An Algorithm for Short-Circuit Current Interval in Distribution Networks with Inverter Type Distributed Generation Based on Affine Arithmetic

During faults in a distribution network,the output power of a distributed generation(DG)may be uncertain.Moreover,the output currents of distributed power sources are also affected by the output power,resulting in uncertainties in the calculation of the short-circuit current at the time of a fault.Additionally,the impacts of such uncertainties around short-circuit currents will increase with the increase of distributed power sources.Thus,it is very important to develop a method for calculating the short-circuit current while considering the uncertainties in a distribution network.In this study,an affine arithmetic algorithm for calculating short-circuit current intervals in distribution networks with distributed power sources while considering power fluctuations is presented.The proposed algorithm includes two stages.In the first stage,normal operations are considered to establish a conservative interval affine optimization model of injection currents in distributed power sources.Constrained by the fluctuation range of distributed generation power at the moment of fault occurrence,the model can then be used to solve for the fluctuation range of injected current amplitudes in distributed power sources.The second stage is implemented after a malfunction occurs.In this stage,an affine optimization model is first established.This model is developed to characterizes the short-circuit current interval of a transmission line,and is constrained by the fluctuation range of the injected current amplitude of DG during normal operations.Finally,the range of the short-circuit current amplitudes of distribution network lines after a short-circuit fault occurs is predicted.The algorithm proposed in this article obtains an interval range containing accurate results through interval operation.Compared with traditional point value calculation methods,interval calculation methods can provide more reliable analysis and calculation results.The range of short-circuit current amplitude obtained by this algorithm is slightly larger than those obtained using the Monte Carlo algorithm and the Latin hypercube sampling algorithm.Therefore,the proposed algorithm has good suitability and does not require iterative calculations,resulting in a significant improvement in computational speed compared to the Monte Carlo algorithm and the Latin hypercube sampling algorithm.Furthermore,the proposed algorithm can provide more reliable analysis and calculation results,improving the safety and stability of power systems.

Optimization of Finned-Tube Heat Exchanger in a Gravity-Assisted Separated Heat Pipe

Finned-tube heat exchanger(FTHE)is often used as an evaporator in commercial products of separated heat pipe(SHP).The working conditions of FTHE in gravity-assisted SHP are significantly different from those working in refrigerators and air conditioners.Although FTHE is widely used in commercial products of SHP,previous research on its characteristics is very limited.In this paper,a mathematical model for a SHP with FTHE as the evaporator and plate heat exchanger as the condenser is established and verified with experiments.Parametric analyses are carried out to investigate the influences of evaporator design parameters:air inlet velocity,number of tube rows,tube diameter,and fin pitch.With the increasing of air velocity,number of tube rows and tube diameter,and the decreasing of fin pitch,the heat transfer rate increases,while the energy efficiency ratio(EER)decreases monotonically.Using the total cost of the ten-year life cycle as the performance index,the structure parameters of the evaporator with a given heat transfer rate are optimized by the method of orthogonal experimental design.It is found that the total cost can differ as large as nearly ten times between groups.Among the three factors investigated,the number of tube rows has a significant impact on the total cost of the evaporator.With more tube rows,the total cost will be less.The impacts of fin pitch and tube diameter are insignificant.These results are of practical importance for the engineering design of FTHE in gravity-assisted SHP.

Large Rabi splitting obtained in Ag‐WS2 strong‐coupling heterostructure with optical microcavity at room temperature

Manipulation of light-matter interaction is critical in modern physics, especially in the strong coupling regime, where the generated half-light, half-matter bosonic quasiparticles as polaritons are important for fundamental quantum science and applications of optoelectronics and nonlinear optics. Two-dimensional transition metal dichalcogenides (TMDs) are ideal platforms to investigate the strong coupling because of their huge exciton binding energy and large absorption coefficients. Further studies on strong exciton-plasmon coupling by combining TMDs with metallic nanostructures have generated broad interests in recent years. However, because of the huge plasmon radiative damping, the observation of strong coupling is significantly limited at room temperature. Here, we demonstrate that a large Rabi splitting (~300 meV) can be achieved at ambient conditions in the strong coupling regime by embedding Ag-WS2 heterostructure in an optical microcavity. The generated quasiparticle with part-plasmon, part-exciton and part-light is analyzed with Hopfield coefficients that are calculated by using three-coupled oscillator model. The resulted plasmon-exciton polaritonic hybrid states can efficiently enlarge the obtained Rabi splitting, which paves the way for the practical applications of polaritonic devices based on ultrathin materials.

Real-time prediction of mechanical behaviors of underwater shield tunnel structure using machine learning method based on structural health monitoring data

Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is rough.Therefore,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)data.An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time series.Then,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h.As a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel.The robustness study is carried out to verify the reliability and the prediction capability of the proposed model.Finally,the ATENet model is compared with some typical models,and the results indicate that it has the best performance.ATENet model is of great value to predict the realtime evolution trend of tunnel structure.

基于时空数据的地下空间基础设施智能监测系统

基于时空大数据的智能感知、机理认知和劣化预知,不仅促进了基础设施安全的发展,同时也是基础设施建设向智能化转变的基础理论和关键技术。地下空间利用的发展,形成了深、大、聚的三大特征和立体的城市布局。然而,与地上的建筑物和桥梁相比,发生在地下的疾病和退化更为隐蔽,难以识别,在建设和服务期间仍然存在许多挑战。针对这一问题,本文总结了现有的方法,并在现实世界的空间安全管理中评估了它们的长处和短处,并在统一的智能监控系统中,讨论关键科学问题和解决方案。

Effects of a rooftop wind turbine on the dispersion of air pollutant behind a cube-shaped building

The concentration distribution of urban air pollutants is closely related to people’s health.As an important utilization form of urban wind power,rooftop wind turbines have been widely used in cities.The wake effect of the rooftop wind turbines will change the flow behind buildings and then affect the pollutant dispersion.To this end,the pollutant dispersion behind the building is studied via the computational fluid dynamics method.The actuator disk model and idealized cube are adopted to model the wind turbine and the building,respectively.The study shows that the rooftop wind turbine can reduce the pollutant mass fraction near the ground and the pedestrian level.Due to the wake effect of the rooftop wind turbine,the turbulent fluctuation behind the building is weakened,and the spanwise pollutant dispersion is suppressed.Besides,the rooftop wind turbine weakens the downwash movement of the building,which enhances the vertical pollutant dispersion.

Conception and Exploration of Using Data as a Service in Tunnel Construction with the NATM

The New Austrian Tunneling Method （NATM） has been widely used in the construction of mountain tun- nels, urban metro lines, underground storage tanks, underground power houses, mining roadways, and so on, The variation patterns of advance geological prediction data, stress-strain data of supporting struc- tures, and deformation data of the surrounding rock are vitally important in assessing the rationality and reliability of construction schemes, and provide essential information to ensure the safety and scheduling of tunnel construction, However, as the quantity of these data increases significantly, the uncertainty and discreteness of the mass data make it extremely difficult to produce a reasonable con- struction scheme; they also reduce the forecast accuracy of accidents and dangerous situations, creating huge challenges in tunnel construction safety, In order to solve this problem, a novel data service system is proposed that uses data-association technology and the NATM, with the support of a big data environ- ment, This system can integrate data resources from distributed monitoring sensors during the construc- tion process, and then identify associations and build relations among data resources under the same construction conditions, These data associations and relations are then stored in a data pool, With the development and supplementation of the data pool, similar relations can then he used under similar con- ditions, in order to provide data references for construction schematic designs and resource allocation, The proposed data service system also provides valuable guidance for the construction of similar projects.

Direct observation of ultrafast plasmonic hot electron transfer in the strong coupling regime

Achieving strong coupling between plasmonic oscillators can significantly modulate their intrinsic optical properties.Here,we report the direct observation of ultrafast plasmonic hot electron transfer from an Au grating array to an MoS_(2) monolayer in the strong coupling regime between localized surface plasmons(LSPs)and surface plasmon polaritons(SPPs).By means of femtosecond pump-probe spectroscopy,the measured hot electron transfer time is approximately 40 fs with a maximum external quantum yield of 1.65%.Our results suggest that strong coupling between LSPs and SPPs has synergetic effects on the generation of plasmonic hot carriers,where SPPs with a unique nonradiative feature can act as an‘energy recycle bin’to reuse the radiative energy of LSPs and contribute to hot carrier generation.Coherent energy exchange between plasmonic modes in the strong coupling regime can further enhance the vertical electric field and promote the transfer of hot electrons between the Au grating and the MoS_(2) monolayer.Our proposed plasmonic strong coupling configuration overcomes the challenge associated with utilizing hot carriers and is instructive in terms of improving the performance of plasmonic opto-electronic devices.

Continuous amino-functionalized University of Oslo 66 membranes as efficacious polysulfide barriers for lithium−sulfur batteries

The shuttle effect of soluble polysulfides is a serious problem impeding the development of lithium−sulfur batteries.Herein,continuous amino-functionalized University of Oslo 66 membranes supported on carbon nanotube films are proposed as ion-permselective interlayers that overcome these issues and show outstanding suppression of the polysulfide shuttle effect.The proposed membrane material has appropriately sized pores,and can act as ionic sieves and serve as barriers to polysulfides transport while allowing the passage of lithium ions during electrochemical cycles,thereby validly preventing the shuttling of polysulfides.Moreover,a fast catalytic conversion of polysulfides is also achieved with the asdeveloped interlayer.Therefore,lithium−sulfur batteries with this interlayer show a desirable initial capacity of 999.21 mAh·g^(-1)at 1 C and a durable cyclic stability with a decay rate of only 0.04%per cycle over 300 cycles.Moreover,a high area capacity of 4.82 mAh·cm^(-2)is also obtained even under increased sulfur loading(5.12 mg·cm^(-2))and a lean-electrolyte condition(E/S=4.8μL·mg^(-1)).

Fully depleted vdW heterojunction based high performance photovoltaic photodetector

Van der Waals(vdW)heterojunctions,with their unique electronic and optoelectronic properties,have become promising candidates for photodetector applications.Amplifying the contribution of the depletion region in vdW heterojunction,which would enhance both of the collection efficiency and speed of the photogenerated carriers,presents an effective strategy for achieving high performance vdW heterojunction photodetectors.Herein,a fully depleted vdW heterojunction photodetector is built on two-dimensional(2D)semiconductor materials(GaTe and InSe)layered on a pattered bottom electrode in vertical structure,in which the generation and motion of carriers are exclusively achieved in the depletion region.Attributed to the intrinsic built-in electric field,the elimination of series resistance and the depletion region confinement of carriers,the as-fabricated photodetector exhibits prominent photovoltaic properties with a high open-circuit voltage of 0.465 V,as well as photoresponse characteristics with outstanding responsivity,detectivity and photoresponse speed of 63.7 A/W,3.88×10^(13)Jones,and 32.7 ms respectively.The overall performance of this fully depleted GaTe/InSe vdW heterojunctions photodetectors are ranking high among the top level of 2D materials based photodetectors.It indicates the device architecture can provide new opportunities for the fabrication of high-performance photodetectors.

Bi-channel near-and far-field optical vortex generator based on a single plasmonic metasurface

With the recent development of the metasurface,generating an optical vortex in optical far or near fields is realized in various ways.However,to generate vortices in both the near and far fields simultaneously is still a challenge,although it has great potential in the future compact and versatile photonic system.Here,a bi-channel optical vortex generator in both the near and far fields is proposed and demonstrated within a single metasurface,where the surface plasmon vortex and the far-field optical vortex can be simultancously generated under circularly polarized light.The ability of generating vortices with arbitrary topological charges is experimentally demon-strated,which agrces well with simulations.This approach provides great freedom to integrate different vortex generators in a single device and ofers new opprtunities for integrated optical communications,trapping,and other related fields.

Pressure and flowrate distribution in central exhaust shaft with multiple randomly operating range hoods

Central flues are now commonly adopted in high-rise residential buildings in China for cooking oil fumes(COF)exhaust.Range hoods of all floors are connected to the central shaft,where oil fumes were gathered and exhausted through the outlet at the building roof.As households may cook and use their range hood at random periods,there is great uncertainty of the amount of COF being exhausted.In addition,users can often adjust the exhaust rate of the range hood according to their needs.As a result,thousands of possible operating conditions consisting of distinct combinations of on/off conditions and fan speed occur randomly in the central COF exhaust system,causing the exhaust performance to vary considerably from condition to condition.This work developed a mathematical model for characterizing the operation of the central COF exhaust system in a high-rise residential building as well as its iterative solving method.Full-scale tests coupled with CFD simulation referring to a real 30-floor building were conducted to validate the proposed model.The results show that the model agreed well with the CFD and experimental data under various system operating conditions.Moreover,the Monte-Carlo method was introduced to simulate the random operating characteristics of the system,and a hundred thousand cases corresponding to distinct system operating conditions were sampled and statistically analyzed.

Localization and characterization of intermittent pollutant source in buildings with ventilation systems:Development and validation of an inverse model

Terrorist attacks through building ventilation systems are becoming an increasing concern.In case pollutants are intentionally released in a building with mechanical ventilation systems,it is critical to localize the source and characterize its releasing curve.Previous inverse modeling studies have adopted the adjoint probability method to identify the source location and used the Tikhonov regularization method to determine the source releasing profile,but the selection of the prediction model and determination of the regularization parameter remain challenging.These limitations can affect the identification accuracy and prolong the computational time required.To address the difficulties in solving the inverse problems,this work proposed a Markov-chain-oriented inverse approach to identify the temporal release rate and location of a pollutant source in buildings with ventilation systems and validated it in an experimental chamber.In the modified Markov chain,the source term was discrete by each time step,and the pollutant distribution was directly calculated with no iterations.The forward Markov chain was reversed to characterize the intermittently releasing profile by introducing the Tikhonov regularization method,while the regularized parameter was determined by an automatic iterative discrepancy method.The source location was further estimated by adopting the Bayes inference.With chamber experiments,the effectiveness of the proposed inverse model was validated,and the impact of the sensor performance,quantity and placement,as well as pollutant releasing curves on the identification accuracy of the source intensity was explicitly discussed.Results showed that the inverse model can identify the intermittent releasing rate efficiently and promptly,and the identification error for pollutant releasing curves with complex waveforms is about 20%.

Au/MXene based ultrafast all-optical switching

All-optical switches have arisen great attention due to their ultrafast speed as compared with electric switches.However,the excellent optical properties and strong interaction of two-dimensional(2D)material MXene show great potentials in next-generation all-optical switching.As a solution,we propose all-optical switching used Au/MXene with switching full width at half maximum(FWHM)operating at 290 fs.Compared with pure MXene,the Au/MXene behaves outstanding performances due to local surface plasmon resonance(LSPR),including broadband differential transmission,strong near-infrared on/off ratio enhancement.Remarkably,this study enhances understanding of Au/MXene based ultrafast all-optical switching red-shifted about 34 nm in comparison to MXene,validating all optical properties of Au/MXene opening the way to the implementation of optical interconnection and optical switching.

Investigation on identification of structural anomalies from polluted data sets using an unsupervised learning method

Civil infrastructure is prone to structural damage due to high geo-stress and other natural disasters,so monitoring is required.Data collected by structural health monitoring(SHM)systems are easily affected by many factors,such as temperature,sensor fluctuation,sensor failure,which can introduce a lot of noise,increasing the difficulty of structural anomaly identification.To address this problem,this paper designs a new process of structural anomaly identification under noisy conditions and offers Civil Infrastructure Denoising Autoencoder(CIDAE),a denoising autoencoder-based deep learning model for SHM of civil infrastructure.As a case study,the effectiveness of the proposed model is verified by experiments on deformation stress data of the Wuhan Yangtze River Tunnel based on finite element simulation.Investigation of the circumferential weld and longitudinal weld data of the case study is also conducted.It is concluded that CIDAE is superior to traditional methods.