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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. [ABSTRACT FROM AUTHOR]

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–TiO2/blood 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. [ABSTRACT FROM AUTHOR]

The traditional model-driven methods are not much efficient to predict the viscosity of nanofluids accurately. This study presents a novel approach of using physics-guided deep learning technique for predicting viscosity of water-based nanofluids from large dataset containing both experimental and simulated data of spherical oxide nanoparticles Al2O3, CuO, SiO2, and TiO2. Further, this study introduces a novel methodology of combining deep learning methods and physics-based models to leverage their complementary strengths. To the best of the author's knowledge, theory-guided deep learning prediction model was never used to predict viscosity before. The theory-guided deep neural networks (TGDNN) model is trained by minimizing the mean square error (MSE) and regularization terms using Adam optimization technique. The investigations reveal that the values of R², RMSE, and AARD% are, respectively, 0.999868, 0.001143, and 2.198887 on experimental testing dataset. The TGDNN model learns non-linear relationship among the input variables from the training data. Additionally, the results show that the proposed method performed better than the other well-known existing theoretical and computer-aided models to predict the viscosity in wide range with high level of accuracy. [ABSTRACT FROM AUTHOR]

A novel approach is presented in this paper to predict the viscosity of nanofluids by developing a deep neural network (DNN) based smart generalized model. This study is conducted with a large experimental dataset containing Al2O3, CuO, SiO2, TiO2, Ag, and Fe2O3 nanoparticles and the DNN model is trained by Nadam optimization technique. This proposed DNN model has the learning capability of non-linearities from a training dataset automatically. The novelty of this study with the advantages of deep learning has been described in this paper. To the best of the author's knowledge, deep learning-based model was never used to predict viscosity before. The detailed analysis of the performance of this DNN model shows that it performs better than any other existing model and overcomes all of their limitations. Also, it gives an excellent prediction on the unseen data and it takes very less amount of time to train this DNN model comparing to other traditional data-driven models. A sensitivity analysis of this smart model has also be presented. This novel DNN-based smart model can predict the viscosity of nanofluids with the highest level of accuracy with a coefficient determination 0.9999. [ABSTRACT FROM AUTHOR]

One of the current challenges of the coronary angioplasty technique for treating atherosclerosis in the artery is to target the desired area using nanoparticles. This study aims to investigate the hybrid Au-Fe 3 O 4 /blood nanofluid flow that passes through a narrowed section of a stenotic artery. The behavior of the Au-Fe 3 O 4 /blood flow in the stenosis region is mathematically examined, taking into account an inclined magnetic field and temperature-dependent thermal conductivity. Additionally, this work analyses the effect of nanolayers on nanofluid flow in the presence of a catheter with a balloon (angioplasty) on its wall. Tube and balloon catheter models are considered individually to compare the results of flow characteristics and heat transmission. Channel boundaries comprise wall properties with thermal slip and second-order velocity slip conditions. A similarity transformation procedure is used to convert the governing equations into highly nonlinear normal differential equations, which are analytically computed using the homotopy perturbation technique. The obtained results reveal an increased velocity profile due to the magnetic force, while an inverse trend occurs due to the thermal conductivity of the nanofluid. Furthermore, interfacial nanolayers of nanoparticles significantly alter the blood temperature to prevent and manage cardiovascular diseases. This rheological model may be useful for cancer diagnosis, tumor-selective photothermal therapy, medical simulation devices, and other applications. [Display omitted] • The current study presents advanced research on hybrid nanofluid flow dynamics. • The study aims to visually examine the hemodynamic characteristics of the fluid. • This model incorporates ferrous nanoparticles to enhance the heat transfer rate. • The study suggests that flow velocity is influenced by magnetic force and variable thermal conductivity. • Interfacial nanolayers alter blood temperature to prevent cardiovascular diseases. • Inserting a balloon catheter is more effective in a narrowed artery than using a catheter tube. [ABSTRACT FROM AUTHOR]