Dynamically identifying regenerative cycles in Simulation-based Optimization Algorithms for Markov chains

Simulation-based algorithms for maximizing the average reward of a parameterized Markov chain often rely upon the existence of a state which is recurrent for all choices of parameter values. The question of which recurrent state should serve to mark the end of a regenerative cycle is a very important practical consideration in applications. Even when all of the states of the process are recurrent, some states tend to be visited more often than others, and lengthy renewal cycles tend to result in high variance estimates of the gradient. To address this difficulty, we analyze a mechanism for adjusting this special state dynamically (i/sup */-adaptation) as applied to the "batch" simulation-based optimization algorithm of a previous paper. We show that the desirable convergence properties of the original "batch" algorithm are retained with i/sup */-adaptation, namely the almost sure convergence of the parameter vector to a critical point.     

Neural-Net Analysis of the Plasticity of ÉP-450 Ferrite–Martensite Steel with Different Alloying Element Concentrations

The results of a computational experiment with a neural-net model simulating the effect of the chemical composition on the plastic properties of ÉP-450 steel are presented. It is shown that computer-aided development of reactor steel with prescribed properties is, in principle, possible.     

Mechanisms and Special Features of the Thermodeformational Behavior of a Channel Pipe and Graphite Elements in RBMK Reactors During Heating Due to an Accident

Two- and three-parameter quadratic fitting curves, characterizing the basic thermal and deformational parameters of the state of the structural components of a RBMK technological channel heated as a result of an accident, are constructed on the basis of numerical simulation. The parameters characterizing the accidental heating are assumed to be the heat flux density on the inner surface of the channel pipe, the pressure in the channel, and the thermal resistance on contact surfaces. The output functions are the temperature of the channel pipe at the moment it sets on the graphite, the rate of heating, the average rate of radial expansion of the pipe before and after it sets on the graphite, and the temperature of the surface of the opening in the block at the moment the pipe sets on the graphite. The fitting relations constructed can be used to obtain real-time estimates of the basic thermal and deformational characteristics of the heating process due to the accident and to develop new integral criteria for the integrity of a damaged RBMK channel.     

SVBR-75/100 Multipurpose Modular Low-Power Fast Reactor with Lead–Bismuth Coolant

The results of development work on the SVBR-75/100 modular fast reactor cooled by lead–bismuth coolant are presented. The design is based on the experience gained operating reactors in nuclear-powered submarines. The SVBR-75/100 reactor is inherently safe because of its low power, the chemical inertness and high boiling point of the lead–bismuth coolant, and the integrated basin-type arrangement of the first-loop equipment. This eliminates the conflict between the safety and economic requirements that is characteristic of conventional-type reactors. The lower power makes it possible to build the reactor in a factory and deliver the reactor using any form of transportation to the nuclear power plant site. All safety systems operate passively. Most of these systems are used as normal-operating systems. This creates conditions for multipurpose applications of such reactor modules in developed and developing countries.     

Structured optimal and robust control with multiple criteria: a convex solution

In this paper, the design of controllers that incorporate structural and multiobjective performance requirements is considered. The control structures under study cover nested, chained, hierarchical, delayed interaction and communications, and symmetric systems. Such structures are strongly related to several modern-day and future applications including integrated flight propulsion systems, platoons of vehicles, micro-electro-mechanical systems, networked control, control of networks, production lines and chemical processes. It is shown that the system classes presented have the common feature that all stabilizing controllers can be characterized by convex constraints on the Youla-Kucera parameter. Using this feature, a solution to a general optimal performance problem that incorporates time domain and frequency domain constraints is obtained. A synthesis procedure is provided which at every step yields a feasible controller together with a measure of its performance with respect to the optimal. Convergence to the optimal performance is established. An example of a multinode network congestion control problem is provided that illustrates the effectiveness of the developed methodology.     

Cr-Mo solid solutions forced by high-energy ball milling

Mixtures of Cr and Mo elemental powders, with the nominal compositions Cr₂₅Mo₇₅, Cr₅₀Mo₅₀, and Cr₇₅Mo₂₅, are processed by high-energy ball milling at ambient temperature. Milling is observed to force the mixing of the immiscible bcc elements Cr and Mo into solid solutions. The lattice parameter of these solid solutions, measured by X-ray diffraction (XRD), displays the expected positive deviation from Vegard’s law. These deviations are compared to the ones predicted by Eshelby’s inclusion model for dilute alloys. The conventional Williamson-Hall approach is shown to fail to determine the grain size in as-milled samples, probably due to the high density of dislocations. Annealing at 700 °C for 10 hours under argon leads to a large reduction in structural defect density, without inducing any significant decomposition. The mixing measured in Cr-Mo is discussed in the broader context of the mechanical mixing forced by ball milling in moderately immiscible systems.     

Cyclic oxidation response of multiphase niobium-based alloys

Cyclic oxidation tests were performed on multiphase Nb-based alloys containing silicide, Laves, and Nb solid solution phases. In particular, the oxidation resistance of six alloys with various compositions (Nb, Ti, Hf, Cr, Ge, and Si) and microstructures was characterized by thermal cycling from ambient temperature to a peak temperature that ranges from 900 °C to 1400 °C. Weight change data were obtained and the corresponding spalled oxides were collected and identified by X-ray diffraction. The results indicated that Nb-based alloys formed a mixture of CrNbO₄, Nb₂O₅, and Nb₂O₅ · TiO₂, with possibly small amounts of SiO₂ or GeO₂. The oxidation resistance was improved when CrNbO₄ formed instead of Nb₂O₅ and Nb₂O₅ · TiO₂. These results were used to assess the influence of microstructure and composition on the oxidation resistance of multiphase Nb-based alloys.     

Micromechanical approach to damage mechanics of composite materials with fabric tensors

The purpose of this study is to apply continuum damage mechanics – introduced through the concept of fabric tensors – to composite materials within the framework of the theory of elasticity. A directional data model of damage mechanics for composite materials will be developed using fabric tensors. The introduction of fabric tensors into the analysis of damage of composite materials will allow for an enhanced and better understood physical meaning of damage. The micromechanical approach will be used here to relate the damage effect through fabric tensors to the behavior of composite materials. In this approach, damage mechanics is introduced separately to the constituents of the composite material through different constituents’ damage effect tensors. The damaged properties of the composite system as a whole can then be obtained by proper homogenization of the damaged properties of the constituents.

The derivation of a generalized formulation of damage evolution will be shown here in a mathematically consistent manner that is based on sound thermodynamic principles. Numerical examples will be presented to show applicability. In addition, damage evolution for the one-dimensional tension case is also illustrated.     

Kernel Spectral Matched Filter for Hyperspectral Imagery

In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral matched filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the matched filter is derived by expressing the spectral matched filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so called kernel trick property of the Mercer kernels. The proposed kernel spectral matched filter is equivalent to a nonlinear matched filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral matched filter is implemented and simulation results on hyperspectral imagery show that the kernel spectral matched filter outperforms the conventional linear matched filter.