Coupled Ostrovsky equations for internal waves in a shear flow

In the context of fluid flows, the coupled Ostrovsky equations arise when two distinct linear long wave modes have nearly coincident phase speeds in the presence of background rotation. In this paper, nonlinear waves in a stratified fluid in the presence of shear flow are investigated both analytically, using techniques from asymptotic perturbation theory, and through numerical simulations. The dispersion relation of the system, based on a three-layer model of a stratified shear flow, reveals various dynamical behaviours, including the existence of unsteady and steady envelope wave packets.Comment: 47 pages, 39 figures, accepted to Physics of Fluid

Sequential algorithm and numerical analysis on mathematical model for thermal control curing process of thermoset composite materials

To reproduce and improve the efficiency of waste composite materials with consistence and high quality, it is important to tailor and control their temperature profile during curing process. Due to this phenomenon, temperature profile during curing process between two layers of composite materials, which are, resin and carbon fibre are visualized in this paper. Thus, mathematical model of 2D convection-diffusion of the heat equation of thick thermoset composite during its curing process is employed for this study. Sequential algorithms for some numerical approximation such as Jacobi and Gauss Seidel are investigated. Finite difference method schemes such as forward, backward and central methods are used to discretize the mathematical modelling in visualizing the temperature behavior of composite materials. While, the physical and thermal properties of materials used from previous studies are fully employed. The comparisons of numerical analysis between Jacobi and Gauss Seidel methods are investigated in terms of time execution, iteration numbers, maximum error, computational and complexity, as well as root means square error (RMSE). The Fourth-order Runge-Kutta scheme is applied to obtain the degree of cure for curing process of composite materials. From the numerical analysis, Gauss Seidel method gives much better output compared to Jacobi method

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Positioning Control of an Antagonistic Pneumatic Muscle Actuated System using Feedforward Compensation with Cascaded Control Scheme

This paper presents a feedforward compensation with cascaded control scheme (FFC) for the positioning control of a vertical antagonistic based pneumatic muscle actuated (PMA) system. Owing to the inherent nonlinearities and time varying parameters exhibited by PMA, conventional fixed controllers unable to demonstrate high positioning performance. Hence, the feedforward compensation with cascaded control scheme is proposed whereby the scheme includes a PID controller coupled with nonlinear control elements. The proposed scheme has a simple control structure in addition to its straightforward design procedures. Though there are nonlinear control elements involved, these elements are derived from the open loop system responses that does not requires any accurate known parameters. Performance of the FFC scheme are then evaluated experimentally and compared to a PID controller with feedforward compensation (FF-PID) in point-to-point motion of different step heights. Overall, the experimental results show that the effectiveness of the proposed FFC scheme in reducing the steady state error to zero in comparison to FF-PID controller for all cases of step heights examined

Parallel computing of numerical schemes and big data analytic for solving real life applications

This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance

Dynamic Mechanical and Gel Content Properties of Irradiated ENR/PVC Blends with TiO2 Nanofillers

Numerous studies reported on irradiated epoxidized natural rubber/polyvinyl chloride (ENR/PVC) blends and the blends were found miscible at all compositional range thus it offers a broad of opportunity in modifying the blend characteristic. Addition of low loading titanium dioxide (TiO2) nanofillers in the ENR/PVC blends has shown a remarkable increment in tensile strength. Thus, this study was initiated to address the effect of TiO2 nanofillers on ENR/PVC blends dynamic mechanical and gel content properties and its morphology upon exposure to electron beam irradiation. ENR/PVC blends with addition of 0, 2 and 6 phr TiO2 nanofillers were first blended in a mixing chamber before being irradiated by an electron beam accelerator at different 0-200 kGy irradiation doses. The influence of TiO2 nanofillers on the irradiation crosslinking of ENR/PVC blends was study based on the dynamic mechanical analysis which was carried out in determining the glass transition temperature and the storage modulus behavior of ENR/PVC blends incorporated with TiO2 nanofillers. Formations of irradiation crosslinking in the blend were investigated by gel content measurement. While, the TiO2 nanofillers distribution were examined by Transmission Electron Microscope (TEM). Upon irradiation, the ENR/PVC/6 phr TiO2 formed the highest value of gel fraction. For dynamic mechanical analysis, it was found that electron beam radiation increased the Tg of all the compositions. The relationship between the crosslinking and the stiffness of the nanocomposites also can be found in this study. The enhancement in the storage modulus and Tg at higher amount of TiO2 in the blend could be correlated to the enhancement of the irradiation-induced crosslinking in the nanocomposites characteristic and also with the higher agglomerations of TiO2 evidence shown from the TEM micrograph examination. Lastly, the dimensions of TiO2 in the blends were found less than 100 nm in diameter which indicates incorporation of TiO2 nanofillers in ENR/PVC blends is potentially to provide the nanocomposites features. Doi: 10.12777/ijse.6.1.24-30 [How to cite this article: Ramlee, N.A., Ratnam, C.T., Alias, N.H., Rahman, M.F.A.. 2014. Dynamic Mechanical and Gel Content Properties of Irradiated ENR/PVC blends with TiO2 Nanofillers. International Journal of Science and Engineering, 6(1),24-30. Doi: 10.12777/ijse.6.1.24-30

Business intelligence readiness factors for higher education institution

Higher Education Institution (HEI) have embarked on the new style of decision-making with the aim to enhance the speed and reliability of decision-making capabilities. One of the hardest challenges in implementing Business Intelligence (BI) is the organization’s readiness towards adopting and implementing BI systems. Currently, few published studies have examined BI readiness in HEI environment. Seeing this challenge, this study aims to contribute in determining the BI readiness factors in HEI specifically in the deployment strategies. Through inductive attention to BI in HEI environment, three broad factors have been identified: a) Organizational – that concerning on business strategies, process and structure, b) Technology – involves the BI system and knowledge for managing including the sources and c) Social – the culture within organization that may influence decision-making and its processes. This paper also makes recommendations for future research

A new version of the CNRM Chemistry-Climate Model, CNRM-CCM: description and improvements from the CCMVal-2 simulations

This paper presents a new version of the Météo-France CNRM Chemistry-Climate Model, so-called CNRM-CCM. It includes some fundamental changes from the previous version (CNRM-ACM) which was extensively evaluated in the context of the CCMVal-2 validation activity. The most notable changes concern the radiative code of the GCM, and the inclusion of the detailed stratospheric chemistry of our Chemistry-Transport model MOCAGE on-line within the GCM. A 47-yr transient simulation (1960–2006) is the basis of our analysis. CNRM-CCM generates satisfactory dynamical and chemical fields in the stratosphere. Several shortcomings of CNRM-ACM simulations for CCMVal-2 that resulted from an erroneous representation of the impact of volcanic aerosols as well as from transport deficiencies have been eliminated. <br><br> Remaining problems concern the upper stratosphere (5 to 1 hPa) where temperatures are too high, and where there are biases in the NO<sub>2</sub>, N<sub>2</sub>O<sub>5</sub> and O<sub>3</sub> mixing ratios. In contrast, temperatures at the tropical tropopause are too cold. These issues are addressed through the implementation of a more accurate radiation scheme at short wavelengths. Despite these problems we show that this new CNRM CCM is a useful tool to study chemistry-climate applications