Calculations of electromagnetic fields in longitudinal irregular TEM-cells

TEM-cells can be used as a standardized field generator for field probe calibration purposes or electromagnetic compatibility measurements. Because of its practical use as a measurement environment, the electromagnetic behavior over a broad range of frequencies is essential. However, without the understanding of wave reflection, mode-conversion, and attenuation, using such a measurement environment is impractical. In this contribution, we calculate the electromagnetic fields in a longitudinal irregular coaxial TEM-cell. Using a semi-analytical approach, we can determine these wave characteristics. The method is based on the projection of Maxwell's equations onto eigenfunctions. This work's primary objective is to examine the effect of irregular deformed boundaries on the electromagnetic field and the resonance frequencies.

Calibration model and numerical simulation of a 7-hole probe based on pressure–velocity parameterization equation

As a sensor of a flow field detection system, a 7-hole probe can detect the flow field velocity and retrieve three-dimensional (3D) information of the flow field. Owing to its simple structure and strong environmental adaptability, it is particularly important to calibrate it when it is widely used in turbine machinery, aerospace, and other fields. To detect the 3D flow field in the middle atmosphere, a novel calibration method based on the potential flow theory is designed using a hemispherical 7-hole probe. The hemispherical 7-hole probe was numerically calibrated through numerical simulation, and the coefficients of the calibration equation are provided. In comparison with the traditional 7-hole probe calibration method, the calibration process is significantly shortened while maintaining good measurement accuracy. The velocity error was less than 5% and the angle error was approximately 0.5°.

Artificial intelligence-based predictive model of nanoscale friction using experimental data

A recent systematic experimental characterisation of technological thin films, based on elaborated design of experiments as well as probe calibration and correction procedures, allowed for the first time the determination of nanoscale friction under the concurrent influence of several process parameters, comprising normal forces, sliding velocities, and temperature, thus providing an indication of the intricate correlations induced by their interactions and mutual effects. This created the preconditions to undertake in this work an effort to model friction in the nanometric domain with the goal of overcoming the limitations of currently available models in ascertaining the effects of the physicochemical processes and phenomena involved in nanoscale contacts. Due to the stochastic nature of nanoscale friction and the relatively sparse available experimental data, meta-modelling tools fail, however, at predicting the factual behaviour. Based on the acquired experimental data, data mining, incorporating various state-of-the-art machine learning (ML) numerical regression algorithms, is therefore used. The results of the numerical analyses are assessed on an unseen test dataset via a comparative statistical validation. It is therefore shown that the black box ML methods provide effective predictions of the studied correlations with rather good accuracy levels, but the intrinsic nature of such algorithms prevents their usage in most practical applications. Genetic programming-based artificial intelligence (AI) methods are consequently finally used. Despite the marked complexity of the analysed phenomena and the inherent dispersion of the measurements, the developed AI-based symbolic regression models allow attaining an excellent predictive performance with the respective prediction accuracy, depending on the sample type, between 72% and 91%, allowing also to attain an extremely simple functional description of the multidimensional dependence of nanoscale friction on the studied variable process parameters. An effective tool for nanoscale friction prediction, adaptive control purposes, and further scientific and technological nanotribological analyses is thus obtained.