This article introduces an affine registration (AR) algorithm to improve the performance of ultra-local (UL) model-free predictive control (MFPC) methods. The UL model is extensively utilized in continuous control set (CCS)-MFPC to estimate lumped parameters. However, in these cases, variations in inductance during operation can significantly impact the input gain coefficient of the UL model. Little work has focused on this aspect, which makes the motivation and novelty of this paper. The AR technique, derived from image processing methodologies, is employed to establish a mapping between predicted states and actual states during the commissioning process. Subsequently, during actual operation, the resolved voltage reference is modified based on the AR matrix to compensate for errors in the UL model. The theoretical control model is rigorously derived, and experimental results are presented to validate the superior performance of the proposed method.

Under high-altitude, low-Reynolds-number conditions, flow instability in confined dual-fan configurations severely limits the propulsion and thermal management efficiency of heavier-than-air aircraft. This study establishes a high-fidelity 3D transient numerical model using curvature-corrected shear stress transport (SST) turbulence modeling, integrated with proper orthogonal decomposition (POD), dynamic mode decomposition (DMD), and nonlinear stability analysis to investigate rotational direction control mechanisms. Results indicate that co-rotating configurations trigger intense low-frequency pulsations and significant flow skewness due to wall-adhesion effects. Conversely, the counter-rotating layout reconstructs vortex topology by forming a strong interaction shear layer, which enhances local momentum exchange and suppresses large-scale coherent structures. While counter-rotation exhibits a higher initial growth rate, its significantly enhanced nonlinear aerodynamic damping forces the flow into a low-amplitude quasi-steady state, reducing inlet non-uniformity by 74% and increasing mass flow by 5.19%. These findings clarify the physical mechanisms of vortex interference in regulating stability and provide critical design insights for optimizing compact propulsion systems in heavier-than-air high-altitude platforms, such as long-endurance UAVs.

In modern industrial systems, twin-shaft mixers are key units for efficient mixing and reactions; their performance directly affects product quality, production cycle, and energy consumption across the chemical, pharmaceutical, food, and lithium-battery-slurry sectors. Systematic elucidation of the mixing mechanisms is hindered by strongly three-dimensional, unsteady, and nonlinear flow fields induced by the complex motions of the two shafts. To address these issues, an advanced coupled numerical model combining the lattice Boltzmann method (LBM) and large-eddy simulation (LES) in an integrated LBM–LES framework is developed, incorporating the Smagorinsky subgrid-scale model to capture small-scale turbulent dissipation under high-Reynolds-number conditions with fidelity. The model enables systematic simulations across configurations with varying blade counts, quantitatively revealing how blade count governs flow structures and mixing performance. The results show that blade count is a key design parameter for performance tuning. A four-blade configuration generates moderately strong, well-distributed turbulence and vortical structures in both the main-shaft and side-shaft regions. The generated turbulence and vortical structures, in turn, promote effective global blending and mass transfer while avoiding localized energy over concentration, unnecessary power loss, and overheating risk, thereby achieving an optimal balance among mixing efficiency, energy consumption, and operational stability. These findings provide a solid theoretical basis and a reliable numerical paradigm for the refined design and performance optimization of industrial mixing equipment.