Dimensional analysis considerations in the engine rotor fragment containment/deflection problem

Dimensional analysis techniques are described and applied to the containment/deflection problem of bursting high-rpm rotating parts of turbojet engines. The use of dimensional analysis to select a feasible set of experiments and to determine the important parameters to be varied is presented. The determination of a containment coefficient based on the nondimensionalized parameters is developed for the reduction of experimental data and as an assist to designers of containment/deflection devices

Experimental and data analysis techniques for deducing collision-induced forces from photographic histories of engine rotor fragment impact/interaction with a containment ring

An analysis method termed TEJ-JET is described whereby measured transient elastic and inelastic deformations of an engine-rotor fragment-impacted structural ring are analyzed to deduce the transient external forces experienced by that ring as a result of fragment impact and interaction with the ring. Although the theoretical feasibility of the TEJ-JET concept was established, its practical feasibility when utilizing experimental measurements of limited precision and accuracy remains to be established. The experimental equipment and the techniques (high-speed motion photography) employed to measure the transient deformations of fragment-impacted rings are described. Sources of error and data uncertainties are identified. Techniques employed to reduce data reading uncertainties and to correct the data for optical-distortion effects are discussed. These procedures, including spatial smoothing of the deformed ring shape by Fourier series and timewise smoothing by Gram polynomials, are applied illustratively to recent measurements involving the impact of a single T58 turbine rotor blade against an aluminum containment ring. Plausible predictions of the fragment-ring impact/interaction forces are obtained by one branch of this TEJ-JET method; however, a second branch of this method, which provides an independent estimate of these forces, remains to be evaluated

Dynamic Network Mechanisms of Relational Integration

A prominent hypothesis states that specialized neural modules within the human lateral frontopolar cortices (LFPCs) support “relational integration” (RI), the solving of complex problems using inter-related rules. However, it has been proposed that LFPC activity during RI could reflect the recruitment of additional “domain-general” resources when processing more difficult problems in general as opposed to RI specifi- cally. Moreover, theoretical research with computational models has demonstrated that RI may be supported by dynamic processes that occur throughout distributed networks of brain regions as opposed to within a discrete computational module. Here, we present fMRI findings from a novel deductive reasoning paradigm that controls for general difficulty while manipulating RI demands. In accordance with the domain- general perspective, we observe an increase in frontoparietal activation during challenging problems in general as opposed to RI specifically. Nonetheless, when examining frontoparietal activity using analyses of phase synchrony and psychophysiological interactions, we observe increased network connectivity during RI alone. Moreover, dynamic causal modeling with Bayesian model selection identifies the LFPC as the effective connectivity source. Based on these results, we propose that during RI an increase in network connectivity and a decrease in network metastability allows rules that are coded throughout working memory systems to be dynamically bound. This change in connectivity state is top-down propagated via a hierarchical system of domain-general networks with the LFPC at the apex. In this manner, the functional network perspective reconciles key propositions of the globalist, modular, and computational accounts of RI within a single unified framework

Universal error corrections for finite semiconductor resistivity in cross-kelvin resistor test structures

The Cross-Kelvin Resistor test structure is commonly used for the extraction of the specific contact resistance of ohmic contacts. Analysis using this structure are generally based on a two-dimensional model that assumes zero voltage drop in the semiconductor layer in the direction normal to the plane of the contact. This paper uses a three-dimensional (3-D) analysis to show the magnitude of the errors introduced by this assumption, and illustrates the conditions under which a 3-D analysis should be used. This paper presents for the first time 3-D universal error correction curves that account for the vertical voltage drop due to the finite depth of the semiconductor laye

An assessment of the strength of knots and splices used as eye terminations in a sailing environment

Research into knots, splices and other methods of forming an eye termination has been limited, despite the fact that they are essential and strongly affect the performance of a rope. The aim of this study was to carry out a comprehensive initial assessment of the breaking strength of eye terminations commonly used in a sailing environment, thereby providing direction for further work in the field. Supports for use in a regular tensile testing machine were specially developed to allow individual testing of each sample and a realistic spread of statistical data to be obtained. Over 180 break tests were carried out on four knots (the bowline, double bowline, figure-of-eight loop and perfection loop) and two splices (three-strand eye splice and braid-on-braid splice). The factors affecting their strength were investigated. A statistical approach to the analysis of the results was adopted. The type of knot was found to have a significant effect on the strength. This same effect was seen in both types of rope construction (three-strand and braid-on-braid). Conclusions were also drawn as to the effect of splice length, eye size, manufacturer and rope diameter on the breaking strength of splices. Areas of development and further investigation were identified

Finite element modeling of misalignment in interconnect vias

Electrical resistance and hence heat generation in semiconductor chips are becoming more significant issues particularily as generations of silicon devices continue to have smaller features. The resistance of interconnect vias is a significant source of heat generation because of the increasing number of these on chips and increases in via resistance due to reduced size. Finite element modeling of voltage drops and current flow through interconnect vias gives information to aid in designing geometry and materials used in forming vias. It can also be used for modeling the thermal distribution in a via and hence the contribution by vias to heating a chip. In this paper we examine the effect of misalignment of the via between the two metal layers M1 and M2 with regard to the interconnect via resistance. The effect of the interface specific contact resistance is examined in particular. Significant misalignment can be tolerated without increasing the via resistance. The heat generation due to electrical current flow in the via materials and interfaces is modelled using the samefinite element mesh and software. The output of the electrical analysis is used as the heat generation input for the therml analysis

Homology of Distributive Lattices

We outline the theory of sets with distributive operations: multishelves and multispindles, with examples provided by semi-lattices, lattices and skew lattices. For every such a structure we define multi-term distributive homology and show some of its properties. The main result is a complete formula for the homology of a finite distributive lattice. We also indicate the answer for unital spindles and conjecture the general formula for semi-lattices and some skew lattices. Then we propose a generalization of a lattice as a set with a number of idempotent operations satisfying the absorption law.Comment: 30 pages, 3 tables, 3 figure

Network mechanisms of intentional learning.

The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flexible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ventral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple distinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus-response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated.This work was supported by Medical Research Council Grant (U1055.01.002.00001.01) and a European Research GrantPCIG13-GA-2013-618351 to AH. JBR is supported by the Wellcome Trust (103838). The authors report no conflicts of interest.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.neuroimage.2015.11.06