Scientia Iranica
Volume:29 Issue: 4, Jul-Aug 2022

In this study first and second law analyses of non-Newtonian nano-fluid flow through an annular cylinder filled with non-Newtonian water- CMC/TiO2 nano-fluid by considering temperature jump and slip velocity were investigated numerically. The single-phase was developed for heat transfer and nano-fluid flow. The impact of Reynolds number, nano-particles volume fraction, temperature jump, and slip velocity on Nusselt numbers and entropy generation were evaluated and the findings were discussed considering non-Newtonian performance of working fluid. The findings indicate that note the higher shear rate in the presence of the interior wall the Nusselt number for the interior wall was higher than outside walls. According to shear-thinning fluid behavior, when the flow has a higher shear rate, apparent viscosity would be small. So, it can be concluded that the apparent viscosity of flow close to the inner wall was low which decreased the impact of viscosity force and improved heat transfer due to convection-advection phenomena. In addition, the findings showed that the entropy generation ratio is very high at the entrance and it decreased along the annular tube. Furthermore, the apparent viscosity of fluid increases by nano-particle volume fraction.

Vortex generator supported Fin-tube heat transfer surface having inline elliptical tubes is investigated by performing numerical simulations governed by finite volume method. The present investigation is aimed at minimizing the air-side thermal resistance by utilizing the rectangular winglet pairs. The thermo-fluid performance of the baseline model is investigated for the circular and elliptical tube configurations. It is found that the former resulted in higher heat transfer whereas the latter reduces the pressure drop. The effect of split winglet pair’s span-wise and stream-wise separation, and attack angle on heat transfer and pressure drop performance is examined in detail and the results were presented in terms of Nu, f and η. Optimum stream wise and span wise locations for the front and rear winglet pair were identified based on highest enhancement factor. It is also found that the optimum attack angle of the front and rear winglet pair is different for maximum enhancement factor.

Recently there has been a surge in the usage of metaheuristic algorithms to design materials with optimum performance. In this article, one such recently proposed metaheuristic algorithm called RPSOLC (Repulsive Particle Swarm Optimization with Local search and Chaotic perturbation) has been used to design diamond-like carbon (DLC) thin films having better hardness. Based on a Box-Behnken design, 15 independent experiments on DLC deposition are conducted in a PECVD (plasma-enhanced chemical vapor deposition) setup by varying the CH4-Argon flow rate, hydrogen flow rate and the deposition temperature. The nano-hardness of the DLCs are evaluated using nano-indention tests. The hardness is then expressed as the function of the three process parameters using a polynomial regression metamodel. Finally, the metamodel is optimized using RPSOLC and compared with optimal predictions of a traditional GA. It is seen that RPSOLC has faster convergence and is more reliable than the GA. In general, a high H2 flow rate along with low CH4-Ar flow rate and high temperature is found to be beneficial in improving the hardness.

Nowadays, with increasing the injuries to the hand, specially fingers, because of saving in time and money, wearable robots are so practical to use in order to rehabilitate the injured part of the body. This exoskeleton is designed to rehabilitate the thumb to do the opposition movement, which is one DOF (degree of freedom) and a passive type with the cable mechanism. These cables are placed by two drums, one on the thumb and the other on the tip of the assistive finger, and have a cable adjusting screw that can be adjusted for fingers of different lengths. To choose the assistive finger from 4 intact fingers, the two perspectives of the cable length changes and finger forces are compared. Finally, with the help of these comparisons, the middle finger was selected as the assistive finger. Furthermore, by safety factor of 36.78, the cable traction forces do not harm the fingers' joins. The most angle difference between thumb and middle is at the 140 degrees angle, at the third joint, the performance of the thumb is better, due to the least angle difference.

An experimental investigation was carried out to comprehend the impact of speed of traverse of tool on tensile strength and micro-structural peculiarities of attained joints during friction stir welding of Cu alloy namely CDA 101 flat plates, with other parameters namely spinning speed of tool (1100 rpm) and downward force (6kN) being constant. A tool with cylindrical tapered pin geometry was made to traverse at distinctive speeds from 20 mm/min to 45 mm/min. It was observed that, the CDA 101 joints fabricated at 20 mm/min was found to be entirely free flaws, with joints fabricated at other speeds of tool traverse possessing several weld flaws. Grains in the center of the zone of stir of the joints obtained at 20 mm/min was uniformly distributed and homogeneous, due to the experience of the exemplary volume of frictional heat and sufficient amount of stirring force. Highest tensile strength of 200.65 MPa (nearly 85.38% of base metal) was exhibited by joint attained at 20 mm/min

In the concerned work, a model is developed to analyze the influence of chemical reaction in a Darcy-Forchheimer nanofluid mass and heat transfer flow over a nonlinearly extending surface with the effect of non-uniform magnetic force. Further, it is presumed that the fluid is viscous and incompressible. A powerful tool of similarity variables is exploited to transform the governing flow model PDEs into ordinary ones which are then solved with the aid of the SOR technique (using MATLAB software). Our outcomes are linked with those presented in earlier literature and found to be in a good connect with them. From the current investigation, it is revealed that the chemical reaction causes an enhancement in the mass transfer rate whereas the Forchheimer parameter tends to devaluate the mass as well as heat transport rate on the surface. The influences of different involved parameters are examined and visualized through tables and diagrams.

Accurate energy production forecasting is critical when planning energy for the economic development of a country. A deep learning approach based on Long Short-Term Memory (LSTM) to forecast one-day-ahead energy production from the run-of-river hydroelectric power plants in Turkey was introduced in the present study. In addition to the LSTM network, three different data-driven methods, namely, adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM), ANFIS with subtractive clustering (SC), and ANFIS with grid partition (GP) were applied. The correlation coefficient (R), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were used as quality metrics for prediction. Predicted values of the LSTM, ANFIS-FCM, ANFIS-SC, and ANFIS-GP models were compared with observed values by evaluating their errors. MAPE values in the testing process are 5.98%, 6.14%, 6.16%, and 6.40% for the LSTM neural network, ANFIS-FCM, ANFIS-SC, and ANFIS-GP models, respectively. The comparison revealed that the LSTM neural network provided high accuracy results in one day-ahead short-term energy production prediction and gave higher performance than other ANFIS models used in the study.

In the experimental study, biofuel is extracted from two distinct waste cooking oil of palm and sunflower oil through a transesterification process. A suitable blend B20 (WCPME 10% + WCSME 10% + Diesel 80%) is prepared by mixing diesel in biofuel. After that, using an ultrasonicator, Al2O3 was mixed in B20 at the distinct proportions of 25, 50, and 100 ppm, respectively, and new ternary blends are developed: B20 + 25Al2O3, B20 + 50Al2O3, and B20 + 100Al2O3. The experiment test was performed on a CRDI engine fuelled by Diesel, B20, B20 + 25Al2O3, B20+50Al2O3, and B20 +100Al2O3 samples at a steady speed of 1500 rpm and different engine loads to evaluate engine performance, combustion, and emission characteristics. The test result depicts that BTE extensively improved by 13.53% and SFC reduced by 20.93% for B20 +100 Al2O3 than B20 at full load. The emission characteristics, for example, CO, and HC were altogether decreased with the mixing of nanoparticles in the correlation of B20 and D100 yet there is a slight increment in NOx emissions than B20 and D100. Higher peak points in CPmax and HRRmax reached for B20 +100Al2O3 because of reduced ignition delay than that of B20 and D100.

In this study, we have proposed a new generalized estimator using auxiliary information for the estimation of population variance in presence and absence of measurement error. The expressions approximate bias and mean square error of the proposed generalized estimator are derive up to the first-order. Several new and existing estimators are found as the special cases of proposed estimator and expressed on various values of optimizing and generalized constants. The proposed estimator is compared mathematically with some existing estimators under certain conditions. The performance of proposed estimator is observed by simulation and real data application under different sample sizes. It is observed that the proposed estimator performed well than other competing estimators in the presence and absence of measurement error.