Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this stud...Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this study presents an innovative bearing fault diagnosis approach predicated on Parameter⁃Optimized Symplectic Geometry Mode Decomposition(POSGMD)and Improved Convolutional Neural Network(ICNN).Firstly,assisted by the relative entropy⁃based adaptive selection of embedding dimension,a POSGMD is presented to adaptively decompose the collected bearing vibration signals into various Symplectic Geometry Components(SGC),which can solve the problem of manual selection of the embedding dimension in the raw Symplectic Geometry Mode Decomposition(SGMD).Meanwhile,the signal reconstruction on the decomposed SGC is conducted based on kurtosis⁃weighted principle to obtain the reconstructed signals.Subsequently,the Continuous Wavelet Transform(CWT)of the reconstructed signals is calculated to generate the corresponding time⁃frequency images as sample set.Finally,an ICNN is introduced for model training and automatic recognition of bearing faults.Two case studies are used to validate the presented methods efficacy.Comparing the presented method with traditional fault diagnosis methods,experimental results show that it can achieve greater identification accuracy and superior anti⁃noise resilience.This work provides a practical and effective solution for fault diagnosis in wind turbine bearings,contributing to the timely detection of faults and the reliable operation of wind turbines or other rotational machinery in industrial applications.展开更多
Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been i...Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been increasing attention on generating highly realistic and consistent driving videos,particularly those involving viewpoint changes guided by the control commands or trajectories of ego vehicles.However,current reconstruction approaches,such as Neural Radiance Fields and 3D Gaussian Splatting,frequently suffer from limited generalization and depend on substantial input data.Meanwhile,2D generative models,though capable of producing unknown scenes,still have room for improvement in terms of coherence and visual realism.To overcome these challenges,we introduce GenScene,a world model that synthesizes front-view driving videos conditioned on trajectories.A new temporal module is presented to improve video consistency by extracting the global context of each frame,calculating relationships of frames using these global representations,and fusing frame contexts accordingly.Moreover,we propose an innovative attention mechanism that computes relations of pixels within each frame and pixels in the corresponding window range of the initial frame.Extensive experiments show that our approach surpasses various state-of-the-art models in driving video generation,and the introduced modules contribute significantly to model performance.This work establishes a new paradigm for goal-oriented video synthesis in autonomous driving,which facilitates on-demand simulation to expedite algorithm development.展开更多
Conventional locking/release mechanisms often face challenges in aircraft wing separation processes,such as excessive impact loads and insufficient synchronization.These may cause structural damage to the airframe or ...Conventional locking/release mechanisms often face challenges in aircraft wing separation processes,such as excessive impact loads and insufficient synchronization.These may cause structural damage to the airframe or attitude instability,seriously compromising mission reliability.To address this engineering challenge,this paper proposes a multi-point low-impact locking/release mechanism based on the mobility model and energy conversion strategy.Through establishing a DOF constraint framework system,this paper systematically analyzes the energy transfer and conversion characteristics during the wing separation process,reveals the generation mechanism of impact loads,and conducts research on low-impact design based on energy conversion strategy.Building on this foundation,a single-point locking/release mechanism employing parallel trapezoidal key shaft structure was designed,which increases frictional contact time and reduces the energy release rate,thereby achieving low-impact characteristics.The mechanism's performance was validated through physical prototype development and systematic functional testing(including unlocking force,synchronization,and impact tests).Experimental results demonstrate:(1)Under 14 kN preload condition,the maximum unlocking force was only 92.54 N,showing a linear relationship with preload that satisfies the"strong-connection/weak-unlock"design requirement;(2)Wing separation was completed within 46 ms,with synchronization time difference among three separation mechanisms stably controlled within 12-14 ms,proving rapid and reliable operation;(3)The unlocking impact acceleration ranged between 26 and 73 g,below the 100 g design limit,confirming the effectiveness of the energy conversion strategy.The proposed low-impact locking/release mechanism design method based on energy conversion strategy resolves the traditional challenges of high impact and synchronization deficiencies.The synergistic optimization mechanism of"structural load reduction and performance improvement"provides a highly reliable technical solution for wing separable mechanisms while offering novel design insights for wing connection/separation systems engineering.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
Conventional ignition methods are proving to be ineffective for low-sensitivity energetic materials,highlighting the need to investigate alternative ignition systems,such as laser-based techniques.Over the past decade...Conventional ignition methods are proving to be ineffective for low-sensitivity energetic materials,highlighting the need to investigate alternative ignition systems,such as laser-based techniques.Over the past decade,lasers have emerged as a promising solution,providing focused energy beams for controllable,efficient,and reliable ignition in the field of energetic materials.This study presents a comparative analysis of two state-of-the-art ignition approaches:direct laser ignition and laser-driven flyer ignition.Experiments were performed using a Neodymium-doped Yttrium Aluminum Garnet(Nd:YAG)laser at different energy beam levels to systematically evaluate ignition onset.In the direct laser ignition test setup,the laser beam was applied directly to the energetic tested material,while laserdriven flyer ignition utilized 40 and 100μm aluminum foils,propelled at velocities ranging from 300 to 1250 m/s.Comparative analysis with the Lawrence and Trott model substantiated the velocity data and provided insight into the ignition mechanisms.Experimental results indicate that the ignition time for the laser-driven flyer method was significantly shorter,with the pyrotechnic composition achieving complete combustion faster compared to direct laser ignition.Moreover,precise ignition thresholds were determined for both methods,providing critical parameters for optimizing ignition systems in energetic materials.This work elucidates the advantages and limitations of each technique while advancing next-generation ignition technology,enhancing the reliability and safety of propulsion systems.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
Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection mo...Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.展开更多
With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has prog...With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has progressed from the fabrication of simple models(1.0)to the fabrication of permanent implants(2.0),tissue engineering scaffolds(3.0),and complex biostructures utilizing living cells(4.0).Nevertheless,significant challenges remain,particularly in accurately replicating the structure and function of host tissues,selecting appropriate materials,and optimizing printing parameters.The integration of artificial intelligence(AI),especially machine learning,provides promising novel opportunities in bioprinting(5.0).This review systematically summarizes the current applications of AI in bioprinting,discussing both construction strategies and application scenarios.It also explores the potential of AI to improve bioprinting in the preparation of complex functional tissues and in situ tissue repair.Overall,the synergy between AI and bioprinting is poised to drive the development of personalized medicine,facilitate high-throughput preparation of in vitro models,and provide robust tools for regenerative medicine and precision healthcare.展开更多
Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,...Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility,adhesion,self-healing,and environmental robustness with excellent sensing metrics.Herein,we report a multifunctional,anti-freezing,selfadhesive,and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes(CoN CNT)embedded in a polyvinyl alcohol-gelatin(PVA/GLE)matrix.Fabricated using a binary solvent system of water and ethylene glycol(EG),the CoN CNT/PVA/GLE organogel exhibits excellent flexibility,biocompatibility,and temperature tolerance with remarkable environmental stability.Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range(40%-95%RH).Freeze-tolerant conductivity under sub-zero conditions(-20℃)is attributed to the synergistic role of CoN CNT and EG,preserving mobility and network integrity.The Co N CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 k Pa^(-1)in the detection range from 0 to 20 k Pa,ideal for subtle biomechanical motion detection.A smart human-machine interface for English letter recognition using deep learning achieved 98%accuracy.The organogel sensor utility was extended to detect human gestures like finger bending,wrist motion,and throat vibration during speech.展开更多
Tissue engineering is an emerging means for resolving the problems of tissue repair and organ replacement in regenerative medicine.Insufficient supply of nutrients and oxygen to cells in large-scale tissues has led to...Tissue engineering is an emerging means for resolving the problems of tissue repair and organ replacement in regenerative medicine.Insufficient supply of nutrients and oxygen to cells in large-scale tissues has led to the demand to prepare blood vessels.Scaffold-based tissue engineering approaches are effective methods to form new blood vessel tissues.The demand for blood vessels prompts systematic research on fabrication strategies of vascular scaffolds for tissue engineering.Recent advances in 3D printing have facilitated fabrication of vascular scaffolds,contributing to broad prospects for tissue vascularization.This review presents state of the art on modeling methods,print materials and preparation processes for fabrication of vascular scaffolds,and discusses the advantages and application fields of each method.Specially,significance and importance of scaffold-based tissue engineering for vascular regeneration are emphasized.Print materials and preparation processes are discussed in detail.And a focus is placed on preparation processes based on 3D printing technologies and traditional manufacturing technologies including casting,electrospinning,and Lego-like construction.And related studies are exemplified.Transformation of vascular scaffolds to clinical application is discussed.Also,four trends of 3D printing of tissue engineering vascular scaffolds are presented,including machine learning,near-infrared photopolymerization,4D printing,and combination of self-assembly and 3D printing-based methods.展开更多
LuGre model has been widely used in friction modeling and compensation.However,the new friction regime,named prestiction regime,cannot be accurately characterized by LuGre model in the latest research.With the extensi...LuGre model has been widely used in friction modeling and compensation.However,the new friction regime,named prestiction regime,cannot be accurately characterized by LuGre model in the latest research.With the extensive experimental observations of friction behaviors in the prestiction,some variables were abstracted to depict the rules in the prestiction regime.Based upon the knowledge of friction modeling,a novel friction model including the presliding regime,the gross sliding regime and the prestiction regime was then presented to overcome the shortcomings of the LuGre model.The reason that LuGre model cannot estimate the prestiction friction was analyzed in theory.Feasibility analysis of the proposed model in modeling the prestiction friction was also addressed.A parameter identification method for the proposed model based on multilevel coordinate search algorithm was presented.The proposed friction compensation strategy was composed of a nonlinear friction observer and a feedforward mechanism.The friction observer was designed to estimate the friction force in the presliding and the gross sliding regimes.And the friction force was estimated based on the model in the prestiction regime.The comparative trajectory tracking experiments were conducted on a simulator of inertially stabilization platforms among three control schemes:the single proportional–derivative(PD)control,the PD with LuGre model-based compensation and the PD with compensator based on the presented model.The experimental results reveal that the control scheme based on the proposed model has the best tracking performance.It reduces the peak-to-peak value(PPV)of tracking error to 0.2 mrad,which is improved almost 50%compared with the PD with LuGre model-based compensation.Compared to the single PD control,it reduces the PPV of error by 66.7%.展开更多
The sensitivity engineering model of the coupling capacitance detector is built to provide the theoretic foundation for designing its circuits and electrodes scientifically. The sensitivity concept model of the capaci...The sensitivity engineering model of the coupling capacitance detector is built to provide the theoretic foundation for designing its circuits and electrodes scientifically. The sensitivity concept model of the capacitance proximity detector is discussed, and the detecting sensitivity of the coupling capacitance detector is analyzed theoretically. Then the sensitivity engineering model, which can reflect the main parameters relationship of the detecting circuit is set up based on the foregoing analyses. It is concluded that: ① the sensitivity is mainly correlative with some parameters including the voltage transmission factor of the demodulator, the oscillating voltage amplitude and the amplitude variation constant of the oscillator; ② the sensitivity is also influenced by the areas of electrodes and the distance between electrodes of the detector.展开更多
To reuse and share the valuable knowledge embedded in repositories of engineering models for accelerating the design process, improving product quality, and reducing costs, it is crucial to devise search engines capab...To reuse and share the valuable knowledge embedded in repositories of engineering models for accelerating the design process, improving product quality, and reducing costs, it is crucial to devise search engines capable of matching 3D models efficiently and effectively. In this paper, an enhanced shape distributions-based technique of using geometrical and topological information to search 3D engineering models represented by polygonal meshes was presented. A simplification method of polygonal meshes was used to simplify engineering model as the pretreatment for generation of sample points. The method of sampling points was improved and a pair of functions that was more sensitive to shape was employed to construct a 2D shape distribution. Experiments were conducted to evaluate the proposed algorithm utilizing the Engineering Shape Benchmark (ESB) database. The experiential results suggest that the search effectiveness is significantly improved by enforcing the simplification and enhanced shape distributions to engineering model retrieval.展开更多
Owing to safety issue and low energy density of liquid lithium-ion batteries(LIBs),all-solid-state lithium metal batteries(ASLMBs)with unique all-solid-state electrolytes(SEs)have attracted wide attentions.This arises...Owing to safety issue and low energy density of liquid lithium-ion batteries(LIBs),all-solid-state lithium metal batteries(ASLMBs)with unique all-solid-state electrolytes(SEs)have attracted wide attentions.This arises mainly from the advantages of the SEs in the suppression of lithium dendrite growth,long cycle life,and broad working temperature range,showing huge potential applications in electronic devices,electric vehicles,smart grids,and biomedical devices.However,SEs suffer from low lithiumion conductivity and low mechanical integrity,slowing down the development of practical ASLMBs.Nanostructure engineering is of great efficiency in tuning the structure and composition of the SEs with improved lithium-ion conductivity and mechanical integrity.Among various available technologies for nanostructure engineering,electrospinning is a promising technique because of its simple operation,cost-effectiveness,and efficient integration with different components.In this review,we will first give a simple description of the electrospinning process.Then,the use of electrospinning technique in the synthesis of various SEs is summarized,for example,organic nanofibrous matrix,organic/inorganic nanofibrous matrix,and inorganic nanofibrous matrix combined with other components.The current development of the advanced architectures of SEs through electrospinning technology is also presented to provide references and ideas for designing high-performance ASLMBs.Finally,an outlook and further challenges in the preparation of advanced SEs for ASLMBs through electrospinning engineering are given.展开更多
A novel method of designing and preparing bone tissue engineering scaffolds with controllable porous structure of both macro channels and micro pores was proposed. The CAD software UG NX3.0 was used to design the macr...A novel method of designing and preparing bone tissue engineering scaffolds with controllable porous structure of both macro channels and micro pores was proposed. The CAD software UG NX3.0 was used to design the macro channels＇ shape, size and distribution. By integrating rapid prototyping and traditional porogen technique, the macro channels and micro pores were formed respectively. The size, shape and quantity of micro pores were controlled by porogen particulates. The sintered β-TCP porous scaffolds possessed connective macro channels of approximately 500 μm and micro pores of 200-400 μm. The porosity and connectivity of micro pores became higher with the increase of porogen ratio, while the mechanical properties weakened. The average porosity and compressive strength offl-TCP scaffolds prepared with porogen ratio of 60wt% were 78.12% and 0.2983 MPa, respectively. The cells＇ adhesion ratio of scaffolds was 67.43%. The ALP activity, OCN content and cells micro morphology indicated that cells grew and proliferated well on the scaffolds.展开更多
Human Adaptive Mechatronics(HAM)includes human and computer system in a closed loop.Elderly person with disabilities,normally carry out their daily routines with some assistance to move their limbs.With the short fall...Human Adaptive Mechatronics(HAM)includes human and computer system in a closed loop.Elderly person with disabilities,normally carry out their daily routines with some assistance to move their limbs.With the short fall of human care takers,mechatronics devices are used with the likes of exoskeleton and exosuits to assist them.The rehabilitation and occupational therapy equipments utilize the electromyography(EMG)signals to measure the muscle activity potential.This paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of the system.Limb characteristics extraction from EMG signal and prediction of optimal controller parameters are modeled.Time and frequency based approach of EMG signal are considered for feature extraction.The models used for estimating motion and muscle parameters from EMG signal for carrying out limb movement predictions are validated.Based on the extracted features,optimal parameters are selected by Modified Lion Optimization(MLO)for controlling the HAM system.Finally,supervised machine learning makes predictions at different points in time for individual sensing using Support Vector Neural Network(SVNN).This model is also evaluated based on optimal parameters of motion estimation and the accuracy level along with different optimization models for various upper limb movements.The proposed model of human adaptive controller predicts the limb movement by 96%accuracy.展开更多
The novel Co-based superalloys are extensively used in gas-powered and jet engine turbines due to their excellent high-temperature performance, achieved by strengthening the L12-γ′ ordered phase. This review present...The novel Co-based superalloys are extensively used in gas-powered and jet engine turbines due to their excellent high-temperature performance, achieved by strengthening the L12-γ′ ordered phase. This review presents an overview of the research progress on oxidation behavior of Co-based superalloys, including oxidation kinetics, oxides morphology, the formation and spallation of oxide layers, and importantly, the synergistic effects of alloying elements on oxidation resistance—a critical area considering the complex interactions with multiple alloying elements. Additionally, this review compares the oxidation resistance of single crystal versus polycrystalline alloys. The effect of phase interface and dislocations on oxidation behavior is also discussed. While significant progress has been achieved, areas necessitating further investigation include optimizing alloy compositions for enhanced oxidation resistance and understanding the long-term stability of oxide layers. The future prospects for Co-based superalloys are promising as ongoing research aims to address the existing challenges and unlock new applications at even higher operating temperatures.展开更多
3D printing technology enhances the combustion characteristics of hybrid rocket fuels by enabling complex geometries. However, improvements in regression rates and energy properties of monotonous 3D printed fuels have...3D printing technology enhances the combustion characteristics of hybrid rocket fuels by enabling complex geometries. However, improvements in regression rates and energy properties of monotonous 3D printed fuels have been limited. This study explores the impact of poly(vinylidene fluoride) and polydopamine-coated aluminum particles on the thermal and combustion properties of 3D printed hybrid rocket fuels. Physical self-assembly and anti-solvent methods were employed for constructing composite μAl particles. Characterization using SEM, XRD, XPS, FTIR, and μCT revealed a core-shell structure and homogeneous elemental distribution. Thermal analysis showed that PVDF coatings significantly increased the heat of combustion for aluminum particles, with maximum enhancement observed in μAl@PDA@PVDF(denoted as μAl@PF) at 6.20 k J/g. Subsequently, 3D printed fuels with varying pure and composite μAl particle contents were prepared using 3D printing. Combustion tests indicated higher regression rates for Al@PF/Resin composites compared to pure resin, positively correlating with particle content. The fluorocarbon-alumina reaction during the combustion stage intensified Al particle combustion, reducing residue size. A comprehensive model based on experiments provides insights into the combustion process of PDA and PVDF-coated droplets. This study advances the design of 3D-printed hybrid rocket fuels, offering strategies to improve regression rates and energy release, crucial for enhancing solid fuel performance for hybrid propulsion.展开更多
A coupled thermal-hydro-mechanical cohesive phase-field model for hydraulic fracturing in deep coal seams is presented.Heat exchange between the cold fluid and the hot rock is considered,and the thermal contribution t...A coupled thermal-hydro-mechanical cohesive phase-field model for hydraulic fracturing in deep coal seams is presented.Heat exchange between the cold fluid and the hot rock is considered,and the thermal contribution terms between the cold fluid and the hot rock are derived.Heat transfer obeys Fourier's law,and porosity is used to relate the thermodynamic parameters of the fracture and matrix domains.The net pressure difference between the fracture and the matrix is neglected,and thus the fluid flow is modeled by the unified fluid-governing equations.The evolution equations of porosity and Biot's coefficient during hydraulic fracturing are derived from their definitions.The effect of coal cleats is considered and modeled by Voronoi polygons,and this approach is shown to have high accuracy.The accuracy of the proposed model is verified by two sets of fracturing experiments in multilayer coal seams.Subsequently,the differences in fracture morphology,fluid pressure response,and fluid pressure distribution between direct fracturing of coal seams and indirect fracturing of shale interlayers are explored,and the effects of the cluster number and cluster spacing on fracture morphology for multi-cluster fracturing are also examined.The numerical results show that the proposed model is expected to be a powerful tool for the fracturing design and optimization of deep coalbed methane.展开更多
The active sound absorption technique excels in mitigating low-frequency sound waves,yet it falls short when dealing with medium and high-frequency sound waves.To enhance the sound-absorbing effect of medium and high-...The active sound absorption technique excels in mitigating low-frequency sound waves,yet it falls short when dealing with medium and high-frequency sound waves.To enhance the sound-absorbing effect of medium and high-frequency sound waves,a novel semi-active sound absorption method has been introduced.This method modulates the surface impedance of a loudspeaker positioned behind the sound-absorbing material,thereby altering the sound absorption coefficient.The theoretical sound absorption coefficient is calculated using MATLAB and compared with the experimental one.Results show that the method can effectively modulates the absorption coefficient in response to varying incident sound wave frequencies,ensuring that it remains at its peak value.展开更多
基金Jiangsu Association for Science and Technology Youth Talent Support Project(Grant No.JSTJ-2024-031)National Natural Science Foundation of China(Grant No.52005265)Natural Science Fund for Colleges and Universities in Jiangsu Province(Grant No.20KJB460002)。
文摘Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this study presents an innovative bearing fault diagnosis approach predicated on Parameter⁃Optimized Symplectic Geometry Mode Decomposition(POSGMD)and Improved Convolutional Neural Network(ICNN).Firstly,assisted by the relative entropy⁃based adaptive selection of embedding dimension,a POSGMD is presented to adaptively decompose the collected bearing vibration signals into various Symplectic Geometry Components(SGC),which can solve the problem of manual selection of the embedding dimension in the raw Symplectic Geometry Mode Decomposition(SGMD).Meanwhile,the signal reconstruction on the decomposed SGC is conducted based on kurtosis⁃weighted principle to obtain the reconstructed signals.Subsequently,the Continuous Wavelet Transform(CWT)of the reconstructed signals is calculated to generate the corresponding time⁃frequency images as sample set.Finally,an ICNN is introduced for model training and automatic recognition of bearing faults.Two case studies are used to validate the presented methods efficacy.Comparing the presented method with traditional fault diagnosis methods,experimental results show that it can achieve greater identification accuracy and superior anti⁃noise resilience.This work provides a practical and effective solution for fault diagnosis in wind turbine bearings,contributing to the timely detection of faults and the reliable operation of wind turbines or other rotational machinery in industrial applications.
基金supported by the Cultivation Program for Major Scientific Research Projects of Harbin Institute of Technology(ZDXMPY20180109).
文摘Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been increasing attention on generating highly realistic and consistent driving videos,particularly those involving viewpoint changes guided by the control commands or trajectories of ego vehicles.However,current reconstruction approaches,such as Neural Radiance Fields and 3D Gaussian Splatting,frequently suffer from limited generalization and depend on substantial input data.Meanwhile,2D generative models,though capable of producing unknown scenes,still have room for improvement in terms of coherence and visual realism.To overcome these challenges,we introduce GenScene,a world model that synthesizes front-view driving videos conditioned on trajectories.A new temporal module is presented to improve video consistency by extracting the global context of each frame,calculating relationships of frames using these global representations,and fusing frame contexts accordingly.Moreover,we propose an innovative attention mechanism that computes relations of pixels within each frame and pixels in the corresponding window range of the initial frame.Extensive experiments show that our approach surpasses various state-of-the-art models in driving video generation,and the introduced modules contribute significantly to model performance.This work establishes a new paradigm for goal-oriented video synthesis in autonomous driving,which facilitates on-demand simulation to expedite algorithm development.
文摘Conventional locking/release mechanisms often face challenges in aircraft wing separation processes,such as excessive impact loads and insufficient synchronization.These may cause structural damage to the airframe or attitude instability,seriously compromising mission reliability.To address this engineering challenge,this paper proposes a multi-point low-impact locking/release mechanism based on the mobility model and energy conversion strategy.Through establishing a DOF constraint framework system,this paper systematically analyzes the energy transfer and conversion characteristics during the wing separation process,reveals the generation mechanism of impact loads,and conducts research on low-impact design based on energy conversion strategy.Building on this foundation,a single-point locking/release mechanism employing parallel trapezoidal key shaft structure was designed,which increases frictional contact time and reduces the energy release rate,thereby achieving low-impact characteristics.The mechanism's performance was validated through physical prototype development and systematic functional testing(including unlocking force,synchronization,and impact tests).Experimental results demonstrate:(1)Under 14 kN preload condition,the maximum unlocking force was only 92.54 N,showing a linear relationship with preload that satisfies the"strong-connection/weak-unlock"design requirement;(2)Wing separation was completed within 46 ms,with synchronization time difference among three separation mechanisms stably controlled within 12-14 ms,proving rapid and reliable operation;(3)The unlocking impact acceleration ranged between 26 and 73 g,below the 100 g design limit,confirming the effectiveness of the energy conversion strategy.The proposed low-impact locking/release mechanism design method based on energy conversion strategy resolves the traditional challenges of high impact and synchronization deficiencies.The synergistic optimization mechanism of"structural load reduction and performance improvement"provides a highly reliable technical solution for wing separable mechanisms while offering novel design insights for wing connection/separation systems engineering.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
文摘Conventional ignition methods are proving to be ineffective for low-sensitivity energetic materials,highlighting the need to investigate alternative ignition systems,such as laser-based techniques.Over the past decade,lasers have emerged as a promising solution,providing focused energy beams for controllable,efficient,and reliable ignition in the field of energetic materials.This study presents a comparative analysis of two state-of-the-art ignition approaches:direct laser ignition and laser-driven flyer ignition.Experiments were performed using a Neodymium-doped Yttrium Aluminum Garnet(Nd:YAG)laser at different energy beam levels to systematically evaluate ignition onset.In the direct laser ignition test setup,the laser beam was applied directly to the energetic tested material,while laserdriven flyer ignition utilized 40 and 100μm aluminum foils,propelled at velocities ranging from 300 to 1250 m/s.Comparative analysis with the Lawrence and Trott model substantiated the velocity data and provided insight into the ignition mechanisms.Experimental results indicate that the ignition time for the laser-driven flyer method was significantly shorter,with the pyrotechnic composition achieving complete combustion faster compared to direct laser ignition.Moreover,precise ignition thresholds were determined for both methods,providing critical parameters for optimizing ignition systems in energetic materials.This work elucidates the advantages and limitations of each technique while advancing next-generation ignition technology,enhancing the reliability and safety of propulsion systems.
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funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.IPP:172-830-2025.
文摘Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.
基金financially supported by the National Natural Science Foundation of China(Nos.32471396,82230071,82172098,82201716,and 61973206)the National Key R&D Program of China(No.2023YFC2411303)+4 种基金the Integrated Project of Major Research Plan of the National Natural Science Foundation of China(No.92249303)the Shanghai Committee of Science and Technology(No.23141900600,Laboratory Animal Research Project)the Shanghai Clinical Research Plan of SHDC2023CRT01the Young Elite Scientist Sponsorship Program by the China Association for Science and Technology(No.YESS20230049)the Baoshan District Health Commission Talents(Excellent Academic Leaders)Program(No.BSWSYX-2024-05)。
文摘With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has progressed from the fabrication of simple models(1.0)to the fabrication of permanent implants(2.0),tissue engineering scaffolds(3.0),and complex biostructures utilizing living cells(4.0).Nevertheless,significant challenges remain,particularly in accurately replicating the structure and function of host tissues,selecting appropriate materials,and optimizing printing parameters.The integration of artificial intelligence(AI),especially machine learning,provides promising novel opportunities in bioprinting(5.0).This review systematically summarizes the current applications of AI in bioprinting,discussing both construction strategies and application scenarios.It also explores the potential of AI to improve bioprinting in the preparation of complex functional tissues and in situ tissue repair.Overall,the synergy between AI and bioprinting is poised to drive the development of personalized medicine,facilitate high-throughput preparation of in vitro models,and provide robust tools for regenerative medicine and precision healthcare.
基金supported by the Basic Science Research Program(2023R1A2C3004336,RS-202300243807)&Regional Leading Research Center(RS-202400405278)through the National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)。
文摘Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility,adhesion,self-healing,and environmental robustness with excellent sensing metrics.Herein,we report a multifunctional,anti-freezing,selfadhesive,and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes(CoN CNT)embedded in a polyvinyl alcohol-gelatin(PVA/GLE)matrix.Fabricated using a binary solvent system of water and ethylene glycol(EG),the CoN CNT/PVA/GLE organogel exhibits excellent flexibility,biocompatibility,and temperature tolerance with remarkable environmental stability.Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range(40%-95%RH).Freeze-tolerant conductivity under sub-zero conditions(-20℃)is attributed to the synergistic role of CoN CNT and EG,preserving mobility and network integrity.The Co N CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 k Pa^(-1)in the detection range from 0 to 20 k Pa,ideal for subtle biomechanical motion detection.A smart human-machine interface for English letter recognition using deep learning achieved 98%accuracy.The organogel sensor utility was extended to detect human gestures like finger bending,wrist motion,and throat vibration during speech.
文摘Tissue engineering is an emerging means for resolving the problems of tissue repair and organ replacement in regenerative medicine.Insufficient supply of nutrients and oxygen to cells in large-scale tissues has led to the demand to prepare blood vessels.Scaffold-based tissue engineering approaches are effective methods to form new blood vessel tissues.The demand for blood vessels prompts systematic research on fabrication strategies of vascular scaffolds for tissue engineering.Recent advances in 3D printing have facilitated fabrication of vascular scaffolds,contributing to broad prospects for tissue vascularization.This review presents state of the art on modeling methods,print materials and preparation processes for fabrication of vascular scaffolds,and discusses the advantages and application fields of each method.Specially,significance and importance of scaffold-based tissue engineering for vascular regeneration are emphasized.Print materials and preparation processes are discussed in detail.And a focus is placed on preparation processes based on 3D printing technologies and traditional manufacturing technologies including casting,electrospinning,and Lego-like construction.And related studies are exemplified.Transformation of vascular scaffolds to clinical application is discussed.Also,four trends of 3D printing of tissue engineering vascular scaffolds are presented,including machine learning,near-infrared photopolymerization,4D printing,and combination of self-assembly and 3D printing-based methods.
基金Projects(51135009,51105371) supported by the National Natural Science Foundation of China
文摘LuGre model has been widely used in friction modeling and compensation.However,the new friction regime,named prestiction regime,cannot be accurately characterized by LuGre model in the latest research.With the extensive experimental observations of friction behaviors in the prestiction,some variables were abstracted to depict the rules in the prestiction regime.Based upon the knowledge of friction modeling,a novel friction model including the presliding regime,the gross sliding regime and the prestiction regime was then presented to overcome the shortcomings of the LuGre model.The reason that LuGre model cannot estimate the prestiction friction was analyzed in theory.Feasibility analysis of the proposed model in modeling the prestiction friction was also addressed.A parameter identification method for the proposed model based on multilevel coordinate search algorithm was presented.The proposed friction compensation strategy was composed of a nonlinear friction observer and a feedforward mechanism.The friction observer was designed to estimate the friction force in the presliding and the gross sliding regimes.And the friction force was estimated based on the model in the prestiction regime.The comparative trajectory tracking experiments were conducted on a simulator of inertially stabilization platforms among three control schemes:the single proportional–derivative(PD)control,the PD with LuGre model-based compensation and the PD with compensator based on the presented model.The experimental results reveal that the control scheme based on the proposed model has the best tracking performance.It reduces the peak-to-peak value(PPV)of tracking error to 0.2 mrad,which is improved almost 50%compared with the PD with LuGre model-based compensation.Compared to the single PD control,it reduces the PPV of error by 66.7%.
文摘The sensitivity engineering model of the coupling capacitance detector is built to provide the theoretic foundation for designing its circuits and electrodes scientifically. The sensitivity concept model of the capacitance proximity detector is discussed, and the detecting sensitivity of the coupling capacitance detector is analyzed theoretically. Then the sensitivity engineering model, which can reflect the main parameters relationship of the detecting circuit is set up based on the foregoing analyses. It is concluded that: ① the sensitivity is mainly correlative with some parameters including the voltage transmission factor of the demodulator, the oscillating voltage amplitude and the amplitude variation constant of the oscillator; ② the sensitivity is also influenced by the areas of electrodes and the distance between electrodes of the detector.
基金The Basic Research of COSTIND,China (No.D0420060521)
文摘To reuse and share the valuable knowledge embedded in repositories of engineering models for accelerating the design process, improving product quality, and reducing costs, it is crucial to devise search engines capable of matching 3D models efficiently and effectively. In this paper, an enhanced shape distributions-based technique of using geometrical and topological information to search 3D engineering models represented by polygonal meshes was presented. A simplification method of polygonal meshes was used to simplify engineering model as the pretreatment for generation of sample points. The method of sampling points was improved and a pair of functions that was more sensitive to shape was employed to construct a 2D shape distribution. Experiments were conducted to evaluate the proposed algorithm utilizing the Engineering Shape Benchmark (ESB) database. The experiential results suggest that the search effectiveness is significantly improved by enforcing the simplification and enhanced shape distributions to engineering model retrieval.
基金financially supported by the National Key Research and Development Project of China for Demonstration of Integrated Utilization of Solid Waste in Distinctive Convergent Areas of Southeast Light Industry Building Materials(2019YFC1904500)the National Natural Science Foundation of China(Grant No.81770222)+4 种基金the Social Development Industry University Research Cooperation Project from the Department of Science and Technology in Fujian(2018Y4002)support by the Award Program for Fujian Minjiang Scholar Professorshipsupport from the Australian Research Grants Council(DP130104648)support from the NSERC Discovery Grant(NSERC RGPIN-2020-04463)McGill Start-Up Grant。
文摘Owing to safety issue and low energy density of liquid lithium-ion batteries(LIBs),all-solid-state lithium metal batteries(ASLMBs)with unique all-solid-state electrolytes(SEs)have attracted wide attentions.This arises mainly from the advantages of the SEs in the suppression of lithium dendrite growth,long cycle life,and broad working temperature range,showing huge potential applications in electronic devices,electric vehicles,smart grids,and biomedical devices.However,SEs suffer from low lithiumion conductivity and low mechanical integrity,slowing down the development of practical ASLMBs.Nanostructure engineering is of great efficiency in tuning the structure and composition of the SEs with improved lithium-ion conductivity and mechanical integrity.Among various available technologies for nanostructure engineering,electrospinning is a promising technique because of its simple operation,cost-effectiveness,and efficient integration with different components.In this review,we will first give a simple description of the electrospinning process.Then,the use of electrospinning technique in the synthesis of various SEs is summarized,for example,organic nanofibrous matrix,organic/inorganic nanofibrous matrix,and inorganic nanofibrous matrix combined with other components.The current development of the advanced architectures of SEs through electrospinning technology is also presented to provide references and ideas for designing high-performance ASLMBs.Finally,an outlook and further challenges in the preparation of advanced SEs for ASLMBs through electrospinning engineering are given.
基金Funded by the Postdoctor Science Fund of China (No. 20070410715) Shanghai Excellent Youth Special Fund (No. 17014)
文摘A novel method of designing and preparing bone tissue engineering scaffolds with controllable porous structure of both macro channels and micro pores was proposed. The CAD software UG NX3.0 was used to design the macro channels＇ shape, size and distribution. By integrating rapid prototyping and traditional porogen technique, the macro channels and micro pores were formed respectively. The size, shape and quantity of micro pores were controlled by porogen particulates. The sintered β-TCP porous scaffolds possessed connective macro channels of approximately 500 μm and micro pores of 200-400 μm. The porosity and connectivity of micro pores became higher with the increase of porogen ratio, while the mechanical properties weakened. The average porosity and compressive strength offl-TCP scaffolds prepared with porogen ratio of 60wt% were 78.12% and 0.2983 MPa, respectively. The cells＇ adhesion ratio of scaffolds was 67.43%. The ALP activity, OCN content and cells micro morphology indicated that cells grew and proliferated well on the scaffolds.
基金This work was supported by the Deanship of Scientific Research,King Khalid University,Kingdom of Saudi Arabia under research Grant Number(R.G.P.2/100/41).
文摘Human Adaptive Mechatronics(HAM)includes human and computer system in a closed loop.Elderly person with disabilities,normally carry out their daily routines with some assistance to move their limbs.With the short fall of human care takers,mechatronics devices are used with the likes of exoskeleton and exosuits to assist them.The rehabilitation and occupational therapy equipments utilize the electromyography(EMG)signals to measure the muscle activity potential.This paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of the system.Limb characteristics extraction from EMG signal and prediction of optimal controller parameters are modeled.Time and frequency based approach of EMG signal are considered for feature extraction.The models used for estimating motion and muscle parameters from EMG signal for carrying out limb movement predictions are validated.Based on the extracted features,optimal parameters are selected by Modified Lion Optimization(MLO)for controlling the HAM system.Finally,supervised machine learning makes predictions at different points in time for individual sensing using Support Vector Neural Network(SVNN).This model is also evaluated based on optimal parameters of motion estimation and the accuracy level along with different optimization models for various upper limb movements.The proposed model of human adaptive controller predicts the limb movement by 96%accuracy.
基金support from the National Natural Science Foundation of China(Nos.52171107,52201203)the Hebei Provincial Natural Science Foundation,China(No.E2021501026)the National Natural Science Foundation of China-Joint Fund of Iron and Steel Research(No.U1960204).
文摘The novel Co-based superalloys are extensively used in gas-powered and jet engine turbines due to their excellent high-temperature performance, achieved by strengthening the L12-γ′ ordered phase. This review presents an overview of the research progress on oxidation behavior of Co-based superalloys, including oxidation kinetics, oxides morphology, the formation and spallation of oxide layers, and importantly, the synergistic effects of alloying elements on oxidation resistance—a critical area considering the complex interactions with multiple alloying elements. Additionally, this review compares the oxidation resistance of single crystal versus polycrystalline alloys. The effect of phase interface and dislocations on oxidation behavior is also discussed. While significant progress has been achieved, areas necessitating further investigation include optimizing alloy compositions for enhanced oxidation resistance and understanding the long-term stability of oxide layers. The future prospects for Co-based superalloys are promising as ongoing research aims to address the existing challenges and unlock new applications at even higher operating temperatures.
基金funded by the National Natural Science Foundation of China(Grant No.06101213)the National Natural Science Foundation of China(Grant No.22105160).
文摘3D printing technology enhances the combustion characteristics of hybrid rocket fuels by enabling complex geometries. However, improvements in regression rates and energy properties of monotonous 3D printed fuels have been limited. This study explores the impact of poly(vinylidene fluoride) and polydopamine-coated aluminum particles on the thermal and combustion properties of 3D printed hybrid rocket fuels. Physical self-assembly and anti-solvent methods were employed for constructing composite μAl particles. Characterization using SEM, XRD, XPS, FTIR, and μCT revealed a core-shell structure and homogeneous elemental distribution. Thermal analysis showed that PVDF coatings significantly increased the heat of combustion for aluminum particles, with maximum enhancement observed in μAl@PDA@PVDF(denoted as μAl@PF) at 6.20 k J/g. Subsequently, 3D printed fuels with varying pure and composite μAl particle contents were prepared using 3D printing. Combustion tests indicated higher regression rates for Al@PF/Resin composites compared to pure resin, positively correlating with particle content. The fluorocarbon-alumina reaction during the combustion stage intensified Al particle combustion, reducing residue size. A comprehensive model based on experiments provides insights into the combustion process of PDA and PVDF-coated droplets. This study advances the design of 3D-printed hybrid rocket fuels, offering strategies to improve regression rates and energy release, crucial for enhancing solid fuel performance for hybrid propulsion.
基金Project supported by the National Natural Science Foundation of China(No.42202314)。
文摘A coupled thermal-hydro-mechanical cohesive phase-field model for hydraulic fracturing in deep coal seams is presented.Heat exchange between the cold fluid and the hot rock is considered,and the thermal contribution terms between the cold fluid and the hot rock are derived.Heat transfer obeys Fourier's law,and porosity is used to relate the thermodynamic parameters of the fracture and matrix domains.The net pressure difference between the fracture and the matrix is neglected,and thus the fluid flow is modeled by the unified fluid-governing equations.The evolution equations of porosity and Biot's coefficient during hydraulic fracturing are derived from their definitions.The effect of coal cleats is considered and modeled by Voronoi polygons,and this approach is shown to have high accuracy.The accuracy of the proposed model is verified by two sets of fracturing experiments in multilayer coal seams.Subsequently,the differences in fracture morphology,fluid pressure response,and fluid pressure distribution between direct fracturing of coal seams and indirect fracturing of shale interlayers are explored,and the effects of the cluster number and cluster spacing on fracture morphology for multi-cluster fracturing are also examined.The numerical results show that the proposed model is expected to be a powerful tool for the fracturing design and optimization of deep coalbed methane.
基金National Natural Science Foundation of China(No.51705545)。
文摘The active sound absorption technique excels in mitigating low-frequency sound waves,yet it falls short when dealing with medium and high-frequency sound waves.To enhance the sound-absorbing effect of medium and high-frequency sound waves,a novel semi-active sound absorption method has been introduced.This method modulates the surface impedance of a loudspeaker positioned behind the sound-absorbing material,thereby altering the sound absorption coefficient.The theoretical sound absorption coefficient is calculated using MATLAB and compared with the experimental one.Results show that the method can effectively modulates the absorption coefficient in response to varying incident sound wave frequencies,ensuring that it remains at its peak value.
