Michele Ferlauto

Rapid removal of ammonium bisulfate (ABS) deposits requires both high-efficiency electromagnetic heating and stable thermal regulation under continuously varying operating conditions. To address this challenge, this paper proposes a resonant ablation equipment (RAE) integrating electromagnetic-thermal coupling analysis, load configuration optimization, and dynamic resonance control. A discrete electromagnetic–thermal model is first established to reveal the relationship between the magnetic energy distribution and heat generation, on which a multi-objective optimization strategy is subsequently developed to determine the optimal annular load configuration. Furthermore, a parameter-dynamic controller that combines dynamic resonance tracking (DRT) and automatic temperature control (ATC) is proposed to maintain resonance and suppress thermal inertia during rapid heating. Both theoretical and experimental results demonstrate that optimized annular loads consistently outperform conventional disk loads and that different optimization objectives yield distinct optimal annular configurations. The fastest-heating configuration achieves an average heating rate of 3.878 °C/s, corresponding to 4.02 times that of a conventional disk load, while the efficiencies of all optimized annular configurations are significantly higher than that of the conventional disk load. Moreover, the proposed controller reduces the thermal response delay by approximately 10 s while maintaining stable zero-voltage switching and resonant operation. The proposed framework establishes a complete model-driven methodology for electromagnetic heating systems and offers a novel solution for rapid, controllable removal of ABS deposits.

Flow control has assumed a key role in many applied aspects of fluid dynamics and propulsion in efforts toward CO₂-neutral growth in air, ground and maritime transportation

Non-contact printed circuit board (PCB) fault diagnosis has been widely applied in PCB detection and maintenance. Due to objective factors such as visual blind spots, low-loss circuit structure design, and frequency insensitivity, traditional algorithms based on visual and temperature features are limited in practice. Therefore, the algorithm based on electromagnetic features containing rich physical connotations and prominent frequency features has received attention in PCB fault diagnosis. Based on the basic principles of electromagnetic physics and PCB fault relationship, this article proposes a scalar magnetic field source feature and further improves feature performance by adding topological relationships of multi-faults to generate the fusion feature. The backbone of the PCB diagnosis model is established on the Transformer architecture, effectively utilizing self-attention and parallel computing mechanisms to explore the inner correlation between each group feature. The paper provides a new non-contact PCB fault diagnosis solution that enriches existing methods. Besides, through actual experiments setting up multi-fault PCBs, the feasibility of our process is proved based on the proposed features and models. The specific multiple indicators Overall Precision (OP), Per Class Precision (CP), Overall Recall (OR), Per Class Recall (CR), Overall F1 Measure (OF1), Per Class F1 Measure (CF1), Accuracy (ACC), Mean Average Precision (mAP) are 98.55%, 94.89%, 98.55%, 95.11%, 98.55%, 95.32%, 96.01%, and 97.27%.

The increasing electrification and integration of advanced controls in modern aircraft designs have significantly raised the number and complexity of installed printed circuit boards (PCBs), posing new challenges for efficient maintenance and rapid failure detection. Despite self-diagnostic features in current avionics systems, circuit damage and multiple simultaneous failures may arise, compromising safety and diagnostic accuracy. To address these challenges, this paper aims to develop a fast, accurate, and non-destructive, multi-failure diagnosis algorithm for PCBs. The proposed method combines a self-attention mechanism with an adaptive graph convolutional neural network to enhance diagnostic precision. A convolutional neural network with residual connections extracts features from scalar magnetic field data, ensuring robust input diversity. The model was tested on a typical dual-phase amplitude boosting circuit with up to four different simultaneous failures, achieving the experimental results of 99.08%, 98.50%, 98.78%, 98.01%, 98.93%, 98.25%, 97.03%, and 99.77% across metrics including overall precision, per-class precision, overall recall, per-class recall, overall F1 measure, and per-class F1 measure. The results demonstrated its effectiveness and feasibility in diagnosing complex PCBs with multiple failures, indicating the algorithm’s potential to improve failure diagnosis performance and offer a promising PCB diagnosis solution in aerospace applications.

The work focuses on the characterization of the flowfield over a truncated linear aerospike by combining theoretical grounds, numerical simulations and experimental tests. The experimental investigations are carried out on a test rig designed at Politecnico di Torino for advanced nozzle testing. Fully three-dimensional CFD analyses are performed on the actual geometry of the experimental nozzle model. At low nozzle pressure ratios (nprs) the analysis combines numerical simulations and experimental testing, which are also used for validating the CFD results. At higher nprs, the flowfield characterization is performed only by three-dimensional CFD analyses. In addition to the validation of the numerical method, the edge effects at different nprs have been observed.

Compressors are an essential component of aircraft engines. Their design and operation must be extremely reliable as engine safety and performance depend greatly on these elements. Axial compressors exhibit instabilities, such as surge or rotating stall, in a region close to the peak of their performance curves. These fluid dynamic instabilities can cause drops in efficiency, stress on the blades, fatigue, and even failures. Compressors are handled therefore by operating with a safety margin far from the surge line. Moreover, models able to predict onset instabilities and to reproduce them are of great interest. A dynamic system able to describe successfully both surge and rotating stall is the model presented by Moore and Greitzer That model has also been used for developing control laws of the compressor dynamics. The present work aims at developing an artificial neural network (ANN) approach able to predict either the permanence of the system in stable working condition or the onset instabilities from a time sequence of the compressor dynamics. Different solutions were tried to find the most suitable model for identifying the system, as well as the effects of the duration of the time sequence on the accuracy of the predicted compressor working conditions. The network was further tried for sequences with different initial values in order to perform a system analysis that included multiple variations from the initial database. The results show how it is possible to identify with high accuracy both rotating stall and surge with the ANN approach. Moreover, the presence of an underlying fluid dynamic model shares some similarities with physically informed AI procedures.

Vortices belong to the most important phenomena in fluid dynamics and play an essential role in many engineering applications. They can act detrimentally by harnessing the flow energy and reducing the efficiency of an aerodynamic device, whereas in other cases, their presence can be exploited to achieve targeted flow conditions. The control of the vortex parameters is desirable in both cases. In this paper, we introduce an optimization strategy for the control of vortices in the wake of a bluff body. Flow modelling is based on RANS and DES computations, validated by experimental data. The algorithm for vortex identification and characterization is based on the triple decomposition of motion. It produces a quantitative measure of vortex strength which is used to define the objective function in the optimization procedure. It is shown how the shape of an aerodynamic device can be altered to achieve the desired characteristics of vortices in its wake. The studied case is closely related to flame holders for combustion applications, but the conceptual approach has a general applicability to vortex control.

Centrifugal pumps have a wide range of applications in the aviation field. The present work focuses on the optimal design of aviation fuel pump impellers by means of an inverse method. The fuel pump impeller is designed here by solving an inverse problem, in which the impeller geometry is found by imposing a target blade loading. As the inverse procedure is inviscid, an iterative process based on RANS is then applied to finally converge to a fully viscous solution. Three representative loading distributions have been investigated, and the final performances are evaluated by RANS computations. Since flow variables, rather than the blade geometry, are imposed on the target flow field, it is found that the impellers designed by way of the inverse method have high efficiency under the conditions without cavitation; among them, the pump impeller with a higher loading at the hub maintains a high efficiency for a wide range of flow conditions and also has better anti-cavitation performances under low inlet pressure conditions. Moreover, cavitation resistance can be improved by adjusting the loading distribution near the blade leading edge using the inverse design tool.

View Video Presentation: https://doi.org/10.2514/6.2023-0714.vid A numerical study on the characteristics of hot flows in a Thrust Optimized Parabolic (TOP) nozzle is conducted. the effects of high temperature on the nozzle performance will be investigated for the Vulcain nozzle in over-expanded conditions. Particular attention is given to the Restricted Shock Separation (RSS) condition, in which recirculation and supersonic annular jets inside the nozzle can be observed. The influence of temperature and gas composition are accounted for through their effect on the thermophysical properties of the mixture, and finally through fully unsteady simulations of a chemically reacting mixture. The obtained results highlight the importance of the modeling approach for the prediction of the separation point. The value of the thrust coefficient has non-negligible variations when considering the least and most accurate models, however it may not be needed to employ a combustion model in order to obtain sufficiently accurate predictions of the thrust coefficient.