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The Method of Moments (MoM) is a non-embedded uncertainty analysis method that has been widely used in Electromagnetic Compatibility (EMC) simulations in recent years due to its two major advantages of high computational efficiency and immunity from dimensional disaster. A random variable sensitivity calculation method based on the Complex Number Method of Moments (CNMoM) is proposed in this paper to improve the accuracy of the MoM in standard deviation prediction and thereby enhance the credibility of EMC simulation uncertainty analysis results. In the parallel cable crosstalk prediction example in the literature, the result of the Monte Carlo Method (MCM) is used as the standard, and the accuracy of the new method proposed in this paper is quantitatively verified using the Feature Selective Validation (FSV) method. Compared with the MoM, the proposed method can significantly improve the calculation accuracy of the standard deviation results without sacrificing simulation efficiency. [ABSTRACT FROM AUTHOR]

For the complex marine environment, the water flow disturbance causes the receiver offset, which leads to the decrease of mutual inductance and the decrease of system efficiency. This paper proposes an estimation method of dynamic coupling coefficient without communication and further realizes the maximum efficiency point tracking (MEPT) on the receiving side. By collecting the effective value of the fundamental current on the receiving side, the equivalent impedance mode equation of mutual inductance is established, and the mutual inductance is identified in real time by numerical solution method. On the basis of the identification results, the impedance matching is realized by the closed-loop controller designed on the receiving side, and the maximum efficiency point tracking of the system is realized. In this paper, the experimental platform is built, and the effectiveness of the method is verified by experiments. The experimental results show that the accuracy of mutual inductance estimation is more than 95%, and the efficiency of the system is improved by 18% after using the maximum efficiency point tracking. [ABSTRACT FROM AUTHOR]

The Stochastic Reduced-Order Models (SROMs) are a non-embedded uncertainty analysis method that has the advantages of high computational efficiency, easy implementation, and no dimensional disasters. Recently, it has been widely used in the field of EMC simulation. In the process of optimizing electromagnetic protection design, the worst-case estimation value is an extremely important uncertainty quantification simulation result. However, the SROMs have a large error in providing this result, which limits its application in the field of EMC simulation prediction. An improved SROM based on the Radial Basis Function (RBF) neural network algorithm is proposed in this paper, which improves the fitness function in the genetic algorithm center clustering process and constructs an RBF neural network model to obtain accurate worst-case estimation results. The accuracy improvement effect of the algorithm proposed in this paper in worst-case estimation is quantitatively verified by using a parallel cable crosstalk prediction example from published literature. [ABSTRACT FROM AUTHOR]

Surrogate models have been gradually promoted in electromagnetic compatibility (EMC) simulation in recent years, and two typical application scenarios are uncertainty analysis and electromagnetic optimization design. The surrogate model can simulate the forward EMC simulation process as accurately as possible with relatively few sampling points. The choice and number of sampling points will directly determine the accuracy of the surrogate model. The purpose investigated by uncertainty analysis and electromagnetic optimization design is different. How to choose appropriate sampling strategies is worth discussing, but there are very few studies in the field at this stage. This paper applies a cascaded cable crosstalk example to explore the accuracy of the surrogate model under different sampling strategies, which provides a theoretical level of suggestion for the application of the surrogate model in EMC simulation. The study enables the surrogate model to be better suited for two application scenarios: uncertainty analysis and electromagnetic optimization design. [ABSTRACT FROM AUTHOR]

The Method of Moments (MoM) is widely used in Electromagnetic Compatibility (EMC) uncertain simulation due to its advantages such as non-embedded simulation, high computational efficiency, and immunity from dimensional disasters. The theoretical research of the MoM has been relatively complete, but many of its key practical issues have not been fully discussed, which will result in the calculation accuracy in practical engineering applications falling short of theoretical expectations. With the help of the Feature Selective Validation (FSV) method, this paper analyzes and discusses two aspects. One is how to reasonably select the perturbation, and the other is the relationship between the uncertainty input size and the accuracy. By solving key practical issues of the MoM, the aim is to further promote it in the EMC field. [ABSTRACT FROM AUTHOR]

In recent years, uncertainty analysis has become a hot topic in the field of Electromagnetic Compatibility (EMC), and nonintrusive uncertainty analysis methods have been widely applied due to their advantage of obtaining results without modifying the original solver. Among them, the Surrogate Model Method has attracted widespread attention from researchers in the field of EMC due to its high computational efficiency and resistance to the curse of dimensionality. However, the issue of determining the convergence of the surrogate models seriously affects the computational efficiency and convenience of this method in practical applications. To address this issue, a convergence determination method for uncertainty analysis surrogate models based on Mean Equivalent Area Method (MEAM) is proposed in this paper. The complete convergence time of the Surrogate Model Method can be accurately determined through iterative calculation by this method, and the effectiveness of the proposed method is verified by calculating parallel cable crosstalk prediction examples from published literature. Finally, based on the proposed convergence determination method, the real-time convergence determination problem of the Surrogate Model Method is also preliminarily discussed in this paper, and by establishing a polynomial relationship, the real-time convergence of the Surrogate Model Method can be roughly determined. [ABSTRACT FROM AUTHOR]

With the advancement of the intelligent process, all kinds of electrical equipment are highly dense in space, and the impact of electromagnetic interference on high-precision electronic equipment cannot be ignored. Metal shielding box is one of the effective means to reduce electromagnetic interference. The heat dissipation holes on the surface of the shielded box are often used to maintain the normal operating temperature of the internal equipment, which will reduce the electromagnetic shielding effectiveness of the box. At the same time, due to the existence of capillary effect, condensation is very easy to occur at the hole gap, and the corrosion caused by it will further reduce the overall shielding effectiveness of the metal box. At present, there are few studies on the integrated prediction of "condensation-corrosion-shielding effectiveness" of metal boxes. Based on the commercial multi-physics simulation software COMSOL, this paper first simulates the condensation of a metal box in a high-humidity environment by constructing temperature, humidity, and moisture transport fields. Then, the current field and deformation field are constructed to predict the corrosion phenomenon at the gap of the metal box, and finally the electromagnetic field is constructed to predict the electromagnetic shielding efficiency of metal boxes at different frequencies. The joint multi-physics coupling simulation of condensation, corrosion, and electromagnetic shielding effectiveness phenomena is realized. [ABSTRACT FROM AUTHOR]

In recent years, uncertainty analysis is a hot topic in the field of electromagnetic compatibility simulation. The actual electromagnetic environment is simulated by considering the randomness of the model input parameters. However, there are currently two key issues that have not been resolved. One is the curse of dimensionality problem that occurs when there are many random variables. The other is how to establish a random input model with generality and portability. In order to address these issues, this paper proposes a new random input modeling method called random variable black box model. When applying to the Stochastic Collocation Method with dimensionality reduction sparse grid strategy, the applicability of this uncertainty analysis method can be extended to any probability density function, then enabling efficient electromagnetic compatibility simulation uncertainty analysis of high-dimensional random variable models and fundamentally solving the curse of dimensionality problem. Finally, this paper implements a joint simulation technology of the MATLAB software and COMSOL software to verify the strong portability of the random variable black box model, ensuring that advanced uncertainty analysis methods can be smoothly introduced into commercial electromagnetic simulation software and expanding the application scope of uncertainty analysis. [ABSTRACT FROM AUTHOR]