Improving composite damage modelling through automatic placement of cohesive elements

Numerous experimental studies of damage in composite laminates have shown that intralaminar (in-plane) matrix cracks lead to interlaminar delamination (out-of-plane) at ply interfaces. The smearing of in-plane cracks over a volume, as a consequence of the use of continuum damage mechanics, does not always effectively capture the full extent of the interaction between the two failure mechanisms. A more accurate representation is obtained by adopting a discrete crack approach via the use of cohesive elements, for both in-plane and out-of-plane damage. The difficulty with cohesive elements is that their location must be determined a priori in order to generate the model; while ideally the position of the crack migration, and more generally the propagation path, should be obtained as part of the problem’s solution. With the aim of enhancing current modelling capabilities with truly predictive capabilities, a concept of automatic insertion of interface elements is utilized. The consideration of a simple traction criterion in relation to material strength, evaluated at each node of the model (or of the regions of the model where it is estimated cracks might form), allows for the determination of initial crack location and subsequent propagation by the insertion of cohesive elements during the course of the analysis. Several experimental results are modelled using the commercial package ABAQUS/Standard with an automatic insertion subroutine developed in this work, and the results are presented to demonstrate the capabilities of this technique.

The Complexity of MAP Inference in Bayesian Networks Specified Through Logical Languages

We study the computational complexity of finding maximum a posteriori configurations in Bayesian networks whose probabilities are specified by logical formulas. This approach leads to a fine grained study in which local information such as context-sensitive independence and determinism can be considered. It also allows us to characterize more precisely the jump from tractability to NP-hardness and beyond, and to consider the complexity introduced by evidence alone.

Inference in Polytrees with Sets of Probabilities

Inferences in directed acyclic graphs associated with probability intervals and sets of probabilities are NP-hard, even for polytrees. We propose: 1) an improvement on Tessem’s A/R algorithm for inferences on polytrees associated with probability intervals; 2) a new algorithm for approximate inferences based on local search; 3) branch-and-bound algorithms that combine the previous techniques. The first two algorithms produce complementary approximate solutions, while branch-and-bound procedures can generate either exact or approximate solutions. We report improvements on existing techniques for inference with probability sets and intervals, in some cases reducing computational effort by several orders of magnitude.