In this study, the heat transfer characteristics of a new class of nanofluids made from mango bark was numerically simulated and studied during turbulent flow through a double pipe heat exchanger. A range of volume fractions was considered for a particle size of 100 nm. A two-phase flow was considered using the mixture model. The mixture model governing equations of continuity, momentum, energy and volume fraction were solved using the finite-volume method. The results showed an increase of the Nusselt number by 68% for a Reynolds number of 5,000 and 45% for a Reynolds number of 13 000, and the heat transfer coefficient of the nanofluid was about twice that of the base fluid. In addition, the Nusselt number decreased by an average value of 0.76 with an increase of volume fraction by 1%. It was also found that there was a range of Reynolds numbers in which the trend of the average heat transfer coefficient of the nanofluid was completely reversed, and several plots showing zones of higher heat transfer which if taken advantage of in design will lead to higher heat transfer while avoiding other zones that have low heat transfer. It is hoped that these results will influence the thermal design of new heat exchangers.

It is essential to investigate the appropriate model for simulating nanofluid flow for different flow regimes because, at present, most previous studies do not agree with each other. It was, therefore, the purpose of this study to present a Computational Fluids Dynamics (CFD) investigation of heat transfer coefficients of internal forced convective flow of nanofluids in a circular tube subject to constant wall heat flux boundary conditions. A complete threedimensional (3D) cylindrical geometry was used. Laminar and turbulent flow regimes were considered. Three two-phase models (mixture model, discrete phase model (DPM) and the combined model of discrete and mixture phases) and the single-phase homogeneous model (SPM) were considered with both constant and variable properties. For the turbulent flow regime, it was found that the DPM with variable properties closely predicted the local heat transfer coefficients with an average deviation of 9%, and the SPM deviated from the DPM model by 2%. It was also found that the mixture and the combined discrete and the mixture phase model gave unrealistic results. For laminar flow, the DPM model with variable properties predicted the heat transfer coefficients with an average deviation of 9%.

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Nanofluids are solid-liquid composites which show higher convective heat transfer performance than conventional heat transfer fluids. However, most of the nanoparticles used are metallic oxides which are known to be toxic both to the environment and humans. Hence, the study of bionanomaterial to which the environment is naturally exposed is an important study. These biomaterials are the additives to the base fluid. Mango leaves-water nanofluid is the nanofluid being studied under laminar flow conditions in a horizontal pipe. The multi-phase mixture model was used to simulate the nanofluid behavior. ANSYS Fluent 18.2 finite volume commercial code was used to discretize and solve the governing equations of flow and energy with residuals set to 10-6 for each governing equation. The Semi-Implicit method for pressure linked equations algorithm [SIMPLE] was used for pressure-velocity coupling. It was observed that the local heat transfer coefficient always decreased with the axis location. A 12% increase of the average heat transfer coefficient was observed for 3% volume fraction of mango leaves-water nanofluid in comparison to the base fluid. Hence there are great prospects for the use of these fluids as heat transfer fluids it being superior to the base fluid in terms of heat transfer characteristics.

This research presents a neural network algorithm to identify the best modeling and simulation methods and assumptions for the most widespread nanofluid combinations. The neural network algorithm is trained using data from earlier nanofluid experiments. A multilayer perceptron with one hidden layer was employed in the investigation. The neural network algorithm and data set were created using the Python Keras module to forecast the average percentage error in the heat transfer coefficient of nanofluid models. Integer encoding was used to encode category variables. A total of 200 trials of different neural networks were taken into consideration. The worst‐case error bound for the chosen architecture was then calculated after 100 runs. Among the eight models examined were the single‐phase, discrete‐phase, Eulerian, mixture, the mixed model of discrete and mixture phases, fluid volume, dispersion, and Buongiorno's model. We discover that a broad range of nanofluid configurations is accurately covered by the dispersion, Buongiorno, and discrete‐phase models. They were accurate for particle sizes (10–100 nm), Reynolds numbers (100–15,000), and volume fractions (2%–3.5%). The accuracy of the algorithm was evaluated using the root mean square error (RMSE), mean absolute error (MAE), and R2 performance metrics. The algorithm's R2 value was 0.80, the MAE was 0.77, and the RMSE was 2.6.

Background This research introduces a novel approach for modelling single-material nanofluids, considering the constituents and characteristics of the fluids under investigation. The primary focus of this study was to develop models for predicting the thermal conductivity of nanofluids using a range of machine learning algorithms, including ensembles, trees, neural networks, linear regression, Gaussian process regressors, and support vector machines. The main body of the abstract To identify the most relevant features for accurate thermal conductivity prediction, the study compared the performance of established feature selection algorithms, such as minimum redundancy, maximum relevance, Ftest, and RReliefF, with a newly proposed feature selection algorithm. The novel algorithm eliminated features lacking direct implications for fluid thermal conductivity. The selected features encompassed temperature as a thermal property of the fluid itself, multiphase features such as volume fraction and particle size, and material features including nanoparticle material and base fluid material, which could be fixed based on any two intensive properties. Statistical methods were employed to select the features accordingly. Results The results demonstrated that the novel feature selection algorithm outperformed the established approaches in predicting the thermal conductivity of nanofluids. The models were evaluated using 5-fold cross-validation, and the best model based on the proposed feature selection algorithm exhibited a root mean squared error of validation of 1.83 and an R-squared value of 0.94. The model achieved a root mean squared error of 1.46 for the test set and an R-squared value of 0.97. Conclusions The developed predictive model holds practical significance by enabling nanofluids' numerical study and optimisation before their creation. This model facilitates the customisation of conventional fluids to attain desired fluid properties, particularly emphasising thermal properties. Additionally, the model permits the exploration of numerous nanofluid variations based on permutations of their features. Consequently, this research contributes valuable insights to the design and optimisation of nanofluid systems, advancing our understanding and application of thermal conductivity in nanofluids.

Background The accurate prediction of viscosity in nanofluids is essential for comprehending their flow behaviour and enhancing their effectiveness in different industries. This research delves into modelling the viscosity of nanofluids and assessing various models through cross-validation techniques. The models are compared based on the root mean square error of the cross-validation sets, serving as the selection criteria. The main body of the abstract: Four feature selection algorithms were evaluated to identify the most influential features for viscosity prediction. The feature selection based on physical meaning was the algorithm that yielded the best results, as outlined in this study. This methodology takes into account the physical relevance of most aspects of the nanofluid's viscosity. To assess the predictive performance of the models, we conducted a cross-validation process, which provided a robust evaluation. We used the root mean squared error of the cross-validation sets to compare the models. This rigorous evaluation identified the most accurate and reliable model for predicting nanofluid viscosity. Results The results showed that the novel feature selection algorithm outclassed the established approaches in predicting the viscosity of single material nanofluid. The proposed feature selection algorithm had a root mean squared error of 0.022 and an r squared value of 0.9941 for the validation set, while for the test set, the root mean squared error was 0.0146, the mean squared error was 0.0157, the r squared value was 0.9924. Conclusions This research provides valuable insights into nanofluid viscosity and offers guidance on choosing the most suitable features for viscosity modelling. The study also highlights the importance of using physical meaning to select features and cross-validation to assess model performance. The models developed in this study can be helpful in predicting nanofluid viscosity and optimising their use in different industrial processes.

This study investigates the single‐phase simulation of nanofluid with a neural network incorporated into the thermophysical properties in governing equations for the single‐phase treatment. The thermophysical properties affected are the viscosity, and the thermal conductivity, as both properties have been the area of contention in the study of nanofluid. The neural network is trained from experimental data gleaned from the available literature. The single phase and neural network are set up and solved using the finite element method in available commercial code. Grid independence was carried out, and the results were validated with experimental data that the neural networks were not trained with. It was observed that the lowest accuracy from the several simulations was 0.679% average percentage error. The results obtained agreed that nanofluids' thermal conductivity and viscosity can be accurately modeled for most single‐material nanofluids and hence reducing the error in the simulations of nanofluids using the single‐phase model which assumes the nanofluids are homogeneous and their properties are enhanced and effective.