The thermocline energy storage tank(TEST)serves as a crucial component in thermal energy storage systems,utilizing the working fluid that enters through a diffuser to store and harness energy.However,the conventional ...The thermocline energy storage tank(TEST)serves as a crucial component in thermal energy storage systems,utilizing the working fluid that enters through a diffuser to store and harness energy.However,the conventional double-plate radial diffuser is ill-suited for a single-medium TEST’s full tank storage due to its unidirectional fluid inflow.There has been a notable lack of optimization analysis of diffusers.Two innovative tubular diffuser designs with reduced cross-sectional areas have been introduced:the annular-arranged diffuser(AAD)and the cross-arranged diffuser(CAD).To elucidate the impact of diffuser designs on energy storage efficiency,a 3D transient computational fluid dynamics(CFD)model was established to simulate a thermocline formation under two diffuser types.The model was validated against experimental data.Results showed that the thermocline of AAD was 11.39%thinner than that of a traditional double-plate diffuser.In the process of charging and discharging,the time-varying thermocline and factors influencing thermocline thickness were analyzed.Results indicate that in the mixed dominant region,increased turbulent kinetic energy correlates with reduced thermocline thickness.Notably,the AAD’s stable thermocline was 4.23%and 5.41%thinner than the CAD’s during charging and discharging,respectively,making the AAD preferable for engineering applications.The effects of tube diameter and orifice opening angle on temperature stratification performance were also examined.The findings suggest that an inclined impact jet and large-diameter tubes are more conducive to temperature stratification.Moreover,an orifice diameter optimization method was developed,which can decrease the thermocline by 6.78%.展开更多
Size reduction of the gas turbines(GT)by reducing the inlet S-shaped diffuser length increases the powerto-weight ratio.It improves the techno-economic features of the GT by lesser fuel consumption.However,this Length...Size reduction of the gas turbines(GT)by reducing the inlet S-shaped diffuser length increases the powerto-weight ratio.It improves the techno-economic features of the GT by lesser fuel consumption.However,this Length reduction of a bare S-shaped diffuser to an aggressive S-shaped diffuser would risk flow separation and performance reduction of the diffuser and the air intake of the GT.The objective of this research is to propose and assess fitted energy promoters(EPs)to enhance the S-shaped diffuser performance by controlling and modifying the flow in the high bending zone of the diffuser.After experimental assessment,the work has been extended to cover more cases by numerical investigations on bare,bare aggressive,and aggressive with energy promoters S-shaped diffusers.Three types of EPs,namely co-rotating low-profile,co-rotating streamline sheet,and trapezoidal submerged EPs were tested with various combinations over a range of Reynolds numbers from 40,000 to 75,000.The respective S-shaped diffusers were simulated by computational fluid dynamics(CFD)using ANSYS software adopting a steady,3D,standard k-εturbulence model to acquire the details of the flow structure,which cannot be visualized in the experiment.The diffuser performance has been evaluated by the performance indicators of static pressure recovery coefficient,total pressure loss coefficient,and distortion coefficient(DC(45°)).The enhancements in the static pressure recovery of the S-shaped aggressive diffuser with energy promoters are 19.5%,22.2%,and 24.5%with EPs at planes 3,4 and 5,respectively,compared to the aggressive bare diffuser.In addition,the installation of the EPs resulted in a DC(45°)reduction at the outlet plane of the diffuser of about 43%at Re=40,000.The enhancements in the performance parameters confirm that aggravating the internal flow eliminates the flow separation and enhances the GT intake efficiency.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
Hard carbon(HC)in sodium-ion batteries is searched by numerous investigations,which can offer the excellent performance of reversible Na^(+)insertion and extraction.The covalent heteroatom doping in HC is recently wor...Hard carbon(HC)in sodium-ion batteries is searched by numerous investigations,which can offer the excellent performance of reversible Na^(+)insertion and extraction.The covalent heteroatom doping in HC is recently worth concentrating,which can dilate the interlayer spacing of graphite to adjust the electrochemical storage performance in carbon anodes.However,the reported doping strategies of the modified HC have only resulted in limited improvement,especially unobvious effects on tuning porous structure.In this study,tannin extract and K_(2)SO_(4) are respectively utilized as carbon source and sulfur source for the fabrication of HC,in which K_(2)SO_(4) can contribute to the heteroatom doping,and the pore forming as well.The tannin-derived sulfur-doped carbon anode shows the excellent cycle stability,achieving a high reversible capacity of 520.5 mAh/g at a current density of 100 mA/g.Even after 500 cycles at a current density of 3 A/g,a high specific capacity of 236.7 mAh/g and a capacity retention rate of 92.6%can be reserved.Compared with the initial carbon,the adsorption energy of Na^(+)is multifold times higher,whereas Na^(+)diffusion energy barriers manyfold decrease.Moreover,the full battery assembled with Na_(3)V_(2)(PO_(4))_(3)/tannin-based HC demonstrates a stable cycling performance.This work can manifest the potentiality of the tannin-based electrode as anode for a high-performance sodium-ion batteries(SIBs),which could especially offer an explanation of Na^(+)storage and solid-electrolyte interface(SEI)stability to the electrochemical performance.展开更多
Reaction-diffusion systems are widely used to describe pattern formation,and various control strategies have been applied to reaction-diffusion systems to achieve control objectives such as boundary control,output fee...Reaction-diffusion systems are widely used to describe pattern formation,and various control strategies have been applied to reaction-diffusion systems to achieve control objectives such as boundary control,output feedback stabilization,and synchronization.However,controlling pattern dynamics in reaction-diffusion systems with fractional-order diffusion remains an unresolved problem.This paper presents a proportional-derivative(PD)control strategy for the Schnakenberg system with fractional-order diffusion and cross-diffusion.Theoretical analysis explores the amplitude equation near the Turing bifurcation threshold,determining the selection and stability of pattern formations.Numerical simulations demonstrate that the PD controller accomplishes the modification of pattern structures and suppression of Turing instability by adjusting only two control parameters.Additionally,it is found that for smaller fractional diffusion order,the region can accommodate more hexagonal and stripe patterns in space.This work contributes to the control of complex pattern dynamics and offers a new approach to enhancing stability in fractional reaction-diffusion systems.展开更多
In this paper,we are concerned with the stability of traveling wavefronts of a Belousov-Zhabotinsky model with mixed nonlocal and degenerate diffusions.Such a system can be used to study the competition among nonlocal...In this paper,we are concerned with the stability of traveling wavefronts of a Belousov-Zhabotinsky model with mixed nonlocal and degenerate diffusions.Such a system can be used to study the competition among nonlocally diffusive species and degenerately diffusive species.We prove that the traveling wavefronts are exponentially stable,when the initial perturbation around the traveling waves decays exponentially as x→-∞,but in other locations,the initial data can be arbitrarily large.The adopted methods are the weighted energy with the comparison principle and squeezing technique.展开更多
With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing r...With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing research attention.However,existing methods still face limitations in balancing multi-frame character consistency and generation efficiency,which restricts their feasibility for large-scale practical applications.To address this issue,this study proposes a modular cloud-based distributed system built on Stable Diffusion.By separating the character generation and story generation processes,and integratingmulti-feature control techniques,a cachingmechanism,and an asynchronous task queue architecture,the system enhances generation efficiency and scalability.The experimental design includes both automated and human evaluations of character consistency,performance testing,and multinode simulation.The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics.In terms of performance,under the experimental environment of this study,dual-node deployment reduces average waiting time by approximately 19%,while the four-node simulation further reduces it by up to 65%.Overall,this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency,highlighting its potential value inmulti-user collaborative story visualization applications.展开更多
Injecting impure CO_(2)for enhanced gas recovery(CO_(2)-EGR)offers a dual benefit by improving natural gas extraction while enabling CO_(2)sequestration.However,the interactions between CO_(2),N_(2),and CH_(4)under re...Injecting impure CO_(2)for enhanced gas recovery(CO_(2)-EGR)offers a dual benefit by improving natural gas extraction while enabling CO_(2)sequestration.However,the interactions between CO_(2),N_(2),and CH_(4)under reservoir conditions require further investigation.This study employs Grand Canonical Monte Carlo(GCMC)and Molecular Dynamics(MD)simulations to quantify the adsorption and diffusion behaviors of CO_(2),N_(2),and CH_(4)in quartz nanopores over a pressure range of 1-24 MPa under varying water saturations and gas compositions.The results indicate that:(1)CO_(2)exhibits the broadest energy distribution and the strongest adsorption stability,occupying about 20%-30%more adsorption sites than CH_(4)or N_(2)and showing the least sensitivity to water saturation,with only a 30%reduction at 50%saturation,compared to 60%for CH_(4),giving CO_(2)a clear competitive advantage.(2)The adsorption and desorption behaviors are strongly pressure dependent,as increasing pressure reduces the adsorption layer area and shifts gas distribution from adsorption dominated to free phase.Competitive adsorption analysis reveals that while CO_(2)dominates displacement at low pressures,mixtures that contain N_(2)achieve higher CH_(4)desorption efficiency above 13 MPa by mitigating diffusion resistance.(3)A higher N_(2)fraction improves CH_(4)diffusion coefficients,thereby facilitating gas mobility and ensuring superior recovery performance under high-pressure conditions.This study advances the fundamental knowledge of microscale gas behavior in tight sandstones and supports the feasibility of impure CO_(2)injection as a practical strategy for sustainable gas production.展开更多
With the development of technology,diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization(CO)problems,particularly in tackling Non-deterministic Polynomial-time hard(NP-hard...With the development of technology,diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization(CO)problems,particularly in tackling Non-deterministic Polynomial-time hard(NP-hard)problems such as the Traveling Salesman Problem(TSP).However,existing diffusion model-based solvers typically employ a fixed,uniform noise schedule(e.g.,linear or cosine annealing)across all training instances,failing to fully account for the unique characteristics of each problem instance.To address this challenge,we present GraphGuided Diffusion Solvers(GGDS),an enhanced method for improving graph-based diffusion models.GGDS leverages Graph Neural Networks(GNNs)to capture graph structural information embedded in node coordinates and adjacency matrices,dynamically adjusting the noise levels in the diffusion model.This study investigates the TSP by examining two distinct time-step noise generation strategies:cosine annealing and a Neural Network(NN)-based approach.We evaluate their performance across different problem scales,particularly after integrating graph structural information.Experimental results indicate that GGDS outperforms previous methods with average performance improvements of 18.7%,6.3%,and 88.7%on TSP-500,TSP-100,and TSP-50,respectively.Specifically,GGDS demonstrates superior performance on TSP-500 and TSP-50,while its performance on TSP-100 is either comparable to or slightly better than that of previous methods,depending on the chosen noise schedule and decoding strategy.展开更多
Prussian blue analogs(PBAs)have emerged as environmentally friendly and structurally tunable cathode materials for aqueous ammonium-ion batteries(AIBs).However,the fundamental role of crystalline H_(2)O in regulating ...Prussian blue analogs(PBAs)have emerged as environmentally friendly and structurally tunable cathode materials for aqueous ammonium-ion batteries(AIBs).However,the fundamental role of crystalline H_(2)O in regulating ammonium-ion storage and transport remains poorly understood.In this study,we present a comprehensive comparison between hydrated NH_(4)NiHCF-H_(2)O and its anhydrous counterpart NH_(4)NiHCF,revealing the critical contribution of interstitial water to electrochemical performance.Structural and spectroscopic analyses confirm that interstitial water forms robust hydrogen bonds with NH_(4)+ions,stabilizing the PBA framework and mitigating structural degradation during cycling.Electrochemical measurements show that NH_(4)NiHCF-H_(2)O delivers a significantly higher specific capacity of 61 mA h g^(−1)at 0.2 C and markedly improved rate performance compared to NH_(4)NiHCF(48 mA h g^(−1)at 0.2 C).Kinetic analysis reveals that interstitial water enhances NH_(4)+diffusion,as evidenced by higher diffusion coefficients.Furthermore,density functional theory(DFT)calculations demonstrate that crystal water acts as a hydrogen bond acceptor,preferentially interacting with NH_(4)+and reducing the migration energy barrier,thereby facilitating fast ion transport.This work provides fundamental insights into the role of crystal water in PBAs and offers a rational design strategy for improving the kinetics,structural stability of PBAs cathodes for AIBs.展开更多
Aqueous hydrogen(H_(2))gas batteries with unmatched lifespan are ideal for grid-scale energy storage,yet their deployment remains limited by the lack of low-cost,efficient,and durable hydrogen electrodes.Here,we repor...Aqueous hydrogen(H_(2))gas batteries with unmatched lifespan are ideal for grid-scale energy storage,yet their deployment remains limited by the lack of low-cost,efficient,and durable hydrogen electrodes.Here,we report a high-throughput and durable gas diffusion electrode(GDE)based on a simply preparable carbon-coated nickel(Ni@C)catalyst and the design of H_(2) diffusion channels.By optimizing the carbon layer structure,a balance between the intrinsic activity and stability of the catalyst can be achieved.This Ni@C catalyst exhibits a hydrogen oxidation reaction(HOR)activity of 44 A g^(-1) as well as remarkable hydrogen evolution reaction(HER)performance.Experimental results and theoretical calculations confirm the electronic interaction between the carbon shell and Ni.In combination with a hydrophobic design,a robust and durable Ni@C-GDE has been fabricated.This electrode achieves a low HOR polarization of only 91 mV at 30 mA cm^(-2),outperforming Pt/C-GDE(154 mV),and operates stably over 4500cycles(3200 h)for HOR/HER reversing.Enabled by this electrode,a 10 Ah Ni-H_(2) battery with an energy density of 156.3 Wh kg^(-1) and cost of 62.2$kWh^(-1) is demonstrated.This work offers a viable strategy for practical and scalable hydrogen gas batteries.展开更多
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h...In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.展开更多
The neutron diffusion equation plays a pivotal role in nuclear reactor analysis.Nevertheless,employing the physics-informed neural network(PINN)method for its solution entails certain limitations.Conventional PINN app...The neutron diffusion equation plays a pivotal role in nuclear reactor analysis.Nevertheless,employing the physics-informed neural network(PINN)method for its solution entails certain limitations.Conventional PINN approaches generally utilize a fully connected network(FCN)architecture that is susceptible to overfitting,training instability,and gradient vanishing as the network depth increases.These challenges result in accuracy bottlenecks in the solution.In response to these issues,the residual-based resample physics-informed neural network(R2-PINN)is proposed.It is an improved PINN architecture that replaces the FCN with a convolutional neural network with a shortcut(S-CNN).It incorporates skip connections to facilitate gradient propagation between network layers.Additionally,the incorporation of the residual adaptive resampling(RAR)mechanism dynamically increases the number of sampling points.This,in turn,enhances the spatial representation capabilities and overall predictive accuracy of the model.The experimental results illustrate that our approach significantly improves the convergence capability of the model and achieves high-precision predictions of the physical fields.Compared with conventional FCN-based PINN methods,R 2-PINN effectively overcomes the limitations inherent in current methods.Thus,it provides more accurate and robust solutions for neutron diffusion equations.展开更多
Crack detection accuracy in computer vision is often constrained by limited annotated datasets.Although Generative Adversarial Networks(GANs)have been applied for data augmentation,they frequently introduce blurs and ...Crack detection accuracy in computer vision is often constrained by limited annotated datasets.Although Generative Adversarial Networks(GANs)have been applied for data augmentation,they frequently introduce blurs and artifacts.To address this challenge,this study leverages Denoising Diffusion Probabilistic Models(DDPMs)to generate high-quality synthetic crack images,enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation.The proposed framework involves a two-stage pipeline:first,DDPMs are used to synthesize high-fidelity crack images that capture fine structural details.Second,these generated samples are combined with real data to train segmentation networks,thereby improving accuracy and robustness in crack detection.Compared with GAN-based approaches,DDPM achieved the best fidelity,with the highest Structural Similarity Index(SSIM)(0.302)and lowest Learned Perceptual Image Patch Similarity(LPIPS)(0.461),producing artifact-free images that preserve fine crack details.To validate its effectiveness,six segmentation models were tested,among which LinkNet consistently achieved the best performance,excelling in both region-level accuracy and structural continuity.Incorporating DDPM-augmented data further enhanced segmentation outcomes,increasing F1 scores by up to 1.1%and IoU by 1.7%,while also improving boundary alignment and skeleton continuity compared with models trained on real images alone.Experiments with varying augmentation ratios showed consistent improvements,with F1 rising from 0.946(no augmentation)to 0.957 and IoU from 0.897 to 0.913 at the highest ratio.These findings demonstrate the effectiveness of diffusion-based augmentation for complex crack detection in structural health monitoring.展开更多
The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-super...The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.展开更多
Amidst the growing global emphasis on nuclear safety,the integrity of nuclear reactor systems has garnered attention in the aftermath of consequential events.Moreover,the rapid development of artificial intelligence t...Amidst the growing global emphasis on nuclear safety,the integrity of nuclear reactor systems has garnered attention in the aftermath of consequential events.Moreover,the rapid development of artificial intelligence technology has provided immense opportunities to enhance the safety and economy of nuclear energy.However,data-driven deep learning techniques often lack interpretability,which hinders their applicability in the nuclear energy sector.To address this problem,this study proposes a hybrid data-driven and knowledge-driven artificial intelligence model based on physics-informed neural networks to accurately compute the neutron flux distribution inside a nuclear reactor core.Innovative techniques,such as regional decomposition,intelligent k_(eff)(effective multiplication factor)search,and k_(eff)inversion,have been introduced for the calculation.Furthermore,hyperparameters of the model are automatically optimized using a whale optimization algorithm.A series of computational examples are used to validate the proposed model,demonstrating its applicability,generality,and high accuracy in calculating the neutron flux within the nuclear reactor.The model offers a dependable strategy for computing the neutron flux distribution in nuclear reactors for advanced simulation techniques in the future,including reactor digital twinning.This approach is data-light,requires little to no training data,and still delivers remarkably precise output data.展开更多
基金supported by the National Natural Science Foundation of China(No.52375274)the Zhejiang Provincial Natural Science Foundation of China(No.LD21E050003)+1 种基金the Key R&D Program of Zhejiang Province(No.2023C01229)the Central Government Fund for Regional Science and Technology Development of China(No.2023ZY1033).
文摘The thermocline energy storage tank(TEST)serves as a crucial component in thermal energy storage systems,utilizing the working fluid that enters through a diffuser to store and harness energy.However,the conventional double-plate radial diffuser is ill-suited for a single-medium TEST’s full tank storage due to its unidirectional fluid inflow.There has been a notable lack of optimization analysis of diffusers.Two innovative tubular diffuser designs with reduced cross-sectional areas have been introduced:the annular-arranged diffuser(AAD)and the cross-arranged diffuser(CAD).To elucidate the impact of diffuser designs on energy storage efficiency,a 3D transient computational fluid dynamics(CFD)model was established to simulate a thermocline formation under two diffuser types.The model was validated against experimental data.Results showed that the thermocline of AAD was 11.39%thinner than that of a traditional double-plate diffuser.In the process of charging and discharging,the time-varying thermocline and factors influencing thermocline thickness were analyzed.Results indicate that in the mixed dominant region,increased turbulent kinetic energy correlates with reduced thermocline thickness.Notably,the AAD’s stable thermocline was 4.23%and 5.41%thinner than the CAD’s during charging and discharging,respectively,making the AAD preferable for engineering applications.The effects of tube diameter and orifice opening angle on temperature stratification performance were also examined.The findings suggest that an inclined impact jet and large-diameter tubes are more conducive to temperature stratification.Moreover,an orifice diameter optimization method was developed,which can decrease the thermocline by 6.78%.
文摘Size reduction of the gas turbines(GT)by reducing the inlet S-shaped diffuser length increases the powerto-weight ratio.It improves the techno-economic features of the GT by lesser fuel consumption.However,this Length reduction of a bare S-shaped diffuser to an aggressive S-shaped diffuser would risk flow separation and performance reduction of the diffuser and the air intake of the GT.The objective of this research is to propose and assess fitted energy promoters(EPs)to enhance the S-shaped diffuser performance by controlling and modifying the flow in the high bending zone of the diffuser.After experimental assessment,the work has been extended to cover more cases by numerical investigations on bare,bare aggressive,and aggressive with energy promoters S-shaped diffusers.Three types of EPs,namely co-rotating low-profile,co-rotating streamline sheet,and trapezoidal submerged EPs were tested with various combinations over a range of Reynolds numbers from 40,000 to 75,000.The respective S-shaped diffusers were simulated by computational fluid dynamics(CFD)using ANSYS software adopting a steady,3D,standard k-εturbulence model to acquire the details of the flow structure,which cannot be visualized in the experiment.The diffuser performance has been evaluated by the performance indicators of static pressure recovery coefficient,total pressure loss coefficient,and distortion coefficient(DC(45°)).The enhancements in the static pressure recovery of the S-shaped aggressive diffuser with energy promoters are 19.5%,22.2%,and 24.5%with EPs at planes 3,4 and 5,respectively,compared to the aggressive bare diffuser.In addition,the installation of the EPs resulted in a DC(45°)reduction at the outlet plane of the diffuser of about 43%at Re=40,000.The enhancements in the performance parameters confirm that aggravating the internal flow eliminates the flow separation and enhances the GT intake efficiency.
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.
基金supported by National Natural Science Foundation of China(Nos.32271791,32171709 and 22475053)Hunan Provincial Natural Science Foundation of China(No.2024JJ7643)Natural Science Foundation of Shanghai(No.22ZR1404100).
文摘Hard carbon(HC)in sodium-ion batteries is searched by numerous investigations,which can offer the excellent performance of reversible Na^(+)insertion and extraction.The covalent heteroatom doping in HC is recently worth concentrating,which can dilate the interlayer spacing of graphite to adjust the electrochemical storage performance in carbon anodes.However,the reported doping strategies of the modified HC have only resulted in limited improvement,especially unobvious effects on tuning porous structure.In this study,tannin extract and K_(2)SO_(4) are respectively utilized as carbon source and sulfur source for the fabrication of HC,in which K_(2)SO_(4) can contribute to the heteroatom doping,and the pore forming as well.The tannin-derived sulfur-doped carbon anode shows the excellent cycle stability,achieving a high reversible capacity of 520.5 mAh/g at a current density of 100 mA/g.Even after 500 cycles at a current density of 3 A/g,a high specific capacity of 236.7 mAh/g and a capacity retention rate of 92.6%can be reserved.Compared with the initial carbon,the adsorption energy of Na^(+)is multifold times higher,whereas Na^(+)diffusion energy barriers manyfold decrease.Moreover,the full battery assembled with Na_(3)V_(2)(PO_(4))_(3)/tannin-based HC demonstrates a stable cycling performance.This work can manifest the potentiality of the tannin-based electrode as anode for a high-performance sodium-ion batteries(SIBs),which could especially offer an explanation of Na^(+)storage and solid-electrolyte interface(SEI)stability to the electrochemical performance.
基金supported by the National Natural Science Foundation of China(62073172)the Natural Science Foundation of Jiangsu Province of China(BK20221329)。
文摘Reaction-diffusion systems are widely used to describe pattern formation,and various control strategies have been applied to reaction-diffusion systems to achieve control objectives such as boundary control,output feedback stabilization,and synchronization.However,controlling pattern dynamics in reaction-diffusion systems with fractional-order diffusion remains an unresolved problem.This paper presents a proportional-derivative(PD)control strategy for the Schnakenberg system with fractional-order diffusion and cross-diffusion.Theoretical analysis explores the amplitude equation near the Turing bifurcation threshold,determining the selection and stability of pattern formations.Numerical simulations demonstrate that the PD controller accomplishes the modification of pattern structures and suppression of Turing instability by adjusting only two control parameters.Additionally,it is found that for smaller fractional diffusion order,the region can accommodate more hexagonal and stripe patterns in space.This work contributes to the control of complex pattern dynamics and offers a new approach to enhancing stability in fractional reaction-diffusion systems.
基金Supported by the National Natural Science Foundation of China(Grant No.12261081).
文摘In this paper,we are concerned with the stability of traveling wavefronts of a Belousov-Zhabotinsky model with mixed nonlocal and degenerate diffusions.Such a system can be used to study the competition among nonlocally diffusive species and degenerately diffusive species.We prove that the traveling wavefronts are exponentially stable,when the initial perturbation around the traveling waves decays exponentially as x→-∞,but in other locations,the initial data can be arbitrarily large.The adopted methods are the weighted energy with the comparison principle and squeezing technique.
文摘With the rapid development of generative artificial intelligence(GenAI),the task of story visualization,which transforms natural language narratives into coherent and consistent image sequences,has attracted growing research attention.However,existing methods still face limitations in balancing multi-frame character consistency and generation efficiency,which restricts their feasibility for large-scale practical applications.To address this issue,this study proposes a modular cloud-based distributed system built on Stable Diffusion.By separating the character generation and story generation processes,and integratingmulti-feature control techniques,a cachingmechanism,and an asynchronous task queue architecture,the system enhances generation efficiency and scalability.The experimental design includes both automated and human evaluations of character consistency,performance testing,and multinode simulation.The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics.In terms of performance,under the experimental environment of this study,dual-node deployment reduces average waiting time by approximately 19%,while the four-node simulation further reduces it by up to 65%.Overall,this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency,highlighting its potential value inmulti-user collaborative story visualization applications.
基金supported by the National Natural Science Foundation of China(Grant No.U23A2022)the National Natural Science Foundation of China(Grant No.52474047)+2 种基金the Natural Science Foundation of Chongqing(Grant No.CSTB2024NSCQ-MSX0951)the Natural Science Foundation of Sichuan Province(Grant No.2025ZNSFSC1357)the National Science and Technology Major Project(Grant No.2025ZD1404307).
文摘Injecting impure CO_(2)for enhanced gas recovery(CO_(2)-EGR)offers a dual benefit by improving natural gas extraction while enabling CO_(2)sequestration.However,the interactions between CO_(2),N_(2),and CH_(4)under reservoir conditions require further investigation.This study employs Grand Canonical Monte Carlo(GCMC)and Molecular Dynamics(MD)simulations to quantify the adsorption and diffusion behaviors of CO_(2),N_(2),and CH_(4)in quartz nanopores over a pressure range of 1-24 MPa under varying water saturations and gas compositions.The results indicate that:(1)CO_(2)exhibits the broadest energy distribution and the strongest adsorption stability,occupying about 20%-30%more adsorption sites than CH_(4)or N_(2)and showing the least sensitivity to water saturation,with only a 30%reduction at 50%saturation,compared to 60%for CH_(4),giving CO_(2)a clear competitive advantage.(2)The adsorption and desorption behaviors are strongly pressure dependent,as increasing pressure reduces the adsorption layer area and shifts gas distribution from adsorption dominated to free phase.Competitive adsorption analysis reveals that while CO_(2)dominates displacement at low pressures,mixtures that contain N_(2)achieve higher CH_(4)desorption efficiency above 13 MPa by mitigating diffusion resistance.(3)A higher N_(2)fraction improves CH_(4)diffusion coefficients,thereby facilitating gas mobility and ensuring superior recovery performance under high-pressure conditions.This study advances the fundamental knowledge of microscale gas behavior in tight sandstones and supports the feasibility of impure CO_(2)injection as a practical strategy for sustainable gas production.
基金supported by the National Science and Technology Council,Taiwan,under grant no.NSTC 114-2221-E-197-005-MY3.
文摘With the development of technology,diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization(CO)problems,particularly in tackling Non-deterministic Polynomial-time hard(NP-hard)problems such as the Traveling Salesman Problem(TSP).However,existing diffusion model-based solvers typically employ a fixed,uniform noise schedule(e.g.,linear or cosine annealing)across all training instances,failing to fully account for the unique characteristics of each problem instance.To address this challenge,we present GraphGuided Diffusion Solvers(GGDS),an enhanced method for improving graph-based diffusion models.GGDS leverages Graph Neural Networks(GNNs)to capture graph structural information embedded in node coordinates and adjacency matrices,dynamically adjusting the noise levels in the diffusion model.This study investigates the TSP by examining two distinct time-step noise generation strategies:cosine annealing and a Neural Network(NN)-based approach.We evaluate their performance across different problem scales,particularly after integrating graph structural information.Experimental results indicate that GGDS outperforms previous methods with average performance improvements of 18.7%,6.3%,and 88.7%on TSP-500,TSP-100,and TSP-50,respectively.Specifically,GGDS demonstrates superior performance on TSP-500 and TSP-50,while its performance on TSP-100 is either comparable to or slightly better than that of previous methods,depending on the chosen noise schedule and decoding strategy.
基金supported by the National Natural Science Foundation of China (52172227)the Natural Science Foundation of Hubei Province (2023AFA114)+2 种基金the Guizhou Provincial Key Technology R&D Program (ZD[2025]019)provided by the Startup Fund (20QD80 and 22QD28)support from the Science&Technology Top Talents Program of Guizhou Province ([2024]349)
文摘Prussian blue analogs(PBAs)have emerged as environmentally friendly and structurally tunable cathode materials for aqueous ammonium-ion batteries(AIBs).However,the fundamental role of crystalline H_(2)O in regulating ammonium-ion storage and transport remains poorly understood.In this study,we present a comprehensive comparison between hydrated NH_(4)NiHCF-H_(2)O and its anhydrous counterpart NH_(4)NiHCF,revealing the critical contribution of interstitial water to electrochemical performance.Structural and spectroscopic analyses confirm that interstitial water forms robust hydrogen bonds with NH_(4)+ions,stabilizing the PBA framework and mitigating structural degradation during cycling.Electrochemical measurements show that NH_(4)NiHCF-H_(2)O delivers a significantly higher specific capacity of 61 mA h g^(−1)at 0.2 C and markedly improved rate performance compared to NH_(4)NiHCF(48 mA h g^(−1)at 0.2 C).Kinetic analysis reveals that interstitial water enhances NH_(4)+diffusion,as evidenced by higher diffusion coefficients.Furthermore,density functional theory(DFT)calculations demonstrate that crystal water acts as a hydrogen bond acceptor,preferentially interacting with NH_(4)+and reducing the migration energy barrier,thereby facilitating fast ion transport.This work provides fundamental insights into the role of crystal water in PBAs and offers a rational design strategy for improving the kinetics,structural stability of PBAs cathodes for AIBs.
基金financially supported by the“National Natural Science Foundation of China”(No.22279082)the“Natural Science Foundation of Sichuan”(2025YFHZ0056)。
文摘Aqueous hydrogen(H_(2))gas batteries with unmatched lifespan are ideal for grid-scale energy storage,yet their deployment remains limited by the lack of low-cost,efficient,and durable hydrogen electrodes.Here,we report a high-throughput and durable gas diffusion electrode(GDE)based on a simply preparable carbon-coated nickel(Ni@C)catalyst and the design of H_(2) diffusion channels.By optimizing the carbon layer structure,a balance between the intrinsic activity and stability of the catalyst can be achieved.This Ni@C catalyst exhibits a hydrogen oxidation reaction(HOR)activity of 44 A g^(-1) as well as remarkable hydrogen evolution reaction(HER)performance.Experimental results and theoretical calculations confirm the electronic interaction between the carbon shell and Ni.In combination with a hydrophobic design,a robust and durable Ni@C-GDE has been fabricated.This electrode achieves a low HOR polarization of only 91 mV at 30 mA cm^(-2),outperforming Pt/C-GDE(154 mV),and operates stably over 4500cycles(3200 h)for HOR/HER reversing.Enabled by this electrode,a 10 Ah Ni-H_(2) battery with an energy density of 156.3 Wh kg^(-1) and cost of 62.2$kWh^(-1) is demonstrated.This work offers a viable strategy for practical and scalable hydrogen gas batteries.
基金funding from the European Commission by the Ruralities project(grant agreement no.101060876).
文摘In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.
基金supported by the Science and Technology on Reactor System Design Technology Laboratory(No.LRSDT12023108)supported in part by the Chongqing Postdoctoral Science Foundation(No.cstc2021jcyj-bsh0252)+2 种基金the National Natural Science Foundation of China(No.12005030)Sichuan Province to unveil the list of marshal industry common technology research projects(No.23jBGOV0001)Special Program for Stabilizing Support to Basic Research of National Basic Research Institutes(No.WDZC-2023-05-03-05).
文摘The neutron diffusion equation plays a pivotal role in nuclear reactor analysis.Nevertheless,employing the physics-informed neural network(PINN)method for its solution entails certain limitations.Conventional PINN approaches generally utilize a fully connected network(FCN)architecture that is susceptible to overfitting,training instability,and gradient vanishing as the network depth increases.These challenges result in accuracy bottlenecks in the solution.In response to these issues,the residual-based resample physics-informed neural network(R2-PINN)is proposed.It is an improved PINN architecture that replaces the FCN with a convolutional neural network with a shortcut(S-CNN).It incorporates skip connections to facilitate gradient propagation between network layers.Additionally,the incorporation of the residual adaptive resampling(RAR)mechanism dynamically increases the number of sampling points.This,in turn,enhances the spatial representation capabilities and overall predictive accuracy of the model.The experimental results illustrate that our approach significantly improves the convergence capability of the model and achieves high-precision predictions of the physical fields.Compared with conventional FCN-based PINN methods,R 2-PINN effectively overcomes the limitations inherent in current methods.Thus,it provides more accurate and robust solutions for neutron diffusion equations.
基金the National Natural Science Foundation of China(Grant No.:52508343)the Fundamental Research Funds for the Central Universities(Grant No.:B250201004).
文摘Crack detection accuracy in computer vision is often constrained by limited annotated datasets.Although Generative Adversarial Networks(GANs)have been applied for data augmentation,they frequently introduce blurs and artifacts.To address this challenge,this study leverages Denoising Diffusion Probabilistic Models(DDPMs)to generate high-quality synthetic crack images,enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation.The proposed framework involves a two-stage pipeline:first,DDPMs are used to synthesize high-fidelity crack images that capture fine structural details.Second,these generated samples are combined with real data to train segmentation networks,thereby improving accuracy and robustness in crack detection.Compared with GAN-based approaches,DDPM achieved the best fidelity,with the highest Structural Similarity Index(SSIM)(0.302)and lowest Learned Perceptual Image Patch Similarity(LPIPS)(0.461),producing artifact-free images that preserve fine crack details.To validate its effectiveness,six segmentation models were tested,among which LinkNet consistently achieved the best performance,excelling in both region-level accuracy and structural continuity.Incorporating DDPM-augmented data further enhanced segmentation outcomes,increasing F1 scores by up to 1.1%and IoU by 1.7%,while also improving boundary alignment and skeleton continuity compared with models trained on real images alone.Experiments with varying augmentation ratios showed consistent improvements,with F1 rising from 0.946(no augmentation)to 0.957 and IoU from 0.897 to 0.913 at the highest ratio.These findings demonstrate the effectiveness of diffusion-based augmentation for complex crack detection in structural health monitoring.
基金supported by the National Natural Science Foundation of China(32471964)。
文摘The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.
文摘Amidst the growing global emphasis on nuclear safety,the integrity of nuclear reactor systems has garnered attention in the aftermath of consequential events.Moreover,the rapid development of artificial intelligence technology has provided immense opportunities to enhance the safety and economy of nuclear energy.However,data-driven deep learning techniques often lack interpretability,which hinders their applicability in the nuclear energy sector.To address this problem,this study proposes a hybrid data-driven and knowledge-driven artificial intelligence model based on physics-informed neural networks to accurately compute the neutron flux distribution inside a nuclear reactor core.Innovative techniques,such as regional decomposition,intelligent k_(eff)(effective multiplication factor)search,and k_(eff)inversion,have been introduced for the calculation.Furthermore,hyperparameters of the model are automatically optimized using a whale optimization algorithm.A series of computational examples are used to validate the proposed model,demonstrating its applicability,generality,and high accuracy in calculating the neutron flux within the nuclear reactor.The model offers a dependable strategy for computing the neutron flux distribution in nuclear reactors for advanced simulation techniques in the future,including reactor digital twinning.This approach is data-light,requires little to no training data,and still delivers remarkably precise output data.
