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
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
Healthcare mechatronics is a typical multidisciplinary field involving machinery,medicine,computer,and automation,which has been widely applied in respiratory therapy,urology robot,rehabilitation exoskeleton,artificia...Healthcare mechatronics is a typical multidisciplinary field involving machinery,medicine,computer,and automation,which has been widely applied in respiratory therapy,urology robot,rehabilitation exoskeleton,artificial heart,etc.Existing progresses has some defects in modeling,design and implementation of healthcare mechatronics.Therefore,exploring new design theories,key technologies and typical applications is an effective to promote the rapid development of this field.展开更多
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
The effect of plasma and charged particle interaction with spacecraft in a low Earth orbit(LEO)environment leads to ion focusing and the formation of an ion void in the downstream region as a result of charging.Simula...The effect of plasma and charged particle interaction with spacecraft in a low Earth orbit(LEO)environment leads to ion focusing and the formation of an ion void in the downstream region as a result of charging.Simulations and investigations using a fixed potential imposed on the spacecraft showed the nonsignificance of geophysical parameter changes to ion focusing.Variation of the temperature ratio(T_(r))contributed only to local ion focusing and manifested as two-ion streamers dispersed at the upper and lower edges of the spacecraft-the outermost layers of the satellite structure at the top and bottom,respectively.A simulation involving changing the ambient plasma density(N_(p))also showed the formation of local ion focusing,in which ions were more concentrated as the density increased.Furthermore,auroral electron density(N_(ae))variation had no clear impact on ion focusing,as indicated by static two-ion structures in the wake field.However,variation of the object potential(ϕ)strongly affected ion focusing formation,leading to distortion of the initial ion void region behind the spacecraft.The formation of ion focusing in this study was subject to the electric field produced by the object potential and the ambipolar electric field resulting from plasma expansion in the downstream region.展开更多
Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstruc...Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.展开更多
Robotic electronic skin(e-skin)is inspired by human skin and endows robots with tactile perception,temperature detection,and environmental interaction capabilities.However,its development is hampered by prolonged desi...Robotic electronic skin(e-skin)is inspired by human skin and endows robots with tactile perception,temperature detection,and environmental interaction capabilities.However,its development is hampered by prolonged design cycles,limited signal enhancement,and weak cognitive abilities.Given that the convergence of artificial intelligence(AI)with e-skin is fundamentally transforming this landscape,the present review highlights the pivotal contributions of AI across the entire development spectrum of robotic e-skin,including design optimization,signal processing,and cognitive enhancement.AI-driven design paradigms dramatically shorten development time and enable the discovery of optimal sensor materials and structures.In signal processing,AI algorithms notably improve the ability to decouple complex sensory data,enabling robust,multimodal,super-resolution sensing.AI endows e-skin with advanced cognitive capabilities,allowing it to interpret intricate tactile information and intelligently respond to external environments.By underscoring the potential of AI throughout the entire development pipeline,this review aims to drive the creation of e-skin with minimal hardware and maximal cognition and thus achieve revolutionary breakthroughs in cutting-edge fields such as human-robot interactions,precise robot control,and soft robotics for environmental exploration.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
“By successfully integrating artificial intelligence(AI)into research workflows,researchers could substantially increase scientific productivity”[1].In biofabrication,AI is dr iving a paradigm shift from empiricism ...“By successfully integrating artificial intelligence(AI)into research workflows,researchers could substantially increase scientific productivity”[1].In biofabrication,AI is dr iving a paradigm shift from empiricism toward intelligen t,data centric manufacturing[2].By integrating computation,automation,and biology,AI gives rise to self-evolving,adaptive systems that learn from data,predict complex behaviors,and autonomously optimize fabrication outcomes.Such systems translate experimental insights into patient-specific and clinically relevant solutions,bridging laboratory research and regenerative therapies[3].This emerging frontier is rapidly advancing from concept to application.This Special Column highlights how AI-driven advanc es in materials,design,and manufacturing are reshaping biof abrication for regenerative medicine and clinical translation.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
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
The development of low-temperature solid oxide fuel cells(LT-SOFCs)is of significant importance for realizing the widespread application of SOFCs.This has stimulated a substantial materials research effort in developi...The development of low-temperature solid oxide fuel cells(LT-SOFCs)is of significant importance for realizing the widespread application of SOFCs.This has stimulated a substantial materials research effort in developing high oxide-ion conductivity in the electrolyte layer of SOFCs.In this context,for the first time,a dielectric material,CaCu_(3)Ti_(4)O_(12)(CCTO)is designed for LT-SOFCs electrolyte application in this study.Both individual CCTO and its heterostructure materials with a p-type Ni_(0.8)Co_(0.15)Al_(0.05)LiO_(2−δ)(NCAL)semiconductor are evaluated as alternative electrolytes in LT-SOFC at 450–550℃.The single cell with the individual CCTO electrolyte exhibits a power output of approximately 263 mW cm^(-2) and an open-circuit voltage(OCV)of 0.95 V at 550℃,while the cell with the CCTO–NCAL heterostructure electrolyte capably delivers an improved power output of approximately 605 mW cm^(-2) along with a higher OCV over 1.0 V,which indicates the introduction of high hole-conducting NCAL into the CCTO could enhance the cell performance rather than inducing any potential short-circuiting risk.It is found that these promising outcomes are due to the interplay of the dielectric material,its structure,and overall properties that led to improve electrochemical mechanism in CCTO–NCAL.Furthermore,density functional theory calculations provide the detailed information about the electronic and structural properties of the CCTO and NCAL and their heterostructure CCTO–NCAL.Our study thus provides a new approach for developing new advanced electrolytes for LT-SOFCs.展开更多
To enhance the mechanical properties of Mo alloys prepared through laser powder bed fusion(LPBF),a hot isostatic pressing(HIP)treatment was used.Results show that following HIP treatment,the porosity decreases from 0....To enhance the mechanical properties of Mo alloys prepared through laser powder bed fusion(LPBF),a hot isostatic pressing(HIP)treatment was used.Results show that following HIP treatment,the porosity decreases from 0.27%to 0.22%,enabling the elements Mo and Ti to diffuse fully and to distribute more uniformly,and to forming a substantial number of low-angle grain boundaries.The tensile strength soars from 286±32 MPa to 598±22 MPa,while the elongation increases from 0.08%±0.02%to 0.18%±0.02%,without notable alterations in grain morphology during the tensile deformation.HIP treatment eliminates the molten pool boundaries,which are the primary source for premature failure in LPBFed Mo alloys.Consequently,HIP treatment emerges as a novel and effective approach for strengthening the mechanical properties of LPBFed Mo alloys,offering a fresh perspective on producing high-performance Mo-based alloys.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
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Healthcare mechatronics is a typical multidisciplinary field involving machinery,medicine,computer,and automation,which has been widely applied in respiratory therapy,urology robot,rehabilitation exoskeleton,artificial heart,etc.Existing progresses has some defects in modeling,design and implementation of healthcare mechatronics.Therefore,exploring new design theories,key technologies and typical applications is an effective to promote the rapid development of this field.
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Kobe Universitythe National Research and Innovation Agency (BRIN)
文摘The effect of plasma and charged particle interaction with spacecraft in a low Earth orbit(LEO)environment leads to ion focusing and the formation of an ion void in the downstream region as a result of charging.Simulations and investigations using a fixed potential imposed on the spacecraft showed the nonsignificance of geophysical parameter changes to ion focusing.Variation of the temperature ratio(T_(r))contributed only to local ion focusing and manifested as two-ion streamers dispersed at the upper and lower edges of the spacecraft-the outermost layers of the satellite structure at the top and bottom,respectively.A simulation involving changing the ambient plasma density(N_(p))also showed the formation of local ion focusing,in which ions were more concentrated as the density increased.Furthermore,auroral electron density(N_(ae))variation had no clear impact on ion focusing,as indicated by static two-ion structures in the wake field.However,variation of the object potential(ϕ)strongly affected ion focusing formation,leading to distortion of the initial ion void region behind the spacecraft.The formation of ion focusing in this study was subject to the electric field produced by the object potential and the ambipolar electric field resulting from plasma expansion in the downstream region.
基金funded by the Directorate of Research and Community Service,Directorate General of Research and Development,Ministry of Higher Education,Science and Technologyin accordance with the Implementation Contract for the Operational Assistance Program for State Universities,Research Program Number:109/C3/DT.05.00/PL/2025.
文摘Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.
基金supported by the National Natural Science Foundation of China(No.52375031)the Dongfang Electric Corporation-Zhejiang University Joint Innovation Research Institutethe Bellwethers Research and Development Plan of Zhejiang Province(No.2023C01045)。
文摘Robotic electronic skin(e-skin)is inspired by human skin and endows robots with tactile perception,temperature detection,and environmental interaction capabilities.However,its development is hampered by prolonged design cycles,limited signal enhancement,and weak cognitive abilities.Given that the convergence of artificial intelligence(AI)with e-skin is fundamentally transforming this landscape,the present review highlights the pivotal contributions of AI across the entire development spectrum of robotic e-skin,including design optimization,signal processing,and cognitive enhancement.AI-driven design paradigms dramatically shorten development time and enable the discovery of optimal sensor materials and structures.In signal processing,AI algorithms notably improve the ability to decouple complex sensory data,enabling robust,multimodal,super-resolution sensing.AI endows e-skin with advanced cognitive capabilities,allowing it to interpret intricate tactile information and intelligently respond to external environments.By underscoring the potential of AI throughout the entire development pipeline,this review aims to drive the creation of e-skin with minimal hardware and maximal cognition and thus achieve revolutionary breakthroughs in cutting-edge fields such as human-robot interactions,precise robot control,and soft robotics for environmental exploration.
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.
文摘“By successfully integrating artificial intelligence(AI)into research workflows,researchers could substantially increase scientific productivity”[1].In biofabrication,AI is dr iving a paradigm shift from empiricism toward intelligen t,data centric manufacturing[2].By integrating computation,automation,and biology,AI gives rise to self-evolving,adaptive systems that learn from data,predict complex behaviors,and autonomously optimize fabrication outcomes.Such systems translate experimental insights into patient-specific and clinically relevant solutions,bridging laboratory research and regenerative therapies[3].This emerging frontier is rapidly advancing from concept to application.This Special Column highlights how AI-driven advanc es in materials,design,and manufacturing are reshaping biof abrication for regenerative medicine and clinical translation.
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.
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National Natural Science Foundation of China(NSFC)supported this work under Grant No.32250410309,11674086,51736006,and 51772080funding from Science and Technology Department of Jiangsu Province under Grant No.BE2022029Shenzhen University under Grant No.86902/000248 also supported part of this work.
文摘The development of low-temperature solid oxide fuel cells(LT-SOFCs)is of significant importance for realizing the widespread application of SOFCs.This has stimulated a substantial materials research effort in developing high oxide-ion conductivity in the electrolyte layer of SOFCs.In this context,for the first time,a dielectric material,CaCu_(3)Ti_(4)O_(12)(CCTO)is designed for LT-SOFCs electrolyte application in this study.Both individual CCTO and its heterostructure materials with a p-type Ni_(0.8)Co_(0.15)Al_(0.05)LiO_(2−δ)(NCAL)semiconductor are evaluated as alternative electrolytes in LT-SOFC at 450–550℃.The single cell with the individual CCTO electrolyte exhibits a power output of approximately 263 mW cm^(-2) and an open-circuit voltage(OCV)of 0.95 V at 550℃,while the cell with the CCTO–NCAL heterostructure electrolyte capably delivers an improved power output of approximately 605 mW cm^(-2) along with a higher OCV over 1.0 V,which indicates the introduction of high hole-conducting NCAL into the CCTO could enhance the cell performance rather than inducing any potential short-circuiting risk.It is found that these promising outcomes are due to the interplay of the dielectric material,its structure,and overall properties that led to improve electrochemical mechanism in CCTO–NCAL.Furthermore,density functional theory calculations provide the detailed information about the electronic and structural properties of the CCTO and NCAL and their heterostructure CCTO–NCAL.Our study thus provides a new approach for developing new advanced electrolytes for LT-SOFCs.
基金National Natural Science Foundation of China(52105385)Stable Support Plan Program of Shenzhen Natural Science Fund(20220810132537001)+2 种基金Guangdong Basic and Applied Basic Research Foundation(2022A1515010781)Joint Fund of Henan Province Science and Technology R&D Program(225200810002)Fundamental Research Funds of Henan Academy of Sciences(240621041)。
文摘To enhance the mechanical properties of Mo alloys prepared through laser powder bed fusion(LPBF),a hot isostatic pressing(HIP)treatment was used.Results show that following HIP treatment,the porosity decreases from 0.27%to 0.22%,enabling the elements Mo and Ti to diffuse fully and to distribute more uniformly,and to forming a substantial number of low-angle grain boundaries.The tensile strength soars from 286±32 MPa to 598±22 MPa,while the elongation increases from 0.08%±0.02%to 0.18%±0.02%,without notable alterations in grain morphology during the tensile deformation.HIP treatment eliminates the molten pool boundaries,which are the primary source for premature failure in LPBFed Mo alloys.Consequently,HIP treatment emerges as a novel and effective approach for strengthening the mechanical properties of LPBFed Mo alloys,offering a fresh perspective on producing high-performance Mo-based alloys.
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.
