Polytechnic School of Algiers

Purpose This paper aims to perform a rigorous analytical evaluation of fully coreless axial-flux permanent magnet (AFPM) machines, targeting high-efficiency propulsion systems in unmanned vehicles. It focuses on the influence of permanent magnet geometries, winding topologies and current excitation types on key performance metrics. Design/methodology/approach A fully analytical framework is developed to evaluate 36 AFPM configurations. Using Coulombian charge and Biot–Savart laws, the magnetic field is modeled precisely. Back-electromotive force (EMF) and torque are derived under both sinusoidal and trapezoidal currents. Comparative analysis is performed based on total harmonic distortion (THD), torque ripple, efficiency and mass power density. Finally, the effectiveness and accuracy of the proposed approach are validated through 3D finite element simulations. Findings Performance is highly sensitive to the interplay between magnet shape, winding design and current waveform. Trapezoidal and curved rectangular windings, especially when paired with cylindrical or trapezoidal magnets and trapezoidal current, offer the best trade offs delivering high EMF, minimal THD, torque ripple reductions over 70% and efficiencies up to 97.5%. In contrast, triangular sector windings consistently yield the weakest performance. Originality/value This work introduces a fast, fully analytical tool for modeling and performance-driven optimization of coreless AFPM machines. It enables accurate, scalable evaluation of complex geometries and excitations without relying on computationally expensive numerical methods. The approach supports rapid design iteration and paves the way for next-generation high-speed, high-efficiency electric propulsion systems.

Ice accretion poses a threat to fixed-wing aerial vehicles as it alters the wings' shape and thus degrades the aerodynamic performance. In manned aircraft, the icing detection system assists the pilot and utilises dedicated sensors. However, in unmanned aerial vehicles (UAVs), onboard icing detection can generally only be achieved using standard sensors in conjunction with dynamical models, because dedicated sensors are rarely available. In this paper, we propose two approaches based on the particle filter for both icing detection and accurate state and aerodynamic parameter estimation in the presence of icing, with different levels of severity. The first approach uses the observation likelihood for icing hypothesis testing with a complement of the Gaussian kernel to compute icing probability. The second approach uses a discrete jump approach based on a Bernoulli process and a subset of particles to test the icing hypothesis for faster icing detection by estimating changes in icing-related aerodynamic parameters. Using both approaches, the simulation results demonstrate improved estimation accuracy compared to an extended Kalman filter (EKF), under both moderate and severe icing conditions. With adequate tuning, the proposed approaches show potential for indirect icing detection in UAVs. They also enable the computation of icing severity and provide a more accurate and reliable estimate of the icing probability compared to the EKF.

This paper proposes two main contributions to fractional-order modeling and control of biological systems that may exhibit chaotic behavior. First, a fractional-order chaotic model is designed to represent a biological enzyme using bifurcation diagrams and fractional orders tuning inspired by the available integer order model. This new approach improves the biological model by introducing physical properties specific to fractional order systems such as the memory effect, fractal properties, tissue heterogeneity and non-local behavior. Furthermore, this makes the use of a more effective, robust and powerful fractional-order control easier and more natural. The second main contribution is to propose a fractional-order sliding mode surface in order to derive a sliding mode control (SMC) controller that is able to stabilize this fractional-order biological system asymptotically. We successfully performed the stability analysis using the Lyapunov theory. Numerical simulations using MATLAB are given to demonstrate the efficiency of the proposed fractional-order controller with a drastic improvement in convergence time comparatively to the integer-order counterpart.

This article presents a novel design for a power-cooling system consisting of a combined organic Rankine cycle and an ejector-expansion refrigeration cycle (ORC–EERC) activated by low-grade thermal energy. The hydrocarbon R600 (butane) is used as the working fluid for both cycles. A thermodynamic analysis has been performed to investigate the performance, in particular the performance indicators (overall coefficient of performance (COP oval ) and working fluid mass flow rate per kW of cooling capacity (MkW)) of the proposed system by comparing it with the conventional (ORC–VCR) system widely used in the literature, which combines the ORC with the vapor compression refrigeration (VCR) cycle under various operating conditions (boiler exit temperatures ( T boil = 60 to 90 °C), condenser temperatures ( T cond = 30 to 55 °C) and evaporator temperatures ( T evap = −15 to 15 °C)). It was found that the ORC–EERC exhibited a higher COP oval and a lower MkW than the ORC–VCR. When the T boil reaches 90 °C and the other input parameters are at typical values, the COP oval of both systems (ORC–EERC and ORC–VCR) reaches 0.4984 and 0.4704, respectively, which represents an increase of 5.95%. On the other hand, the MkW of both systems reaches 0.0074 and 0.0084, respectively, which represents a decrease of 11.90%. Overall, the study shows that the ORC–EERC driven by low-grade thermal energy can be considered a promising system to replace the ORC–VCR.