Search Results (182)

This study aims to investigate the effects of phase synchronisation on tonal noise reduction in a multi-rotor UAV using an electronic phase-locking system. Experiments at the University of Bristol explored the impact of relative phase angle, propeller spacing, and blade geometry on acoustic performance, including psychoacoustic annoyance. Results show that increasing the phase angle consistently reduces the sound pressure level (SPL) due to destructive interference. For the two-bladed configuration, the highest noise reduction occurred at relative phase angle $\Delta \psi ={90}^{\circ }$, with a 19 dB decrease at the first blade-passing frequency (BPF). Propeller spacing had minimal impact when phase synchronisation was applied. The pitch-to-diameter ($P/D$) ratio also influenced results: for $P/D=0.55$, reductions ranged from 13–18 dB; and for $P/D=1.0$, reductions ranged from 10–20 dB. Maximum psychoacoustic annoyance was observed when propellers were in phase ($\Delta \psi =0^{\circ }$), while annoyance decreased with increasing phase angle, confirming the effectiveness of phase control for noise mitigation. For the five-bladed configuration, the highest reduction of 15 dB occurred at $\Delta \psi ={36}^{\circ }$, with annoyance levels also decreasing with phase offset. Full article

The general framework of Entropic Dynamics (ED) is used to construct non-relativistic models of relational Quantum Mechanics from well-known inference principles—probability, entropy and information geometry. Although only partially relational—the absolute structures of simultaneity and Euclidean geometry are still retained—these models provide a useful testing ground for ideas that will prove useful in the context of more realistic relativistic theories. The fact that in ED the positions of particles have definite values, just as in classical mechanics, has allowed us to adapt to the quantum case some intuitions from Barbour and Bertotti’s classical framework. Here, however, we propose a new measure of the mismatch between successive states that is adapted to the information metric and the symplectic structures of the quantum phase space. We make explicit that ED is temporally relational and we construct non-relativistic quantum models that are spatially relational with respect to rigid translations and rotations. The ED approach settles the longstanding question of what form the constraints of a classical theory should take after quantization: the quantum constraints that express relationality are to be imposed on expectation values. To highlight the potential impact of these developments, the non-relativistic quantum model is parametrized into a generally covariant form and we show that the ED approach evades the analogue of what in quantum gravity has been called the problem of time. Full article

Millimeter-wave frequencies are crucial for meeting the high-capacity, low-latency demands of 5G communication systems, thereby driving the need for compact, high-gain antenna arrays capable of efficient beamforming. This paper presents the design, simulation, fabrication, and experimental validation of a compact, high-efficiency 1 × 6 linear series-fed microstrip patch antenna array for 5G millimeter-wave communication operating at 28 GHz. The proposed antenna is fabricated on a low-loss Rogers RO3003 substrate and incorporates an integrated symmetric two-way microstrip power divider to ensure balanced feeding and phase uniformity across elements. The antenna achieves a simulated peak gain of 11.5 dBi and a broad simulated impedance bandwidth of 30.21%, with measured results confirming strong impedance matching and a return loss better than −20 dB. The far-field radiation patterns demonstrate a narrow, highly directive beam in the E-plane, and the H-plane results reveal beam tilting behavior, validating the antenna’s capability for passive beam steering through feedline geometry and element spacing (~0.5λ). Surface current distribution analysis confirms uniform excitation and efficient radiation, further validating the design’s stability. The fabricated prototype shows excellent agreement with the simulation, with minor discrepancies attributed to fabrication tolerances. These results establish the proposed antenna as a promising candidate for applications requiring compact, high-gain, and beam-steerable solutions, such as 5G mm-wave wireless communication systems, point-to-point wireless backhaul, and automotive radar sensing. Full article

Multi-modal remote sensing image (MRSI) matching suffers from severe nonlinear radiometric distortions and geometric deformations, and conventional feature-based techniques are generally ineffective. This study proposes a novel and robust MRSI matching method using the side window filter (MSWF). First, a novel side window scale space is constructed based on the side window filter (SWF), which can preserve shared image contours and facilitate the extraction of feature points within this newly defined scale space. Second, noise thresholds in phase congruency (PC) computation are adaptively refined with the Weibull distribution; weighted phase features are then exploited to determine the principal orientation of each point, from which a maximum index map (MIM) descriptor is constructed. Third, coarse position, orientation, and scale information obtained through global matching are employed to estimate image-pair geometry, after which descriptors are recalculated for precise correspondence search. MSWF is benchmarked against eight state-of-the-art multi-modal methods—six hand-crafted (PSO-SIFT, LGHD, RIFT, RIFT2, HAPCG, COFSM) and two learning-based (CMM-Net, RedFeat) methods—on three public datasets. Experiments demonstrate that MSWF consistently achieves the highest number of correct matches (NCM) and the highest rate of correct matches (RCM) while delivering the lowest root mean square error (RMSE), confirming its superiority for challenging MRSI registration tasks. Full article

We present novel proofs of the Riemann hypothesis by extending the standard complex Riemann zeta function into a quaternionic algebraic framework. Utilizing λ-regularization, we construct a symmetrized form that ensures analytic continuation and restores critical-line reflection symmetry, a key structural property of the Riemann ξ(s) function. This formulation reveals that all nontrivial zeros of the zeta function must lie along the critical line Re(s) = 1/2, offering a constructive and algebraic resolution to this fundamental conjecture. Our method is built on convexity and symmetrical principles that generalize naturally to higher-dimensional hypercomplex spaces. We also explore the broader implications of this framework in quantum statistical physics. In particular, the λ-regularized quaternionic zeta function governs thermodynamic properties and phase transitions in Bose–Einstein condensates. This quaternionic extension of the zeta function encodes oscillatory behavior and introduces critical hypersurfaces that serve as higher-dimensional analogues of the classical critical line. By linking the spectral features of the zeta function to measurable physical phenomena, our work uncovers a profound connection between analytic number theory, hypercomplex geometry, and quantum field theory, suggesting a unified structure underlying prime distributions and quantum coherence. Full article

Two-phase expansion processes have emerged as a promising technology for enhancing energy efficiency in power generation, refrigeration, waste heat recovery systems (for example, partially evaporated organic Rankine cycle, organic flash cycle, and trilateral flash cycle), oil and gas, and other applications. However, despite their potential, widespread adoption is hindered by inherent challenges, particularly energy losses that reduce operational efficiency. This review systematically evaluates the current state of two-phase expansion technologies, focusing on the root causes, impacts, and mitigation strategies for expansion losses. This work used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Using the PRISMA framework, 52 relevant publications were identified from Scopus and Web of Science to conduct the systematic review. A preliminary co-occurrence analysis of keywords was also conducted using VOSviewer version 1.6.20. Three clusters were observed in this co-occurrence analysis. However, the results may not be significant. Therefore, the extended work was done through a comprehensive analysis of experimental and simulation studies from the literature. This study identifies critical loss mechanisms in key components of two-phase expanders, such as the nozzle, diffuser, rotor, working chamber, and vaneless space. Also, losses arising from wetness, such as droplet formation, interfacial friction, and non-equilibrium phase transitions, are examined. These phenomena degrade performance by disrupting flow stability, increasing entropy generation, and causing mechanical erosion. Several losses in the turbine and volumetric expanders operating in two-phase conditions are reported. Ejectors, throttling valves, and flashing flow systems that exhibit similar challenges of losses are also discussed. This review discusses the mitigation and the strategy to minimize the two-phase expansion losses. The geometry of the inlet of the two-phase expanders plays an important role, which also needs improvement to minimize losses. The review highlights recent advancements in addressing these challenges and shows optimization opportunities for further research. Full article

Rapid urbanization, excessive motorization, and the imperative to reduce carbon footprints are driving the search for sustainable urban space solutions. One promising approach involves the effective design of small-scale architecture, such as parking shelters, optimized for structural material consumption and resilience to vehicle impacts. This research employed a novel approach during the initial design phase. Genetic algorithms and optimization techniques were utilized to define the optimal geometries of steel structures, focusing on the height of the conoidal roof and the shape and arrangement of columns. The subsequent analysis included static and strength calculations, dimensioning, and evaluating structural responses to exceptional loading, incorporating novel impact scenarios. The analysis yielded several key insights into the structural efficiency, dynamic behavior, and design optimization of the shelters. The research revealed that both roof geometry and column shape and arrangement significantly influenced material consumption and design effectiveness. The findings indicated that shelters with four straight, vertical, non-corner columns exhibited the most favorable dynamic behavior and highest impact resistance. These shelters also facilitated easy parking for both single-module and double-module roof types. The research findings provide a foundation for the parametric design of functional and structurally resilient parking shelters that cater to urban transportation needs and ecological objectives. Full article

The Automatic Modulation Classification (AMC) for underwater acoustic signals enables more efficient utilization of the acoustic spectrum. Deep learning techniques significantly improve classification performance. Hence, they can be applied in AMC work to improve the underwater acoustic (UWA) communication. This paper is based on the adoption of Hough Transform (HT) and Edge Detection (ED) to enhance modulation classification, especially for a small dataset. Deep neural models based on basic Convolutional Neural Network (CNN), Visual Geometry Group-16 (VGG-16), and VGG-19 trained on constellation diagrams transformed using HT are adopted. The objective is to extract features from constellation diagrams projected onto the Hough space. In addition, we use Orthogonal Frequency Division Multiplexing (OFDM) technology, which is frequently utilized in UWA systems because of its ability to avoid multipath fading and enhance spectrum utilization. We use an OFDM system with the Discrete Cosine Transform (DCT), Cyclic Prefix (CP), and equalization over the UWA communication channel under the effect of estimation errors. Seven modulation types are considered for classification, including Phase Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM) (2/8/16-PSK and 4/8/16/32-QAM), with a Signal-to-Noise Ratio (SNR) ranging from −5 to 25 dB. Simulation results indicate that our CNN model with HT and ED at perfect channel estimation, achieves a 94% classification accuracy at 10 dB SNR, outperforming benchmark models by approximately 40%. Full article

Fused filament fabrication (FFF) is a 3D printing technology that has been successfully demonstrated aboard the International Space Station (ISS), proving its suitability for space applications. In this study, we aimed to apply FFF to the 3D printing of recycled space beverage packaging, made of LDPE and a PET-Aluminum-LDPE (PAL) trilaminate. To minimize material waste and optimize the experimental process, we first conducted numerical simulations of additive manufacturing. Using Digimat-AM 2021.1 software, we analyzed residual stresses and warpage in an LDPE/PAL composite with a 10 wt% filler content, processed through the FFF technique. Three key printing parameters, including printing speed and infill pattern, were varied across different levels to assess their impact. Once the optimal combination of parameters for minimizing residual stresses and warpage was identified, we proceeded with the experimental phase, printing objects of increasing complexity to validate the correlation between numerical predictions and the 3D-printed models. The successful fabrication of all geometries under optimized conditions confirmed the numerical predictions, particularly the reduction in warpage and residual stress, validating the material’s viability for additive manufacturing. These findings support the potential application of the LDPE/PAL composite for in situ resource utilization strategies in long-term space missions. Full article

The roughness of the Earth’s surface dictates the nature of air flow across it. Detailed meteorological data that are necessary to access the aerodynamic roughness ($z_0$) are not widely collected and, as such, the geometry of a surface can be used to estimate $z_0$. Here, we present a novel formulation, and the corresponding computer code, to compute $z_0$ based on the Lettau (1969) geometric approach. The new code produces a mean $z_0$, as well as a histogram of all $z_0$ values for each individual roughness element (e.g., 10 s of thousand for the 1000 × 1000 grids) discretized using watersheds, as well as directional $z_0$ diagrams, which can be matches with the wind rose for the location. The formulation includes two parameters that may optionally be applied to smooth the surface before calculating $z_0$. By calculating $z_0$ as a function of these two parameters, we demonstrate the sensitivity of the $z_0$ value to these parameter choices. Since a large portion of the Earth’s surface is snow covered during some parts of the year, and the roughness of the snow surface varies over the snow season and over space, we apply the code to three snow surface datasets. Each surface is during a different phases of the snowpack. Each surface is evaluated at two resolutions). These surfaces are: fresh snow accumulation (1 m² at 1 and 10 mm), peak accumulation (1 km² at 1 and 10 m) and ablation sun cups (25 m² at 5 and 50 mm). Full article

We propose Phase-Adaptive Federated Learning (PAFL), a novel framework for privacy-preserving personalized travel itinerary generation that dynamically balances privacy and utility through a phase-dependent aggregation mechanism inspired by phase-change materials. (1) PAFL’s primary objective is to dynamically optimize the privacy–utility trade-off in federated travel recommendation systems through phase-adaptive anonymization. The phase parameter φ ∈ [0, 1] operates as a tunable control variable that continuously adjusts the latent space geometry between differentially private (φ→1) and utility-optimized (φ→0) representations via a thermodynamic-inspired transformation. Conventional federated learning approaches often rely on static privacy-preserving techniques, which either degrade recommendation quality or inadequately protect sensitive user data; PAFL addresses this limitation through three key innovations: a latent-space phase transformer, a differential privacy-gradient inverter with mathematically provable reconstruction bounds (εt ≤ 1.0), and a lightweight sequential transformer. (2) PAFL’s core innovation lies in its phase-adaptive mechanism that dynamically balances privacy preservation through differential privacy and utility maintenance via gradient inversion, governed by the tunable phase parameter φ. Experimental results demonstrate statistically significant improvements, with 18.7% higher HR@10 (p < 0.01) and 62% lower membership inference risk compared to state-of-the-art methods, while maintaining εtotal < 2.3 over 100 training rounds. The framework advances federated learning for sensitive recommendation tasks by establishing a new paradigm for adaptive privacy–utility optimization. Full article

This study quantitatively analyzes the effects of various ventilation parameters on airflow stability in confined spaces ventilated by opposed jets, a common configuration in high-density settings. Using large eddy simulations (LES), we evaluate how changes in supply velocity, airflow configuration, enclosure geometry, and thermal gradients influence airflow dynamics. Findings show that higher supply velocities, up to 1.92 m/s, lead to a measurable increase in oscillation period (from 7.7 s to 11.3 s) and reduce small-scale flow disturbances. The free jet configuration exhibits higher oscillation amplitude and a more disordered structure compared to the attached jet, resulting in uneven airflow distribution. Aspect ratio has a pronounced effect, with increased ratios extending oscillation periods from 10.6 s to 18.1 s and intensifying turbulence. Thermal gradients, with floor temperatures rising from 15 °C to 35 °C, and the oscillation period are increased, further dispersing airflow and reducing stability. Phase space reconstruction and power spectral analysis provide quantitative benchmarks for oscillation frequencies and patterns, correlating velocity time series with airflow structural changes. The findings from this study can serve as a foundation for future research on thermal comfort and air quality management in enclosed environments. Full article

Metallic materials additive manufacturing is extremely challenging nowadays, while aircraft manufacturers are trying to adapt the newly developed technology to produce parts of complex geometry with minimum materials losses. Skyrora is a company focused on the production of several launch vehicles and rockets with the aim of becoming a commercial provider for access to space. One of the Skyrora goals is to develop innovative and long-term solutions for future growth, and, within the Horizon European project “MADE-3D”, aims to improve the rocket propulsion system of the launch vehicle Skyrora XL by exploiting multi-materials during the production phase by additive manufacturing. The main goal of the present investigation is to document the already existing production phases of the “conventional” Skyrora vacuum nozzle printed with Inconel 718 to provide a baseline in terms of weight, manufacturing cost, lead processing time and CO₂ equivalent emissions of the under-development multi-material demonstrator. Full article

As an evolutionary advancement to conventional synthetic aperture radar (SAR), passive bistatic SAR (PBSAR) utilizing geostationary orbit (GEO) satellite signals demonstrates significant potential for high-resolution imaging. However, PBSAR faces dual challenges in computational efficiency and phase error compensation. Traditional accelerated back-projection (BP) variants developed from monostatic SAR are incompatible with PBSAR’s geometry, and autofocus BP (AFBP) methods exhibit prohibitive computational costs and inadequate space-variant phase error handling. This study first develops a bistatic ground Cartesian back-projection (BGCBP) algorithm through subimage wavenumber spectrum correction, specifically adapted to GEO-satellite-based PBSAR. Compared to conventional BP, the BGCBP achieves an order-of-magnitude complexity reduction without resolution degradation. Building upon this foundation, we propose a subimage autofocus BGCBP (SIAF-BGCBP) methodology, synergistically integrating autofocus processing with BGCBP’s accelerated framework. SIAF-BGCBP reduces phase estimation’s complexity by 90% through subimage pixel density optimization while maintaining estimation accuracy. Further enhancement of SIAF-BGCBP via geometric inversion would enable the precise compensation of space-variant phase errors while remaining efficient. Simulations and real-environment experiments verify the effectiveness of the proposed methods. Full article

This paper presents a multi-fidelity optimization procedure for aircraft wing design, implemented in the early stages of the aircraft design process. Since wing shape is a key factor that influences aerodynamic performance, having an accurate estimate of its efficiency at the conceptual design phase is highly beneficial for aircraft designers. This study introduces a comprehensive optimization framework for designing the wing of a Class I fixed-wing mini-UAV with electric propulsion, focusing on maximizing aerodynamic efficiency and operational performance. Utilizing Class-Shape Transformation (CST) in combination with Surrogate-Based Optimization (SBO) techniques, the research first optimizes the airfoil shape to identify the most suitable airfoil for the UAV wing. Subsequently, SBO techniques are applied to generate wing geometries with varying characteristics, including aspect ratio ($AR$), taper ratio ($\lambda$), quarter-chord sweep angle (${\mathrm{\Lambda }}_{0.25}$), and tip twist angle ($\epsilon$). These geometries are then evaluated using both low- and high-fidelity aerodynamic simulations. The integration of SBO techniques enables an efficient exploration of the design space while minimizing the computational costs associated with iterative simulations. Specifically, the proposed SBO framework enhances the wing’s aerodynamic characteristics by optimizing the lift-to-drag ratio and reducing drag. Full article