MECHANICAL PROPERTIES OF FIBER REINFORCED POLYMER COMPOSITE – A REVIEW

ABSTRACT. Fiber reinforced plastic composites or as called fiber-reinforced polymer, consists many rewards such as its ultimate strength, its low density, and easier processing procedures. Thus, the fiber-Reinforced polymer is used in many fields for instance: in automotive, constructions, as well as in aerospace. Merging two or more fibers into one mixture polymer matrix guides to develop the hybrid composite. The mechanical properties of single fiber-reinforced polymer composite can be improved by the process of Hybridization. The resin and aggregates can be seen as a polymer concrete composite material signs. The (FRPs) are used instead of the steel in both rehabilitation and construction projects because of their non-corrosive and their light weight Thus they are non-magnetic, inert from the prospective of the chemical composition, also they can be applied easily. In addition, they are suitable economically for strengthening, rehabilitating and seismic retrofit of columns, beams, joints, and many more other uses in structures. In this paper, the mechanical properties of the Fiber-Reinforced Polymer (FRP) is presented in this paper

MrLazy: Lazy runtime label propagation for MapReduce

Organisations are starting to publish datasets containing potentially sensitive information in the Cloud; hence it is important there is a clear audit trail to show that involved parties are respecting data sharing laws and policies. Information Flow Control (IFC) has been proposed as a solution. However, fine-grained IFC has various deployment challenges and runtime overhead issues that have limited wide adoptation so far. In this paper we present MrLazy, a system that practically addresses some of these issues for MapReduce. Within one trust domain, we relax the need of continuously checking policies. We instead rely on lineage (information about the origin of a piece of data) as a mechanism to retrospectively apply policies on-demand. We show that MrLazy imposes manageable temporal and spatial overheads while enabling fine-grained data regulation

A primer on provenance

Better understanding data requires tracking its history and context.</jats:p

Turbulence in a network of rigid fibers

The effect of a network of fixed rigid fibers on fluid flow is investigated by means of three-dimensional direct numerical simulations using an immersed boundary method for the fluid-structure coupling. Different flows are considered (i.e., cellular, parallel, and homogeneous isotropic turbulent flow) to identify the modification of the classic energy budget occurring within canopies or fibrous media, as well as particle-laden flows. First, we investigate the stabilizing effect of the network on the Arnold-Beltrami-Childress cellular flow, showing that, the steady configuration obtained for a sufficiently large fiber concentration mimics the single-phase stable solution at a lower Reynolds number. Focusing on the large-scale dynamics, the effect of the drag exerted by the network on the flow can be effectively modeled by means of a Darcy\u27s friction term. For the latter, we propose a phenomenological expression that is corroborated when extending our analysis to the Kolmogorov parallel flow and homogeneous isotropic turbulence. Furthermore, we examine the overall energy distribution across the various scales of motion, highlighting the presence of small-scale activity with a peak in the energy spectra occurring at the wave number corresponding to the network spacing

Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data

Analysis of compressible turbulent flows is essential for applications related to propulsion, energy generation, and the environment. Here, we present BLASTNet 2.0, a 2.2 TB network-of-datasets containing 744 full-domain samples from 34 high-fidelity direct numerical simulations, which addresses the current limited availability of 3D high-fidelity reacting and non-reacting compressible turbulent flow simulation data. With this data, we benchmark a total of 49 variations of five deep learning approaches for 3D super-resolution - which can be applied for improving scientific imaging, simulations, turbulence models, as well as in computer vision applications. We perform neural scaling analysis on these models to examine the performance of different machine learning (ML) approaches, including two scientific ML techniques. We demonstrate that (i) predictive performance can scale with model size and cost, (ii) architecture matters significantly, especially for smaller models, and (iii) the benefits of physics-based losses can persist with increasing model size. The outcomes of this benchmark study are anticipated to offer insights that can aid the design of 3D super-resolution models, especially for turbulence models, while this data is expected to foster ML methods for a broad range of flow physics applications. This data is publicly available with download links and browsing tools consolidated at https://blastnet.github.io.Comment: Accepted in Advances in Neural Information Processing Systems 36 (NeurIPS 2023). 55 pages, 21 figures. v2: Corrected co-author name. Keywords: Super-resolution, 3D, Neural Scaling, Physics-informed Loss, Computational Fluid Dynamics, Partial Differential Equations, Turbulent Reacting Flows, Direct Numerical Simulation, Fluid Mechanics, Combustio

Practical whole-system provenance capture

Data provenance describes how data came to be in its present form. It includes data sources and the transformations that have been applied to them. Data provenance has many uses, from forensics and security to aiding the reproducibility of scientific experiments. We present CamFlow, a whole-system provenance capture mechanism that integrates easily into a PaaS offering. While there have been several prior whole-system provenance systems that captured a comprehensive, systemic and ubiquitous record of a system’s behavior, none have been widely adopted. They either A) impose too much overhead, B) are designed for long-outdated kernel releases and are hard to port to current systems, C) generate too much data, or D) are designed for a single system. CamFlow addresses these shortcoming by: 1) leveraging the latest kernel design advances to achieve efficiency; 2) using a self-contained, easily maintainable implementation relying on a Linux Security Module, NetFilter, and other existing kernel facilities; 3) providing a mechanism to tailor the captured provenance data to the needs of the application; and 4) making it easy to integrate provenance across distributed systems. The provenance we capture is streamed and consumed by tenant-built auditor applications. We illustrate the usability of our implementation by describing three such applications: demonstrating compliance with data regulations; performing fault/intrusion detection; and implementing data loss prevention. We also show how CamFlow can be leveraged to capture meaningful provenance without modifying existing applications.Engineering and Applied Science