By integrating topology optimization and lattice-based optimization,a novel multi-scale design method is proposed to create solid-lattice hybrid structures and thus to improve the mechanical performance as well as red...By integrating topology optimization and lattice-based optimization,a novel multi-scale design method is proposed to create solid-lattice hybrid structures and thus to improve the mechanical performance as well as reduce the structural weight.To achieve this purpose,a two-step procedure is developed to design and optimize the innovative structures.Initially,the classical topology optimization is utilized to find the optimal material layout and primary load carrying paths.Afterwards,the solid-lattice hybrid structures are reconstructed using the finite element mesh based modeling method.And lattice-based optimization is performed to obtain the optimal crosssection area of the lattice structures.Finally,two typical aerospace structures are optimized to demonstrate the effectiveness of the proposed optimization framework.The numerical results are quite encouraging since the solid-lattice hybrid structures obtained by the presented approach show remarkably improved performance when compared with traditional designs.展开更多
This paper deals with the concurrent multi-scale optimization design of frame structure composed of glass or carbon fiber reinforced polymer laminates. In the composite frame structure, the fiber winding angle at the ...This paper deals with the concurrent multi-scale optimization design of frame structure composed of glass or carbon fiber reinforced polymer laminates. In the composite frame structure, the fiber winding angle at the micro-material scale and the geometrical parameter of components of the frame in the macro-structural scale are introduced as the independent variables on the two geometrical scales. Considering manufacturing requirements, discrete fiber winding angles are specified for the micro design variable. The improved Heaviside penalization discrete material optimization interpolation scheme has been applied to achieve the discrete optimization design of the fiber winding angle. An optimization model based on the minimum structural compliance and the specified fiber material volume constraint has been established. The sensitivity information about the two geometrical scales design variables are also deduced considering the characteristics of discrete fiber winding angles. The optimization results of the fiber winding angle or the macro structural topology on the two single geometrical scales, together with the concurrent two-scale optimization, is separately studied and compared in the paper. Numerical examples in the paper show that the concurrent multi-scale optimization can further explore the coupling effect between the macro-structure and micro-material of the composite to achieve an ultralight design of the composite frame structure. The novel two geometrical scales optimization model provides a new opportunity for the design of composite structure in aerospace and other industries.展开更多
In this paper,to present a lightweight-developed front underrun protection device(FUPD)for heavy-duty trucks,plain weave carbon fiber reinforced plastic(CFRP)is used instead of the original high-strength steel.First,t...In this paper,to present a lightweight-developed front underrun protection device(FUPD)for heavy-duty trucks,plain weave carbon fiber reinforced plastic(CFRP)is used instead of the original high-strength steel.First,the mechanical and structural properties of plain carbon fiber composite anti-collision beams are comparatively analyzed from a multi-scale perspective.For studying the design capability of carbon fiber composite materials,we investigate the effects of TC-33 carbon fiber diameter(D),fiber yarn width(W)and height(H),and fiber yarn density(N)on the front underrun protective beam of carbon fiber compositematerials.Based on the investigation,a material-structure matching strategy suitable for the front underrun protective beam of heavy-duty trucks is proposed.Next,the composite material structure is optimized by applying size optimization and stack sequence optimization methods to obtain the higher performance carbon fiber composite front underrun protection beam of commercial vehicles.The results show that the fiber yarn height(H)has the greatest influence on the protective beam,and theH1matching scheme for the front underrun protective beamwith a carbon fiber composite structure exhibits superior performance.The proposed method achieves a weight reduction of 55.21% while still meeting regulatory requirements,which demonstrates its remarkable weight reduction effect.展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the backgroun...Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.展开更多
Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet th...Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.展开更多
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t...Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.展开更多
Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To ...Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.展开更多
The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and dispos...The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and disposing of this mining waste.This study employs a macro-meso-micro testing method to investigate the effects of the waste rock grading index(WGI)and loading rate(LR)on the uniaxial compressive strength(UCS),pore structure,and micromorphology of CTWB materials.Pore structures were analyzed using scanning electron microscopy(SEM)and mercury intrusion porosimetry(MIP).The particles(pores)and cracks analysis system(PCAS)software was used to quantitatively characterize the multi-scale micropores in the SEM images.The key findings indicate that the macroscopic results(UCS)of CTWB materials correspond to the microscopic results(pore structure and micromorphology).Changes in porosity largely depend on the conditions of waste rock grading index and loading rate.The inclusion of waste rock initially increases and then decreases the UCS,while porosity first decreases and then increases,with a critical waste rock grading index of 0.6.As the loading rate increases,UCS initially rises and then falls,while porosity gradually increases.Based on MIP and SEM results,at waste rock grading index 0.6,the most probable pore diameters,total pore area(TPA),pore number(PN),maximum pore area(MPA),and area probability distribution index(APDI)are minimized,while average pore form factor(APF)and fractal dimension of pore porosity distribution(FDPD)are maximized,indicating the most compact pore structure.At a loading rate of 12.0 mm/min,the most probable pore diameters,TPA,PN,MPA,APF,and APDI reach their maximum values,while FDPD reaches its minimum value.Finally,the mechanism of CTWB materials during compression is analyzed,based on the quantitative results of UCS and porosity.The research findings play a crucial role in ensuring the successful application of CTWB materials in deep metal mines.展开更多
Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric n...Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious an...Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.展开更多
Wearable devices with efficient thermal management and electromagnetic interference(EMI) shielding are highly desirable for improving human comfort and safety. Herein, a multifunctional wearable carbon fibers(CF) @ po...Wearable devices with efficient thermal management and electromagnetic interference(EMI) shielding are highly desirable for improving human comfort and safety. Herein, a multifunctional wearable carbon fibers(CF) @ polyaniline(PANI)/silver nanowires(Ag NWs) composites with a “branch-trunk” interlocked micro/nanostructure were achieved through "three-in-one" multi-scale design. The reasonable assembly of the three kinds of one-dimensional(1D) materials can fully exert their excellent properties i.e., the superior flexibility of CF, the robustness of PANI, and the splendid conductivity of Ag NWs. Consequently, the constructed flexible composite demonstrates enhanced mechanical properties with a tensile stress of 1.2 MPa, which was almost 6 times that of the original material. This is mainly attributed to the fact that the PNAI(branch) was firmly attached to the CF(trunk) through polydopamine(PDA), forming a robust interlocked structure. Meanwhile, the composite possesses excellent thermal insulation and heat preservation capacity owing to the synergistically low thermal conductivity and emissivity. More importantly, the conductive path of the composite established by the three 1D materials greatly improved its EMI shielding property and Joule heating performance at low applied voltage. This work paves the way for rational utilization of the intrinsic properties of 1D materials, as well as provides a promising strategy for designing wearable electromagnetic protection and thermal energy management devices.展开更多
In the contemporary era,lithium-ion batteries have gained considerable attention in various industries such as 3C products,electric vehicles and energy storage systems due to their exceptional properties.With the rapi...In the contemporary era,lithium-ion batteries have gained considerable attention in various industries such as 3C products,electric vehicles and energy storage systems due to their exceptional properties.With the rapid progress in the energy storage sector,there is a growing demand for greater energy density in lithium-ion batteries.While the use of thick electrodes is a straightforward and effective approach to enhance the energy density of battery,it is hindered by the sluggish reaction dynamics and insufficient mechanical properties.Therefore,we comprehensively review recent advances in the field of thick electrodes for lithium-ion batteries to overcome the bottlenecks in the development of thick electrodes and achieve efficient fabrication for high-performance lithium-ion batteries.Initially,a systematic analysis is performed to identify the factors affecting the performance of the thick electrodes.the correlation between electrode materials,structural parameters,and performance is also investigated.Subsequently,the viable strategies for constructing thick electrodes with improved properties are summarize,including high throughput,high conductivity and low tortuosity,in both material development and structural design.In addition,recent advances in efficient fabrication methods for thick electrode fabrication are reviewed,with a comprehensive assessment of their merits,limitations,and applicable scenarios.Finally,a comprehensive overview of the multiscale design and manufacturing process for thick electrodes in lithium-ion batteries is provided,accompanied by valuable insights into design considerations that are crucial for future advances in this area.展开更多
Design parameters at different scales in the pre-design phase could significantly impact both building energy consumption and photovoltaic(PV)power generation potential.However,existing studies often overlook the syne...Design parameters at different scales in the pre-design phase could significantly impact both building energy consumption and photovoltaic(PV)power generation potential.However,existing studies often overlook the synergistic effects of design parameters across multiple scales(block-building-facade scales)when evaluating these aspects.This paper aims to propose a workflow for the assessing building energy consumption and PV power generation potential of office blocks applicable in the pre-schematic design phase considering the synergistic influence of multi-scale design parameters,using building typology and parametric modelling approach.The study proposed a multi-scale design parameter classification system combined with parametric modelling.The study investigated 80 office blocks in Wuhan as the study case,which were classified into array type and enclosed type.Correlation analysis and multiple regression equations were used to quantify the single versus synergistic effects of different scale design parameters.Results suggest that focusing solely on a single scale during the pre-design stage is typically inadequate for understanding building energy potential.In contrast,multi-scale synergistic analysis boosts energy use intensity(EUI)by 7.56%and net energy use intensity(NEUI)by 33.96%.Under multi-scale synergistic conditions,the EUI of array type is more influenced by the building design parameters,while the NEUI is effected by the balance of multi-scales design parameters.While the EUI of enclosed types exhibit balanced effects across multi-scale design parameters,with NEUI results aligning closely with PV power generation potential.Multiple regression equations highlight building density and shape factor as key influencers for both array and enclosure layouts.This study offers designers a flexible and scalable workflow for evaluating building energy consumption and PV power generation potential in the pre-design phase.The findings can guide nearly-zero energy urban block planning to achieve a balance between energy supply and demand.展开更多
A new thermal protection design method for hypersonic vehicle's leading edge is proposed, which can effectively reduce temperature of the leading edge without additional cooling measures. This method reduces the l...A new thermal protection design method for hypersonic vehicle's leading edge is proposed, which can effectively reduce temperature of the leading edge without additional cooling measures. This method reduces the leading-edge's temperature by the multi-scale collaborative design of the macroscopic thermal optimization and the mesoscopic woven structures of Three-dimensional Orthogonal Woven Ceramic Matrix Composites(TOCMC). The macroscopic thermal optimization is achieved by designing different mesoscopic woven structures in different regions to create combined heat transfer channels to dredge the heat. The combined heat transfer channel is macroscopically represented by the anisotropic thermal conductivity of TOCMC. The thermal optimization multiple linear regression model is established to optimize the heat transport channel, which predicts Theoretical Optimal Thermal Conductivity Configuration(TOTCC) in different regions to achieve the lowest leading-edge temperature. The function-oriented mesostructure design method is invented to design the corresponding mesostructure of TOCMC according to the TOTCC, which consists of universal thermal conductivity prediction formulas for TOCMC. These universal formulas are firstly derived based on the thermal resistance network method, which is verified by experiments with an error of 6.25%. The results show that the collaborative design method can effectively reduce the leading edge temperature by about 12.8% without adding cooling measures.展开更多
Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer ...Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer composite plate by explosive welding.The microscopic properties of each bonding interface were elucidated through field emission scanning electron microscope and electron backscattered diffraction(EBSD).A methodology combining finite element method-smoothed particle hydrodynamics(FEM-SPH)and molecular dynamics(MD)was proposed for the analysis of the forming and evolution characteristics of explosive welding interfaces at multi-scale.The results demonstrate that the bonding interface morphologies of TC4/Al 6063 and Al 6063/Al 7075 exhibit a flat and wavy configuration,without discernible defects or cracks.The phenomenon of grain refinement is observed in the vicinity of the two bonding interfaces.Furthermore,the degree of plastic deformation of TC4 and Al 7075 is more pronounced than that of Al 6063 in the intermediate layer.The interface morphology characteristics obtained by FEM-SPH simulation exhibit a high degree of similarity to the experimental results.MD simulations reveal that the diffusion of interfacial elements predominantly occurs during the unloading phase,and the simulated thickness of interfacial diffusion aligns well with experimental outcomes.The introduction of intermediate layer in the explosive welding process can effectively produce high-quality titanium/aluminum alloy composite plates.Furthermore,this approach offers a multi-scale simulation strategy for the study of explosive welding bonding interfaces.展开更多
The demand of high-end electromagnetic wave absorbing materials puts forward higher requirements on comprehensive performances of small thickness,lightweight,broadband,and strong absorption.Herein,a novel multi-layer ...The demand of high-end electromagnetic wave absorbing materials puts forward higher requirements on comprehensive performances of small thickness,lightweight,broadband,and strong absorption.Herein,a novel multi-layer stepped metamaterial absorber with gradient electromagnetic properties is proposed.The complex permittivity and permeability of each layer are tailored via the proportion of carbonyliron and carbon-fiber dispersing into the epoxy resin.The proposed metamaterial is further optimized via adjusting the electromagnetic parameters and geometric sizes of each layer.Comparing with the four-layer composite with gradient electromagnetic properties which could only realize reflection loss(RL)of less than−6 dB in 2.0-40 GHz,the optimized stepped metamaterial with the same thickness and electromagnetic properties realizes less than−10 dB in the relevant frequency range.Additionally,the RL of less than−15 dB is achieved in the frequency range of 11.2-21.4 GHz and 28.5-40 GHz.The multiple electromagnetic wave absorption mechanism is discussed based on the experimental and simulation results,which is believed to be attributed to the synergy effect induced by multi-scale structures of the metamaterial.Therefore,combining multi-layer structures and periodic stepped structures into a novel gradient absorbing metamaterial would give new insights into designing microwave absorption devices for broadband electromagnetic protections.展开更多
High-insulation,long-life thermal barrier coatings(TBCs)decrease the service temperature of superalloys and improve the service life of gas turbines.Improvement in the thermal insulation properties of the coating main...High-insulation,long-life thermal barrier coatings(TBCs)decrease the service temperature of superalloys and improve the service life of gas turbines.Improvement in the thermal insulation properties of the coating mainly depends on the optimization of the TBC structure.An important challenge for TBCs is maintaining a high performance during thermal exposure without degradation,as the pore-rich structure of the topcoat would inevitably be transformed by sintering.A low-thermalconductivity anti-sintering coating can overcome the tradeoff between thermal insulation and sinter degradation.In this review,the design,preparation,and serviceability evaluation of a low-thermal-conductivity anti-sintering coating will be discussed.Furthermore,directions for potential development are introduced.This paper provides a comprehensive understanding of the structured tailoring of TBCs for better thermal insulation and anti-sintering performance.展开更多
基金supported by National Key Research and Development Program(No.2017YFB1102800)Key Project of NSFC(Nos.51790171 and 51761145111)NSFC for Excellent Young Scholars(No.11722219)。
文摘By integrating topology optimization and lattice-based optimization,a novel multi-scale design method is proposed to create solid-lattice hybrid structures and thus to improve the mechanical performance as well as reduce the structural weight.To achieve this purpose,a two-step procedure is developed to design and optimize the innovative structures.Initially,the classical topology optimization is utilized to find the optimal material layout and primary load carrying paths.Afterwards,the solid-lattice hybrid structures are reconstructed using the finite element mesh based modeling method.And lattice-based optimization is performed to obtain the optimal crosssection area of the lattice structures.Finally,two typical aerospace structures are optimized to demonstrate the effectiveness of the proposed optimization framework.The numerical results are quite encouraging since the solid-lattice hybrid structures obtained by the presented approach show remarkably improved performance when compared with traditional designs.
基金financial support for this research was provided by the Program (Grants 11372060, 91216201) of the National Natural Science Foundation of ChinaProgram (LJQ2015026 ) for Excellent Talents at Colleges and Universities in Liaoning Province+3 种基金the Major National Science and Technology Project (2011ZX02403-002)111 project (B14013)Fundamental Research Funds for the Central Universities (DUT14LK30)the China Scholarship Fund
文摘This paper deals with the concurrent multi-scale optimization design of frame structure composed of glass or carbon fiber reinforced polymer laminates. In the composite frame structure, the fiber winding angle at the micro-material scale and the geometrical parameter of components of the frame in the macro-structural scale are introduced as the independent variables on the two geometrical scales. Considering manufacturing requirements, discrete fiber winding angles are specified for the micro design variable. The improved Heaviside penalization discrete material optimization interpolation scheme has been applied to achieve the discrete optimization design of the fiber winding angle. An optimization model based on the minimum structural compliance and the specified fiber material volume constraint has been established. The sensitivity information about the two geometrical scales design variables are also deduced considering the characteristics of discrete fiber winding angles. The optimization results of the fiber winding angle or the macro structural topology on the two single geometrical scales, together with the concurrent two-scale optimization, is separately studied and compared in the paper. Numerical examples in the paper show that the concurrent multi-scale optimization can further explore the coupling effect between the macro-structure and micro-material of the composite to achieve an ultralight design of the composite frame structure. The novel two geometrical scales optimization model provides a new opportunity for the design of composite structure in aerospace and other industries.
基金supported by the Guangxi Science and Technology Plan and Project(Grant Numbers 2021AC19131 and 2022AC21140)Guangxi University of Science and Technology Doctoral Fund Project(Grant Number 20Z40).
文摘In this paper,to present a lightweight-developed front underrun protection device(FUPD)for heavy-duty trucks,plain weave carbon fiber reinforced plastic(CFRP)is used instead of the original high-strength steel.First,the mechanical and structural properties of plain carbon fiber composite anti-collision beams are comparatively analyzed from a multi-scale perspective.For studying the design capability of carbon fiber composite materials,we investigate the effects of TC-33 carbon fiber diameter(D),fiber yarn width(W)and height(H),and fiber yarn density(N)on the front underrun protective beam of carbon fiber compositematerials.Based on the investigation,a material-structure matching strategy suitable for the front underrun protective beam of heavy-duty trucks is proposed.Next,the composite material structure is optimized by applying size optimization and stack sequence optimization methods to obtain the higher performance carbon fiber composite front underrun protection beam of commercial vehicles.The results show that the fiber yarn height(H)has the greatest influence on the protective beam,and theH1matching scheme for the front underrun protective beamwith a carbon fiber composite structure exhibits superior performance.The proposed method achieves a weight reduction of 55.21% while still meeting regulatory requirements,which demonstrates its remarkable weight reduction effect.
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金financially supported byChongqingUniversity of Technology Graduate Innovation Foundation(Grant No.gzlcx20253267).
文摘Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.
基金Tianmin Tianyuan Boutique Vegetable Industry Technology Service Station(Grant No.2024120011003081)Development of Environmental Monitoring and Traceability System for Wuqing Agricultural Production Areas(Grant No.2024120011001866)。
文摘Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
文摘Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.
基金Project(2022YFC2904103)supported by the National Key Research and Development Program of ChinaProjects(52374112,52274108)supported by the National Natural Science Foundation of China+1 种基金Projects(BX20220036,BX20230041)supported by the Postdoctoral Innovation Talents Support Program,ChinaProject(2232080)supported by the Beijing Natural Science Foundation,China。
文摘The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and disposing of this mining waste.This study employs a macro-meso-micro testing method to investigate the effects of the waste rock grading index(WGI)and loading rate(LR)on the uniaxial compressive strength(UCS),pore structure,and micromorphology of CTWB materials.Pore structures were analyzed using scanning electron microscopy(SEM)and mercury intrusion porosimetry(MIP).The particles(pores)and cracks analysis system(PCAS)software was used to quantitatively characterize the multi-scale micropores in the SEM images.The key findings indicate that the macroscopic results(UCS)of CTWB materials correspond to the microscopic results(pore structure and micromorphology).Changes in porosity largely depend on the conditions of waste rock grading index and loading rate.The inclusion of waste rock initially increases and then decreases the UCS,while porosity first decreases and then increases,with a critical waste rock grading index of 0.6.As the loading rate increases,UCS initially rises and then falls,while porosity gradually increases.Based on MIP and SEM results,at waste rock grading index 0.6,the most probable pore diameters,total pore area(TPA),pore number(PN),maximum pore area(MPA),and area probability distribution index(APDI)are minimized,while average pore form factor(APF)and fractal dimension of pore porosity distribution(FDPD)are maximized,indicating the most compact pore structure.At a loading rate of 12.0 mm/min,the most probable pore diameters,TPA,PN,MPA,APF,and APDI reach their maximum values,while FDPD reaches its minimum value.Finally,the mechanism of CTWB materials during compression is analyzed,based on the quantitative results of UCS and porosity.The research findings play a crucial role in ensuring the successful application of CTWB materials in deep metal mines.
基金financial support from the National Key Research and Development Program of China(2022YFB3804905)National Natural Science Foundation of China(22375047,22378068,and 22378071)+1 种基金Natural Science Foundation of Fujian Province(2022J01568)111 Project(No.D17005).
文摘Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
基金the National Key Research and Development Program of China (Grant No.2022YFF0711400)the National Space Science Data Center Youth Open Project (Grant No. NSSDC2302001)
文摘Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.
基金supported by the National Nature Science Foundation of China (Nos. 51971111, 52273247)the facilities in the Center for Microscopy and Analysis at Nanjing University of Aeronautics and Astronautics and the Fund of Prospective Layout of Scientific Research for NUAA (Nanjing University of Aeronautics and Astronautics (No. ILA220461A22)。
文摘Wearable devices with efficient thermal management and electromagnetic interference(EMI) shielding are highly desirable for improving human comfort and safety. Herein, a multifunctional wearable carbon fibers(CF) @ polyaniline(PANI)/silver nanowires(Ag NWs) composites with a “branch-trunk” interlocked micro/nanostructure were achieved through "three-in-one" multi-scale design. The reasonable assembly of the three kinds of one-dimensional(1D) materials can fully exert their excellent properties i.e., the superior flexibility of CF, the robustness of PANI, and the splendid conductivity of Ag NWs. Consequently, the constructed flexible composite demonstrates enhanced mechanical properties with a tensile stress of 1.2 MPa, which was almost 6 times that of the original material. This is mainly attributed to the fact that the PNAI(branch) was firmly attached to the CF(trunk) through polydopamine(PDA), forming a robust interlocked structure. Meanwhile, the composite possesses excellent thermal insulation and heat preservation capacity owing to the synergistically low thermal conductivity and emissivity. More importantly, the conductive path of the composite established by the three 1D materials greatly improved its EMI shielding property and Joule heating performance at low applied voltage. This work paves the way for rational utilization of the intrinsic properties of 1D materials, as well as provides a promising strategy for designing wearable electromagnetic protection and thermal energy management devices.
基金supported by the National Natural Science Foundation of China(U22A20193,U22A20438)the Key R&D Plan of Hubei Province(2023BAB036).
文摘In the contemporary era,lithium-ion batteries have gained considerable attention in various industries such as 3C products,electric vehicles and energy storage systems due to their exceptional properties.With the rapid progress in the energy storage sector,there is a growing demand for greater energy density in lithium-ion batteries.While the use of thick electrodes is a straightforward and effective approach to enhance the energy density of battery,it is hindered by the sluggish reaction dynamics and insufficient mechanical properties.Therefore,we comprehensively review recent advances in the field of thick electrodes for lithium-ion batteries to overcome the bottlenecks in the development of thick electrodes and achieve efficient fabrication for high-performance lithium-ion batteries.Initially,a systematic analysis is performed to identify the factors affecting the performance of the thick electrodes.the correlation between electrode materials,structural parameters,and performance is also investigated.Subsequently,the viable strategies for constructing thick electrodes with improved properties are summarize,including high throughput,high conductivity and low tortuosity,in both material development and structural design.In addition,recent advances in efficient fabrication methods for thick electrode fabrication are reviewed,with a comprehensive assessment of their merits,limitations,and applicable scenarios.Finally,a comprehensive overview of the multiscale design and manufacturing process for thick electrodes in lithium-ion batteries is provided,accompanied by valuable insights into design considerations that are crucial for future advances in this area.
基金supported by the National Natural Science Foundation(No.52378020)Open Foundation of the State Key Laboratory of Subtropical Building and Urban Science(No.2023KA02)+1 种基金Fundamental Research Funds for the Central Universities(YCJJ20230576)Program for HUST Academic Frontier Youth Team(No.2019QYTD10).
文摘Design parameters at different scales in the pre-design phase could significantly impact both building energy consumption and photovoltaic(PV)power generation potential.However,existing studies often overlook the synergistic effects of design parameters across multiple scales(block-building-facade scales)when evaluating these aspects.This paper aims to propose a workflow for the assessing building energy consumption and PV power generation potential of office blocks applicable in the pre-schematic design phase considering the synergistic influence of multi-scale design parameters,using building typology and parametric modelling approach.The study proposed a multi-scale design parameter classification system combined with parametric modelling.The study investigated 80 office blocks in Wuhan as the study case,which were classified into array type and enclosed type.Correlation analysis and multiple regression equations were used to quantify the single versus synergistic effects of different scale design parameters.Results suggest that focusing solely on a single scale during the pre-design stage is typically inadequate for understanding building energy potential.In contrast,multi-scale synergistic analysis boosts energy use intensity(EUI)by 7.56%and net energy use intensity(NEUI)by 33.96%.Under multi-scale synergistic conditions,the EUI of array type is more influenced by the building design parameters,while the NEUI is effected by the balance of multi-scales design parameters.While the EUI of enclosed types exhibit balanced effects across multi-scale design parameters,with NEUI results aligning closely with PV power generation potential.Multiple regression equations highlight building density and shape factor as key influencers for both array and enclosure layouts.This study offers designers a flexible and scalable workflow for evaluating building energy consumption and PV power generation potential in the pre-design phase.The findings can guide nearly-zero energy urban block planning to achieve a balance between energy supply and demand.
基金co-supported by the Science Center for Gas Turbine Project, China (No. P2022-B-Ⅱ-025-001)the National Science and Technology Major Project, China (No. Y2019-Ⅰ-0018-0017)。
文摘A new thermal protection design method for hypersonic vehicle's leading edge is proposed, which can effectively reduce temperature of the leading edge without additional cooling measures. This method reduces the leading-edge's temperature by the multi-scale collaborative design of the macroscopic thermal optimization and the mesoscopic woven structures of Three-dimensional Orthogonal Woven Ceramic Matrix Composites(TOCMC). The macroscopic thermal optimization is achieved by designing different mesoscopic woven structures in different regions to create combined heat transfer channels to dredge the heat. The combined heat transfer channel is macroscopically represented by the anisotropic thermal conductivity of TOCMC. The thermal optimization multiple linear regression model is established to optimize the heat transport channel, which predicts Theoretical Optimal Thermal Conductivity Configuration(TOTCC) in different regions to achieve the lowest leading-edge temperature. The function-oriented mesostructure design method is invented to design the corresponding mesostructure of TOCMC according to the TOTCC, which consists of universal thermal conductivity prediction formulas for TOCMC. These universal formulas are firstly derived based on the thermal resistance network method, which is verified by experiments with an error of 6.25%. The results show that the collaborative design method can effectively reduce the leading edge temperature by about 12.8% without adding cooling measures.
基金Opening Foundation of Key Laboratory of Explosive Energy Utilization and Control,Anhui Province(BP20240104)Graduate Innovation Program of China University of Mining and Technology(2024WLJCRCZL049)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_2701)。
文摘Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer composite plate by explosive welding.The microscopic properties of each bonding interface were elucidated through field emission scanning electron microscope and electron backscattered diffraction(EBSD).A methodology combining finite element method-smoothed particle hydrodynamics(FEM-SPH)and molecular dynamics(MD)was proposed for the analysis of the forming and evolution characteristics of explosive welding interfaces at multi-scale.The results demonstrate that the bonding interface morphologies of TC4/Al 6063 and Al 6063/Al 7075 exhibit a flat and wavy configuration,without discernible defects or cracks.The phenomenon of grain refinement is observed in the vicinity of the two bonding interfaces.Furthermore,the degree of plastic deformation of TC4 and Al 7075 is more pronounced than that of Al 6063 in the intermediate layer.The interface morphology characteristics obtained by FEM-SPH simulation exhibit a high degree of similarity to the experimental results.MD simulations reveal that the diffusion of interfacial elements predominantly occurs during the unloading phase,and the simulated thickness of interfacial diffusion aligns well with experimental outcomes.The introduction of intermediate layer in the explosive welding process can effectively produce high-quality titanium/aluminum alloy composite plates.Furthermore,this approach offers a multi-scale simulation strategy for the study of explosive welding bonding interfaces.
基金financially supported by the National Natural Science Foundation of China (No. 52102113)the Nature Science Foundation of Shaanxi in China (No. 2022JQ-323)+1 种基金the Creative Research Foundation of the Science and Technology on Thermostructural Composite Materials LaboratoryNatural Science Foundation and Department of Education of Shaanxi in China (No. 21JK0912)
文摘The demand of high-end electromagnetic wave absorbing materials puts forward higher requirements on comprehensive performances of small thickness,lightweight,broadband,and strong absorption.Herein,a novel multi-layer stepped metamaterial absorber with gradient electromagnetic properties is proposed.The complex permittivity and permeability of each layer are tailored via the proportion of carbonyliron and carbon-fiber dispersing into the epoxy resin.The proposed metamaterial is further optimized via adjusting the electromagnetic parameters and geometric sizes of each layer.Comparing with the four-layer composite with gradient electromagnetic properties which could only realize reflection loss(RL)of less than−6 dB in 2.0-40 GHz,the optimized stepped metamaterial with the same thickness and electromagnetic properties realizes less than−10 dB in the relevant frequency range.Additionally,the RL of less than−15 dB is achieved in the frequency range of 11.2-21.4 GHz and 28.5-40 GHz.The multiple electromagnetic wave absorption mechanism is discussed based on the experimental and simulation results,which is believed to be attributed to the synergy effect induced by multi-scale structures of the metamaterial.Therefore,combining multi-layer structures and periodic stepped structures into a novel gradient absorbing metamaterial would give new insights into designing microwave absorption devices for broadband electromagnetic protections.
基金financially supported by the Natural Science Basic Research Program of Shaanxi(No.2019JQ380)the Fundamental Research Funds for the Central Universities,CHD(No.300102500102)
文摘High-insulation,long-life thermal barrier coatings(TBCs)decrease the service temperature of superalloys and improve the service life of gas turbines.Improvement in the thermal insulation properties of the coating mainly depends on the optimization of the TBC structure.An important challenge for TBCs is maintaining a high performance during thermal exposure without degradation,as the pore-rich structure of the topcoat would inevitably be transformed by sintering.A low-thermalconductivity anti-sintering coating can overcome the tradeoff between thermal insulation and sinter degradation.In this review,the design,preparation,and serviceability evaluation of a low-thermal-conductivity anti-sintering coating will be discussed.Furthermore,directions for potential development are introduced.This paper provides a comprehensive understanding of the structured tailoring of TBCs for better thermal insulation and anti-sintering performance.
