Mechanical behavior of a continuous fiber reinforced aluminum matrix composite subjected to transverse and thermal loading

The transverse properties of an aluminum alloy metal matrix composite reinforced by continuous alumina fibers were investigated. The composite is subjected to both mechanical and cyclic thermal loading. The results of an experimental program indicate that the shakedown concept of structural mechanics provides a means of describing the material behavior. When the loading conditions are within the shakedown region, the material finally responds in an elastic manner after initial plastic response, and for loading conditions outside the shakedown region, the material exhibits a rapid incremental plastic strain accumulation. The failure strain varies by an order of magnitude according to the operating conditions. Hence, for high mechanical and low thermal loading, the failure strains is small; for low mechanical and high thermal loading, the failure strain is large

Reduction of thermal stresses in continuous fiber reinforced metal matrix composites with interface layers

The potential of using an interface layer to reduce thermal stresses in the matrix of composites with a mismatch in coefficients of thermal expansion of fiber and matrix was investigated. It was found that compliant layers, with properties of readily available materials, do not have the potential to reduce thermal stresses significantly. However, interface layers with high coefficient of thermal expansion can compensate for the mismatch and reduce thermal stresses in the matrix significantly

Optimization of interface layers in the design of ceramic fiber reinforced metal matrix composites

The potential of using an interface layer to reduce thermal stresses in the matrix of composites with a mismatch in coefficients of thermal expansion (CTE) of fiber and matrix was investigated. It was found that the performance of the layer can be defined by the product of the CTE and the thickness, and that a compensating layer with a sufficiently high CTE can reduce the thermal stresses in the matrix significantly. A practical procedure offering a window of candidate layer materials is proposed

Intersectional inequalities and the U.S. opioid crisis:Challenging dominant narratives and revealing heterogeneities

Dominant narratives of prescription opioid misuse (POM) in the U.S. have portrayed it as an issue primarily affecting White communities. In this study we explore POM as reported in data from the 2015 National Survey on Drug Use and Health, using an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). We map the risk of POM through a series of multilevel models with individuals (N = 43,409) nested within strata formed by the intersections of gender, race/ethnicity, income, and age. We find meaningful heterogeneity between and within strata. The ten strata with the greatest risk for POM were comprised of individuals identifying as White, African American, and non-White Hispanic, and included individuals of low, medium, and high income. We uncover intersections of social position with high risk for POM that are often excluded from dominant narratives, including young high-income African American women. Intersectional approaches are essential for advancing our understanding of health inequalities and unfolding epidemics such as that of POM in the U.S

Materials with periodic internal structure: Computation based on homogenization and comparison with experiment

The combination of thermal and mechanical loading expected in practice means that constitutive equations of metal matrix composites must be developed which deal with time-independent and time-dependent irreversible deformation. Also, the internal state of composites is extremely complicated which underlines the need to formulate macroscopic constitutive equations with a limited number of state variables which represent the internal state at the micro level. One available method for calculating the macro properties of composites in terms of the distribution and properties of the constituent materials is the method of homogenization whose formulation is based on the periodicity of the substructure of the composite. A homogenization procedure was developed which lends itself to the use of the finite element procedure. The efficiency of these procedures, to determine the macroscopic properties of a composite system from its constituent properties, was demonstrated utilizing an aluminum plate perforated by directionally oriented slits. The selection of this problem is based on the fact that, extensive experimental results exist, the macroscopic response is highly anisotropic, and that the slits provide very high stress gradients which severely test the effectiveness of the computational procedures. Furthermore, both elastic and plastic properties were investigated so that the application to practical systems with inelastic deformation should be able to proceed without difficulty. The effectiveness of the procedures was rigorously checked against experimental results and with the predictions of approximate calculations. Using the computational results it is illustrated how macroscopic constitutive equations can be expressed in forms of the elastic and limit load behavior

Node re-ordering as a means of anomaly detection in time-evolving graphs

© Springer International Publishing AG 2016. Anomaly detection is a vital task for maintaining and improving any dynamic system. In this paper, we address the problem of anomaly detection in time-evolving graphs, where graphs are a natural representation for data in many types of applications. A key challenge in this context is how to process large volumes of streaming graphs. We propose a pre-processing step before running any further analysis on the data, where we permute the rows and columns of the adjacency matrix. This pre-processing step expedites graph mining techniques such as anomaly detection, PageRank, or graph coloring. In this paper, we focus on detecting anomalies in a sequence of graphs based on rank correlations of the reordered nodes. The merits of our approach lie in its simplicity and resilience to challenges such as unsupervised input, large volumes and high velocities of data. We evaluate the scalability and accuracy of our method on real graphs, where our method facilitates graph processing while producing more deterministic orderings. We show that the proposed approach is capable of revealing anomalies in a more efficient manner based on node rankings. Furthermore, our method can produce visual representations of graphs that are useful for graph compression