Fast prototype and rapid construction of three-dimensional and multi-scaled pitcher for controlled drainage by systematic biomimicry

Biomimetic materials that use natural wisdom to solve practical problems are developing rapidly.The trend for systematic biomimicry is towards in-situ characterization of naturalcreatures with high spatial resolutions.Furthermore,rapid reconstruction of digital twin models with the same complex features as the prototype is indispensable.However,it faces bottlenecks and limits in fast characterization and fabrication,precise parameter optimization,geometricdeviations control,and quality prediction.To solve these challenges,here,we demonstrate astate-of-the-art method taking advantage of micro-computed tomography and three-dimensional printing for the fast characterization of the pitcher plant Nepenthes x ventrata and fabrication of its biomimetic model to obtain a superior drainage controller with multiscale structures withprecise surface morphology optimization and geometric deviation control.Thefilm-rupture-based drainage dynamic and mechanisms are characterized by x-ray and high-speed videography,which determines the crucial structures for unique directionaldrainage.Then the optimized artificial pitchers are further developed into sustained drainage devices with novel applications,such as detection,reaction,and smoke control.

Additive manufacturing of magnesium matrix composites: Comprehensive review of recent progress and research perspectives

The magnesium matrix composites(MMCs) formed by introducing reinforcements to magnesium alloys overcome the limitations of the mechanical properties to a certain extent, presenting unique and excellent properties that any component does not have, such as high specific stiffness and specific strength, good dimensional stability, outstanding shock absorption performance, excellent electromagnetic shielding and hydrogen storage characteristics, etc. As an emerging manufacturing technology, additive manufacturing(AM) is based on the design of threedimensional(3D) data model to obtain 3D objects through layer-by-layer processing, which possesses the advantages of short manufacturing cycle, high material utilization rate, high degree of design freedom, excellent mechanical properties and the ability to fabricate complex structural components. Combining the high stiffness and high strength properties of MMCs and the technical advantages of AM forming complex structural parts with high performance, the prepared AM MMCs have huge potential advantages and broad application prospects in new high-tech industries such as automobile, aerospace, consumer electronics and biomedicine, etc. This paper reviews the research progress in the field of AM MMCs, mainly introduces the main AM technologies, including selective laser melting(SLM), electron beam selective melting(EBSM), laser engineered net shaping(LENS) and wire and arc additive manufacturing(WAAM). The formation mechanism and control methods of the typical defects including balling effect, porosity, poor fusion, loss of alloy elements and cracks produced during AM are discussed. The main challenges of AM MMCs are proposed from the aspects of composition design and the preparation of powder raw material. The relationship between the microstructure and mechanical properties, corrosion performance and biocompatibility of AM MMCs are elaborated in detail. The application potential of AM MMCs in various fields at present and in the future is introduced. Finally, the development direction and urgent problems to be solved in the AM MMCs are prospected.

Effect of Ni content on the wear behavior of Al-Si-Cu-Mg-Ni/SiC particles composites

In recent years,the addition of Ni has been widely acknowledged to be capable of enhancing the mechanical properties of Al-Si alloys.However,the effect of Ni on the wear behaviors of Al-Si alloys and Al matrix composites,particularly at elevated temperat-ures,remains an understudied area.In this study,Al-Si-Cu-Mg-Ni/20wt%SiC particles(SiCp)composites with varying Ni contents were prepared by using a semisolid stir casting method.The effect of Ni content on the dry sliding wear behavior of the prepared compos-ites was investigated through sliding tests at 25 and 350℃.Results indicated that theθ-Al_(2)Cu phase gradually diminished and eventually disappeared as the Ni content increased from 0wt%to 3wt%.This change was accompanied by the formation and increase inδ-Al_(3)CuNi andε-Al_(3)Ni phases in microstructures.The hardness and ultimate tensile strength of the as-cast composites improved,and the wear rates of the composites decreased from 5.29×10^(−4)to 1.94×10^(−4)mm^(3)/(N∙m)at 25℃and from 20.2×10^(−4)to 7×10^(−4)mm^(3)/(N∙m)at 350℃with the increase in Ni content from 0wt%to 2wt%.The enhancement in performance was due to the presence of strengthening network structures and additional Ni-containing phases in the composites.However,the wear rate of the 3Ni composite was approximately two times higher than that of the 2Ni composite due to the fracture and debonding of theε-Al_(3)Ni phase.Abrasive wear,delamination wear,and oxidation wear were the predominant wear mechanisms of the investigated composites at 25℃,whereas delamination wear and oxid-ation wear were dominant during sliding at 350℃.

Dynamic range and linearity improvement for zero-field single-beam atomic magnetometer

Zero-field single-beam atomic magnetometers with transverse parametric modulation for ultra-weak magnetic field detection have attracted widespread attention recently.In this study,we present a comprehensive response model and propose a modification method of conventional first harmonic response by introducing the second harmonic correction.The proposed modification method gives improvement in dynamic range and reduction of linearity error.Additionally,our modification method shows suppression of response instability caused by optical intensity and frequency fluctuations.An atomic magnetometer with single-beam configuration is built to compare the performance between our proposed method and the conventional method.The results indicate that our method’s magnetic field response signal achieves a 5-fold expansion of dynamic range from 2 nT to 10 nT,with the linearity error decreased from 5%to 1%.Under the fluctuations of 5%for optical intensity and±15 GHz detuning of frequency,the proposed modification method maintains intensityrelated instability less than 1%and frequency-related instability less than 8%while the conventional method suffers 15%and 38%,respectively.Our method is promising for future high-sensitive and long-term stable optically pumped atomic sensors.

Atomic Spin Polarization Controllability Analysis:A Novel Controllability Determination Method for Spin-Exchange Relaxation-Free Co-Magnetometers

This paper investigates the atomic spin polarization controllability of spin-exchange relaxation-free co-magnetometers(SERFCMs).This is the first work in the field of controllability analysis for the atomic spin ensembles systems,whose dynamic behaviors of spin polarization are described by the Bloch equations.Based on the Bloch equations,a state-space model of the atomic spin polarization for SERFCM is first established,which belongs to a particular class of nonlinear systems.For this class of nonlinear systems,a novel determination method for the global state controllability is proposed and proved.Then,this method is implemented in the process of controllability analysis on the atomic spin polarization of an actual SERFCM.Moreover,a theoretically feasible and reasonable solution of the control input is proposed under some physical constraints,with whose limitation of realistic conditions,the controller design can be accomplished more practically and more exactly.Finally,the simulation results demonstrate the feasibility and validation of the proposed controllability determination method.

N-type core-shell heterostructured Bi2S3@Bi nanorods/polyaniline hybrids for stretchable thermoelectric generator

With the growing need on distributed power supply for portable electronics,energy harvesting from environment becomes a promising solution.Organic thermoelectric(TE)materials have advantages in intrinsic flexibility and low thermal conductivity,thus hold great prospect in applications as a flexible power generator from dissipated heat.Nevertheless,the weak electrical transport behaviors of organic TE materials have severely impeded their development.Moreover,compared with p-type organic TE materials,stable and high-performance n-type counterparts are more difficult to obtain.Here,we developed a n-type polyaniline-based hybrid with core-shell heterostructured Bi;S;@Bi nanorods as fillers,showing a Seebeck coefficient-159.4μV/K at room temperature.Further,a couple of n/p legs from the PANI-based hybrids were integrated into an elastomer substrate forming a stretchable thermoelectric generator(TEG),whose function to output stable voltages responding to temperature differences has been demonstrated.The in situ output performance of the TEG under stretching could withstand up to 75%elongation,and stability test showed little degradation over a one-month period in the air.This study provides a promising strategy to develop stable and high thermopower organic TEGs harvesting heat from environment as long-term power supply.

Accurate prediction of the critical heat flux for pool boiling on the heater substrate

While the influence of liquid qualities,surface morphology,and operating circumstances on critical heat flux(CHF)in pool boiling has been extensively studied,the effect of the heater substrate has not.Based on the force balance analysis,a theoretical model has been developed to accurately predict the CHF in pool boiling on a heater substrate.An analytical expression for the CHF of a heater substrate is obtained in terms of the surface thermophysical property.It is indicated that the ratio of thermal conductivity(k)to the product of density(ρ)and specific heat(cp)is an essential substrate property that influences the CHF.By modifying the well-known force-balance-based CHF model(Kandlikar model),the thermal characteristics of the substrate are taken into consideration.The bias of predicted CHF values are within 5%compared with the experimental results.

数字孪生增强的复合材料质量预测

复合材料以其优异的性能被广泛应用于许多领域。复合材料的质量缺陷会导致其构件的性能下降,成为潜在的事故隐患。当前国内外研究者通常采用实验或仿真的方法对复合材料的质量进行预测。然而,由于固化环境的不确定性和动态、静态特征考虑不全面,因此难以准确预测复合材料的质量。为了解决这一问题,本文首先建立了复合材料的数字孪生(DT)模型,然后通过实现静态热压罐DT虚拟模型与可变复合材料DT虚拟模型的耦合,完成复合材料固化过程数字孪生模型的构建。基于该固化过程模型,生成模拟数据来增加动态特征,从而提高质量预测的准确性。最后基于获取的数据,使用极限学习机(ELM)构建复合材料质量预测模型,并通过结果分析验证了所提方法的有效性。

Enhancing thermoelectric performance of p-type SnTe through manipulating energy band structures and decreasing electronic thermal conductivity

SnTe has received considerable attention as an environmentally friendly alternative to the representative thermoelectric material of PbTe.However,excessive hole carrier concentration in SnTe results in an extremely low Seebeck coefficient and high thermal conductivity,which makes it exhibit relatively inferior thermoelectric properties.In this work,the thermoelectric performance of p-type SnTe is enhanced through regulating its energy band structures and reducing its electronic thermal conductivity by combining Bi doping with CdSe alloying.First,the carrier concentration of SnTe is successfully suppressed via Bi doping,which significantly decreases the electronic thermal conductivity.Then,the convergence and flattening of the valence bands by alloying CdSe effectively improves the effective mass of SnTe while restraining its carrier mobility.Finally,a maximum figure of merit(ZT) of~ 0.87 at 823 K and an average ZT of~ 0.51 at 300-823 K have been achieved in Sn_(0.96)Bi_(0.04)Te-5%CdSe.Our results indicate that decreasing the electronic thermal conductivity is an effective means of improving the performance of thermoelectric materials with a high carrier concentration.

Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system

As one of the core modules for air traffic flow management,Air Traffic Flow Prediction(ATFP)in the Multi-Airport System(MAS)is a prerequisite for demand and capacity balance in the complex meteorological environment.Due to the challenge of implicit interaction mechanism among traffic flow,airspace capacity and weather impact,the Weather-aware ATFP(Wa-ATFP)is still a nontrivial issue.In this paper,a novel Multi-faceted Spatio-Temporal Graph Convolutional Network(MSTGCN)is proposed to address the Wa-ATFP within the complex operations of MAS.Firstly,a spatio-temporal graph is constructed with three different nodes,including airport,route,and fix to describe the topology structure of MAS.Secondly,a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather,which can effectively address the complex impact of severe weather,e.g.,thunderstorms.Thirdly,to capture the latent connections of nodes,an adaptive graph connection constructor is designed.The experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area,China,validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance.

Single sample electromagnetic spectrum recognition utilizing fractional Fourier transform

Electromagnetic Spectrum(EMS)recognition is vital in spectrum control,interference location,electronic countermeasures,etc.However,samples of high-value targets are incredibly scarce,even single,and are easily overwhelmed by noise and numerous low-value targets,resulting in poor recognition accuracy using traditional methods.Furthermore,the great similarity between samples from the same manufacturer,model,and batch,makes Specific Emitter Identification(SEI)with the EMS especially challenging.Based on the powerful extension and extraction ability of the Fractional Fourier Transform(FrFT)for detailed features,this paper proposes a novel algorithm for the EMS recognition under a single-sample condition.The proposed method constructs a feature matrix FrFT-M from the results of the FrFT under specific orders for each sample.Then,the most relevant item,obtained by analyzing the correlations among FrFT-Ms between the unidentified sample and known samples,determines the optimal recognition.Three simple tests are conducted,including two simulations considering fifteen basic waveforms and six typical radar signals,and one experiment using STM32 microcontroller boards.The detection results of simulated and experimental data show that the accuracies of all three cases are higher than 86%,even for samples of the same model.Our method is promising and may have significant value in other fields.

Effect of Nd addition on microstructure and tensile properties of laser additive manufactured TC11 titanium alloy

Single-layer and multilayer laser additive manufacturing(LAM)for TC11 alloy with different Nd additions was conducted and the effect of Nd addition on microstructure and properties was studied.With the addition of Nd,the aspect ratio of melting pools of single-layer specimens increases and the columnar-to-equiaxed transition occurs.The originalβgrain size andαplate width of TC11−1.0Nd are significantly reduced compared with those of pure TC11 specimens.It is proposed that the evenly distributed fine Nd_(2)O_(3) precipitates of about 1.51μm are formed preferentially during rapid solidification of melting pool,and they serve as heterogeneous nucleation particles to refine the microstructure in the subsequent solidification and solid-state phase transformation.Due to the multiple effects of Nd on the microstructure,the ultimate tensile strength of TC11−1.0Nd increases,while the yield strength,ductility and microhardness decrease compared with those of pure TC11.

Dual Attention Based Feature Pyramid Network

Object detection could be recognized as an essential part of the research to scenarios such as automatic driving and pedestrian detection, etc. Among multiple types of target objects, the identification of small-scale objects faces significant challenges. We would introduce a new feature pyramid framework called Dual Attention based Feature Pyramid Network(DAFPN), which is designed to avoid predicament about multi-scale object recognition. In DAFPN, the attention mechanism is introduced by calculating the topdown pathway and lateral pathway, where the spatial attention, as well as channel attention, would participate, respectively, such that the pyramidal feature maps can be generated with enhanced spatial and channel interdependencies, which bring more semantical information for the feature pyramid. Using the COCO data set, which consists of a considerable quantity of small-scale objects, the experiments are implemented. The analysis results verify the optimized performance of DAFPN compared with the original Feature Pyramid Network(FPN) specifically for the identification on a small scale. The proposed DAFPN is promising for object detection in an era full of intelligent machines that need to detect multi-scale objects.

Adaptive Maxwell’s Equations Derived Optimization and Its Application in Antenna Array Synthesis

In this paper,a self-adaptive method for the Maxwell’s Equations Derived Optimization(MEDO)is proposed.It is implemented by applying the Sequential Model-Based Optimization(SMBO)algorithm to the iterations of the MEDO,and achieves the automatic adjustment of the parameters.The proposed method is named as adaptive Maxwell’s equations derived optimization(AMEDO).In order to evaluate the performance of AMEDO,eight benchmarks are used and the results are compared with the original MEDO method.The results show that AMEDO can greatly reduce the workload of manual adjustment of parameters,and at the same time can keep the accuracy and stability.Moreover,the convergence of the optimization can be accelerated due to the dynamical adjustment of the parameters.In the end,the proposed AMEDO is applied to the side lobe level suppression and array failure correction of a linear antenna array,and shows great potential in antenna array synthesis.

GSTAR:an innovative software platform for processing space geodetic data at the observation level

To meet the demands for the data combination with multiple space geodetic techniques at the observation level,we developed a new software platform with high extensibility and computation efficiency,named space Geodetic SpatioTemporal data Analysis and Research software(GSTAR).Most of the modules in the GSTAR are coded in C++with object-oriented programming.The layered modular theory is adopted for the design of the software,and the antenna-based data architecture is proposed for users to construct personalized geodetic application scenarios easily.The initial performance of the GSTAR software is evaluated by processing the Global Navigation Satellite System(GNSS)data collected from 315 globally distributed stations over two and a half years.The accuracy of GNSS-based geodetic products is evaluated by comparing them with those released by International GNSS Service(IGS)Analysis Centers(AC).Taking the products released by European Space Agency(ESA)as reference,the Three-Dimension(3D)Root-Mean-Squares(RMS)of the orbit differences are 2.7/6.7/3.3/7.7/21.0 cm and the STandard Deviations(STD)of the clock differences are 19/48/16/32/25 ps for Global Positioning System(GPS),GLObal NAvigation Satellite System(GLONASS),Galileo navigation satellite system(Galileo),BeiDou Navigation Satellite System(BDS),Medium Earth Orbit(MEO),and BDS Inclined Geo-Synchronous Orbit(IGSO)satellites,respectively.The mean values of the X and Y components of the polar coordinate and the Length of Day(LOD)with respect to the International Earth Rotation and Reference Systems Service(IERS)14 C04 products are-17.6 microarc-second(μas),9.2μas,and 14.0μs/d.Compared to the IGS daily solution,the RMSs of the site position differences in the north/east/up direction are 1.6/1.5/3.9,3.8/2.4/7.6,2.5/2.4/7.9 and 2.7/2.3/7.4 mm for GPS-only,GLONASS-only,Galileo-only,and BDS-only solution,respectively.The RMSs of the differences of the tropospheric Zenith Path Delay(ZPD),the north gradients,and the east gradients are 5.8,0.9,and 0.9 mm with respect to the IGS products.The X and Y components of the geocenter motion estimated from GPS-only,Galileo-only,and BDS-only observations well agree with IGS products,while the Z component values are much nosier where anomalous harmonics in GNSS draconitic year can be found.The accuracies of the above products calculated by the GSTAR are comparable with those from different IGS ACs.Compared to the precise scientific orbit products,the 3D RMS of the orbit differences for the two Gravity Recovery and Climate Experiment Follow-on(GRACE-FO)satellites is below 1.5 cm by conducting Precise Point Positioning with Ambiguity Resolution(PPP-AR).In addition,a series of rapid data processing algorithms are developed,and the operation speed of the GSTAR software is 5.6 times faster than that of the Positioning and Navigation Data Analyst(PANDA)software for the quad-system precise orbit determination procedure.

A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning,flight scheduling,airport operation,and passenger service.Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity,which brings critical challenges to accurate flight delay prediction.In recent years,Graph Convolutional Networks(GCNs)have become popular in flight delay prediction due to the advantage in extracting complicated relationships.However,most of the existing GCN-based methods have failed to effectively capture the spatial-temporal information in flight delay prediction.In this paper,a Geographical and Operational Graph Convolutional Network(GOGCN)is proposed for multi-airport flight delay prediction.The GOGCN is a GCN-based spatial-temporal model that improves node feature representation ability with geographical and operational spatial-temporal interactions in a graph.Specifically,an operational aggregator is designed to extract global operational information based on the graph structure,while a geographical aggregator is developed to capture the similar nature among spatially close airports.Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.

Flow characteristics of integrated motor-pump assembly with phosphate ester medium for aerospace electro-hydrostatic actuators

Motor-pump assembly is the core component of the Aerospace Electro-hydrostatic Actuator(EHA).Thus,the design of the motor pump can be very challenging under conditions of high speed and wide pressure range,especially in particular working mediums.Our ultimate goal is to pursue better flow characteristics under a wide range of working conditions.In this paper,we built a sub-model of the main friction interfaces and a model of single-shaft coaxial motor-pump assembly adopting the method of hierarchical modeling.The experimental investigation of the output characteristics was mainly carried out in a phosphate ester medium environment.Then,the flow characteristics were compared and analyzed with the simulation results.Results indicated that the flow characteristics of the motor-pump assembly could be accurately simulated by the model and quite severe in a low speed and high-pressure environment.

Modeling and analysis of oil frictional loss in wet-type permanent magnet synchronous motor for aerospace electro-hydrostatic actuator

To improve the power density and simplify the seal structure,the Wet-Type Permanent Magnet Synchronous Motor(WTPMSM)technique has been applied to aerospace Electro-Hydrostatic Actuators(EHA).In a WTPMSM,the stator and the rotor are both immersed in the aviation hydraulic oil.Although the heat dissipation performance of the WTPMSM can be enhanced,the aviation hydraulic oil will cost an extra oil frictional loss in the narrow airgap of the WTPMSM.This paper proposes an accurate oil frictional loss model for the WTPMSM,in which the wide speed range(0–20 kr/min)and the narrowness of the airgap(0.5–1.5 mm)are its features.Firstly,the mechanism of the oil frictional loss in the airgap of the WTPMSM is revealed.Then an accurate oil frictional loss model is proposed considering the nonlinear influence caused by the Taylor vortex.Furthermore,the influence of motor dimensions on oil frictional loss is analyzed.Finally,the proposed oil frictional loss model is verified by experiments,which provides a guideline for engineers to follow in the WTPMSM design.

Microstructure and Mechanical Properties of an Ultrahigh-strength Titanium alloy Ti-4.5Al-5Mo-5V-6Cr-1Nb Prepared Using Laser Directed Energy Deposition and Forging:A Comparative Study

The application of titanium alloys in aerospace put forward the requirement for higher strength.Additive manu-facturing is a promising method for the efficient and economical processing of titanium alloys.However,research on the additive manufacturing of ultrahigh-strength titanium alloys is still limited.The mechanisms of microseg-regation for high alloying elements and poor plasticity are still not clear.In this study,an ultrahigh-strength titanium alloy Ti-4.5Al-5Mo-5V-6Cr-1Nb(TB18)was prepared using two methods:laser direct energy deposi-tion(LDED)and forging.The LDEDed alloy contains three zones with similar grain morphologies but different microstructure.The microsegregation of the alloy is limited due to the rapid solidification and almost eliminated after the thermal cycle and solution treatment.With stress relief treatment,the LDEDed alloy exhibits anisotropic mechanical properties.After solution and aging treatments,its ultimate strength is enhanced;however,its plas-ticity is relatively lower than that of the wrought alloy with equally high strength.The excellent balance of the strength and plasticity of the wrought alloy can be ascribed to the formation of𝛼WGB and multiscale𝛼laths,which provides enlightenment for optimizing the properties of the LDEDed alloy.

Combinatorial screening via high-throughput preparation:Thermoelectric performance optimization for n-type Bi-Te-Se film with high average zT>1

Thermoelectric materials have drawn extensive interest due to the direct conversion between electricity and heat,however,it is usually a time-consuming process for applying traditional“sequential”meth-ods to grow materials and investigate their properties,especially for thermoelectric films that typically require fine microstructure control.High-throughput experimental approaches can effectively accelerate materials development,but the methods for high-throughput screening of the microstructures require further study.In this work,a combinatorial high-throughput optimization solution of material properties is proposed for the parallel screening and optimizing of composition and microstructure,which involves two distinctive types of high-throughput fabrication approaches for thin films,along with a new portable multiple discrete masks based high-throughput preparation platform.Thus,Bi_(2)Te_(3-x)Se_(x)thin film library with 196 throughputs for locating the optimized composition is obtained in one growth cycle.In addition,another thin film library composed of 31 materials with traceable process parameters is built to further investigate the relationship between microstructure,process,and thermoelectric performance.Through high-throughput screening,the Bi_(2)Te_(2.9)Se_(0.1)film with(00l)orientation is prepared with a peak zT value of 1.303 at 353 K along with a high average zT value of 1.047 in the interval from 313 to 523 K.This method can be also extended to the discovery of other functional thin films with a rapid combinatorial screening of the composition and structure to accelerate material optimization.