Digital twin (DT), primarily a virtual replica of any conceivable physical entity, is a highly transformative technology with profound implications. Whether it be a product development, design optimisation, performance improvement, or predictive maintenance, digital twins are changing the ways work is undertaken in various industries with multifarious business applications. Aerospace industry, including its manufacturing base, is one such keen adopter of digital twins with an unprecedented interest in their bespoke design, development, and implementation across wider operations and critical functions. This, however, comes with some misconceptions about the digital twin technology and lack of understanding with respect to its optimal implementation. For instance, equating digital twin to an intelligent model while ignoring the essential components of data acquisition and visualisation, misleads the creators into building digital shadow or digital models, instead of the actual digital twin. This paper unfolds such intricacies of digital twin technology for the aerospace community in particular and others in general so as to remove the fallacies that affect their effective realisation for safety-critical systems. It comprises a comprehensive survey of digital twins and their constituent elements. Elaborating their characteristic state-of-the-art composition along with corresponding limitations, three dimensions of the future digital twins for aerospace sector, termed as aero-Digital Twins (aero-DTs), are proposed as an outcome of this survey. These include the interactive, standardisation, and cognitive dimensions of digital twins, which if leveraged diligently could help the aero-DT research and development community quadruple the efficiency of existing and future aerospace systems as well as their associated processes.

Electric motors find widespread application across various industrial fields. The pursuit of enhanced comprehensive electric motors performance has consistently drawn significant attention, prompting extensive research in this domain. With the rapid and profound integration of artificial intelligence (AI) technology into diverse sectors, an increasing array of AI-based methodologies is being employed in electric motors research. This review aims to comprehensively delineate the applications of AI within the realm of electric motors, specifically focusing into three pivotal stages: electric motors design, drive control, and health maintenance, covering a full-cycle research and development of electric motors. The review commences by outlining the elucidation of the fundamental principles and components of AI. Subsequently, the encompassed input signals, characteristic methodologies, and AI techniques are reviewed and analyzed as demonstrated by pertinent research cases. Furthermore, a synthesis of the distinctive attributes characterizing various techniques is presented. Conclusively, this article offers an unprecedented review of AI-based technology throughout the entirety of the electric motors research cycle. Considering the existing body of knowledge, this article stands as the pioneering endeavor to encapsulate the expansive domain of AI's influence in electric motors research.

The proliferation of Power Electronic Converters (PECs) has had a pervasive affect in a variety of industries including the power generation, automotive and aerospace sectors, where their use brings reliability to the forefront, especially in applications where safety critical and harsh environments are experienced. Continuous improvements in the power density and efficiency through extensive research into new semiconductor technologies, passive components, circuit topologies and control methodologies has seen the performance of PECs improve indubitably. However, the manifestation of stresses occurring from significant heating due to high currents and switching frequencies; which over time can cause degradation in the performance of PEC components is still a real concern. This paper outlines a methodology for monitoring the degradation of PECs over the operational lifetime by utilizing a component level, physics based Digital Twin (DT). As well as providing a methodology for the real time comparison of parameters to realize the onset of faults subjected to operational stresses, the DT also provides a novel method of training a classifier by simulating the faults within the PEC, a process that in reality, would be difficult to achieve from a physical device. Feature extraction is via Wavelet Scattering and classification is provided using a Support Vector Machine (SVM) approach. The overall approach is one that is novel and expandable to a wide range of PEC topologies and will be beneficial to the optimization and maintenance of PECs in a variety of platforms.

This paper presents the use of physics of failure (PoF) methodology to infer fast and accurate lifetime predictions for power electronics at the printed circuit board (PCB) level in early design stages. It is shown that the ability to accurately model silicon–metal layers, semiconductor packaging, printed circuit boards (PCBs), and assemblies allows, for instance, the prediction of solder fatigue failure due to thermal, mechanical, and manufacturing conditions. The technique allows a life-cycle prognosis of the PCB, taking into account the environmental stresses it will encounter during the period of operation. Primarily, it involves converting an electronic computer aided design (eCAD) circuit layout into computational fluid dynamic (CFD) and finite element analysis (FEA) models with accurate geometries. From this, stressors, such as thermal cycling, mechanical shock, natural frequency, and harmonic and random vibrations, are applied to understand PCB degradation, and semiconductor and capacitor wear, and accordingly provide a method for high-fidelity power PCB modelling, which can be subsequently used to facilitate virtual testing and digital twinning for aircraft systems and sub-systems.

The detection of degradations and resulting failures in electronic components/systems is of paramount importance for complex industrial applications including nuclear power reactors, aerospace, automotive, and space applications. There is an increasing acceptance of the importance of detection of failures and degradations in electronic components and of the prospect of system-level health monitoring to make a key contribution to detecting and predicting any impending failures. This paper describes a parametric system identification-based health-monitoring method for detecting aging degradations of passive components in switch-mode power converters (SMPCs). A nonparametric system response is identified by perturbing the system with an optimized multitone sinusoidal signal of the order of mVs. The parametric system model is estimated from nonparametric system response using recursive weighted least-square (WLS) algorithm. Finally, the power-stage component values, including their parasitics, are extracted from numerator and denominator coefficients based on the assumed Laplace system model. These extracted component values provide direct diagnostic information of any degradation or anomalies in the components and the system. A proof of concept is initially verified on a simple point-of-load (POL) converter but the same methodology can be applied to other topologies of SMPC.

Financial costs and energy savings are considered to be more critical on average for computationally intensive workflows, as such workflows which generally require extended execution times, and thus, require efficient energy consumption and entail a high financial cost. Through the effective utilization of scheduled gaps, the total execution time in a workflow can be decreased by placing uncompleted tasks in the gaps through approximate computations. In the current research, a novel approach based on multi-objective optimization is utilized with CloudSim as the underlying simulator in order to evaluate the VM (virtual machine) allocation performance. In this study, we determine the energy consumption, CPU utilization, and number of executed instructions in each scheduling interval for complex VM scheduling solutions to improve the energy efficiency and reduce the execution time. Finally, based on the simulation results and analyses, all of the tested parameters are simulated and evaluated with a proper validation in CloudSim. Based on the results, multi-objective PSO (particle swarm optimization) optimization can achieve better and more efficient effects for different parameters than multi-objective GA (genetic algorithm) optimization can.