Abdullah Algarni

Damage assessment of random multiwalled carbon nanotube-reinforced polymer nanocomposites

Abstract The paper presents a numerical procedure to evaluate the mechanical properties and predict the damage initiation of random multiwalled carbon nanotube-reinforced polymer nanocomposites (MWCNT-RPNC). The Hashin-Shtrikman (H-S) random prediction model is used to compute the properties of the reinforced polymer matrix, whereas the Chamis model is used to compute the lamina properties and the Hashin progressive damage model within the ABAQUS environment is used as a finite element analysis (FEA) tool to predict the damage initiation in the reinforced composite material. Experimental testing is employed to validate the numerical results and to adjust the H-S prediction model for MWCNT-RPNC.

Toward Exascale Computing Systems: An Energy Efficient Massive Parallel Computational Model

The emerging Exascale supercomputing system expected till 2020 will unravel many scientific mysteries. This extreme computing system will achieve a thousand-fold increase in computing power compared to the current petascale computing system. The forthcoming system will assist system designers and development communities in navigating from traditional homogeneous to the heterogeneous systems that will be incorporated into powerful accelerated GPU devices beside traditional CPUs. For achieving ExaFlops (10^18 calculations per second) performance through the ultrascale and energy-efficient system, the current technologies are facing several challenges. Massive parallelism is one of these challenges, which requires a novel energy-efficient parallel programming (PP) model for providing the massively parallel performance. In the current study, a new parallel programming model has been proposed, which is capable of achieving massively parallel performance through coarse-grained and fine-grained parallelism over inter-node and intra-node architectural-based processing. The suggested model is a tri-level hybrid of MPI, OpenMP and CUDA that is computable over a heterogeneous system with the collaboration of traditional CPUs and energy-efficient GPU devices. Furthermore, the developed model has been demonstrated by implementing dense matrix multiplication (DMM). The proposed model is considered an initial and leading model for obtaining massively parallel performance in an Exascale computing system.

Polymer composite reinforced with nanoparticles produced from graphitic carbon-rich fly ash:

Carbon nanotubes and graphene are considered effective reinforcement materials for various polymers because of their superior properties. However, they are expensive and difficult to separate and incorporate individually into matrix systems because of their tendency to exist in clustered form. In this work, carbon nanoparticles produced from graphitic carbon-rich fly ash by high-energy ball milling are evaluated as a reinforcement in a high-performance epoxy matrix system. They were used in various weight fractions ranging from 0.1 to 2 wt.%. The obtained carbon nanoparticles have an average particle size of around 20 nm, while XPS spectrum shows active carbonyl groups on their surfaces. The mechanical tensile properties of the carbon nanoparticles/epoxy nanocomposite, including their Youngs modulus, stiffness, and load at fracture, were investigated. Moreover, the effect of ethanol as a dispersion medium was studied. The obtained results indicate that the Youngs modulus and load at fracture changed only slightly upon the addition of carbon nanoparticles to the epoxy matrix system. On the other hand, the stiffness was improved by 60% over that of the pure epoxy matrix system. This improvement was obtained at 0.6 wt.% carbon nanoparticle content. The test results indicate that ethanol is effective in modifying the nanocomposite mechanical properties. Additionally, results show that low-cost CNPs might be useful as a reinforcement material for high-stiffness products.

Loop Block Profiling with Performance Prediction

With increase in the complexity of High Performance Computing systems, the complexity of applications has increased as well. To achieve better performance by effectively exploiting parallelism from High Performance Computing architectures, we need to analyze/identify various parameters such as, the code hotspot (kernel), execution time, etc of the program. Statistics say that a program usually spends 90% of the time in executing less than 10% of the code. If we could optimize even some small portion of the 10% of the code that takes 90% of the execution time we have a high probability of getting better performance. So we must find the bottleneck, that is the part of the code which takes a long time to run which is usually called the hotspot. Profiling provides a solution to the question: which portions of the code should be optimized/parallelized, for achieving better performance. In this research work we develop a light-weight profiler that gives information about which portions of the code is the hotspot and estimates the maximum speedup that could be achieved, if the hotspot is parallelized. Keywords—Profiling, Loop Block Profile, Code Analysis, Performance Prediction, Speedup Estimation

A Framework for Faster Porting of Scientific Applications Between Heterogeneous Clouds

The emergence of pay-as-you-use compute clouds has enabled scientists to experiment with the latest processor architectures and accelerators. However, the lack of standardization in cloud computing, more specifically in the interoperability context, makes the task of portability of applications between clouds challenging. Two main tasks that users of multi-vendor clouds will need to perform are porting cost analysis and faster source-to-source translation. Cost analysis is essential to help evaluate the feasibility and cost of portability. And any automation of the source-to-source translation step will help developers perform the translation faster while taking advantage of platform-specific features. This paper presents a framework that assists a developer in performing these two tasks. The first task is achieved using the Maintainability Analyzer module which generates unique funnel shaped patterns that give an insight about the maintainability of an application and its potential for porting. Different scientific applications from various domains, that were developed using different programming paradigms, were evaluated using this module. For the second task, a set of modules use a knowledge repository to perform source-to-source translations while ensuring the maintainability of the generated code. The framework has been tested with different architecture and library combinations with promising results.

Location Privacy in Smart Cities Era

In recent years, smart city concept was proposed to provide sustainable development to the cities and improve the quality of citizens’ life by utilizing the information and communication technologies. To achieve that, smart city applications are expected to use IoT infrastructure to collect and integrate data continuously about the environment and citizens, and take actions based on the constructed knowledge. Indeed, identification and tracking technologies are essential to develop such context-aware applications. Therefore, citizens are expected to be surrounded by smart devices which continuously identify, track and process their daily activities. Location privacy is one of the important issues which should be addressed carefully. Preserving location privacy means that the released sensitive location data of citizens are used only for the desired purpose. In reality, adopting the citizens’ for smart city applications depends on their trust on the used technologies. In this paper, we review smart city architectures, frameworks, and platforms to highlight to what extent preserving location privacy is addressed. We show that preserving location privacy in smart city applications does not get the required attention. We discuss the issues, which we think should be addressed to improve location privacy preservation for smart city applications. Accordingly, we propose a location privacy preservation system for smart city applications.

Failure Analysis in Hybrid Composite Laminates Using Acoustic Emission and Microscopy

Fiber Reinforced Plastic (FRP) composite materials are widely used in many applications especially in aircraft manufacturing because they offer outstanding strength to weight ratio compared to other materials such as aluminum alloys. The use of hybrid composite materials is potentially an effective cost saving design while maintaining strength and stiffness requirements. In this work, Woven Carbon Fibers (WCFs) along with Unidirectional Glass Fibers (UDGFs) are added to a an aerospace-rated epoxy matrix system to produce a hybrid carbon and glass fibers reinforced plastic composite plates. The manufacturing method used here is a conventional vacuum bagging technique and the stacking sequence achieved consists of a symmetric and balanced laminate (±451WCF, 03UDGF, ±451WCF) to simulate the layup usually adopted for helicopter composite blades constructions. Then, tensile static tests samples are cut according to ASTM standard using a diamond blade and tested using a servohydraulic test machine. Acoustic Emission (AE) piezoelectric sensors (transducers) are attached to the samples surface using a special adhesive. Stress waves that are released at the moments of various failure modes are then recorded by the transducers in the form of AE hits and events (a burst of hits) after they pass through pre-amplifiers. Tests are incrementally paused at load levels that represent significant AE hits activity which usually corresponds to certain failure modes. The unbroken samples are then thoroughly investigated using a high resolution microscopy. The multi load level test-and-inspect method combined with AE and microscopy techniques is considered here to be an innovation in the area of composite failure analysis and damage characterization as it has not been carried out before. Results are found to show good correlation between AE hits concentration zones and the specimens damage location observed by microscopy. Waveform analysis is also carried out to classify the damage type based on the AE signal strength energy, frequency and amplitude. Most of the AE activity is found to initiate from early matrix cracking that develops into delamination. Whereas little fiber failure activity has been observed at the initial stages of the load curve. The results of this work are expected to clear the conflicting reports reported in the literature regarding the correlation of AE hits characteristics (e.g. amplitude level) with damage type in FRP composite materials. In addition, the use of a hybrid design is qualitatively assessed here using AE and microscopy techniques for potential cost savings purposes without jeopardizing the weight and strength requirements as is the case in a typical aircraft composite structural design.Copyright