Arabian Journal for Science and Engineering

Cobalt–chromium (Co–Cr)-based alloys have been used extensively as medical implants, but the ion release and the corrosion products can affect their mechanical integrity and biocompatibility. One of the solutions is to surface coat the substrate with hydroxyapatite via electrophoretic deposition technique. Two variables—pH of electrolyte and current density—were used to examine the electrochemical behavior of the coated sample. An experimental strategy was developed based on the response surface methodology together with the analysis of variance to verify the precision of the mathematical models and their relative parameters. Close agreement was observed between the predicted models and the experimental results. The pH value of electrolyte was a more significant factor than current density in increasing the corrosion potential (E corr) of the substrate. The maximum E corr was obtained with a current density of 12 mA cm−2 and a pH value of 4.71.

Bandwidth limitations in the wireless communication bands have motivated the investigation and exploration of wireless access technologies like massive multiple-input multiple-output (MIMO) networks. The performance of MIMO networks dramatically depends upon the techniques exploited for channel estimations. The existing methods of channel estimation have failed to resolve the inter-symbol interference (ISI) effects efficiently. This paper presents a new channel estimation method based on machine learning. The presence of ISI in the MIMO-orthogonal frequency division multiplexing (MIMO-OFDM) network introduces errors in the decision device at the receiver. This study aims to limit the impact of ISI in the transmitting and receiving filter designs to convey digital data with the lowest error rate. The Elman recurrent neural network (E-RNN) algorithm was employed herein to estimate the channel in MIMO-OFDM considering reliability and scalability. A low peak-to-average power ratio (PAPR), reduced bit error rate (BER), high capacity, high throughput, and improved mean squared error (MSE) performance are achieved using the E-RNN approach. The obtained PAPR value for the proposed E-RNN is 0.1272 after 40 epochs. Variations of cumulative distribution function (CDF) for various channel capacities are plotted. Also, these channel estimation parameters exploiting recurrent neural network (RNN), artificial neural network (ANN), convolutional neural network (CNN), and deep neural network (DNN) methods are compared.

The impact of some external input parameters on electron, ozone $$\hbox {O}_3$$, negative $$\hbox {O}^{-}$$ and positive $$\hbox {O}_2^{+}$$ ions, metastable singlet delta-state $$\hbox {O}_{2}(a^{1}\varDelta _{g})$$ molecule and atomic oxygen O formation is investigated using a numerical simulation. A one-dimensional, self-consistent fluid model of a dual radio-frequency capacitively coupled discharge operating on pure oxygen is developed to explore the evolution of the species density profiles as functions of gas pressure $$p_g$$, driving high-frequency $$f_{hf}$$, inter-electrode gap distance d and driving voltage waveform $$V_{hf}$$. The proposed model incorporates five main species and 24 dominant reaction channels. Simulation results show that the time-averaged density profiles of electron, ozone $$\hbox {O}_3$$, negative $$\hbox {O}^{-}$$ and positive $$\hbox {O}_2^{+}$$ ions decrease when the gas pressure increases. However, the density of the metastable singlet delta-state $$\hbox {O}_{2}(a^{1}\varDelta _{g})$$ molecule and atomic oxygen O increase when the gas pressure increases. The electron density significantly increases with increased $$f_{hf}$$ until a maximum peak is reached at $$40.68~ \hbox {MHz}$$, and then it drops almost linearly at frequencies greater than $$40.68~\hbox {MHz}$$. However, the negative ions $$\hbox {O}^{-}$$ density increases over a range of frequencies from 27.12 to $$67.80~ \hbox {MHz}$$, then it decreases slightly as $$f_{hf}$$ increases further. Therefore, when $$f_{hf}$$ increases, it does enhance the production of the metastable $$\hbox {O}_{2}(a^{1}\varDelta _{g})$$ and the oxygen O atoms, whereas the $$\hbox {O}_2^{+}$$ density is decreased. It is also shown that an increase in the inter-electrode gap distance causes a noticeably decrease in the formation of the various species in the discharge. Furthermore, a significant increase in the atomic oxygen O and the metastable singlet delta-state $$\hbox {O}_{2}(a^{1}\varDelta _{g})$$ densities is displayed as $$V_{hf}$$ increases. Comparisons are made with recent simulation models and experimental data, and a qualitative agreement is obtained.

In this study, the method regarding preparation of a tea residue-based activated carbon (AC) was provided. The physicochemical property and adsorption performance of the obtained AC were also investigated. The preparation of AC mainly includes the carbonization at 350 °C and activation by potassium hydroxide (KOH). The activation parameters including temperature, time and dosage of KOH are crucial to the physicochemical property of AC samples. According to the ANOVA analysis, the optimum activation conditions are determined as activation temperature of 900 °C, activation time of 60 min and mixture ratio of char and KOH of 3:2. The N2 adsorption/desorption and scanning electron microscopy (SEM) characterizations confirm that AC obtained at the above-mentioned optimum activation conditions, designated as AC7, possesses well-developed porosity. The Langmuir model well describes the adsorption equilibrium of o-cresol on sample AC7. The maximum o-cresol adsorption capacity obtained from the Langmuir model increases with the elevated temperature. The maximum adsorption capacity of o-cresol on sample AC7 could reach up to 476 mg g−1, which is higher than that of other reported AC samples. The adsorption kinetics of o-cresol on sample AC7 follows the pseudo-second-order model. The generated pseudo-second-order rate constant of o-cresol adsorption on sample AC7 rises with the operating temperature. The adsorption thermodynamics study shows that o-cresol adsorption on sample AC7 is spontaneous and endothermic. Apart from the physisorption, the chemisorption does exist between o-cresol and sample AC7. Particularly, the π–π dispersion interaction plays a dominant role during o-cresol adsorption on sample AC7.

The effect of three different types of time periodic modulations on the Rayleigh–Bénard magnetic system involving Newtonian nanoliquids is studied. Multiple-scale analysis (homogenization method) is used to arrive at the Ginzburg–Landau equation. The curiosity in the work is to know the individual effects of (1) rotation, (2) gravity and (3) temperature modulations on Rayleigh–Bénard magnetoconvection in weakly electrically conducting Newtonian nanoliquids. A significant effort in this research is devoted toward linear and nonlinear stability analyses as well as the homogenization method which leads to the Ginzburg–Landau evolution equation. Although several studies have concluded similar results for nanoliquids compared with those of pure base fluids, many fundamental issues like the choice of phenomenological models for the thermo-physical properties and “the” best type of nanoparticles are not well understood. This research focuses on several important issues involving mathematical and computational problems arising in heat transfer analysis in the presence of nanoliquids. Effects of various nanoliquid parameters, frequency and amplitude of modulation on heat transport are analyzed. This investigation focuses on five nanoliquids, with water as a carrier liquid and five nanoparticles, viz. copper, copper oxide, silver, alumina and titania. Enhanced heat transport was observed for rotation, gravity and temperature modulations. In the case of rotation modulation, it is found that increase in the amplitude of modulation results in a decrease in the critical Rayleigh number and thereby to an increase in the mean Nusselt number. The increase in the amplitude of the gravity modulation is shown to enhance the heat transport, whereas increase in frequency is to inhibit the heat transport. Two types of temperature modulations are considered, viz. in-phase (synchronous) and out-of-phase (asynchronous) temperature modulations with the assumption that the boundary temperatures vary sinusoidally with time. The amplitudes of modulation are considered to be very small. In the case of in-phase modulation, there is no significant difference between the heat transports in the presence and in the absence of temperature modulation. On this reason, out-of-phase temperature modulation is used to either enhance or diminish heat transport by suitably adjusting the frequency and phase difference of the modulated temperature. The effect of magnetic field, in all three cases of modulations, is to inhibit the onset of convection and thereby diminish the heat transport.

Insufficient hole cleaning in deviated eccentric annulus yields many drilling problems due to excessive accumulation of cuttings. The majority of the implemented studies in such issues are computational or experimental making them complicated, expensive, and time-consuming. This paper presents a statistical data-driven model to predict cuttings concentration (CA), as a hole cleaning efficiency indicator, under different presumed eccentricity (ε) values of 0.0, 0.4, and 0.8. The model was constructed on the basis of Buckingham Pi theorem utilizing oil field data collected from six offshore deviated wells for developing reliable CA predictive tool. Least linear regression approach was employed for each ε value considering seven dimensionless groups of drilling parameters, rheological parameters, cuttings density, and hole cleaning efficiency indicators, such as transport velocity ratio (VTR), carrying capacity index (CCI), and equivalent circulating density (ECD). The field data were filtered and divided into 75% for training and 25% for validation. Furthermore, a sensitivity study was conducted by parametric differentiation of independent variables regarding the developed CA correlations. Results showed that, VTR, CCI followed by ECD had the highest significance, especially in eccentric annuli. In contrast, cuttings density had the lowest significance in all cases. The proposed method outperformed Duan and Khaled models with root mean square error of 1.45, 1.77, 2.89, and mean absolute percentage error of 34.6, 41.3, 54.5% for ε = 0.0, 0.4, and 0.8, respectively. This study could provide promising and practical methodology in estimating CA and improving deviated drilling efficiency up to 61.8° from vertical.