Nanofluids have been known as practical materials to ameliorate heat transfer within diverse industrial systems. The current work presents an empirical study on forced convection effects of Al2O3–water nanofluid within an annulus tube. A laminar flow regime has been considered to perform the experiment in high Reynolds number range using several concentrations of nanofluid. Also, the boundary conditions include a constant uniform heat flux applied on the outer shell and an adiabatic condition to the inner tube. Nanofluid particle is visualized with transmission electron microscopy to figure out the nanofluid particles. Additionally, the pressure drop is obtained by measuring the inlet and outlet pressure with respect to the ambient condition. The experimental results showed that adding nanoparticles to the base fluid will increase the heat transfer coefficient (HTC) and average Nusselt number. In addition, by increasing viscosity effects at maximum Reynolds number of 1140 and increasing nanofluid concentration from 1% to 4% (maximum performance at 4%), HTC increases by 18%. 相似文献
The main objective of the present work is to modify the traditional mapping method for the simulation of distributive mixing of multiphase flows in geometries involving moving parts such as, internal mixers or twin-screw extruders without a limitation on their geometrical periodicity. The periodicity condition, limits the results of traditional mapping method to tracking mapping mesh between specific discrete time intervals or distances for that geometry is repeated, hence, result is only for fixed orientation of rotors. Imaginary domain method is introduced to track mapping mesh from one state to the next free of geometrical periodicity limitations. In this work the method is introduced and its applicability and accuracy is discussed in details. A two-dimensional (2D) simulation of mixing of two Newtonian fluids with different viscosities in an intermeshing internal mixer is used as a test case study. In this example the key issues of ability to predict mixing state in details for all orientations of rotors is presented. To reduce diffusion errors of mapping method in the boundaries of the rotors, mapping mesh refinement technique that relies upon one single reference mesh is also presented. 相似文献
Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (Vp) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R2) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples.
The study of the movement of solids in multiphase reactors using radioactive particle tracking is currently limited to fairly modest particle velocities because of count‐rate limitations of the detection system. In this work, this restriction was overcome by increasing the activity of the radioactive tracer, by decreasing the sampling time interval and by modifying the particle tracking software to recognize which detectors were saturated and to use only the data from the remaining unsaturated detectors. Higher tracer activity resulted in lower standard deviation of the calculated tracer coordinates. 相似文献