Field-to-Wire Coupling Model for the Common Mode in Random Bundles of Twisted-Wire Pairs

Abstract

magnetic modeling still represents a challenging problem as concerns prediction of crosstalk and field-to-wire coupling. Indeed, deterministic modeling based on the representation of specific and arbitrary cable geometries would be an exercise in futility, due to the high sensitivity of the induced noise to different random configurations of the bundle. Consequently, statistical models are more suited, since they are based on the description of model parameters and results in terms of random variables and their moments (e.g., mean, standard deviation, etc.) [1]-[3]. Though the complexity of the involved electromagnetic problem prevents analytical closed-form solutions, the numerical Monte Carlo method (that is, repeated-run simulations carried out on several random samples of the bundle) is a common viable approach. Particularly, repeated-run analysis requires two fundamental tools: 1) a method to generate a physically sound geometry of random-bundle samples; 2) a fast solution method, so to optimize the computational burden associated with several simulations [4]. These aspects are here treated with a specific Manuscript

Modeling electromagnetic wave coupling and mode conversion effects in multitwisted bundle of twisted‐wire pairs (MTB‐TWP) above ground plane

“…It is observed that good agreement is achieved between our model and FEKO results, although there are some deviations at low frequencies in the broad-side case. These deviations occurred because the currents are extremely small at very low frequencies, and they are very sensitive to any slight change of geometrical parameters of the MTB-TWP, with the similar phenomena shown in the previous studies, 8,33 respectively (Figure 8).…”

“…The extension of the equivalent cable bundle method for modeling crosstalk in complex cable bundles was done in the previous study . More recently, Pignari et al have carried out very effective studies on modeling field‐to‐wire coupling in bundles of TWP with different geometries, respectively. Because of the geometrical feature of TWP as well as TWP bundles, both analytical and numerical methods can be implemented for predicting their input‐output responses or crosstalk among them, while analytical or semianalytical approaches can save much time for simulating many TWPs in comparison with the commercial software FEKO…”

Worst-Case Wideband Radiated IEMI for Unshielded and Shielded Cables: A Statistical Analysis of the Main Influencing Parameters

“…Specifically, the first structure under analysis represents a harness composed of an arbitrary number of wires and interconnected to terminal units. Since radiated susceptibility is characterized by a dominant CM coupling mechanism [8], in Fig. 1(a) the multi-wire harness is reduced to a single equivalent wire describing the propagation of CM voltages/currents [11].…”

Perturbative Reformulation of the Stochastic Galerkin Method for Statistical Analysis of Wiring Structures with Several Random Parameters

“…Statistical techniques recently gained increasing attention from the Electromagnetic Compatibility (EMC) community, owing to the need to handle systems in which some parameters may randomly vary either due to manufacturing tolerances or due to uncertainty in the knowledge of their exact value. This is, for instance, the case of complex wiring harnesses, whose geometrical characteristics, such as precise position of wires in the bundle, are often hard to be controlled [1][2][3][4]. Since these random variations may significantly impact EMC performance, modeling and propagating the uncertainty associated with these parameters become of paramount importance in order to provide a statistical characterization (in terms of statistical moments) of relevant electrical quantities.…”