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Ensuring safety and security in urban air mobility is of utmost significance. As air traffic becomes more concentrated in urban regions, instances of flight conflicts are on the rise. The complex urban morphology, micro-environmental factors and various flight risks significantly impact flight safety. This paper introduces a comprehensive framework to address these challenges. The framework incorporates random 3D city layouts, encompassing city buildings and terrains to establish a corridor graph structure. Leveraging this graph, a multi-view representation learning approach is proposed, which employs graph neural networks, recurrent neural networks (RNN) and contrastive learning to effectively manage tactical conflicts. Through rigorous testing across diverse scenarios, including the incorporation of uncertainties such as wind turbulence, the model’s performance is extensively evaluated. The conclusive results underscore the robustness and efficacy of the proposed approach in ensuring safety and resolving conflicts within the dynamic urban air mobility landscape.
The assessment of soil–structure interaction (SSI) under dynamic loading conditions remains a challenging task due to the complexities of modeling this system and the interplay of SSI effects, which is also characterized by uncertainties across varying loading scenarios. This field of research encompasses a wide range of engineering structures, including underground tunnels. In this study, a surrogate model based on a regression ensemble model has been developed for real-time assessment of underground tunnels under dynamic loads. The surrogate model utilizes synthetic data generated using Latin hypercube sampling, significantly reducing the required dataset size while maintaining accuracy. The synthetic dataset is constructed using an accurate numerical model that integrates the two-and-a-half-dimensional singular boundary method for modeling wave propagation in the soil with the finite element method for structural modeling. This hybrid approach allows for a precise representation of the dynamic interaction between tunnels and the surrounding soil. The validation and optimization algorithms are evaluated for two problems: underground railway tunnels with circular and rectangular cross-sections, both embedded in a homogenous full-space medium. Both geometrical and material characteristics of the underground tunnel are incorporated into the optimization process. The optimization target is to minimize elastic wave propagation in the surrounding soil. The results demonstrate that the proposed optimization framework, which combines the Bayesian optimization algorithm with surrogate models, effectively explores trade-offs among multiple design parameters. This enables the design of underground railway tunnels that achieve an optimal balance between elastic wave propagation performance, material properties, and geometric constraints.
The jet from a model-scale, internally mixed nozzle produced a loud howling when operated at jet Mach numbers between 0.80 and 1.00. Discrete tones dominated the noise radiated to the far field and powerful oscillations were present in the jet. To explain these observations, this paper leverages a blend of experimental acoustic and flow measurements and modal analyses thereof via the spectral proper orthogonal decomposition, computational fluid dynamics simulations and local, linear stability analyses of vortex-sheet models for the flow inside the nozzle. This blend of experiments, computations and theory makes clear the cause of the howling, what sets its characteristic frequency and how it may be suppressed. The flow around a small-radius, convex bend just upstream of the final-nozzle exit led to a pocket of locally supersonic flow that was terminated by a shock. The shock was strong enough to separate the boundary layer, but neither the attached nor separated states were stable. A periodic, shock-induced separation of the boundary layer resulted, and this shock-wave/boundary-layer interaction coupled with a natural acoustic mode of the nozzle’s interior in a feedback phenomenon of sorts. Acoustic tones and large flow oscillations were produced at the associated natural frequency of the nozzle’s interior.
Nonlinear dynamical systems often allow for multiple statistically stationary states for the same values of the control parameters. Herein, we introduce a framework that selectively eliminates specific nonlinear triad interactions, thereby suppressing emergence of a particular state, and enabling the emergence of another. The methodology is applied to yield the multiple convection-roll states in two-dimensional planar Rayleigh–Bénard convection (e.g. Wang et al., 2020, Phys. Rev. Lett., vol. 125, 074501) in the turbulent regime. The intrusive framework presented here is based on the observation that the characteristic wavenumber associated with the mean horizontal size of the convection rolls mediates triadic scale interactions resulting in both kinetic energy and temperature variance cascades that are dominant energy transfer processes in a statistically stationary state. Suppression of these cascades mediated by a candidate wavenumber hinders the formation of the convection rolls at that wavenumber. Consequently, convection rolls are formed at another candidate wavenumber which is allowed to mediate energy to establish the cascade processes. In case no stable convection-roll states are possible, this technique does not tend to yield any convection rolls, making it a suitable method for discovering multiple states. Whereas in previous investigations the signature of different states in the initial condition in simulations yielded the multiple states, the present method alleviates such dependence of the emergence of multiple states on initial conditions. It is also demonstrated that accurate predictions of statistical quantities, such as Nusselt number and volume-averaged Reynolds numbers, can also be obtained using this method. The convection-roll states yielded using this technique may be used as initial conditions for direct simulations quickly converging to the target roll state without taking long convergence routes involving state transitions. Additionally, because only one state can possibly emerge in each simulation, this technique can empirically designate each of the multiple states with respect to their stability.
The transient dynamics of a wake vortex, modelled as a strong swirling $q$-vortex, is investigated with a focus on optimal transient growth driven by continuous eigenmodes associated with continuous spectra. The pivotal contribution of viscous critical-layer eigenmodes (Lee and Marcus, J. Fluid Mech. vol. 967, 2023, p. A2) amongst the entire eigenmode families to optimal perturbations is numerically confirmed, using a spectral collocation method for a radially unbounded domain that ensures correct analyticity and far-field behaviour. The consistency of the numerical method across different sensitivity tests supports the reliability of the results and provides flexibility for tuning. Both axisymmetric and helical perturbations with axial wavenumbers of order unity or less are examined through linearised theory and nonlinear simulations, yielding results that align with existing literature on energy growth curves and optimal perturbation structures. The initiation process of transient growth is also explored, highlighting its practical relevance. Inspired by ice crystals in contrails, the backward influence of inertial particles on the vortex flow, particularly through particle drag, is emphasised. In the pursuit of optimal transient growth, particles are initially distributed at the periphery of the vortex core to disturb the flow. Two-way coupled vortex–particle simulations reveal clear evidence of optimal transient growth during ongoing vortex–particle interactions, reinforcing the robustness and significance of transient growth in the original nonlinear vortex system over finite time periods.
A path being planned for an unmanned aerial vehicle (UAV) or its armed conformation called the unmanned combat aerial vehicle (UCAV) has a critical importance on the flight safety and success of the task including reconnaissance, surveillance, monitoring or destroying. The Back-and-Forth (BaF) algorithm is one of the most recent greedy techniques that executes a heuristic approach for generating two backbone candidates and a combination procedure for bringing advantageous segments of them in order to solve the geometric description of the path planning problem. This study was devoted to a thread-based parallel implementation of the BaF algorithm, also named the multi-threaded BaF (tBaF for short). The threads of the tBaF algorithm increase their search bounds gradually according to the assigned thread indexes and execute the heuristic approach and combination procedure of the BaF for calculating paths. A set of detailed experiments was carried out with the aim of evaluating the performance of the tBaF, and its results were compared with the BaF and 14 other meta-heuristic-based path planners over three battlefield scenarios and their 12 test cases containing the preidentified enemy threats. Comparative studies showed that the different local and global search capabilities of the threads gained with the newly introduced thread-based organisation give a significant contribution to the path planning performance and allow the tBaF to be ranked among the top three planners even though it requires at least from four to eight times less function calls than the tested meta-heuristics.
Supersonic free jets are extensively employed across a range of applications, especially in high-tech industries such as semiconductor processing and aerospace propulsion. Due to the difficulties involved in flow measurement, previous research on supersonic free jets has primarily focused on investigating near-field shockwave structures, with quantitative experimental analysis of the far-field zone being relatively scarce. However, physical understanding of the far-field flow, particularly post-shockwave energy dissipation, holds significant importance for the application and utilisation of these jets in vacuum environments. Therefore, this study aims to provide a robust experimental foundation for a rarefied supersonic free jet through the analysis of the flow field in both the near- and far-field zones. Nanometre-sized tracer particles and molecules were utilised to measure the rarefied supersonic jet flow field using particle image velocimetry and acetone molecular tagging velocimetry, respectively. The experiments revealed that in rarefied conditions, the supersonic jet exhibits a one-barrel shockwave structure in the near field, and after passing the Mach disk, a long annular viscous layer develops downstream. Experimental data on the jet velocity profile and width demonstrated a transition to a laminar flow regime in the far-field zone. This transition aligns with the theoretically inferred flow regimes based on the complex Reynolds number. The velocity profile and potential core length of the laminar flow regime could be modelled using a bi-modal distribution, which represents the summation of symmetric Gaussian distributions.
This study addresses challenges in sensor fusion for accurate and robust joint orientation estimation in human movement analysis using wearable inertial measurement units (IMUs). A magnetometer-free refined Kalman filter (KF) approach is presented and validated to address various indoor environmental constraints and challenges posed by human movement. These include variability in motion and dynamics, as well as magnetic disturbances caused by ferromagnetic materials or electronic interferences. Our proposed approach utilizes a Kalman-filter-based framework that analyzes the accelerometer’s alignment with the Earth’s frame to estimate orientation and correct gyroscope readings, eliminating reliance on magnetometer inputs. The algorithm was tested on both controlled robotic movements and real-world upper-limb-motion-monitoring scenarios. First, a comparative analysis was conducted on the double-stage Kalman filter (DSKF) and complementary filter using the collected robot motion encoder data. The results demonstrated superior performance in orientation estimation, particularly in yaw measurements, where the proposed method significantly improved accuracy. It achieved a lower root mean square error (RMSE = $ {2.447}^{\circ } $) and mean absolute error (MAE = $ {2.006}^{\circ } $), outperforming both the DSKF and complementary filter approaches. Additionally, the study’s findings were validated against a standard motion capture system, revealing error metrics within generally acceptable ranges ($ \le 12.4\% $ of the joint range of motion [ROM]) and strong correlation coefficients ($ {r}^2>0.89 $). However, some deviations were observed during complex motion cycle intervals, highlighting opportunities for further refinement. These findings suggest that the proposed approach presents a promising alternative for human joint orientation estimation in industrial settings with magnetic distortions.
Ventilated cavities in the wake of a two-dimensional bluff body are studied experimentally via time-resolved X-ray densitometry. With a systematic variation of flow velocity and gas injection rate, expressed as Froude number ($\textit{Fr}$) and ventilation coefficient ($C_{qs}$), four cavities with different closure types are identified. A regime map governed by $\textit{Fr}$ and $C_{qs}$ is constructed to estimate flow conditions associated with each cavity closure type. Each closure exhibits a different gas ejection mechanism, which in turn dictates the cavity geometry and the pressure in the cavity. Three-dimensional cavity closure is seen to exist for the supercavities at low $\textit{Fr}$. However, closure is nominally two-dimensional for supercavities at higher $\textit{Fr}$. At low $C_{qs}$, cavity closure is seen to be wake-dominated, while supercavities are seen to have interfacial perturbation near the closure at higher $C_{qs}$, irrespective of $\textit{Fr}$. With the measured gas fraction, a gas balance analysis is performed to quantify the gas ejection rate at the transitional cavity closure during its formation. For a range of $\textit{Fr}$, the transitional cavity closure is seen to be characterised by re-entrant flow, whose intensity depends on the flow inertia, dictating the gas ejection rates. Two different ventilation strategies were employed to systematically investigate the formation and maintenance gas fluxes. The interaction of wake and gas injection is suspected to dominate the cavity formation process and not the maintenance, resulting in ventilation hysteresis. Consequently, the ventilation gas flux required to maintain the supercavity is significantly less than the gas flux required to form the supercavity.
Ultra-thin liquid sheets generated by impinging two liquid jets are crucial high-repetition-rate targets for laser ion acceleration and ultra-fast physics, and serve widely as barrier-free samples for structural biochemistry. The impact of liquid viscosity on sheet thickness should be comprehended fully to exploit its potential. Here, we demonstrate experimentally that viscosity significantly influences thickness distribution, while surface tension primarily governs shape. We propose a thickness model based on momentum exchange and mass transport within the radial flow, which agrees well with the experiments. These results provide deeper insights into the behaviour of liquid sheets and enable accurate thickness control for various applications, including atomization nozzles and laser-driven particle sources.
Cavitation bubble pulsation and liquid jet loads are the main causes of hydraulic machinery erosion. Methods to weaken the load influences have always been hot topics of related research. In this work, a method of attaching a viscous layer to a rigid wall is investigated in order to reduce cavitation pulsations and liquid jet loads, using both numerical simulations and experiments. A multiphase flow model incorporating viscous effects has been developed using the Eulerian finite element method (EFEM), and experimental methods of a laser-induced bubble near the viscous layer attached on a rigid wall have been carefully designed. The effects of the initial bubble–wall distance, the thickness of the viscous layer, and the viscosity on bubble pulsation, migration and wall pressure load are investigated. The results show that the bubble migration distance, the normalised thickness of the oil layer and the wall load generally decrease with the initial bubble–wall distance or the oil-layer parameters. Quantitative analysis reveals that when the initial bubble–wall distance remains unchanged, there exists a demarcation line for the comparison of the bubble period and the reference period (the bubble period without viscous layer under the same initial bubble–wall distance), and a logarithmic relationship is observed that $\delta \propto \log_{10} \mu ^*$, where $\delta =h/R_{max}$ is the thickness of the viscous layer h normalised by the maximum bubble radius $R_{max}$, $\mu ^* = \mu /({R_{max }}\sqrt {{\rho }{{\mathop {P}\nolimits } _{{atm}}}})$ is the dynamic viscosity $\mu$ normalised by water density $ \rho $ and atmospheric pressure $P_{atm}$. The results of this paper can provide technical support for related studies of hydraulic cavitation erosion.
This self-contained and detailed text, will benefit students in gaining thorough understanding of fundamental concepts and applications of different manufacturing processes. Starting from basic knowledge, the readers are guided through important manufacturing processes including metal casting, metal forming and shaping processes, powder metallurgy, gas welding, electric arc welding and cutting processes. Description of shell moulding, explosive forming and electro-hydraulic forming is given in detail. Numerous review questions, fill in the blanks and multiple choice questions are included throughout the text to help the reader self-test their understanding of the subject matter. This text is the ideal resource for mechanical, industrial and production engineering undergraduates taking an introductory, single-semester course in manufacturing processes.
The radome of weather radars can be covered with a layer of water, degrading the quality of the radar products. Considering a simplified setup with a planar replica of the Swiss weather radars’ radome, we measure and model analytically its scattering parameters, with and without water. The measured reflectance of the dry radome replica agrees well with the one modeled according to the manufacturer specifications. Water forms droplets on the hydrophobic surface, but water films thicker than 1 mm can be created. Meteorologically more realistic thinner water films are expected on old radomes that have become hydrophilic with aging. Using hygroscopic silk and cotton tissues, we empirically imitate water films as thin as less than 0.1 mm. The measurements align with the simple analytical model of uniform plane wave incidence on the radome and water film but could be further improved by taking refraction and bending of the radome replica into account. Simulations with the General Reflector Antenna Software Package (GRASP) from TICRA complement the study for a representative setup with a spherical radome.
Heating, Ventilation, and Air Conditioning (HVAC) systems are major energy consumers in buildings, challenging the balance between efficiency and occupant comfort. While prior research explored generative AI for HVAC control in simulations, real-world validation remained scarce. This study addresses this gap by designing, deploying, and evaluating “Office-in-the-Loop,” a novel cyber-physical system leveraging generative AI within an operational office setting. Capitalizing on multimodal foundation models and Agentic AI, our system integrates real-time environmental sensor data (temperature, occupancy, etc.), occupants’ subjective thermal comfort feedback, and historical context as input prompts for the generative AI to dynamically predict optimal HVAC temperature setpoints. Extensive real-world experiments demonstrate significant energy savings (up to 47.92%) while simultaneously improving comfort (up to 26.36%) compared to baseline operation. Regression analysis confirmed the robustness of our approach against confounding variables like outdoor conditions and occupancy levels. Furthermore, we introduce Data-Driven Reasoning using Agentic AI, finding that prompting the AI for data-grounded rationales significantly enhances prediction stability and enables the inference of system dynamics and cost functions, bypassing the need for traditional reinforcement learning paradigms. This work bridges simulation and reality, showcasing generative AI’s potential for efficient, comfortable building environments and indicating future scalability to large systems like data centers.
Engineering mechanics is the branch of engineering that applies the laws of mechanics in design, and is at the core of every machine that is designed. This book offers a comprehensive discussion of the fundamental theories and principles of engineering mechanics. It begins by explaining the laws and idealization of mechanics, and then establishes the equation of equilibrium for a rigid body and free body diagram (FBD), along with their applications. Chapters on method of virtual work and mechanical vibration discuss in detail important topics such as principle of virtual work, potential energy and equilibrium and free vibration. The book also introduces the elastic spring method for finding deflection in beams and uses a simple integration method to calculate centroid and moment of inertia. This volume will serve as a useful textbook for undergraduates and engineering students studying engineering mechanics.
There are four forces in our universe. Two act only at the very smallest scales and one only at the very biggest. For everything inbetween, there is electromagnetism. The theory of electromagnetism is described by four gloriously simple and beautiful vector calculus equations known as the Maxwell equations. These are the first genuinely fundamental equations that we meet in our physics education and they survive, essentially unchanged, in our best modern theories of physics. They also serve as a blueprint for what subsequent laws of physics look like. This textbook takes us on a tour of the Maxwell equations and their many solutions. It starts with the basics of electric and magnetic phenomena and explains how their unification results in waves that we call light. It then describes more advanced topics such as superconductors, monopoles, radiation, and electromagnetism in matter. The book concludes with a detailed review of the mathematics of vector calculus.
In this chapter, we (a) introduce the notion of a convex problem in cone-constrained form, (b) present the Lagrange function of a cone-constrained convex problem, (c) prove the convex programming Duality Theorem in cone-constrained form, and (d) discuss conic programming and conic duality, and present the conic programming Duality Theorem.
In this chapter we present convex programming optimality conditions in both sadde point form and Karush--Kuhn--Tucker form for mathematical programming, and also optimality conditions for cone-constrained convex programs and for conic problems. We conclude the chapter by revisiting linear programming duality as a special case of conic duality and reproducing the classical results on the dual of a linearly constrained convex quadratic minimization problem.