Models for nuclear smuggling interdiction

We describe two stochastic network interdiction models for thwarting nuclear smuggling. In the first model, the smuggler travels through a transportation network on a path that maximizes the probability of evading detection, and the interdictor installs radiation sensors to minimize that evasion probability. The problem is stochastic because the smuggler's origin-destination pair is known only through a probability distribution at the time when the sensors are installed. In this model, the smuggler knows the locations of all sensors and the interdictor and the smuggler “agree” on key network parameters, namely the probabilities the smuggler will be detected while traversing the arcs of the transportation network. Our second model differs in that the interdictor and smuggler can have differing perceptions of these network parameters. This model captures the case in which the smuggler is aware of only a subset of the sensor locations. For both models, we develop the important special case in which the sensors can only be installed at border crossings of a single country so that the resulting model is defined on a bipartite network. In this special case, a class of valid inequalities reduces the computation time for the identical-perceptions model.     

An approach to identify the optimal configurations and reconfiguration processes for design of reconfigurable machine tools

A reconfigurable machine tool (RMT) is a special machine that can deliver different machining functions through reconfiguration processes among its configurations during the machine utilisation stage. In this research, a new approach is developed to identify the optimal configurations and the reconfiguration processes for design of the RMTs. In this work, a generic design AND-OR tree is used to model different design solution candidates, their machine configurations and parameters of these configurations. A specific design solution is created from the generic design AND-OR tree through tree-based search and modelled by different machine configurations. For a reconfiguration process between two machine configurations, a generic process AND-OR graph is used to model reconfiguration operation candidates, sequential constraints among operations and operation parameters. A graph-based search is used to generate all feasible reconfiguration process candidates from the generic process AND-OR graph. The optimal design is identified by multi-level and multi-objective hybrid optimisation. A case study is developed to show how this new approach is used for the optimal design of a RMT.     

Setting procurement safety lead-times for assembly systems

This paper describes a practical method for setting safety lead-times for purchased components in assembly systems with uncertainty in the supply process. The approach is specifically designed to be used in an MRP procurement environment. A two-stage production model in which suppliers were uncapacitated with stochastic lead times was developed. Then, a combinatorial optimization method that took advantage of structural properties was developed to produce an optimal integer solution of safety lead-times. In addition to yielding a practical lead-time setting tool, the model led to some interesting policy insights: (1) the flexibility achieved by bringing parts in early may have value even in situations with ideal suppliers; (2) purchasing components in batches offers some of the same protection as does safety lead time, but is less precise and subsequently more expensive; and (3) ignoring the complicating issues of MRP procurement results in far from optimal results.     

Experimental investigation of impact on composite laminates with protective layers

This paper presents an experimental study of low energy impacts on composite plates covered with a protective layer. In service, composite materials are subjected to low energy impacts. Such impacts can generate damage in the material that results in significant reduction in material strength. In order to reduce the damage severity, one solution is to add a mechanical protection on composite structures. The protection layer is made up of a low density energy absorbent material (hollow spheres) of a certain thickness and a thin layer of composite laminate (Kevlar). Energy absorption ability of these protective layers can be deduced from the load/displacement impact curves. First, two configurations of protection are tested on an aluminium plate in order to identify their performance against impact, then the same are tested on composite plates. Test results from force–displacement curves and C-scan control are compared and discussed and finally a comparison of impact on composite plates with and without protection is made for different configurations.     

Fault Diagnosis Improvement Using Dynamic Fault Model in Optimal Sensor Placement: A Case Study of Steam Turbine

Health data are collected dominantly through sensors mounted on different locations in the system. Optimization of sensor network has a significant influence on the reliability of system health prognostics process. In this research, the effect of sensors reliability is studied on their placement optimization. Sensors are considered in this study as components in system failure model. This study is aimed to use ‘Priority AND’ gate for evaluating the effect of time dependencies of sensors as well as components failure on the optimal sensor placement. Because of that, PAND gate is added to the fault tree between all sensors and their corresponding components to develop the failure model of each sensor placement scenario. For calculating the probability of top event, a Monte Carlo‐based algebraic approach is proposed. In algebraic approach, temporal operator ‘BEFORE’ is proposed for calculating the probability of ‘PAND’ gate. The result of using ‘BEFORE’ operator is an analytical solution for probability of each cut sequence. Because of the complexity of analytical solution in practical problems, a Monte Carlo simulation is utilized on the solution in this research. Then the probability of each cut sequence is calculated. Consequently, the probability of top event for each scenario is obtained. Finally, all scenarios are ranked based on top event probabilities. As a case study, optimization of sensor placement has been demonstrated on steam turbine and results are discussed. Copyright © 2016 John Wiley & Sons, Ltd.     

Efficient Routing Protection Algorithm Based on Optimized Network Topology

Network failures are unavoidable and occur frequently. When the network fails, intra-domain routing protocols deploying on the Internet need to undergo a long convergence process. During this period, a large number of messages are discarded, which results in a decline in the user experience and severely affects the quality of service of Internet Service Providers (ISP). Therefore, improving the availability of intra-domain routing is a trending research question to be solved. Industry usually employs routing protection algorithms to improve intra-domain routing availability. However, existing routing protection schemes compute as many backup paths as possible to reduce message loss due to network failures, which increases the cost of the network and impedes the methods deployed in practice. To address the issues, this study proposes an efficient routing protection algorithm based on optimized network topology (ERPBONT). ERPBONT adopts the optimized network topology to calculate a backup path with the minimum path coincidence degree with the shortest path for all source purposes. Firstly, the backup path with the minimum path coincidence with the shortest path is described as an integer programming problem. Then the simulated annealing algorithm ERPBONT is used to find the optimal solution. Finally, the algorithm is tested on the simulated topology and the real topology. The experimental results show that ERPBONT effectively reduces the path coincidence between the shortest path and the backup path, and significantly improves the routing availability.     

基于逆问题求解的汽车操纵性能优化

给出一种基于逆问题求解的人-车-路闭环系统操纵性能优化的方法.利用径向基函数神经网络建立了汽车侧向位移与方向盘转角及其它响应之间的映射关系,由跟踪路径反求出方向盘转角及汽车的其它响应,进而计算闭环系统的操纵性能评价指标并进行优化.该方法是在不同汽车方案具有相同实际行驶路径的基础上对操纵性能进行分析并优化,从而得到的最优汽车方案在跟踪某一典型路径时具有最好的操纵性能.     

Cold-standby redundancy optimization for nonrepairable systems

A solution methodology is described and demonstrated to determine optimal design configurations for nonrepairable series-parallel systems with cold-standby redundancy. This problem formulation considers non-constant component hazard functions and imperfect switching. The objective of the redundancy allocation problem is to select from available components and to determine an optimal design configuration to maximize system reliability. For cold-standby redundancy, other formulations have generally required exponential component time-to-failure and perfect switching assumptions. For this paper, there are multiple component choices available for each subsystem and component time-to-failure is distributed according to an Erlang distribution. Optimal solutions are determined based on an equivalent problem formulation and integer programming. Compared to other available algorithms, the methodology presented here more accurately models many engineering design problems with cold-standby redundancy. Previously, it has been difficult to determine optimal solutions for this class of problems or even lo efficiently calculate system reliability. The methodology is successfully demonstrated on a large problem with 14 subsystems.     

Averaged stress intensity factors for embedded elliptical cracks

As an extension of well-known two-degree-of-freedom models describing virtual crack extensions in weight function applications for surface crack configurations different possibilities are suggested of four-degree-of-freedom models used for embedded elliptical cracks. As a result, the geometric function for an elliptical crack in a strip of finite width is presented for tension and bending. FEM-calculations have been carried out to check the quality of the solution.     

Optimal design methodologies for configuration of supply chains

This paper describes a methodology developed for designing an optimal configuration for a supply chain. A typical configuration for a supply chain consists of defining components of the system, assigning values to characteristics parameters of each component and setting operation policies for governing the interrelationships among these components. As such, each configuration will be defined by a set of values for quantitative parameters of the system as well as a set of policy and qualitative characteristics. Examples of quantitative variable include inventory levels and frequency of ordering where as location of distribution centres and mode of transportation between suppliers and the original equipment manufacturers (OEM) are the decision variables of policy and qualitative nature. The methodology presented here consists of a supply chain model builder coupled with two optimisation algorithms that automatically build a sequence of configurations that systematically move towards an optimum design. A combination of mixed integer programming and a genetic algorithm is used to determine simultaneously the values of quantitative as well as policy variables. The solution consists of strategic decisions regarding facility locations, stocking locations, supplier selection, production policies, production capacities, and transportation modes.     

Switched Reluctance Motor Design Using Neural-Network Method With Static Finite-Element Simulation

The paper describes a neural network method for optimal design of a switched reluctance motor (SRM). The approach maximizes average torque while minimizing torque ripple, considering mainly the stator and rotor geometry parameters. Before optimization takes place, an experimental validation of the SRM model, based on the finite-element method, is performed. The validation predicts average torque and torque ripple characteristics for several motor configurations while stator and rotor pole arcs are varied. The numerical results are highly nonlinear, and a function approximation of the data is therefore difficult to implement. We therefore interpolate the data by using a neural network based on a generalized radial basis function. The computed results allow us to search for optimum motor parameters. The optimum design was confirmed by numerical field solutions.     

Application of the hybrid finite element, finite difference technique to the solution of multistrand conductor problems

An algorithm for the application of a hybrid finite element, finite difference technique for the solution of linear conductor problems has been developed. This paper describes the implementation of the technique to single layer helically spiralling conductor strand geometries. Such conductor configurations occur in hollow conductors with magnetically neutral spacers. However, the technique can also be used as an approximation to the solution of expanded conductor or ACSR conductor problems if it is assumed that the core wires remain substantially magnetically neutral. The influences of conductor pitch and strand diameter or equivalent circuit parameters are illustrated by considering a number of different configurations. Theoretical samples which are physically similar to actual ACSR specimens are analyzed in order to infer the inductive role of the core strand.     

Performances of the mixed-gases Joule-Thomson refrigeration cycles for cooling fixed-temperature heat loads

Numerous mixed-gases refrigeration cycle configurations based on Joule-Thomson effects were developed in the past several decades. In this paper, comprehensive thermodynamic analyses were made on two typical cycle configurations to learn their performance for cooling fixed-temperature heat loads. One is the single-stage cycle without phase separators; the other is the auto-cascade refrigeration cycle which has at least one phase separator. An exergy model was developed to analyze the thermodynamic performance of those refrigeration cycles. Comprehensive comparisons were made on the performance of the recuperative throttling cycles using multicomponent mixture as refrigerant, including extensive simulations and optimizations of mixtures and cycle configurations. The results show that the auto-cascade cycle can improve thermodynamic performance in the case of using mixtures with increased fraction of high-boiling components, however, degrade the performance when using mixtures with increased fraction of low-boiling components. The results also show that the mixed refrigerant is the most important designing parameter in the design of such mixed-gases refrigeration system. Different cycle configuration has different optimal mixture composition. When using optimal mixtures, both cycles (separation and non-separation) can provide approximately equal performance.     

Crack kinking of a delamination at an inclined core junction interface in a sandwich beam

The paper considers a general interface delamination and crack kinking from an inclined core junction in a sandwich beam. This particular problem is relevant for a newly developed peel-stopper component for sandwich structures.A finite element model (FEM) was developed and calibrated against a known model by He and Hutchinson. The numerically and analytically determined solution coefficients were in a perfect agreement with each other, so the necessary generalisation of results can be obtained through the application of FEM-analyses. The FE-model was used to determine solution coefficients for a number of interface compositions of practical interest. As expected some of the coefficients were quite sensitive to the specific material combination, which confirms that accurate solution strategies are important.The solution coefficients obtained were further applied to the analysis of the crack propagation and kinking process in three different sandwich beam configurations, each of which contained an inclined junction of 20°, 30°, or 40°. The objective was to examine how the core junction angle and the fracture mechanical properties of the sandwich components influenced the crack kinking tendency. The latter is vital for the design and functionality of a newly developed peel-stopper. It was shown that smaller core junction angles will lead to longer crack propagation (delamination) along the core-core interface prior to a possible kinking. The physical insight obtained is essential for optimal design of peel-stoppers.     

Identification of the optimal product configuration and parameters based on individual customer requirements on performance and costs in one-of-a-kind production

One-of-a-kind production (OKP) aims at manufacturing products based on the requirements from individual customers while maintaining the high quality and efficiency of mass production. This research addresses the issues in identifying the optimal product configuration and its parameters based on individual customer requirements on performance and costs of products. In this work, variations of product configurations and parameters in an OKP product family are modeled by an AND-OR tree and parameters of the nodes in this tree. Different product configurations with different parameters are evaluated by performance and cost measures. These evaluation measures are converted into comparable customer satisfaction indices using the non-linear relations between the evaluation measures and the customer satisfaction indices. The optimal product configuration and its parameters with the maximum overall customer satisfaction index are identified by genetic programming and constrained optimization. A case study to identify the optimal configuration and its parameters of window products in an industrial company is used to demonstrate the effectiveness of the introduced approach.     

Supplier selection and order allocation in closed-loop supply chain systems using hybrid Monte Carlo simulation and goal programming

Supplier selection is an important strategic design decision in closed-loop supply chain systems. In addition, and after identifying the candidate suppliers, optimal order allocations are also considered as crucial tactical decisions. This research presents a multi-objective optimisation model to select the best suppliers and configure manufacturing and refurbishing facilities with the optimal number of parts and products in a closed-loop supply chain network. The objective functions in this research are formulated as total profit, total defective parts, total late delivered parts and economic risk factors of the candidate suppliers. The proposed multi-objective model is solved by hybrid Monte Carlo simulation integrated with three different variants of goal programming method. The effectiveness of the mathematical model and the proposed solution algorithms in obtaining Pareto-optimal solutions is demonstrated in a numerical example adopted from a real case study.     

A Neural Network Approach to Find The Cumulative Failure Distribution: Modeling and Experimental Evidence

The failure prediction of components plays an increasingly important role in manufacturing. In this context, new models are proposed to better face this problem, and, among them, artificial neural networks are emerging as effective. A first approach to these networks can be complex, but in this paper, we will show that even simple networks can approximate the cumulative failure distribution well. The neural network approach results are often better than those based on the most useful probability distribution in reliability, the Weibull. In this paper, the performances of multilayer feedforward basic networks with different network configurations are tested, changing different parameters (e.g., the number of nodes, the learning rate, and the momentum). We used a set of different failure data of components taken from the real world, and we analyzed the accuracy of the approximation of the different neural networks compared with the least squares method based on the Weibull distribution. The results show that the networks can satisfactorily approximate the cumulative failure distribution, very often better than the least squares method, particularly in cases with a small number of available failure times. Copyright © 2015 John Wiley & Sons, Ltd.     

Statistical definition of locking bandwidth in an array of synchronised microwave oscillators

Arrays of N weakly coupled oscillators are considered in different configurations. The locking bandwidth for these arrays is defined statistically. Various factors affecting the locking bandwidth and the effect of the coupling network topology on locking bandwidth are studied by solving the dynamic equations of the array numerically. The analytical locking bandwidth for two configurations of arrays is computed and the results are compared with the numerical solution of dynamic equations.