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Gives introductory remarks about chapter 1 of this group of 31 papers, from ISEF 1999 Proceedings, in the methodologies for field analysis, in the electromagnetic community. Observes that computer package implementation theory contributes to clarification. Discusses the areas covered by some of the papers ‐ such as artificial intelligence using fuzzy logic. Includes applications such as permanent magnets and looks at eddy current problems. States the finite element method is currently the most popular method used for field computation. Closes by pointing out the amalgam of topics.

To present a new parallel method for solving differential equations that describe transient states in physical systems.

The proposed speculative method first solves a differential equation with a large integration step to determine initial data for parallel computations in sub‐intervals of time, then speculatively computes in parallel solutions in all the sub‐intervals with a smaller integration step and finally composes the final solution from the speculatively computed ones. The basic numerical method applied is the well‐known Runge‐Kutta algorithm.

The speculative method allows important reduction of the computation time of sequential algorithms. The speed‐up of the speculative method that we propose, as compared to the sequential execution, depends on the number of sub‐intervals that are defined inside the total analysed time interval. The speed‐up increases almost linearly with the number of sub‐intervals. The good accuracy of computations in the presented example was obtained.

The proposed method can be applied to non‐linear systems without discontinuity points and to stable systems (i.e. systems insensitive to the selection of initial conditions).

The method can be especially applied for long‐lasting computations with a slow convergence of state variables values along with the decrease of integration steps.

The paper presents an original parallel method for solving differential equations, which significantly speeds up transient states analysis in physical systems.

For part models with complex shape features or freeform shapes, the existing build orientation determination methods may have issues, such as difficulty in defining features and costly computation. To deal with these issues, this paper aims to introduce a new statistical method to develop fast automatic decision support tools for additive manufacturing build orientation determination.

The proposed method applies a non-supervised machine learning method, K-Means Clustering with Davies–Bouldin Criterion cluster measuring, to rapidly decompose a surface model into facet clusters and efficiently generate a set of meaningful alternative build orientations. To evaluate alternative build orientations at a generic level, a statistical approach is defined.

A group of illustrative examples and comparative case studies are presented in the paper for method validation. The proposed method can help production engineers solve decision problems related to identifying an optimal build orientation for complex and freeform CAD models, especially models from the medical and aerospace application domains with much efficiency.

The proposed method avoids the limitations of traditional feature-based methods and pure computation-based methods. It provides engineers a new efficient decision-making tool to rapidly determine the optimal build orientation for complex and freeform CAD models.

The purpose of this paper is the fast and accurate modelling of surface-mounted Axial-Flux Permanent-Magnet (AFPM) machines equipped with cylindrical magnets using quasi-3D approach. Furthermore, the accuracy of the method is improved by using leakage coefficient, saturation coefficient and an appropriate permeance function.

Quasi-3D approach is used for fast and accurate modelling of AFPM machines. Air-gap flux density distribution, induced back EMF, and produced cogging torque are calculated using the proposed method with reasonable accuracy.

The results obtained by quasi-3D approach compared to Finite-Element-Analyses (FEA) shows how accurate, fast and efficient this method is. It is proved that, this method can be successfully applied to evaluate the performance of the AFPM machines.

Effectiveness and accuracy of quasi-3D approach is assessed on different AFPM machines. Furthermore, to increase the accuracy of computations, the effects of the magnetic potential drop at iron parts of the machine are taken into account by using a saturation coefficient. Besides, the influence of the slot opening on the flux density distribution is taken into account by using an appropriate relative permeance function.

A fundamental disadvantage of three‐dimensional finite element (FE) simulations is high computational cost when compared to two‐dimensional models. The purpose of this paper is to present an approach to minimize the computation time by achieving the same simulation accuracy.

The applied approach for avoiding high computational cost is the multi‐slice method. This paper presents the adoption of this method to a tubular linear motor.

It is demonstrated that the multi‐slice method is applicable for tubular linear motors. Furthermore, the number of slices and thereby computation time is minimized at the same accuracy of the simulation results.

The results of this paper offer a faster computation of skewed linear motors. At this juncture, the results are independent from the deployed FE solver.

The methods developed and proved permit a faster and more accurate design of tubular linear motors.

One of the main issues which microelectronics industry encounter is reliability as feature sizes scale down to nano-design level. The purpose of this paper is to provide a probabilistic transfer matrix based to find the accurate and efficient method of finding circuit’s reliability.

The proposed method provides a probabilistic description of faulty behavior and is well-suited to reliability and error susceptibility calculations. The proposed method offers accurate circuit reliability calculations in the presence of reconvergent fanout. Furthermore, a binary probability matrix is used to not only resolve signals correlation problem but also improve the accuracy of the obtained reliability in the presence of reconverging signals.

The results provide the accuracy and computation time of reliability evaluation for ISCAS85 benchmark schemes. Also, simulations have been conducted on some digital circuits involving LGSynth’91 circuits. Simulation results show that proposed solution is a fast method with less complexity and gives an accurate reliability value in comparison with other methods.

The proposed method is the only scheme giving the low calculation time with high accuracy compared to other schemes. The library-based method also is able to evaluate the reliability of every scheme independent from its circuit topology. The comparison exhibits that a designer can save its evaluation time in terms of performance and complexity.

The purpose of this paper is to present the benefits of using the Lagrangian relaxation (LR) and subgradient methods in scenario studies for wavelength division multiplexing (WDM) network planning. The problem of WDM network planning for a given set of lightpath demands in a mesh topology network is to select lightpath routes and then allocate wavelength channels to the lightpaths. In WDM network planning, a scenario study is to find out the network performance under different lightpath demands and/or different network resource configurations.

A scenario study must solve a series of related static WDM network planning problems. Each static WDM network planning problem is an optimization problem, and may be formulated as an integer linear programming problem, which can be solved by the proposed Lagrangian relaxation and subgradient methods. This paper uses the Lagrange multipliers that are obtained from previous scenarios as initial Lagrange multiplier values for other related scenarios.

This approach dramatically reduces the computation time for related scenarios. For small to medium variations of scenarios, the method reduces the computation time by several folds. The proposed method is the first method that effectively considers the relations between related scenarios, and uses such relations to improve the computation efficiency of scenario studies in WDM network planning.

The method improves the efficiency of a scenario study in WDM network planning. By using it, many “what‐if” type of scenario study questions can be answered quickly.

Unlike other existing methods that treat each scenario individually, this method effectively uses the information of the relation between different scenarios to improve the overall computation efficiency.

This paper aims to develop an efficient numerical method for nonlinear transient heat conduction problems with local radiation boundary conditions and nonlinear heat sources.

Based on the physical characteristic of the transient heat conduction and the distribution characteristic of the Green’s function, a quasi-superposition principle is presented for the transient heat conduction problems with local nonlinearities. Then, an efficient method is developed, which indicates that the solution of the original nonlinear problem can be derived by solving some nonlinear problems with small structures and a linear problem with the original structure. These problems are independent of each other and can be solved simultaneously by the parallel computing technique.

Within a small time step, the nonlinear thermal loads can only induce significant temperature responses of the regions near the positions of the nonlinear thermal loads, whereas the temperature responses of the remaining regions are very close to zero. According to the above physical characteristic, the original nonlinear problem can be transformed into some nonlinear problems with small structures and a linear problem with the original structure.

An efficient and accurate numerical method is presented for transient heat conduction problems with local nonlinearities, and some numerical examples demonstrate the high efficiency and accuracy of the proposed method.

The paper proposed a fast procedure for solving the reliability-based robust design optimization (RBRDO) by modifying the RBRDO formulation and transforming it into a series of RBRDO subproblems. Then for each subproblem, the objective function, constraint function and reliability index are approximated using Taylor series expansion, and their approximate forms depend on the deterministic design vector rather than the random vector and the uncertain estimation in the inner loop of RBRDO can be avoided. In this way, it can greatly reduce the evaluation number of performance function. Lastly, the trust region method is used to manage the above sequential RBRDO subproblems for convergence.

As is known, RBRDO is nested optimization, where the outer loop updates the design vector and the inner loop estimate the uncertainties. When solving the RBRDO, a large evaluation number of performance functions are needed. Aiming at this issue, the paper proposed a fast integrated procedure for solving the RBRDO by reducing the evaluation number for the performance functions. First, it transforms the original RBRDO problem into a series of RBRDO subproblems. In each subproblem, the objective function, constraint function and reliability index caused are approximated using simple explicit functions that solely depend on the deterministic design vector rather than the random vector. In this way, the need for extensive sampling simulation in the inner loop is greatly reduced. As a result, the evaluation number for performance functions is significantly reduced, leading to a substantial reduction in computation cost. The trust region method is then employed to handle the sequential RBRDO subproblems, ensuring convergence to the optimal solutions. Finally, the engineering test and the application are presented to illustrate the effectiveness and efficiency of the proposed methods.

The paper proposes a fast procedure of solving the RBRDO can greatly reduce the evaluation number of performance function within the RBRDO and the computation cost can be saved greatly, which makes it suitable for engineering applications.

The standard deviation of the original objective function of the RBRDO is replaced by the mean and the reliability index of the original objective function, which are further approximated by using Taylor series expansion and their approximate forms depend on the deterministic design vector rather than the random vector. Moreover, the constraint functions are also approximated by using Taylor series expansion. In this way, the uncertainty estimation of the performance functions (i.e. the mean of the objective function, the constraint functions) and the reliability index of the objective function are avoided within the inner loop of the RBRDO.

Accurate numerical differentiation of approximate data by methods based on Green's second identity often involves singular or nearly singular integrals over domains or their boundaries. This paper applies the finite part integration concept to evaluate such integrals and to generate suitable quadrature formulae. The weak singularity involved in first derivatives is removable; the strong singularities encountered in computing higher derivatives can be reduced. To find derivatives on or near the edge of the integration region, special treatment of boundary integrals is required. Values of normal derivative at points on the edge are obtainable by the method described. Example results are given for derivatives of analytically known functions, as well as results from finite element analysis.