STRESS AND FAILURE ANALYSIS OF WOVEN COMPOSITES USING AN AUTOMATED CONFORMAL MESH METHOD

This paper presents a progressive damage modeling study to investigate the damage and failure behaviors of woven composites using an automated structured conformal meshing model developed by the authors. The composite domain consists of two materials: yarns and matrix. Yarns impregnated by matrix are considered to be homogenous and transversely isotropic. Each yarn element has its local coordinate system oriented according to the yarn’s centroid path. Properties of yarn are obtained using a micromechanics-based homogenization method. The matrix is considered to be homogenous and isotropic. Maximum stress is used as the damage initiation criteria under tensile loading. Damage evolution is governed by the material property degradation method. The meshing algorithm is based on a realistic micro-geometry generated using the Digital Fabric Mechanics Analyzer (DFMA), and thus is applicable to a wide variety of woven architectures. Numerical damage predictions are discussed and compared with previous numerical studies and experimental data to support the validity of the proposed model.

Sample Path Properties of Generalized Random Sheets with Operator Scaling

We consider operator scaling $$\alpha $$ α -stable random sheets, which were introduced in Hoffmann (Operator scaling stable random sheets with application to binary mixtures. Dissertation Universität Siegen, 2011). The idea behind such fields is to combine the properties of operator scaling $$\alpha $$ α -stable random fields introduced in Biermé et al. (Stoch Proc Appl 117(3):312–332, 2007) and fractional Brownian sheets introduced in Kamont (Probab Math Stat 16:85–98, 1996). We establish a general uniform modulus of continuity of such fields in terms of the polar coordinates introduced in Biermé et al. (2007). Based on this, we determine the box-counting dimension and the Hausdorff dimension of the graph of a trajectory over a non-degenerate cube $$I \subset {\mathbb {R}}^d$$ I ⊂ R d .

Survey on Power-Aware Optimization Solutions for MANETs

Mobile ad hoc networks (MANETs) possess numerous and unique characteristics, such as high channel error-rate, severe link-layer contentions, frequent link breakage (due to node mobility), and dissimilar path properties (e.g., bandwidth, delay, and loss rate) that make these networks different from the traditional ones. These characteristics seriously interfere with communication and hence, ultimately degrade the overall performance in terms of end-to-end delay, packet delivery ratio, network throughput, and network overhead. The traditional referenced layered strict architecture is not capable of dealing with MANET characteristics. Along with this, the most important apprehension in the intent of MANETs is the battery-power consumption, which relies on non-renewable sources of energy. Even though improvements in battery design have not yet reached that great a level, the majority of the routing protocols have not emphasized energy consumption at all. Such a challenging aspect has gained remarkable attention from the researchers, which inspired us to accomplish an extensive literature survey on power-aware optimization approaches in MANETs. This survey comprehensively covers power-aware state-of-the-art schemes for each suggested group, major findings, crucial structures, advantages, and design challenges. In this survey, we assess the suggested power-aware policies in the past in every aspect so that, in the future, other researchers can find new potential research directions.

Spectral projections correlation structure for short-to-long range dependent processes

Let X = (Xt)t≥0 be a stochastic process issued from $x \in \mathbb{R}$ that admits a marginal stationary measure v, i.e. vPtf = vf for all t ≥ 0, where $\textbf{P}_t\,f(x)= \mathbb{E}_x[f(\textbf{X}_t)]$ . In this paper, we introduce the (resp. biorthogonal) spectral projections correlation functions which are expressed in terms of projections.” Also, update first published online date, if available. into the eigenspaces of Pt (resp. and of its adjoint in the weighted Hilbert space L2 (v)). We obtain closed-form expressions involving eigenvalues, the condition number and/or the angle between the projections in the following different situations: when X = X with X = (Xt)t ≥ 0 being a Markov process, X is the subordination of X in the sense of Bochner, and X is a non-Markovian process which is obtained by time-changing X with an inverse of a subordinator. It turns out that these spectral projections correlation functions have different expressions with respect to these classes of processes which enables to identify substantial and deep properties about their dynamics. This interesting fact can be used to design original statistical tests to make inferences, for example, about the path properties of the process (presence of jumps), distance from symmetry (self-adjoint or non-self-adjoint) and short-to-long-range dependence. To reveal the usefulness of our results, we apply them to a class of non-self-adjoint Markov semigroups studied in Patie and Savov (to appear, Mem. Amer. Math. Soc., 179p), and then time-change by subordinators and their inverses.