. According to the graphical outcomes, hybrid nanoparticles are comparatively more effective than unitary nanoparticles in the blood.

The viscosity of nanofluids can be influenced by many physical factors so it is difficult to obtain an accurate prediction model using only traditional theoretical model-driven methods or data-driven black-box models. This study proposes a modern approach named Physics Guided Deep Neural Network (PGDNN) for viscosity prediction that combines the data-driven models and physics-based theoretical models to achieve their complementary strengths and develop the modeling of physical processes. This PGDNN model is applied with a large number of data points (9000 data points) containing both experimental and simulated data of spherical nanoparticles Al2O3, CuO, SiO2, and TiO2. Further, this technique overcomes the overfitting issue and performs better than other traditional models while predicting unseen data. As far as we know, the PGDNN model is novel and not even used earlier to predict the viscosity of nanofluids. The learning performance of the proposed model is analyzed using different statistical performance indicators and Bayesian optimization is used for hyper-parameter tuning. Then, epistemic uncertainty quantification is performed to estimate the confidence level of the proposed model. Our PGDNN model outperformed various previous theoretical and computer-aided models with R-2=0.9961 and RMSE = 0.0312. Moreover, a sensitivity analysis is performed and the results show that the volume fraction of particle is the most and viscosity of a base fluid is the second most significant parameters to determine the viscosity of nanofluids.

Operating fluids are always a significant factor for not achieving a good enough performance of heat transfer equipment and also for growing the energy costs. To resolve this issue, nanofluids are considered a potential choice for conventional heat transfer fluids due to their efficiency for the improvement of overall thermal performance. The aim of this research is to propose a physics-guided machine learning approach by incorporating physics-based relations at the initial stage and into traditional loss functions for predicting the thermal conductivity of water-based nanofluids using a wide range of both experimental and simulated data of nanoparticles Al2O3, CuO, and TiO2. Further, smart connectionist methods, viz., ridge regression, lasso regression, random forest, extreme gradient boosting (XGBOOST (XGB)), and black-box multilayer perceptron (MLP) are applied to compare the present physics-aware MLP model based on different statistical indicators. The accuracy analyses reveal that the use of physical views to monitor the learning of neural networks shows better results with mean absolute percentage error (MAPE) ¼ 0.7075%, root-mean-squared error (RMSE) ¼ 0.0042 W/mK, and R2 ¼ 0.9525. The temperature and volume concentration variations are discussed graphically. Furthermore, the outcomes of applied algorithms confirm that the well-known theoretical and computer-aided models show substandard results than the proposed model.