Jawaharlal Nehru Technological University, Hyderabad (JNTU Hyderabad) is a public university, located in Hyderabad, Telangana. Founded in 1965 as the Nagarjuna Sagar Engineering College, it was established as a university in 1972 by The Jawaharlal Nehru Technological University Act, 1972. The university is situated at Kukatpally Housing Board Region in Hyderabad of India.
This study explores the combined effects of aluminum oxide (Al2Os)/graphene oxide (GO) hybrid nanofluids in 50:50 and 80:20 ratios, offering a notable improvement over conventional Al2Os or GO nanofluids. It delivers a thorough comparison of thermophysical properties such as thermal conductivity and viscosity and heat transfer performance across water, Al2Os nanofluids, and the Al2Os/GO hybrids. Nanofluids at 0.1-0.5 % volume concentrations were tested in a horizontal circular pipe under constant heat flux and turbulent flow with an inlet temperature of 60 degrees C. The maximum Nu enhancements of 64, 56 and 41 % were noted for Al2Os/GO (50:50), Al2Os/GO (80:20), and Al2Os nanofluids, respectively at 0.5 vol%, compared to water. The maximum pressure drop of Al2O3/GO (50:50) nanofluid is 5.64 and 8.3 % greater than that of Al2O3/GO (80:20) and Al2O3 nanofluid, respectively at 0.5 vol%. The peak thermal performance index of 1.56, 1.48, and 1.33 is observed for Al2Os/GO (50:50), Al2Os/GO (80:20), and Al2Os nanofluids. The integration of a multi-layer perceptron artificial neural network further enhances accuracy in predicting thermal performance, surpassing the precision of conventional empirical models. The adopted model showed excellent predictive accuracy, with correlation coefficients of 0.98493 in training, 0.9837 in validation, and 0.98698 in testing.
The advanced heat transfer applications of nanofluids make it important to investigate their viscous properties. For various engineering applications, accurate measurement of dynamic viscosity of nanofluid plays a major role. This research aims to provide accurate and reliable soft computational algorithms to predict the viscosity of fly ash nanofluids and fly ash-Cu hybrid nanofluids accurately using measured data. In this work, the dynamic viscosity of water-based stable fly ash nanofluid and fly ash-Cu (80:20% by volume) hybrid nanofluid for the concentration range of 0-4 vol.% determined in the temperature range of 30-60 degrees C experimentally. The nanoparticles are characterized by SEM, TEM, and DLS techniques. The stability of the nanofluids assessed by Zeta potential measurement. Outcomes show that the viscosity of hybrid nanofluid is found to be higher than fly ash nanofluid. Correlations are suggested using multiple linear regression analysis to predict the nanofluid viscosity based on the obtained results. We have adopted a novel soft computing technique, namely ANN and MGGP to optimize the dynamic viscosity data of nanofluids obtained experimentally. The MGGP model predicts the viscosity values for fly ash nanofluid by (R = 0.99988, RMSE = 0.0019, and MAPE = 0.25%). In addition, statical analysis reveals that the MGGP model has excellent performance for fly ash-Cu/Water hybrid nanofluid viscosity (R = 0.9975, RMSE = 0.0063, and MAPE = 0.664%). In contrast to the ANN method and multiple linear regression analysis, the MGGP approach exhibits better results in estimating nanofluid viscosity.
Dynamic viscosity is among the most significant transport properties used in the fields of radiative heat transfer engineering and advanced fluid mechanics. However, measuring the dynamic viscosity of nanofluids is challenging, expensive, and time-consuming. Additionally, existing theoretical and empirical correlations, while yielding reasonable outcomes, are limited to constrained operating conditions. To address this issue, different artificial intelligence techniques have been applied to estimate dynamic viscosity. Nevertheless, this research has often been confined to a restricted number of nanoparticles. In this study, experimental datasets from 30 sources were used to develop machine learning models that can precisely estimate the viscosity in various nanofluids formed by seven different nanoparticles mixed with eight distinct base fluids. These models consider volume fraction of the nanoparticles, density of nanoparticles, nanoparticle size, base fluid type, and temperature as input parameters, with the target variable being the dynamic viscosity of the nanofluids. The deep learning model was compared against established algorithms such as random forests, bagging, decision trees, gradient boosting and k-nearest neighbors. Our deep learning model achieved superior performance with a determination coefficient (R2) of 0.9892, a root mean square error (RMSE) of 0.0086, and an average absolute relative deviation (AARD%) of ±19.25.
The advancement of materials in the last few decades has guided the development of many hard-to-machine materials, such as superalloys. These alloys have poor machinability characteristics. This paper examines the machinability performance characteristics of Iron-based A286 Nickel superalloy by varying the turning process parameters using uncoated and physical vapor deposition (PVD) coated inserts. Experiments were performed with an L16 orthogonal array using minimum quantity lubrication (MQL) machining and dry machining environments. The accomplishment of the turning process was evaluated in reference to the cutting forces and surface roughness. Optimum turning parameters to decrease the surface roughness and cutting forces using MQL and dry machining environments with PVD-coated and uncoated tools were found using analysis of means methodology. Results have indicated that feed rate would greatly influence surface roughness when using the uncoated tool in dry and MQL machining circumstances. The depth of cut would affect cutting force and feed force more using uncoated tool and PVD coated tool with MQL and dry machining environments. Tool wear results have revealed that PVD coated tool inserts by MQL machining would result in less tool wear than uncoated tools. Regression models were developed from the experimental outcomes to predict the performance characteristics. The coefficient of determination observed was more than 98%.
By using sunlight to prepare meals, solar cookers offer a more environmentally responsible option to conventional techniques. They can provide enough heat to boil water, prepare meals, or disinfect food by concentrating sunlight, which provides a sustainable way to lower energy use and lessen environmental effect. Present work shows the analysis of truncated conical type solar cooker (TCSC) with side and bottom loading of food into it and resist to heat flow through the composite slab. Aluminum tray, saw dust and outer casing comprises of composite wall. Drastic temperature drop can be seen at the slab due to the insulation provided. Various cooking vessels were used to place the food/thermic fluid inside and measured the temperature using thermocouple with temperature indicator. TCSC is loaded with thermic fluid in the cooking vessel (CV), one is placed on lugs and the other on floor of the tray. There was an increase in temperature of CV placed on lugs compared to vessel on floor. Difference of 10 degrees - 20 degrees was noted between CV with and without lugs in conical type cooker. Comparing CV kept on floor and lugs, lugs shown 10.4% increase in temperature of CV, reduces time for cooking.
