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The purpose of this paper is to expose computational methods as applied to engineering systems and evolutionary processes with randomness in external actions and inherent parameters.

In total, two approaches are distinguished that rely on solvers from deterministic algorithms. Probabilistic analysis is referred to as the approximation of the response by a Taylor series expansion about the mean input. Alternatively, stochastic simulation implies random sampling of the input and statistical evaluation of the output.

Beyond the characterization of random response, methods of reliability assessment are discussed. Concepts of design improvement are presented. Optimization for robustness diminishes the sensitivity of the system to fluctuating parameters.

Deterministic algorithms available for the primary problem are utilized for stochastic analysis by statistical Monte Carlo sampling. The computational effort for the repeated solution of the primary problem depends on the variability of the system and is usually high. Alternatively, the analytic Taylor series expansion requires extension of the primary solver to the computation of derivatives of the response with respect to the random input. The method is restricted to the computation of output mean values and variances/covariances, with the effort determined by the amount of the random input. The results of the two methods are comparable within the domain of applicability.

The present account addresses the main issues related to the presence of randomness in engineering systems and processes. They comprise the analysis of stochastic systems, reliability, design improvement, optimization and robustness against randomness of the data. The analytical Taylor approach is contrasted to the statistical Monte Carlo sampling throughout. In both cases, algorithms known from the primary, deterministic problem are the starting point of stochastic treatment. The reader benefits from the comprehensive presentation of the matter in a concise manner.

This paper aims to develop a method to improve the accuracy of tolerance analysis considering the spatial distribution characteristics of part surface morphology (SDCPSM) and local surface deformations (LSD) of planar mating surfaces during the assembly process.

First, this paper proposes a skin modeling method considering SDCPSM based on Non-Gaussian random field. Second, based on the skin model shapes, an improved boundary element method is adopted to solve LSD of nonideal planar mating surfaces, and the progressive contact method is adopted to obtain relative positioning deviation of mating surfaces. Finally, the case study is given to verify the proposed approach.

Through the case study, the results show that different SDCPSM have different influences on tolerance analysis, and LSD have nonnegligible and different influence on tolerance analysis considering different SDCPSM. In addition, the LSD have a greater influence on translational deviation along the z-axis than rotational deviation around the x- and y-axes.

The surface morphology with different spatial distribution characteristics leads to different contact behavior of planar mating surfaces, especially when considering the LSD of mating surfaces during the assembly process, which will have further influence on tolerance analysis. To address the above problem, this paper proposes a tolerance analysis method with skin modeling considering SDCPSM and LSD of mating surfaces, which can help to improve the accuracy of tolerance analysis.

Secure communication is very important for computer networks. Thereby, authentication is one of the most eminent preconditions. In ad hoc networks, common authentication schemes are not applicable since public key infrastructures with a centralized certification authority are hard to deploy in ad hoc networking environments. This paper aims to investigate these issues.

In order to overcome these issues, the paper proposes and evaluates a security concept based on a distributed certification facility. Thereby, a network is divided into clusters with one special head node each. These cluster head nodes perform administrative functions and hold shares of a network key used for certification. New nodes start to participate in the network as guests; they can only become full members with a network‐signed certificate after their authenticity has been warranted by some other members. Access to resources and services within the ad hoc network is controlled using authorization certificates.

The feasibility of this concept was verified by simulations. Three different models for node mobility were used in order to include realistic scenarios as well as to make the results comparable to other work. The simulation results include an evaluation of the log‐on times, availability, and communication overhead.

The paper introduces a cluster‐based architecture to realize a distributed public key infrastructure that is highly adapted to the characteristics of ad hoc networks.

We propose a simplified method to simulate damage evolution in heterogeneous media from geodesic propagation calculations. The method introduced for the case of porous media (polycrystalline graphite), was generalized to multiphase media, and then to a continuous variation of local fracture energy. It is based on a minimization of the fracture energy criterion, ignoring the local variations of the stored strain energy. With this simplification, the microcracking process is simulated by very efficient algorithms, involving a low calculation cost, to extract minimal paths on graphs with edges valued according to the local fracture energy. From the simulations, made on micrographs in materials or on random microstructure simulations, we get images of the possible microcracks paths, to be compared with real cracking of materials, and an estimation of the effective toughness of heterogeneous materials. Our approach is illustrated from two‐dimensional simulations corresponding to various types of microstructure involving the following micro‐geometrical distributions of the local fracture energy: isotropic and anisotropic two‐phase media, polycrystal with cleavage and intergranular fracture, material with a continuous distribution of surface energy.

Availability of military systems is of major concern for military planners at both tactical (battle) level and at strategic level (long‐term national planning). Availability factors critically affect the operational effectiveness during military operations. Military systems are complex and lend themselves to simulation approach for availability estimation as analytical solutions are extremely difficult. The purpose of this paper is to discuss the method of systems modeling to approach the simulation for availability estimation of military systems.

Availability measures are needed for two main domains of application: peacetime operations and battlefield situations. Availability measures include not only inherent availability of interest to designers/manufacturers, but also operational availability and field/service availability. The simulation approach adopted here involves discrete event simulation (DES) techniques using Monte Carlo methods since a network of events can be included in the model. A system engineering approach is emphasized, starting with system representation and characterisation, and using system aggregation techniques.

Modeling involves hierarchical models and network diagrams for events. First the system is described by a hierarchical model; the events and transitions are represented with state transition diagrams (STD). The simulation scheme would be based on initial resources or inventory as military operations proceed, with random variates for event times or rates. The availability as a function of time A(t) is arrived at. The reliability and maintainability models are simulated with probability distributions or using empirical distributions. The methods of data collection and analysis, and sensitivity analysis are mentioned. The methodology is explained with two case studies from the authors' work. The approaches of other workers in recent years are summarised.

The paper shows that the simulation models can suitably be modified to include their applications for army and navy military operations. Also, with proper data on all major subsystems of interest for the weapon platform and accurate past war data, it is possible to fine‐tune the models for online use during military campaigns. The availability figures thus obtained may also be used for procurement decisions for long‐term and strategic planning.

The purpose of this paper is to propose a method with capability of short-time implementation.

This paper was directed using both experimental tests and simulations to propose a comprehensive method for lifetime estimation of the solder joints.

A new method with good agreement with experimental tests has been proposed.

It is confirmed that paper is original.

This paper explores the emergence and coordination of synchrony in networked groups like those that develop integrated product platforms in collaborative ecosystems. While synchronized actions are an important objective for many groups, interorganizational network theory has yet to explore synchrony in depth perhaps because it does not fit the typical diffusion models this research relies upon. By adding organizationally realistic features – sparse network structure and intentional coordination – to the firefly model from theoretical biology, I take some first steps in understanding synchrony in organizational groups. Like diffusion, synchrony is more effective in denser networks, but unlike diffusion clustering decelerates synchrony’s emergence. Coordination by a few group members accelerates group-wide synchrony, and benefits the coordinating organizations with a higher likelihood that it converges to the coordinating organization’s preferred rhythm. This likelihood of convergence to an organization’s preferred rhythm – what I term synchrony performance – increases in denser networks, but is not dependent on tie strength and clustering.

This paper aims to propose a framework for optimizing the pose in the assembly process of the non-ideal parts considering the manufacturing deviations and contact deformations. Furthermore, the accuracy of the method would be verified by comparing it with the other conventional methods for calculating the optimal assembly pose.

First, the surface morphology of the parts with manufacturing deviations would be modeled to obtain the skin model shapes that can characterize the specific geometric features of the part. The model can provide the basis for the subsequent contact deformation analysis. Second, the simulated non-nominal components are discretized into point cloud data, and the spatial position of the feature points is corrected. Furthermore, the evaluation index to measure the assembly quality has been established, which integrates the contact deformations and the spatial relationship of the non-nominal parts’ key feature points. Third, the improved particle swarm optimization (PSO) algorithm combined with the finite element method is applied to the process of solving the optimal pose of the assembly, and further deformation calculations are conducted based on interference detection. Finally, the feasibility of the optimal pose prediction method is verified by a case.

The proposed method has been well suited to solve the problem of the assembly process for the non-ideal parts with complex geometric deviations. It can obtain the reasonable assembly optimal pose considering the constraints of the surface morphological features and contact deformations. This paper has verified the effectiveness of the method with an example of the shaft-hole assembly.

The method proposed in this paper has been well suited to the problem of the assembly process for the non-ideal parts with complex geometric deviations. It can obtain the reasonable assembly optimal pose considering the constraints of the surface morphological features and contact deformations. This paper has verified the method with an example of the shaft-hole assembly.

The different surface morphology influenced by manufacturing deviations will lead to the various contact behaviors of the mating surfaces. The assembly problem for the components with complex geometry is usually accompanied by deformation due to the loading during the contact process, which may further affect the accuracy of the assembly. Traditional approaches often use worst-case methods such as tolerance offsets to analyze and optimize the assembly pose. In this paper, it is able to characterize the specific parts in detail by introducing the skin model shapes represented with the point cloud data. The dynamic changes in the parts' contact during the fitting process are also considered. Using the PSO method that takes into account the contact deformations improve the accuracy by 60.7% over the original method that uses geometric alignment alone. Moreover, it can optimize the range control of the contact to the maximum extent to prevent excessive deformations.

The primary purpose of this paper is to explore possible locations for parity cache within disk array model and describe the disk array model development process.

A dynamic, discrete event simulation model, based on modular, bottom‐up approach, is initiated from a single disk, and then extended on disk array.

Parity information within array model cannot be stored on individual disk cache, instead it should be stored in array cache. If model is used for simulation of single disk array then an approach with separate parity cache should be used, while an approach where a parity cache is part of array cache should be used for simulation of more complex storage systems with numerous arrays.

The proposed model does not include any read‐ahead cache policy, and only a full‐stripe writes can be performed. As a result, the model should be used only for sequential read or write of large files because then those limitations do not influence simulation output.

The novelty depicted in this paper is an approach, in array modelling, with parity cache merged into array cache. Also, the achieved bandwidth for the resulting simulation model differs from 1.05 to 1.7 per cent from the measured one on the experimental array.