An Iterative Method for Split Variational Inclusion Problem and Split Fixed Point Problem for Averaged Mappings

In this paper, we use resolvent operator technology to construct a viscosity approximate algorithm to approximate a common solution of split variational inclusion problem and split fixed point problem for an averaged mapping in real Hilbert spaces. Further, we prove that the sequences generated by the proposed iterative method converge strongly to a common solution of split variational inclusion problem and split fixed point problem for averaged mappings which is also the unique solution of the variational inequality problem. The results presented here improve and extend the corresponding results in this area.

Unsteady Cavitating Flow around a Hydrofoil Simulated Using the Partially-Averaged Navier-Stokes Model

Numerical simulations of unsteady cavitating flow around a NACA66-mod hydrofoil were performed using the partially-averaged Navier-Stokes method with different values of the resolution control parameters (f k =1.0-0.2,fε =1).With decreasing fk,the predicted cavitating flow becomes unsteady as the time-averaged turbulent viscosity at the rear part of the attached cavity is gradually reduced.For fk =0.9 and 0.8,the cavity becomes unstable and its length dramatically expands and shrinks,but the calculation fails to predict the vapor cloud shedding behavior observed experimentally.With smaller fk less than 0.7,the cloud shedding behavior is simulated numerically and the predicted cavity shedding frequency increases.With fk =0.2,the whole cavitating flow evolution can be reasonably reproduced including the cavity growth/destabilization observed previously.The reentrant flow along the suction surface of the hydrofoil is the main trigger to cause the vapor cloud shedding.The wall pressure along the hydrofoil surface oscillates greatly due to the dynamic cavity shedding.Comparing the simulations and experiments,it is confirmed that for the PANS method,resolution control parameters of fk =0.2and fε =1 are recommended for numerical simulations of unsteady cavitating flows.Thus,the present study shows that the PANS method is an effective approach for predicting unsteady cavitating flow over hydrofoils.

MULTI-SCALE COHERENT STRUCTURES IN TURBULENT BOUNDARY LAYER DETECTED BY LOCALLY AVERAGED VELOCITY STRUCTURE FUNCTIONS

The time sequence of longitudinal velocity component at different vertical locations in turbulent boundary layer was finely measured in a wind tunnel. The concept of coarse_grained velocity structure functions, which describes the relative motions of straining and compressing for multi_scale eddy structures in turbulent flows, was put forward based on the theory of locally multi_scale average. Based on the consistency between coarse_grained velocity structure function and Harr wavelet transformation,detecting method was presented, by which the coherent structures and their intermittency was identified by multi_scale flatness factor calculated by locally average structure function. Phase_averaged evolution course for multi_scale coherent eddy structures in wall turbulence were extracted by this conditional sampling to educe scheme. The dynamics course of multi_scale coherent eddy structures and their effects on statistics of turbulent flows were studied.

Time-Averaged Behaviour at the Critical Parameter Point of Transition to Spatiotemporal Chaos

A time-averaged behaviour is found to be important for investigating the critical behaviour in parameter space for the transition from temporal chaos to spatiotemporal chaos by using an energy representation. Considering any wave solution as a superposition of the steady wave with its perturbation wave, we find that when approaching the critical parameter point the averaged positive interaction energy for the k = 1 mode becomes competitive with the negative one, with the summation displaying a scaling behaviour of power law.

Baroclinic pressure gradient difference schemes of subtracting the local averaged density stratification in sigma coordinates models

Much has been written of the error in computing the baroclinic pressure gradient (BPG) with sigma coordinates in ocean or atmospheric numerical models. The usual way to reduce the error is to subtract area-averaged density stratification of the whole computation region. But if there is great difference between the area-averaged and the local averaged density stratification, the error will be obvious. An example is given to show that the error from this method may be larger than that from no correction sometimes. The definition of local area is put forward. Then, four improved BPG difference schemes of subtracting the local averaged density stratification are designed to reduce the error. Two of them are for diagnostic calculation (density field is fixed), and the others are for prognostic calculation (density field is not fixed). The results show that the errors from these schemes all significantly decrease.

The Properties of Expected Scattering and Averaged Scattering and Their Applications to Texture Classification

In order to further improve the effectiveness of image processing,it is necessary that an efficient invariant representation is stable to deformation applied to images.This motivates the study of image representations defining an Euclidean metric stable to these deformation.This paper mainly focuses on two aspects.On the one hand,in this paper,two properties of expected scattering and averaged scattering,i.e.,Lipschitz continuity and translation invariance,are proved in detail.These properties support that excepted scattering and averaged scattering are invariant,stable and informative representations.On the other hand,the issue of texture classification based on expected scattering and averaged scattering has been analyzed respectively in this study.Energy features,which are based on expected scattering and averaged scattering,are calculated and used for classification.Experimental results show that starting with the seventh feature,the two approaches can achieve good performance in texture image classification.

Second-order error analysis of the averaged L1 scheme L1 for time-fractional initial-value and subdiffusion problems

Fractional initial-value problems(IVPs) and time-fractional initial-boundary value problems(IBVPs), each with a Caputo temporal derivative of order α ∈(0, 1), are considered. An averaged variant of the well-known L1 scheme is proved to be O(N^(-2)) convergent for IVPs on suitably graded meshes with N points, thereby improving the O(N^(-(2-α))) convergence rate of the standard L1 scheme. The analysis relies on a delicate decomposition of the temporal truncation error that yields a sharp dependence of the order of convergence on the degree of mesh grading used. This averaged L1 scheme can be combined with a finite difference or piecewise linear finite element discretization in space for IBVPs, and under a restriction on the temporal mesh width, one gets again O(N^(-2)) convergence in time, together with O(h^(2)) convergence in space,where h is the spatial mesh width. Numerical experiments support our results.

Intelligent Voltage Control Method in Active Distribution Networks Based on Averaged Weighted Double Deep Q-network Algorithm

High penetration of distributed renewable energy sources and electric vehicles(EVs)makes future active distribution network(ADN)highly variable.These characteristics put great challenges to traditional voltage control methods.Voltage control based on the deep Q-network(DQN)algorithm offers a potential solution to this problem because it possesses humanlevel control performance.However,the traditional DQN methods may produce overestimation of action reward values,resulting in degradation of obtained solutions.In this paper,an intelligent voltage control method based on averaged weighted double deep Q-network(AWDDQN)algorithm is proposed to overcome the shortcomings of overestimation of action reward values in DQN algorithm and underestimation of action reward values in double deep Q-network(DDQN)algorithm.Using the proposed method,the voltage control objective is incorporated into the designed action reward values and normalized to form a Markov decision process(MDP)model which is solved by the AWDDQN algorithm.The designed AWDDQN-based intelligent voltage control agent is trained offline and used as online intelligent dynamic voltage regulator for the ADN.The proposed voltage control method is validated using the IEEE 33-bus and 123-bus systems containing renewable energy sources and EVs,and compared with the DQN and DDQN algorithms based methods,and traditional mixed-integer nonlinear program based methods.The simulation results show that the proposed method has better convergence and less voltage volatility than the other ones.

Libration-Generated Average Convection in a Rotating Flat Layer with Horizontal Axis

The study of average convection in a rotating cavity subjected to modulated rotation is an interesting area for the development of both fundamental and applied science.This phenomenon finds application in the field of mass transfer and fluid flow control,relevant examples being crystal growth under reduced gravity and fluid mixing in microfluidic devices for cell cultures.In this study,the averaged flow generated by the oscillating motion of a fluid in a planar layer rotating about a horizontal axis is experimentally investigated.The boundaries of the layer are maintained at constant temperatures,while the lateral cylindrical wall is thermally insulated.It is demonstrated that libration results in intense oscillatory fluid motion,which in turn produces a time-averaged flow.For the first time,quantitative measures for the instantaneous velocity field are obtained using the Particle Image Velocimetry technique.It is revealed that the flow has the form of counter-rotating vortices.The vortex circulations sense changes during a libration cycle.An increase in the rotation rate and amplitude of the cavity libration results in an increase in the flow intensity.The heat transfer and time-averaged velocity are examined accordingly as a function of the dimensionless oscillation frequency,and resonant excitation of heat transfer and average oscillation velocity are revealed.The threshold curve for the onset of the averaged convection is identified in the plane of control parameters(dimensionless rotational velocity and pulsation Reynolds number).It is found that an increase in the dimensionless rotational velocity has a stabilizing effect on the onset of convection.

Structure-preserving algorithms for guiding center dynamics based on the slow manifold of classical Pauli particle

The classical Pauli particle(CPP) serves as a slow manifold, substituting the conventional guiding center dynamics. Based on the CPP, we utilize the averaged vector field(AVF) method in the computations of drift orbits. Demonstrating significantly higher efficiency, this advanced method is capable of accomplishing the simulation in less than one-third of the time of directly computing the guiding center motion. In contrast to the CPP-based Boris algorithm, this approach inherits the advantages of the AVF method, yielding stable trajectories even achieved with a tenfold time step and reducing the energy error by two orders of magnitude. By comparing these two CPP algorithms with the traditional RK4 method, the numerical results indicate a remarkable performance in terms of both the computational efficiency and error elimination. Moreover, we verify the properties of slow manifold integrators and successfully observe the bounce on both sides of the limiting slow manifold with deliberately chosen perturbed initial conditions. To evaluate the practical value of the methods, we conduct simulations in non-axisymmetric perturbation magnetic fields as part of the experiments,demonstrating that our CPP-based AVF method can handle simulations under complex magnetic field configurations with high accuracy, which the CPP-based Boris algorithm lacks. Through numerical experiments, we demonstrate that the CPP can replace guiding center dynamics in using energy-preserving algorithms for computations, providing a new, efficient, as well as stable approach for applying structure-preserving algorithms in plasma simulations.

Experimental Study of Thermal Convection and Heat Transfer in Rotating Horizontal Annulus

A genuine technological issue–the thermal convection of liquid in a rotating cavity–is investigated experimentally.The experiments are conducted within a horizontal annulus with isothermal boundaries. The inner boundaryof the annulus has a higher temperature, thus exerting a stabilising influence on the system. It is shown that whenthe layer rotation velocity diminishes, two-dimensional azimuthally periodic convective rolls, rotating togetherwith the cavity, emerge in a threshold manner. The development of convection is accompanied by a significantintensification of heat transfer through the layer. It is shown that the averaged thermal convection excitation inthe form of a system of two-dimensional rolls occurs against the background of oscillations of a non-isothermalfluid in the cavity reference frame caused by the gravity field. The excitation threshold and the structure ofconvective rolls are consistent with the results of the earlier theoretical studies by the authors performed usingthe equations of “vibrational” convection obtained by the averaging method. Furthermore, the experiments haverevealed a new type of averaged flow in the form of a spatially periodic system of toroidal vortices. It is shown thata steady streaming, excited by the inertial oscillations of the fluid, is responsible for the generation of the toroidalvortices. These flows develop in a non-threshold manner and are most clearly manifested in a case of resonantexcitation of one of the inertial modes.

Oscillatory Dynamics of a Spherical Solid in a Liquid in an Axisymmetric Variable Cross Section Channel

The dynamics of a solid spherical body in an oscillating liquid flow in a vertical axisymmetric channel of variable cross section is experimentally studied.It is shown that the oscillating liquid leads to the generation of intense averaged flows in each of the channel segments.The intensity and direction of these flows depend on the dimensionless oscillating frequency.In the region of studied frequencies,the dynamics of the considered body is examined when the primary vortices emerging in the flow occupy the whole region in each segment.For a fixed frequency,an increase in the oscillation amplitude leads to a phase-inclusion holding effect,i.e.,the body occupies a quasi-stationary position in one of the cells of the vertical channel,while oscillating around its average position.It is also shown that the oscillating motion of a liquid column generates an averaged force acting on the body,the magnitude of which depends on the properties of the body and its position in the channel.The quasi-stationary position is determined by the relative density and size of the body,as well as the dimensionless frequency.The behavior of the body as a function of the amplitude and frequency of fluid oscillation and relative size is discussed in detail.Such findings may be used in the future to control the position of a phase inclusion and/or to strengthen mass transfer effects in a channel of variable cross section by means of fluid oscillations.

Investigating the Effects of Injection Pipe Orientation on Mixing and Heat Transfer for Fluid Flow Downstream a T-Junction

At T-junctions, where hot and cold streams flowing in pipes join and mix, significant temperature fluctuations can be created in very close neighborhood of the pipe walls. The wall temperature fluctuations cause cyclical thermal stresses which may induce fatigue cracking. Temperature fluctuation is of crucial importance in many engineering applications and especially in nuclear power plants. This is because the phenomenon leads to thermal fatigue and might subsequently result in failure of structural material. Therefore, the effects of temperature fluctuation in piping structure at mixing junctions in nuclear power systems cannot be neglected. In nuclear power plant, piping structure is exposed to unavoidable temperature differences in a bid to maintain plant operational capacity. Tightly coupled to temperature fluctuation is flow turbulence, which has attracted extensive attention and has been investigated worldwide since several decades. The focus of this study is to investigate the effects of injection pipe orientation on flow mixing and temperature fluctuation for fluid flow downstream a T-junction. Computational fluid dynamics (CFD) approach was applied using STAR CCM+ code. Four inclination angles including 0 (90), 15, 30 and 45 degrees were studied and the mixing intensity and effective mixing zone were investigated. K-omega SST turbulence model was adopted for the simulations. Results of the analysis suggest that, effective mixing of cold and hot fluid which leads to reduced and uniform temperature field at the pipe wall boundary, is achieved at 0 (90) degree inclination of the branch pipe and hence may lower thermal stress levels in the structural material of the pipe. Turbulence mixing, pressure drop and velocity distribution were also found to be more appreciable at 0 (90) degree inclination angle of the branch pipe relative to the other orientations studied.

Regional averaged controllability for hyperbolic parameter dependent systems

The purpose of this paper is to extend the notion of regional controllability for hyperbolic parameter dependent systems.The key idea is the characterization of the averaged regional control with minimal energy.This control steers the state average(with respect to such a parameter)towards the desired state only on a given part of the system evolution domain.In this paper,we give the precis definition and the properties of this new concept.Then,we use an approach based on an extension of the Hilbert uniqueness method devoted to the calculation of the control in two different cases:zone control and pointwise control.

Preweaning period is a critical window for rumen microbial regulation of average daily gain in Holstein heifer calves

Background Rumen bacterial groups can affect growth performance,such as average daily gain(ADG),feed intake,and efficiency.The study aimed to investigate the inter-relationship of rumen bacterial composition,rumen fermentation indicators,serum indicators,and growth performance of Holstein heifer calves with different ADG.Twelve calves were chosen from a trail with 60 calves and divided into higher ADG(HADG,high pre-and post-weaning ADG,n=6)and lower ADG(LADG,low pre-and post-weaning ADG,n=6)groups to investigate differences in bacterial composition and functions and host phenotype.Results During the preweaning period,the relative abundances of propionate producers,including g_norank_f_Butyricicoccaceae,g_Pyramidobacter,and g_norank_f_norank_o_Clostridia_vadin BB60_group,were higher in HADG calves(LDA>2,P<0.05).Enrichment of these bacteria resulted in increased levels of propionate,a gluconeogenic precursor,in preweaning HADG calves(adjusted P<0.05),which consequently raised serum glucose concentrations(adjusted P<0.05).In contrast,the relative abundances of rumen bacteria in post-weaning HADG calves did not exert this effect.Moreover,no significant differences were observed in rumen fermentation parameters and serum indices between the two groups.Conclusions The findings of this study revealed that the preweaning period is the window of opportunity for rumen bacteria to regulate the ADG of calves.

Development of SOM combustion model for Reynolds-averaged and large-eddy simulation of turbulent combustion and its validation by DNS

The derivation and closure methods of the second-order moment (SOM) combus- tion model are proposed. The application of this model to Reynolds averaged (RANS) and large-eddy simulation (LES) of turbulent swirling diffusion combustion, jet diffusion combustion, and bluff-body stabilized premixed combustion is sum- marized. It is indicated that the SOM model is much better than the eddy-beak-up (EBU) and presumed PDF models widely used in commercial software and engi- neering. The SOM modeling results are close to those obtained using the most accurate but much more complex PDF equation model. Moreover, it can save much more computation time than the PDF equation model. Finally, the SOM model is validated by the direct numerical simulation (DNS) of turbulent reacting channel flows.

Influence of Non-Axisymmetric Terms on Circumferentially Averaged Method in Fan/Compressor

The governing equations are derived by circumferentially averaging the three-dimensional （3D） Navier-Stokes equations, which are solved using a time marching finite volume approach. Both Euler throughflow model and Navier-Stokes （N-S） throughflow model are employed to investigate the performance and flow fields of a highly loaded transonic single-stage fan ATS-2 and a four-stage fan. The results are compared with the experimental and three-dimensional computational results. It shows that the throughflow models can provide reasonable perform- ance characteristics and N-S throughflow model gives better predictions in endwall regions. A throughflow com- putation in which all the non-axisymmetric terms are included has been performed at off-design condition and the radial distributions of the flow field can be well described.

基于改进YOLOv8-CEFG的垃圾检测算法研究

随着我国经济的高速发展和人口的急剧增加,生活垃圾污染问题日益突出。然而,传统的人工垃圾检测方法效率低且主观性强。为提高复杂场景下垃圾检测的准确率,文章提出了一种基于改进YOLOv8的垃圾检测模型。为提升检测精度和识别效果,引入了EMA(Exponential Moving Average)注意力机制。实验结果显示,YOLOv8-CEFG在垃圾检测数据集上表现优异,最终其mAP达到了88.8%,比原始YOLOv8模型提高了3.1%。YOLOv8-CEFG算法能较好地满足实时检测任务的需求,并展示了良好的检测效果。

Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull distribution.The control chart developed supports the examination of the mean lifespan variation for a particular product in the process of manufacturing.Three control limit levels are used:the warning control limit,inner control limit,and outer control limit.Together,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control chart.The control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control charts.Finally,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.

A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human lives.The detonation of these landmines results in thousands of casualties reported worldwide annually.Therefore,there is a pressing need to employ diverse landmine detection techniques for their removal.One effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic field.It can generate a contour plot or heat map that visually represents the magnetic field strength.Despite the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith risks.Edge computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine detection.By processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field data.It enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the process.Furthermore,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during transmission.This paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and localization.We have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset traces.By simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry images.The trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.