Interference mechanism of trailing edge flap shedding vortices with rotor wake and aerodynamic characteristics

A robust Reynolds-Averaged Navier-Stokes(RANS)based solver is established to predict the complex unsteady aerodynamic characteristics of the Active Flap Control(AFC)rotor.The complex motion with multiple degrees of freedom of the Trailing Edge Flap(TEF)is analyzed by employing an inverse nested overset grid method.Simulation of non-rotational and rotational modes of blade motion are carried out to investigate the formation and development of TEF shedding vortex with high-frequency deflection of TEF.Moreover,the mechanism of TEF deflection interference with blade tip vortex and overall rotor aerodynamics is also explored.In nonrotational mode,two bundles of vortices form at the gap ends of TEF and the main blade and merge into a single TEF vortex.Dynamic deflection of the TEF significantly interferes with the blade tip vortex.The position of the blade tip vortex consistently changes,and its frequency is directly related to the frequency of TEF deflection.In rotational mode,the tip vortex forms a helical structure.The end vortices at the gap sides co-swirl and subsequently merge into the concentrated beam of tip vortices,causing fluctuations in the vorticity and axial position of the tip vortex under the rotor.This research concludes with the investigation on suppression of Blade Vortex Interaction(BVI),showing an increase in miss distance and reduction in the vorticity of tip vortex through TEF phase control at a particular control frequency.Through this mechanism,a designed TEF deflection law increases the miss distance by 34.7%and reduces vorticity by 11.9%at the target position,demonstrating the effectiveness of AFC in mitigating BVI.

Mach reflection and pressure/heating loads on V-shaped blunt leading edges with variable cross-sections and crotches

The primary Mach Reflection(MR)and pressure/heating loads on V-shaped Blunt Leading Edges(VBLEs)with variable elliptic cross-sections and conic crotches are theoretically investigated in this study.The simplified continuity method is used to forecast the shock configurations.The theoretical predictions and the numerical simulations for the Mach stem and the triple point as well as the curved shock accord well.Based on the theoretical model,an analysis of the impact of the axial ratio a/b of the cross-sectional shape and the eccentricity e of the crotch sweep path on shock structures is carried out.The shock configurations obtained from the theoretical model enable the derivation of the transition boundaries between the primary MR and the same family Regular Reflection(sRR).It is found that the increase of a/b and e can both facilitate the primary MR to sRR transition.The resulting transition and the corresponding generation of the wall pressure and heat flux are then investigated.The results indicate that higher values of the ratio a/b can significantly reduce the wall pressure and heating loads by inducing the primary MR to sRR transition.Conversely,the increase in the eccentricity e results in increased loads,despite causing the same transition.

Intensity enhancement of Raman active and forbidden modes induced by naturally occurred hot spot at GaAs edge

Edge structures are ubiquitous in the processing and fabrication of various optoelectronic devices.Novel physical properties and enhanced light–matter interactions are anticipated to occur at crystal edges due to the broken spatial translational symmetry.However,the intensity of first-order Raman scattering at crystal edges has been rarely explored,although the mechanical stress and edge characteristics have been thoroughly studied by the Raman peak shift and the spectral features of the edge-related Raman modes.Here,by taking Ga As crystal with a well-defined edge as an example,we reveal the intensity enhancement of Raman-active modes and the emergence of Raman-forbidden modes under specific polarization configurations at the edge.This is attributed to the presence of a hot spot at the edge due to the redistributed electromagnetic fields and electromagnetic wave propagations of incident laser and Raman signal near the edge,which are confirmed by the finite-difference time-domain simulations.Spatially-resolved Raman intensities of both Raman-active and Raman-forbidden modes near the edge are calculated based on the redistributed electromagnetic fields,which quantitatively reproduce the corresponding experimental results.These findings offer new insights into the intensity enhancement of Raman scattering at crystal edges and present a new avenue to manipulate light–matter interactions of crystal by manufacturing various types of edges and to characterize the edge structures in photonic and optoelectronic devices.

Femtosecond-laser sharp shaping of millimeter-scale geometries with vertical sidewalls

As femtosecond(fs)laser machining advances from micro/nanoscale to macroscale,approaches capable of machining macroscale geometries that sustain micro/nanoscale precisions are in great demand.In this research,an fs laser sharp shaping approach was developed to address two key challenges in macroscale machining(i.e.defects on edges and tapered sidewalls).The evolution of edge sharpness(edge transition width)and sidewall tapers were systematically investigated through which the dilemma of simultaneously achieving sharp edges and vertical sidewalls were addressed.Through decreasing the angle of incidence(AOI)from 0◦to−5◦,the edge transition width could be reduced to below 10µm but at the cost of increased sidewall tapers.Furthermore,by analyzing lateral and vertical ablation behaviors,a parameter-compensation strategy was developed by gradually decreasing the scanning diameters along depth and using optimal laser powers to produce non-tapered sidewalls.The fs laser ablation behaviors were precisely controlled and coordinated to optimize the parameter compensations in general manufacturing applications.The AOI control together with the parameter compensation provides a versatile solution to simultaneously achieve vertical sidewalls as well as sharp edges of entrances and exits for geometries of different shapes and dimensions.Both mm-scale diameters and depths were realized with dimensional precisions below 10µm and surface roughness below 1µm.This research establishes a novel strategy to finely control the fs laser machining process,enabling the fs laser applications in macroscale machining with micro/nanoscale precisions.

ANALYSIS OF SLAB EDGING BY A 3-D RIGID VISCO-PLASTIC FINITE ELEMENT METHOD

3-D rigid visco-plastic finite element method (FEM) is used in the analysisof metal forming processes, including strip and plate rolling, shape rolling, slab edging, specialstrip rolling. The shifted incomplete Cholesky decomposition of the stiffness matrix with thesolution of the equations for velocity increment by the conjugate gradient method is combined. Thistechnique, termed the shifted ICCG method, is then employed to solve the slab edging problem. Theperformance of this algorithm in terms of the number of iterations, friction variation, shiftedparameter psi and the results of simulation for processing parameters are analysed. Numerical testsand application of this technique verify the efficiency and stability of the shifted ICCG method inthe analysis of slab edging.

Tool Failure Analysis in High Speed Milling of Titanium Alloys

In high speed milling of titanium alloys the high rate of tool failure is the main reason for its high manufacturing cost. In this study,fractured tools which were used in a titanium alloys 5-axis milling process have been observed both in the macro scale using a PG-1000 light microscope and in the micro scale using a Scanning Electron Microscope (SEM) respectively. These observations indicate that most of these tool fractures are the result of tool chipping. Further analysis of each chipping event has shown that beachmarks emanate from points on the cutting edge. This visual evidence indicates that the cutting edge is failing in fatigue due to cyclical mechanical and/or thermal stresses. Initial analyses explaining some of the outlying conditions for this phenomenon are discussed. Future analysis regarding determining the underlying causes of the fatigue phenomenon is then outlined.

光谱物理推出Millennia Edge单纵模绿光激光器——具有业界最低噪声和紧凑结构的单纵模连续激光器

光谱物理，Newport集团下属的知名品牌推出了MillenniaEdge单纵模连续绿光激光器。该532nm激光器具仃低光学噪声，优良的光束质量，光束指向稳定性以及市场上最紧凑的结构。MillenniaEdge是泵浦钛宝石激光器和其他激光器的理想选择，包括载波包络相位（CEP）稳定系统，以及全息术和干涉测量方向的应用。

Analysis of Fracture of Pre⁃sintered Zirconia Material in Neck Milling

With the continuous improvement of the accuracy requirements of clinical base or base tooth and denture,the design of denture neck edge is becoming thinner and thinner,and the presintering zirconia dioxide billet for denture making evacuation and low strength material mechanical characteristics make the certain probability of denture neck edge fragmentation and peeling in the cutting process.Based on this phenomenon,the abnormal wear is the main reason,and a method of high frequency ultrasonic time cleaning to reduce the fracture of the denture neck in actual cutting is proposed.

基于高空平台的边缘计算卸载:网络、算法和展望

高空平台(high altitude platform,HAP)技术与多接入边缘计算(multi-access edge computing,MEC)技术的结合将MEC服务器部署区域由地面扩展到空中,打破传统地面MEC网络的局限性,为用户提供无处不在的计算卸载服务。针对基于HAP的MEC卸载研究进行综述,首先,从HAP计算节点的优势、网络组成部分、网络结构、主要挑战及其应对技术4个方面分析基于HAP的MEC网络;其次,分别从图论、博弈论、机器学习、联邦学习等理论的角度对基于HAP的MEC卸载算法进行横向分析和纵向对比;最后,指出基于HAP的MEC卸载技术目前存在的问题,并对该技术的未来研究方向进行展望。

240 nm AlGaN-based deep ultraviolet micro-LEDs:size effect versus edge effect

240 nm AlGaN-based micro-LEDs with different sizes are designed and fabricated.Then,the external quantum efficiency(EQE)and light extraction efficiency(LEE)are systematically investigated by comparing size and edge effects.Here,it is revealed that the peak optical output power increases by 81.83%with the size shrinking from 50.0 to 25.0μm.Thereinto,the LEE increases by 26.21%and the LEE enhancement mainly comes from the sidewall light extraction.Most notably,transversemagnetic(TM)mode light intensifies faster as the size shrinks due to the tilted mesa side-wall and Al reflector design.However,when it turns to 12.5μm sized micro-LEDs,the output power is lower than 25.0μm sized ones.The underlying mechanism is that even though protected by SiO2 passivation,the edge effect which leads to current leakage and Shockley-Read-Hall(SRH)recombination deteriorates rapidly with the size further shrinking.Moreover,the ratio of the p-contact area to mesa area is much lower,which deteriorates the p-type current spreading at the mesa edge.These findings show a role of thumb for the design of high efficiency micro-LEDs with wavelength below 250 nm,which will pave the way for wide applications of deep ultraviolet(DUV)micro-LEDs.

Edge enhanced depth perception with binocular meta-lens

The increasing popularity of the metaverse has led to a growing interest and market size in spatial computing from both academia and industry.Developing portable and accurate imaging and depth sensing systems is crucial for advancing next-generation virtual reality devices.This work demonstrates an intelligent,lightweight,and compact edge-enhanced depth perception system that utilizes a binocular meta-lens for spatial computing.The miniaturized system comprises a binocular meta-lens,a 532 nm filter,and a CMOS sensor.For disparity computation,we propose a stereo-matching neural network with a novel H-Module.The H-Module incorporates an attention mechanism into the Siamese network.The symmetric architecture,with cross-pixel interaction and cross-view interaction,enables a more comprehensive analysis of contextual information in stereo images.Based on spatial intensity discontinuity,the edge enhancement eliminates illposed regions in the image where ambiguous depth predictions may occur due to a lack of texture.With the assistance of deep learning,our edge-enhanced system provides prompt responses in less than 0.15 seconds.This edge-enhanced depth perception meta-lens imaging system will significantly contribute to accurate 3D scene modeling,machine vision,autonomous driving,and robotics development.

Air-Ground Collaborative Mobile Edge Computing:Architecture,Challenges,and Opportunities

By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.

Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

A Fully-Integrated Memristor Chip for Edge Learning

It is still challenging to fully integrate computing in memory chip as edge learning devices.In recent work published on Science,a fully-integrated chip based on neuromorphic memristors was developed for edge learning as artificial neural networks with functionality of synapses,dendrites,and somas.A crossbar-array memristor chip facilitated edge learning including hardware realization,learning algorithm,and cycle-parallel sign-and threshold-based learning(STELLAR)scheme.The motion control and demonstration platforms were executed to improve the edge learning ability for adapting to new scenarios.

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart

False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad data.Existing FDIA detection methods usually employ complex neural networkmodels to detect FDIA attacks.However,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection model.To address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative confrontation.Furthermore,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal characteristics.Finally,efficient FDIA attack samples can be sequentially generated through interactive adversarial learning.Extensive simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.

Progress and prospects of EOR technology in deep,massive sandstone reservoirs with a strong bottom-water drive

The Triassic massive sandstone reservoir in the Tahe oilfield has a strong bottom-water drive and is characterized by great burial depth,high temperature and salinity,a thin pay zone,and strong heterogeneity.At present,the water-cut is high in each block within the reservoir;some wells are at an ultrahigh water-cut stage.A lack of effective measures to control water-cut rise and stabilize oil production have necessitated the application of enhanced oil recovery(EOR)technology.This paper investigates the development and technological advances for oil reservoirs with strong edge/bottom-water drive globally,and compares their application to reservoirs with characteristics similar to the Tahe oilfield.Among the technological advances,gas injection from the top and along the direction of structural dip has been used to optimize the flow field in a typical bottom-water drive reservoir.Bottom-water coning is restrained by gas injection-assisted water control.In addition,increasing the lateral driving pressure differential improves the plane sweep efficiency which enhances oil recovery in turn.Gas injection technology in combination with technological measures like channeling prevention and blocking,and water plugging and profile control,can achieve better results in reservoir development.Gas flooding tests in the Tahe oilfield are of great significance to identifying which EOR technology is the most effective and has the potential of large-scale application for improving development of deep reservoirs with a strong bottomwater drive.

Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.

UAV-assisted cooperative offloading energy efficiency system for mobile edge computing

Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure.Mitigating this has greatly developed the application and integration of UAV and Mobile Edge Computing(MEC)to the Internet of Things(loT).However,problems such as multi-user and huge data flow in large areas,which contradict the reality that a single UAV is constrained by limited computing power,still exist.Due to allowing UAV collaboration to accomplish complex tasks,cooperative task offloading between multiple UAVs must meet the interdependence of tasks and realize parallel processing,which reduces the computing power consumption and endurance pressure of terminals.Considering the computing requirements of the user terminal,delay constraint of a computing task,energy constraint,and safe distance of UAV,we constructed a UAV-Assisted cooperative offloading energy efficiency system for mobile edge computing to minimize user terminal energy consumption.However,the resulting optimization problem is originally nonconvex and thus,difficult to solve optimally.To tackle this problem,we developed an energy efficiency optimization algorithm using Block Coordinate Descent(BCD)that decomposes the problem into three convex subproblems.Furthermore,we jointly optimized the number of local computing tasks,number of computing offloaded tasks,trajectories of UAV,and offloading matching relationship between multi-UAVs and multiuser terminals.Simulation results show that the proposed approach is suitable for different channel conditions and significantly saves the user terminal energy consumption compared with other benchmark schemes.

IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.