Riccardo Trinchero

This paper compares the assessment of freight train running safety according to two European rules, namely the UIC IRS 40421 and the EN 14363, under emergency braking operations. The paper relies on a combination of longitudinal train dynamics (LTD) simulations for the evaluation of the in-train forces and multibody (MB) simulations for the calculation of the running safety indexes derived from the wheel-rail contact forces. The results confirm that the IRS 40421 is more conservative when assessing the safety of families of freight trains with a statistical perspective. Nonetheless, in individual operating scenarios, the IRS 40421 can predict safe conditions when MB simulations calculate running safety indexes above the limits prescribed by the EN 14363. To improve the running safety assessment from the outputs of LTD simulations, the paper suggests the introduction of kernel regressions, acting as digital twins of MB simulations, for the prediction of the running safety indexes. Digital twins improve the consistency with the classification of derailment conditions based on the outputs of the MB simulations. The findings highlight the complementary value of the two rules and the potential of digital twins to support the reliable assessment of freight train running safety.

This letter introduces a mask-based surrogate modeling framework that generalizes the feature-based paradigm for efficient microwave design exploration using a limited number of full-wave simulations. Unlike feature-based approaches, which extract scalar performance metrics, the proposed method reconstructs the complete $S$ -parameter boundary contour within the operating band. This preserves each design’s parametric signature and enables on-demand evaluation of multiple performance specifications without model retraining. The framework employs two vector-valued kernel ridge regression models to predict boundary coordinates from geometric parameters. Validation on a three-port circulator demonstrates accurate reconstruction and rapid exploration of thousands of candidate designs.

This paper presents a method for generating accurate and efficient macromodels of high-speed I/O buffers. Extending existing techniques, the proposed approach enables a modular and scalable model generation tool based on machine-learning. Given the limitations of traditional methods, this work leverages kernel regression to develop SPICE-compliant models. Two compression schemes, random selection and Nyström approximation are used and thoroughly compared to reduce the number of expansion terms, with beneficial effects in terms of compactness of the SPICE implementation. The effectiveness of the method in terms of model accuracy and efficiency is stressed through real devices and typical signal and power integrity co-simulations.

Large-scale pipeline networks may be subject to wide-area and spatially nonuniform external electromagnetic interference. This paper presents a generalized and efficient algorithm for the calculation of inductive and conductive coupling to pipeline networks based on the two-port equivalent circuit. In the modified transmission line model of the pipeline, the external inteference are characterized by using only longitudinal voltage sources. In addition, the nonlinear polarization of coating breakdown is incorporated into the circuit model of the network. The linear pipe segments can be independently reduced to the equivalent-pi circuit with analytical parameters, which enables model order reduction of the network. Such techniques can be applied to arbitrary nonuniform excitation fields. Several test cases are used to illustrate the response characteristics of pipelines excited by interference with different spatial patterns, including geomagnetic disturbances and HVDC earth return currents.

As low-cost Internet-of-Thing-based PD monitors for medium-voltage apparatuses in distribution power systems increase, developing an effective PD de-noising algorithm is crucial to improve their robustness to on-site noise. Yet, de-noising PD signals in the monitoring devices is challenging primarily due to three critical reasons, i.e., high-level field noises, uncertain PD waveform, and limited computing resources. This work describes an adaptive and efficient PD de-noising algorithm based on the improved spectral decomposition of the noisy PD signal. PD pulses are accurately extracted from the noisy signal by selecting the dominant components via a low-rank singular value decomposition of the time-frequency spectrogram of the signal, thus reducing the size of the involved matrices and the computational complexity. The performance of the proposed de-noising algorithm is first demonstrated on a synthetic PD signal and compared with state-of-the-art alternatives implemented on three embedded systems commonly used for PD monitoring. Finally, the strength and the effectiveness of the proposed approach are further validated on experimental data based on the measurement of Internet-of-Thing-based PD monitors for 35-kV switchgears.

In this paper, an accurate modeling of on-chip copper interconnects with surface roughness is performed considering the parametric variability. This modeling is highly accurate as per-unit-length parameters of the on-chip rough copper interconnects are extracted via full wave EM solver. Further, a space-mapped artificial neural network (ANN) is developed for accurate prediction of eye height and eye width from the geometrical and material parameters of the rough copper interconnects. The novel space-mapping ANN developed in this work is more efficient in terms of accuracy and requires fewer training samples when compared to conventional ANNs.

This article proposes a method to analyze the coupling of overhead lines excited by indirect lightning above the dispersive soil, considering the change of soil water percentage. The whole process is achieved through analytical equations. First, the two-dimensional finite-difference time-domain method with convolution perfectly matched layer boundary based on the Debye representation of frequency-dependent ground is used to calculate the lightning electromagnetic field. In the frequency domain, the vector fitting method is introduced to deal with the frequency-dependent part of the governing equations. The recursive convolution is then combined with the integration by parts to process the convolution part of the governing equations of overhead lines in the time domain, enhancing the robustness of the solution. The resulting iterative equation is in turn derived, allowing to solve the problem at hand, for line loads defined by either R–C parallel or R–L series loads. The results highlight that, with respect to purely resistive loads, inductive loads increase the coupling voltage and reduce the coupling current, whereas capacitive loads do the opposite. In addition, the coupling value is verified to be more sensitive to changes in water percentage at low soil water quantity.