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In this investigation, the individual and synergetic influences of the SiC whiskers and WC particles on the various features of TiC were studied. Fully dense specimens were secured when 40 vol% SiC additive was added to the TiC and TiC-WC systems. The nucleation and growth of the new SiC grains were found as the two main mechanisms positively affecting the sinterability of TiC. Although WC particles were completely dissolved into the TiC matrix in the TiC-WC system, they were entrapped in the newly grown SiC grains in the TiC-WC-SiC system, contributing to strengthening of the specimen. The highest values of flexural strength (590 MPa) and Vickers hardness (2525 HV 0.5 kg) were attained for the sample codoped with SiC whiskers and WC particles. [ABSTRACT FROM AUTHOR]

This investigation intended to assess the influence of SiC morphology on the sinterability and physical-mechanical features of TiB 2 -SiC composites. For this aim, different volume percentages of SiC particles and SiC whiskers were introduced to TiB 2 samples hot-pressed at 1950 °C for 2 h under an external pressure of 25 MPa. The characterization of as-sintered specimens was carried out using X-ray diffraction, optical microscopy, and scanning electron microscopy. The relative density studies revealed that SiC w had a more significant impact on the sinterability of TiB 2 -based composites. The XRD investigation confirmed the production of an in-situ TiC phase during the hot-pressing; however, some peaks related to the graphitized carbon also appeared in the patterns of SiC w -doped ceramics. The addition of 25 vol% SiC p halved the average grain size of TiB 2 while introducing the same content of SiC w decreased this value by just around 20%. Finally, the highest Vickers hardness and fracture toughness were obtained for the sample reinforced with 25 vol% SiC w , standing at 29.3 GPa and 6.1 MPa m1/2, respectively. [ABSTRACT FROM AUTHOR]

Using single layer microchannels accompanied by nanofluids is one of the most practical solutions in thermal management of high power density devices. The main challenge in cooling systems of electronic devices is to provide a uniform temperature distribution. In the present study, fluid flow and heat transfer in a fractal microchannel heatsink have been simulated employing the computational fluid dynamics (CFD) method. The fractal microchannel is used to achieve uniform temperature distribution. Thermal performance of single-walled carbon nanotubes (SWCNT) and multi-walled carbon nanotubes (MWCNT) dispersed in the two base fluids of water and kerosene in a fractal microchannel at Reynolds (Re) numbers of 1500 to 3000 are investigated. It should be noted that the nanofluids have been simulated by the two-phase mixture model. The results indicated that the use of fractals silicon microchannel leads to having a uniform temperature distribution. Based on the results, at maximum Re number when the working fluid is water, Nu number and pumping power are 20.9 and 0.033 W whereas, in kerosene flow at the same condition, Nu number and pumping power are 6 and 0.054 W, respectively. According to the obtained results, using the SWCNT nanoparticle compared with the MWCNT nanoparticle leads to a significant enhancement in the Nusselt (Nu) number. This difference is more pronounced by increasing the Re number and nanoparticle volume fraction. In addition, the results indicated that at the same Re number and nanoparticle volume fraction, the performance evaluation criterion of the water-based nanofluid is 4 times higher than that of the kerosene-based nanofluid. So the use of the water as the working fluid with the SWCNT nanoparticle for cooling in the fractal silicon microchannel is recommended. [ABSTRACT FROM AUTHOR]

There are many debates on the preparation methods and the role of ultrasonication on the stability, thermophysical properties, and heat transfer performance of nanofluids. The present study, which is the continuation of the authors previous study, the effects of ultrasonication on the thermal and fluid dynamic performance of MWCNT-water nanofluid, over a different range of temperatures and solid concentrations, based on the thermophysical properties of the nanofluid, has been investigated. The effects of ultrasonication time on the stability and thermophysical properties of the nanofluid were studied over 30 days of the samples preparation. The thermophysical properties of the nanofluid have been experimentally measured at the optimum ultrasonication time. Using the experimental data, and employing different figures-of-merit, the effects that the addition of MWCNTs had on the heat transfer effectiveness and pumping power have been studied. It was confirmed that the nanofluid is a good heat transfer fluid, with a negligible penalty in pumping power. The thermal and fluid dynamic performance of the nanofluid in a microchannel heat sink has also been studied, by comparing the enhancement ratio of the convective heat transfer coefficient and the increase in pumping power. [ABSTRACT FROM AUTHOR]

The impact of adding 40 vol% SiC whiskers on the sinterability, flexural strength, and hardness of TiC ceramics was studied. The hot-pressing route was employed to fabricate the TiC–SiC composite, as well as the monolithic TiC ceramic. The microstructural studies and X-ray diffractometry revealed the precipitation of carbon within the TiC matrix owing to the formation of non-stoichiometric TiC x during the hot-pressing. The addition of SiC whiskers noticeably improved the sinterability of TiC, attaining a fully dense composite sample. The nucleation and growth of new SiC grains were identified as the dominant factors in enhancing the relative density of the TiC-40 vol% SiC composite. Moreover, the SiC additive had an effective impact on both flexural strength and hardness of TiC, standing at 543 MPa and 2280 HV 0.5 kg , respectively. [ABSTRACT FROM AUTHOR]

The objective of this research was to evaluate the impact of the morphology of SiC additive on the microstructure development, sintering behavior, hardness, and fracture toughness of TiB 2 -based ceramics. For this purpose, three different samples were produced via the hot-pressing method at 1950 °C under 25 MPa for 120 min. Subsequently, the samples were characterized by XRD, SEM, and optical microscopy. The samples containing SiC particles (SiC p) reached their theoretical densities. XRD results manifested the formation of an in-situ TiC during the hot-pressing. Thermodynamical investigation and SEM fractographs revealed the role of a liquid phase in boosting the sintering behavior of as-sintered ceramics. The SEM images also confirmed the in-situ generation of B 4 C in the specimens doped with SiC whiskers (SiC w). Finally, the most significant Vickers hardness (28.9 GPa) and fracture toughness (6.4 MPa m1/2) was secured for the sample toughened by 30 vol% SiC w , whereas the lowest values, namely a Vickers hardness of 18.1 GPa and a fracture toughness of 5.1 MPa m1/2, were obtained for the composites doped with 30 vol% SiC p , and 15 vol% SiC w + 15 vol% SiC p , respectively. [ABSTRACT FROM AUTHOR]

• Herding Behaviors of Grasshopper and Harris hawk are evaluated. • The GOA-ANN outperforms the HHO-ANN. • Computational weights and biases of the MLP optimized prior to hybrid algorithms. This work deals with proposing two novel predictors of soil compression coefficient (SCC) through hybridizing the artificial neural network (ANN) by using grasshopper optimization algorithm (GOA) and Harris hawks optimization (HHO) metaheuristic techniques. The SCC is considered as a function of twelve key factors of the soil, collocated from a local project at Hai Phong city (Vietnam). After creating the HHO-ANN and GOA-ANN ensembles, the best structure of them is determined by a sensitivity analysis process. Each model predicted the SCC and comparing the responses with target values revealed that both metaheuristic algorithms enhance the accuracy of the ANN. In details, applying the GOA and HHO resulted in reducing the ANN leaning error (root mean square error) by 14.96% and 10.88%, as well as the prediction error by 7.14% and 4.76%, respectively. [ABSTRACT FROM AUTHOR]

The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone. [ABSTRACT FROM AUTHOR]

Here, an environmentally friendly bottom-up method introduced as an easy, inexpensive, and affordable method for synthesize of graphene-like nanostructures. Results indicated that the presence of FeCl 3 and oxalic acid could improve the quality of the final product impressively. FeCl 3 plays key role for the generation of the high-quality graphene because it acts as both template and catalyst for the formation of graphene. The products were characterized by XRD, FT-IR, TGA, SEM, Raman and TEM analyses. The photocatalytic degradation ability of (graphene/iron oxide/titanium dioxide) Gr/Fe 3 O 4 /TiO 2 nanocomposite for the removal of rhodamine B dyes from wastewater was reported. The obtained adsorbent with porous structure not only exhibited fast adsorption rate, superior adsorption capacity, but also presented convenient magnetic separation characteristic. Synthesized magnetic nanostructures can be separated from reaction mixtures by an external magnet. As a result of its attractive features such as; simple preparation, superior catalytic activity and magnetic recyclability the synthesized nanocomposite obtained in this work provides a new insight to enhance the photocatalytic efficiency. Image 1 • Graphene-like nanosheets produced by a series of green reducing agents via a nontoxic process. • The electrical conductivity was checked and band gap of nanostructures was about 2.65 eV. • Gr/Fe3O4/TiO2 nanocomposite indicated acceptable magnetic behavior for magnetic separation. • Gr/Fe 3 O 4 /TiO 2 nanocomposite indicated photocatalytic activity for the removal of rhodamine B dyes. [ABSTRACT FROM AUTHOR]

In this paper, the Multi-Layer Perceptron (MLP) neural network is optimized with two metaheuristic algorithms, namely Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) for estimating the heating load (HL) and cooling load (CL) of the energy efficient buildings with the residential use. To achieve this, a dataset composed of eight independent factors namely, relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution, along with the two dependent variables of HL and CL is provided. Out of 768 samples, 80:20 ratio is considered to select the training and testing datasets randomly. Through a trial and error process, the optimal parameters of the MLP, ABC-MLP and PSO-MLP networks are determined. Three well-known criteria including coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) are utilized to measure the accuracy of the used models. The results reveal that applying the ABC and PSO algorithms, helps the MLP to perform more efficiently. In this sense, the increase of R2 from 0.8933 to 0.9349 and 0.9370 for the HL, and from 0.8872 to 0.8969 and 0.8997 for the CL prediction show that the outputs of the ensemble models (i.e., ABC-MLP and PSO-MLP) are more correlated with the actual data. Also, the MAE decreases as 22.32% and 24.28% for the HL, and 10.36% and 12.00% for the CL respectively by applying the ABC and PSO. Besides, the RSME decreases as 22.48% and 23.86% for the HL, and 6.06% and 7.56% for the CL modeling respectively with using the ABC and PSO. It is also deduced that the PSO outperforms the ABC in the performance enhancement of the MLP. • Neural network is optimized to predict heating and cooling loads of a residential building. • Through a trial and error process, optimal parameters of different models are determined. • Applying metaheuristic algorithms helps neural network to perform more efficiently. • Particle swarm optimization outperforms artificial bee colony in improving neural network. [ABSTRACT FROM AUTHOR]