Dissolution-Seepage Coupled Analysis through Formations Containing Soluble Materials

Seepage flow can dissolve particulate soluble materials contained in soil layers and rock formations. The above-mentioned dissolution increases the porosity of the formation and hence seepage flow, which in turn progressively increases the dissolution rate. Due to progressive dissolution, several dams around the world have lost functionality or even failed. Dissolution propagation can be modeled as progress of a solution front, with its progression and resulting excess seepage coupled in the analysis. This is made possible in this paper by simultaneously solving the governing differential equation of seepage and the equation expressing progress of the solution front. The outcome (coupled differential equation) is nonlinear and transient, since both porosity and coefficient of permeability vary with the advancement of the solution front through the medium. The finite-element method is used to solve the resulting nonlinear partial differential equation. Using several examples, influence of material properties and geometry characteristics on the solution front progress and the resulting excess seepage loss is evaluated. Furthermore, effectiveness of different countermeasures (e.g., positive cutoffs and their positions) in dam foundations are studied. Contaminant transport can also be easily modeled and analyzed after applying some modifications into the approach.     

Inverse optimality in the class of Hopfield neural networks with input nonlinearity

This paper presents the chaos suppression problem in the class of Hopfield neural networks (HNNs) with input nonlinearity using inverse optimality approach. Using the inverse optimality technique and based on Lyapunov stability theory, a stabilizing control law, which is optimal with respect to meaningful cost functional, is determined to achieve global asymptotically stability in the closed-loop system. Numerical simulation is performed on a four-dimensional hyper-chaotic HNN to demonstrate the effectiveness of the proposed method.     

Intelligent facial emotion recognition and semantic-based topic detection for a humanoid robot

Automatic perception of human affective behaviour from facial expressions and recognition of intentions and social goals from dialogue contexts would greatly enhance natural human robot interaction. This research concentrates on intelligent neural network based facial emotion recognition and Latent Semantic Analysis based topic detection for a humanoid robot. The work has first of all incorporated Facial Action Coding System describing physical cues and anatomical knowledge of facial behaviour for the detection of neutral and six basic emotions from real-time posed facial expressions. Feedforward neural networks (NN) are used to respectively implement both upper and lower facial Action Units (AU) analysers to recognise six upper and 11 lower facial actions including Inner and Outer Brow Raiser, Lid Tightener, Lip Corner Puller, Upper Lip Raiser, Nose Wrinkler, Mouth Stretch etc. An artificial neural network based facial emotion recogniser is subsequently used to accept the derived 17 Action Units as inputs to decode neutral and six basic emotions from facial expressions. Moreover, in order to advise the robot to make appropriate responses based on the detected affective facial behaviours, Latent Semantic Analysis is used to focus on underlying semantic structures of the data and go beyond linguistic restrictions to identify topics embedded in the users’ conversations. The overall development is integrated with a modern humanoid robot platform under its Linux C++ SDKs. The work presented here shows great potential in developing personalised intelligent agents/robots with emotion and social intelligence.     

The effects of surface-modified graphene nanoplatelets on the sliding wear properties of basalt fibers-reinforced epoxy composites

Possessing unique designs and properties absent in conventional materials, nanocomposites have made a remarkable imprint in science and technology. This is particularly true regarding the polymer matrix composites when they are further reinforced with nanoparticles. In this study, the effects of different weight percentages (0, 0.1, 0.2, 0.3, 0.4, and 0.5) of surface-modified graphene nanoplatelets (GNPs) on the microhardness and wear properties of basalt fibers/epoxy composites were investigated. The GNPs were surface modified by silane, and the composites were made by the hand lay-up method. The wear tests were conducted under two different loads of 20 and 40 N. The best wear properties were achieved at 0.3 wt % GNPs as a result of the GNPs' self-lubrication property and the formation of a stable transfer/lubricating film at the pin and disk interface. Moreover, the friction coefficient was lower at the higher normal load of 40 N. The microscopic studies by FESEM and SEM showed that the presence of GNPs up to 0.3 wt % led to the stability of the transfer/lubricating film by enhancing the adhesion of the basalt fibers to the epoxy resin. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47986.     

Combined extraction processes of lipid from Chlorella vulgaris microalgae: microwave prior to supercritical carbon dioxide extraction

Extraction yields and fatty acid profiles from freeze-dried Chlorella vulgaris by microwave pretreatment followed by supercritical carbon dioxide (MW-SCCO(2)) extraction were compared with those obtained by supercritical carbon dioxide extraction alone (SCCO(2)). Work performed with pressure range of 20-28 Mpa and temperature interval of 40-70 °C, gave the highest extraction yield (w/w dry weight) at 28 MPa/40 °C. MW-SCCO(2) allowed to obtain the highest extraction yield (4.73%) compared to SCCO(2) extraction alone (1.81%). Qualitative and quantitative analyses of microalgae oil showed that palmitic, oleic, linoleic and α-linolenic acid were the most abundant identified fatty acids. Oils obtained by MW-SCCO(2) extraction had the highest concentrations of fatty acids compared to SCCO(2) extraction without pretreatment. Native form, and microwave pretreated and untreated microalgae were observed by scanning electronic microscopy (SEM). SEM micrographs of pretreated microalgae present tearing wall agglomerates. After SCCO(2), microwave pretreated microalgae presented several micro cracks; while native form microalgae wall was slightly damaged.     

Quality assessment of 3D synthesized images based on structural and textural distortion

Depth image based rendering (DIBR) is a popular technique for rendering virtual 3D views in stereoscopic and autostereoscopic displays. The quality of DIBR-synthesized images may decrease due to various factors, e.g., imprecise depth maps, poor rendering techniques, inaccurate camera parameters. The quality of synthesized images is important as it directly affects the overall user experience. Therefore, the need arises for designing algorithms to estimate the quality of the DIBR-synthesized images. The existing 2D image quality assessment metrics are found to be insufficient for 3D view quality estimation because the 3D views not only contain color information but also make use of disparity to achieve the real depth sensation. In this paper, we present a new algorithm for evaluating the quality of DIBR generated images in the absence of the original references. The human visual system is sensitive to structural information; any deg radation in structure or edges affects the visual quality of the image and is easily noticeable for humans. In the proposed metric, we estimate the quality of the synthesized view by capturing the structural and textural distortion in the warped view. The structural and textural information from the input and the synthesized images is estimated and used to calculate the image quality. The performance of the proposed quality metric is evaluated on the IRCCyN IVC DIBR images dataset. Experimental evaluations show that the proposed metric outperforms the existing 2D and 3D image quality metrics by achieving a high correlation with the subjective ratings.

     

Performance analysis of hybrid/single nanofluids on augmentation of heat transport in lid-driven undulated cavity

This numerical study reveals the heat transfer performance of hybrid/single nanofluids inside a lid-driven sinusoidal trapezoidal-shaped enclosure. The right and left inclined surfaces of the trapezium have been considered as insulated, whereas the bottom sinusoidal wavy and the flat top surfaces of the enclosure as hot and cold, respectively. The governing partial differential equations of fluid's velocity and temperature have been resolved by applying the finite element method. The implications of Prandtl number (4.2-6.2), Richardson number (0.1-10.0), undulation number (0-3), nanoparticles volume fraction (0%-3%), and nanofluid/base fluid (water, water–copper (Cu), water–Cu–carbon nanotube, water–Cu–copper oxide (CuO), water–Cu–TiO₂, and water–Cu–Al₂O₃) on the velocity and temperature profiles have been studied. Simulated findings have been represented by means of streamlines, isothermal lines, and average Nusselt number of above-mentioned hybrid nanofluids for varying the governing parameters. The comparison of heat transfer rates using hybrid nanofluids and pure water has been also shown. The heat transfer rate is increased about 15% for varying Richardson number from 0.1 to 10.0. Blending of two nanoparticles suspension in base fluid has a higher heat transfer rate—approximately 5% than a mononanoparticle. Moreover, a higher average Nusselt number is obtained by 14.7% using the wavy surface than the flat surface of the enclosure. Thus, this study showed that applying hybrid nanofluid may be beneficial to obtain expected thermal performance.     

Effect of annealing process on the growth and surface properties of Au–ZnO nanowire films grown by chemical routes

Au–ZnO nanowire films have been synthesized by chemical routes, electrochemical deposition (ECD) and chemical bath deposition (CBD) techniques, on zinc foil followed by annealing in air at 400 °C. X-ray diffraction patterns reveal formation of the ZnO wurtzite structure along with binary phases Au₃Zn and AuZn₃. Scanning electron microscopy shows the presence of ZnO nanowires having several micrometers in length and less than 120 nm in diameter synthesized by ECD and in the range of 70–400 nm using the CBD technique. During the annealing process, different surface morphologies originating from different catalytic effects of Au atoms/layers were observed. In addition, the effect of synthesis routes on crystalline quality and optical properties were studied by Raman and photoluminescence spectrometers indicating varying concentration of defects on the films. The Raman results indicate that Au–ZnO nanowire film prepared by chemical bath deposition route had better crystalline quality.