The illicit trafficking of special nuclear materials(SNMs)poses a grave threat to global security and necessitates the development of effective nuclear material identification methods.This study investigated a method ...The illicit trafficking of special nuclear materials(SNMs)poses a grave threat to global security and necessitates the development of effective nuclear material identification methods.This study investigated a method to isotopically identify the SNMs,including^(233,235,238)U,^(239-242)Pu,and^(232)Th,based on the detection of delayedγ-rays from photofission fragments.The delayedγ-ray spectra resulting from the photofission of SNMs irradiated by a 14 MeVγbeam with a total of 10~9 were simulated using Geant4.Three high-yield fission fragments,namely^(138)Cs,^(89)Rb,and^(94)Y,were selected as candidate fragments for SNM identification.The yield ratios of these three fragments were calculated,and the results from the different SNMs were compared.The yield ratio of^(138)Cs/^(89)Rb was used to identify most SNMs,including^(233,235,238)U,^(242)Pu,and^(232)Th,with a confidence level above 95%.To identify^(239-241)Pu with the same confidence,a higher total number of 10^(11)γbeams is required.However,although the^(94)Y/^(89)Rb ratio is suitable for elementally identifying SNMs,isotopic identification is difficult.In addition,the count rate of the delayedγabove 3 MeV can be used to rapidly detect the presence of nuclear materials.展开更多
In order to improve the traditional teaching model of traditional Chinese medicine identification course for graduate students of pharmacy,this paper described the research on constructing and practicing the blended t...In order to improve the traditional teaching model of traditional Chinese medicine identification course for graduate students of pharmacy,this paper described the research on constructing and practicing the blended teaching model of"online+offline"based on the teaching concept of TfU by taking the course of"Authentic Medicinal Materials and Quality of Traditional Chinese Medicine"as an example.In the preparatory phase,through resource integration and course content decomposition,it identifies"generative topics"to engage students in pre-class online discussions.During the instructional phase,"comprehension-oriented objectives"are established based on learning analytics,followed by the implementation of"understanding-focused activities"for guided inquiry in offline classrooms.The post-class phase employs online extended materials to conduct"sustained assessment"through evaluations and summaries,thereby continuously optimizing subsequent teaching practices.This pedagogical framework not only effectively cultivates investigative research thinking among graduate students but also enhances standardized management and scientific development of the teaching team.The practical research outcomes and experiences derived from this model can provide valuable references for analogous course reforms.展开更多
Bi-based transition metal oxide(Bi_(5)Nb_(3)O_(15))has become a highly hopeful anode material for lithium-ion batteries(LIBs)due to its large theoretical capacity and affordable availability.Unfortunately,poor conduct...Bi-based transition metal oxide(Bi_(5)Nb_(3)O_(15))has become a highly hopeful anode material for lithium-ion batteries(LIBs)due to its large theoretical capacity and affordable availability.Unfortunately,poor conductivity,as well as volume expansion and pulverization during repeated reactions will result in bad specific capacity and inferior cycling stability.Hence,Bi_(5)Nb_(3)O_(15)@C anode materials for LIBs were successfully synthesized using sucrose as a carbon source through a two-step high-temperature solid-phase method.Physical characterizations and electrochemical tests suggest that the highly conductive carbon shell derived from sucrose provides fast channels for Li^(+)transport and greatly reduces the charge transfer resistance.Moreover,ex situ scanning electron microscopy(SEM)indicates that the presence of carbon effectively suppresses the aggregation and pulverization of Bi_(5)Nb_(3)O_(15) particles in the reaction process,effectively ensuring the integrity of Bi_(5)Nb_(3)O_(15) particles.Benefiting from the above merits,the C-modified Bi_(5)Nb_(3)O_(15),especially Bi_(5)Nb_(3)O_(15)@8%C(BNO-C3),holds charge capacity of 414.6 and 281.4 mAh·g^(−1) at 0.1 and 0.5 A·g^(−1),respectively.Additionally,the high specific capacity of 379.5 mAh·g^(−1) is much greater than that of the bare Bi_(5)Nb_(3)O_(15)(only 158.7 mAh·g^(−1))after 200 cycles.Importantly,cyclic voltammetry(CV)combined with ex situ X-ray diffraction(XRD)confirms the conversion reaction between Bi_(5)Nb_(3)O_(15) and Bi during cycling.This work provides a method for suppressing volume expansion and pulverization during cycling of Bi-based transition metal oxides and constructing high-performance LIBs anode materials.展开更多
Soft materials,with the sensitivity to various external stimuli,exhibit high flexibility and stretchability.Accurate prediction of their mechanical behaviors requires advanced hyperelastic constitutive models incorpor...Soft materials,with the sensitivity to various external stimuli,exhibit high flexibility and stretchability.Accurate prediction of their mechanical behaviors requires advanced hyperelastic constitutive models incorporating multiple parameters.However,identifying multiple parameters under complex deformations remains a challenge,especially with limited observed data.In this study,we develop a physics-informed neural network(PINN)framework to identify material parameters and predict mechanical fields,focusing on compressible Neo-Hookean materials and hydrogels.To improve accuracy,we utilize scaling techniques to normalize network outputs and material parameters.This framework effectively solves forward and inverse problems,extrapolating continuous mechanical fields from sparse boundary data and identifying unknown mechanical properties.We explore different approaches for imposing boundary conditions(BCs)to assess their impacts on accuracy.To enhance efficiency and generalization,we propose a transfer learning enhanced PINN(TL-PINN),allowing pre-trained networks to quickly adapt to new scenarios.The TL-PINN significantly reduces computational costs while maintaining accuracy.This work holds promise in addressing practical challenges in soft material science,and provides insights into soft material mechanics with state-of-the-art experimental methods.展开更多
Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effecti...Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effective energy storage systems.Ad-vances in materials science have led to the develop-ment of hybrid nanomaterials,such as combining fil-amentous carbon forms with inorganic nanoparticles,to create new charge and energy transfer processes.Notable materials for electrochemical energy-stor-age applications include MXenes,2D transition met-al carbides,and nitrides,carbon black,carbon aerogels,activated carbon,carbon nanotubes,conducting polymers,carbon fibers,and nanofibers,and graphene,because of their thermal,electrical,and mechanical properties.Carbon materials mixed with conducting polymers,ceramics,metal oxides,transition metal oxides,metal hydroxides,transition metal sulfides,trans-ition metal dichalcogenide,metal sulfides,carbides,nitrides,and biomass materials have received widespread attention due to their remarkable performance,eco-friendliness,cost-effectiveness,and renewability.This article explores the development of carbon-based hybrid materials for future supercapacitors,including electric double-layer capacitors,pseudocapacitors,and hy-brid supercapacitors.It investigates the difficulties that influence structural design,manufacturing(electrospinning,hydro-thermal/solvothermal,template-assisted synthesis,electrodeposition,electrospray,3D printing)techniques and the latest car-bon-based hybrid materials research offer practical solutions for producing high-performance,next-generation supercapacitors.展开更多
Latent heat thermal energy storage(TES)effectively reduces the mismatch between energy supply and demand of renewable energy sources by the utilization of phase change materials(PCMs).However,the low thermal conductiv...Latent heat thermal energy storage(TES)effectively reduces the mismatch between energy supply and demand of renewable energy sources by the utilization of phase change materials(PCMs).However,the low thermal conductivity and poor shape stability are the main drawbacks in realizing the large-scale application of PCMs.Promisingly,developing composite PCM(CPCM)based on porous supporting mate-rial provides a desirable solution to obtain performance-enhanced PCMs with improved effective thermal conductivity and shape stability.Among all the porous matrixes as supports for PCM,three-dimensional carbon-based porous supporting material has attracted considerable attention ascribing to its high ther-mal conductivity,desirable loading capacity of PCMs,and excellent chemical compatibility with various PCMs.Therefore,this work systemically reviews the CPCMs with three-dimensional carbon-based porous supporting materials.First,a concise rule for the fabrication of CPCMs is illustrated in detail.Next,the experimental and computational research of carbon nanotube-based support,graphene-based support,graphite-based support and amorphous carbon-based support are reviewed.Then,the applications of the shape-stabilized CPCMs including thermal management and thermal conversion are illustrated.Last but not least,the challenges and prospects of the CPCMs are discussed.To conclude,introducing carbon-based porous materials can solve the liquid leakage issue and essentially improve the thermal conductivity of PCMs.However,there is still a long way to further develop a desirable CPCM with higher latent heat capacity,higher thermal conductivity,and more excellent shape stability.展开更多
Pitch produced by the lique-faction of coal was divided into two frac-tions:soluble in toluene(TS)and insol-uble in toluene but soluble in pyridine(TI-PS),and their differences in molecu-lar structure and oxidation ac...Pitch produced by the lique-faction of coal was divided into two frac-tions:soluble in toluene(TS)and insol-uble in toluene but soluble in pyridine(TI-PS),and their differences in molecu-lar structure and oxidation activity were studied.Several different carbon materi-als were produced from them by oxida-tion in air(350℃,300 mL/min)fol-lowed by carbonization(1000℃ in Ar),and the effect of the cross-linked structure on their structure and sodium storage properties was investigated.The results showed that the two pitch fractions were obviously different after the air oxidation.The TS fraction with a low degree of condensation and abundant side chains had a stronger oxidation activity and thus introduced more cross-linked oxygen-containing functional groups C(O)―O which prevented carbon layer rearrangement during the carbonization.As a result,a disordered hard carbon with more defects was formed,which improved the electrochemical performance.Therefore,the carbon materials derived from TS(O-TS-1000)had an obvious disordered structure and a larger layer spacing,giving them better sodium storage perform-ance than those derived from the TI-PS fraction(O-TI-PS-1000).The specific capacity of O-TS-1000 was about 250 mAh/g at 20 mA/g,which was 1.67 times higher than that of O-TI-PS-1000(150 mAh/g).展开更多
In recent years,sodium-ion batteries(SIBs)have become one of the hot discussions and have gradually moved toward industrialization.However,there are still some shortcomings in their performance that have not been well...In recent years,sodium-ion batteries(SIBs)have become one of the hot discussions and have gradually moved toward industrialization.However,there are still some shortcomings in their performance that have not been well addressed,including phase transition,structural degradation,and voltage platform.High entropy materials have recently gained significant attention from researchers due to their effects on thermodynamics,dynamics,structure,and performance.Researchers have attempted to use these materials in sodium-ion batteries to overcome their problems,making it a modification method.This paper aims to discuss the research status of high-entropy cathode materials for sodium-ion batteries and summarize their effects on sodium-ion batteries from three perspectives:Layered oxide,polyanion,and Prussian blue.The infiuence on material structure,the inhibition of phase transition,and the improvement of ion diffusivity are described.Finally,the advantages and disadvantages of high-entropy cathode materials for sodium-ion batteries are summarized,and their future development has prospected.展开更多
Novel hydrogen storage materials have propelled progress in hydrogen storage technologies.Magnesium hydride(MgH_(2))is a highly promising candidate.Nevertheless,several drawbacks,including the need for elevated therma...Novel hydrogen storage materials have propelled progress in hydrogen storage technologies.Magnesium hydride(MgH_(2))is a highly promising candidate.Nevertheless,several drawbacks,including the need for elevated thermal conditions,sluggish dehydrogena-tion kinetics,and high thermodynamic stability,limit its practical application.One effective method of addressing these challenges is cata-lyst doping,which effectively boosts the hydrogen storage capability of Mg-based materials.Herein,we review recent advancements in catalyst-doped MgH_(2) composites,with particular focus on multicomponent and high-entropy catalysts.Structure-property relationships and catalytic mechanisms in these doping strategies are also summarized.Finally,based on existing challenges,we discuss future research directions for the development of Mg-based hydrogen storage systems.展开更多
Finding materials with specific properties is a hot topic in materials science.Traditional materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high co...Finding materials with specific properties is a hot topic in materials science.Traditional materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high costs.With the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new materials.Especially through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties.This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse design.Then,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application scenarios.Finally,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are discussed.The authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.展开更多
Two sets of alloys,Mg-Zn-Ca-xNi(0≤x≤5),have been developed with tunable corrosion and mechanical properties,optimized for fracturing materials.High-zinc artificial aged(T6)Mg-12Zn-0.5Ca-x Ni(0≤x≤5)series,featuring...Two sets of alloys,Mg-Zn-Ca-xNi(0≤x≤5),have been developed with tunable corrosion and mechanical properties,optimized for fracturing materials.High-zinc artificial aged(T6)Mg-12Zn-0.5Ca-x Ni(0≤x≤5)series,featuring a straightforward preparation method and the potential for manufacturing large-scale components,exhibit notable corrosion rates up to 29 mg cm^(-2)h^(-1)at 25℃ and 643 mg cm^(-2)h^(-1)at 93℃.The high corrosion rate is primary due to the Ni–containing second phases,which intensify the galvanic corrosion that overwhelms their corrosion barrier effect.Low-zinc rolled Mg-1.5Zn-0.2Ca-x Ni(0≤x≤5)series,characterizing excellent deformability with an elongation to failure of~26%,present accelerated corrosion rates up to 34 mg cm^(-2)h^(-1)at 25℃ and 942 mg cm^(-2)h^(-1)at 93℃.The elimination of corrosion barrier effect via deformation contributes to the further increase of corrosion rate compared to the T6 series.Additionally,Mg-Zn-Ca-xNi(0≤x≤5)alloys exhibit tunable ultimate tensile strengths ranging from~190 to~237 MPa,depending on their specific composition.The adjustable corrosion rate and mechanical properties render the Mg-Zn-Ca-x Ni(0≤x≤5)alloys suitable for fracturing materials.展开更多
As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and...As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.展开更多
As global energy demand increases and environmental standards tighten,the development of efficient,eco-friendly energy conversion and storage technologies becomes crucial.Solid oxide cells(SOCs)show great promise beca...As global energy demand increases and environmental standards tighten,the development of efficient,eco-friendly energy conversion and storage technologies becomes crucial.Solid oxide cells(SOCs)show great promise because of their high energy conversion efficiency and wide range of applications.Highentropy materials(HEMs),a novel class of materials comprising several principal elements,have attracted significant interest within the materials science and energy sectors.Their distinctive structural features and adaptable functional properties offer immense potential for innovation across various applications.This review systematically covers the basic concepts,crystal structures,element selection,and major synthesis strategies of HEMs,and explores in detail the specific applications of these materials in SOCs,including its potential as air electrodes,fuel electrodes,electrolytes,and interconnects(including barrier coatings).By analyzing existing studies,this review reveals the significant advantages of HEMs in enhancing the performance,anti-poisoning,and stability of SOCs;highlights the key areas and challenges for future research;and looks into possible future directions.展开更多
In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant ...In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.展开更多
Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on ...Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.展开更多
Minimum quantity lubrication(MQL),as a new sustainable and eco-friendly alternative cooling/lubrication technology that addresses the limitations of dry and wet machining,utilizes a small amount of lubricant or coolan...Minimum quantity lubrication(MQL),as a new sustainable and eco-friendly alternative cooling/lubrication technology that addresses the limitations of dry and wet machining,utilizes a small amount of lubricant or coolant to reduce friction,tool wear,and heat during cutting processes.MQL technique has witnessed significant developments in recent years,such as combining MQL with other sustainable techniques to achieve optimum results,using biodegradable lubricants,and innovations in nozzle designs and delivery methods.This review presents an in-depth analysis of machining characteristics(e.g.,cutting forces,temperature,tool wear,chip morphology and surface integrity,etc.)and sustainability characteristics(e.g.,energy consumption,carbon emissions,processing time,machining cost,etc.)of conventional MQL and hybrid MQL techniques like cryogenic MQL,Ranque-Hilsch vortex tube MQL,nanofluids MQL,hybrid nanofluid MQL and ultrasonic vibration assisted MQL in machining of aeronautical materials.Subsequently,the latest research and developments are analyzed and summarized in the field of MQL,and provide a detailed comparison between each technique,considering advantages,challenges,and limitations in practical implementation.In addition,this review serves as a valuable source for researchers and engineers to optimize machining processes while minimizing environmental impact and operational costs.Ultimately,the potential future aspects of MQL for research and industrial execution are discussed.展开更多
Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.Howev...Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.However,the widened hydraulic excitation frequency may satisfy the blade resonance due to the time variation in the velocity and angle of attack of the ocean current,even resulting in blade fatigue and destructively interfering with grid stability.A key parameter that determines the resonance amplitude of the blade is the hydrodynamic damping ratio(HDR).However,HDR is difficult to obtain due to the complex fluid-structure interaction(FSI).Therefore,a literature review was conducted on the hydrodynamic damping characteristics of blade-like structures.The experimental and simulation methods used to identify and obtain the HDR quantitatively were described,placing emphasis on the experimental processes and simulation setups.Moreover,the accuracy and efficiency of different simulation methods were compared,and the modal work approach was recommended.The effects of key typical parameters,including flow velocity,angle of attack,gap,rotational speed,and cavitation,on the HDR were then summarized,and the suggestions on operating conditions were presented from the perspective of increasing the HDR.Subsequently,considering multiple flow parameters,several theoretical derivations and semi-empirical prediction formulas for HDR were introduced,and the accuracy and application were discussed.Based on the shortcomings of the existing research,the direction of future research was finally determined.The current work offers a clear understanding of the HDR of blade-like structures,which could improve the evaluation accuracy of flow-induced vibration in the design stage.展开更多
Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from b...Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from both academia and industry.However,the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation.In addition,previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time,especially when these are accompanied by sudden failures under dynamic conditions.This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors.The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation.The maximum error for each actuator is less than0.06%and 0.07%,with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases,respectively.These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions,even in the case of a sudden malfunction.The research results emonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.展开更多
It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in...It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in logging curves,this study establishes a grayscale-phase model based on high-resolution grayscale curves using clustering analysis algorithms for shale lithofacies identification,working with the Shahejie For-mation,Bohai Bay Basin,China.The grayscale phase is defined as the sum of absolute grayscale and relative amplitude as well as their features.The absolute grayscale is the absolute magnitude of the gray values and is utilized for evaluating the material composition(mineral composition+total organic carbon)of shale,while the relative amplitude is the difference between adjacent gray values and is used to identify the shale structure type.The research results show that the grayscale phase model can identify shale lithofacies well,and the accuracy and applicability of this model were verified by the fitting relationship between absolute grayscale and shale mineral composition,as well as corresponding re-lationships between relative amplitudes and laminae development in shales.Four lithofacies are iden-tified in the target layer of the study area:massive mixed shale,laminated mixed shale,massive calcareous shale and laminated calcareous shale.This method can not only effectively characterize the material composition of shale,but also numerically characterize the development degree of shale laminae,and solve the problem that difficult to identify millimeter-scale laminae based on logging curves,which can provide technical support for shale lithofacies identification,sweet spot evaluation and prediction of complex continental lacustrine basins.展开更多
Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials.Exploring the underlying factors that affect impact sensitivity and detonation performances as well as ...Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials.Exploring the underlying factors that affect impact sensitivity and detonation performances as well as exploring how to obtain materials with desired properties remains a long-term challenge.Machine learning with its ability to solve complex tasks and perform robust data processing can reveal the relationship between performance and descriptive indicators,potentially accelerating the development process of energetic materials.In this background,impact sensitivity,detonation performances,and 28 physicochemical parameters for 222 energetic materials from density functional theory calculations and published literature were sorted out.Four machine learning algorithms were employed to predict various properties of energetic materials,including impact sensitivity,detonation velocity,detonation pressure,and Gurney energy.Analysis of Pearson coefficients and feature importance showed that the heat of explosion,oxygen balance,decomposition products,and HOMO energy levels have a strong correlation with the impact sensitivity of energetic materials.Oxygen balance,decomposition products,and density have a strong correlation with detonation performances.Utilizing impact sensitivity of 2,3,4-trinitrotoluene and the detonation performances of 2,4,6-trinitrobenzene-1,3,5-triamine as the benchmark,the analysis of feature importance rankings and statistical data revealed the optimal range of key features balancing impact sensitivity and detonation performances:oxygen balance values should be between-40%and-30%,density should range from 1.66 to 1.72 g/cm^(3),HOMO energy levels should be between-6.34 and-6.31 eV,and lipophilicity should be between-1.0 and 0.1,4.49 and 5.59.These findings not only offer important insights into the impact sensitivity and detonation performances of energetic materials,but also provide a theoretical guidance paradigm for the design and development of new energetic materials with optimal detonation performances and reduced sensitivity.展开更多
基金supported by the National Key Research and Development Program(No.2022YFA1603300)the National Natural Science Foundation of China(Nos.U2230133,12305266,11921006,12405282)National Grand Instrument Project(No.2019YFF01014400)。
文摘The illicit trafficking of special nuclear materials(SNMs)poses a grave threat to global security and necessitates the development of effective nuclear material identification methods.This study investigated a method to isotopically identify the SNMs,including^(233,235,238)U,^(239-242)Pu,and^(232)Th,based on the detection of delayedγ-rays from photofission fragments.The delayedγ-ray spectra resulting from the photofission of SNMs irradiated by a 14 MeVγbeam with a total of 10~9 were simulated using Geant4.Three high-yield fission fragments,namely^(138)Cs,^(89)Rb,and^(94)Y,were selected as candidate fragments for SNM identification.The yield ratios of these three fragments were calculated,and the results from the different SNMs were compared.The yield ratio of^(138)Cs/^(89)Rb was used to identify most SNMs,including^(233,235,238)U,^(242)Pu,and^(232)Th,with a confidence level above 95%.To identify^(239-241)Pu with the same confidence,a higher total number of 10^(11)γbeams is required.However,although the^(94)Y/^(89)Rb ratio is suitable for elementally identifying SNMs,isotopic identification is difficult.In addition,the count rate of the delayedγabove 3 MeV can be used to rapidly detect the presence of nuclear materials.
基金Supported by Guangxi Degree and Postgraduate Education Reform Project(JGY2023213,JGY2023211).
文摘In order to improve the traditional teaching model of traditional Chinese medicine identification course for graduate students of pharmacy,this paper described the research on constructing and practicing the blended teaching model of"online+offline"based on the teaching concept of TfU by taking the course of"Authentic Medicinal Materials and Quality of Traditional Chinese Medicine"as an example.In the preparatory phase,through resource integration and course content decomposition,it identifies"generative topics"to engage students in pre-class online discussions.During the instructional phase,"comprehension-oriented objectives"are established based on learning analytics,followed by the implementation of"understanding-focused activities"for guided inquiry in offline classrooms.The post-class phase employs online extended materials to conduct"sustained assessment"through evaluations and summaries,thereby continuously optimizing subsequent teaching practices.This pedagogical framework not only effectively cultivates investigative research thinking among graduate students but also enhances standardized management and scientific development of the teaching team.The practical research outcomes and experiences derived from this model can provide valuable references for analogous course reforms.
基金supported by the National Natural Science Foundation of China(No.52374301)Hebei Provincial Natural Science Foundation(No.E2024501010)+2 种基金Shijiazhuang Basic Research Project(No.241790667A)the Fundamental Research Funds for the Central Universities(No.N2423054)the Performance Subsidy Fund for Key Laboratory of Dielectric and Electrolyte Functional Material Hebei Province(No.22567627H).
文摘Bi-based transition metal oxide(Bi_(5)Nb_(3)O_(15))has become a highly hopeful anode material for lithium-ion batteries(LIBs)due to its large theoretical capacity and affordable availability.Unfortunately,poor conductivity,as well as volume expansion and pulverization during repeated reactions will result in bad specific capacity and inferior cycling stability.Hence,Bi_(5)Nb_(3)O_(15)@C anode materials for LIBs were successfully synthesized using sucrose as a carbon source through a two-step high-temperature solid-phase method.Physical characterizations and electrochemical tests suggest that the highly conductive carbon shell derived from sucrose provides fast channels for Li^(+)transport and greatly reduces the charge transfer resistance.Moreover,ex situ scanning electron microscopy(SEM)indicates that the presence of carbon effectively suppresses the aggregation and pulverization of Bi_(5)Nb_(3)O_(15) particles in the reaction process,effectively ensuring the integrity of Bi_(5)Nb_(3)O_(15) particles.Benefiting from the above merits,the C-modified Bi_(5)Nb_(3)O_(15),especially Bi_(5)Nb_(3)O_(15)@8%C(BNO-C3),holds charge capacity of 414.6 and 281.4 mAh·g^(−1) at 0.1 and 0.5 A·g^(−1),respectively.Additionally,the high specific capacity of 379.5 mAh·g^(−1) is much greater than that of the bare Bi_(5)Nb_(3)O_(15)(only 158.7 mAh·g^(−1))after 200 cycles.Importantly,cyclic voltammetry(CV)combined with ex situ X-ray diffraction(XRD)confirms the conversion reaction between Bi_(5)Nb_(3)O_(15) and Bi during cycling.This work provides a method for suppressing volume expansion and pulverization during cycling of Bi-based transition metal oxides and constructing high-performance LIBs anode materials.
基金supported by the National Natural Science Foundation of China(Nos.12172273 and 11820101001)。
文摘Soft materials,with the sensitivity to various external stimuli,exhibit high flexibility and stretchability.Accurate prediction of their mechanical behaviors requires advanced hyperelastic constitutive models incorporating multiple parameters.However,identifying multiple parameters under complex deformations remains a challenge,especially with limited observed data.In this study,we develop a physics-informed neural network(PINN)framework to identify material parameters and predict mechanical fields,focusing on compressible Neo-Hookean materials and hydrogels.To improve accuracy,we utilize scaling techniques to normalize network outputs and material parameters.This framework effectively solves forward and inverse problems,extrapolating continuous mechanical fields from sparse boundary data and identifying unknown mechanical properties.We explore different approaches for imposing boundary conditions(BCs)to assess their impacts on accuracy.To enhance efficiency and generalization,we propose a transfer learning enhanced PINN(TL-PINN),allowing pre-trained networks to quickly adapt to new scenarios.The TL-PINN significantly reduces computational costs while maintaining accuracy.This work holds promise in addressing practical challenges in soft material science,and provides insights into soft material mechanics with state-of-the-art experimental methods.
文摘Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effective energy storage systems.Ad-vances in materials science have led to the develop-ment of hybrid nanomaterials,such as combining fil-amentous carbon forms with inorganic nanoparticles,to create new charge and energy transfer processes.Notable materials for electrochemical energy-stor-age applications include MXenes,2D transition met-al carbides,and nitrides,carbon black,carbon aerogels,activated carbon,carbon nanotubes,conducting polymers,carbon fibers,and nanofibers,and graphene,because of their thermal,electrical,and mechanical properties.Carbon materials mixed with conducting polymers,ceramics,metal oxides,transition metal oxides,metal hydroxides,transition metal sulfides,trans-ition metal dichalcogenide,metal sulfides,carbides,nitrides,and biomass materials have received widespread attention due to their remarkable performance,eco-friendliness,cost-effectiveness,and renewability.This article explores the development of carbon-based hybrid materials for future supercapacitors,including electric double-layer capacitors,pseudocapacitors,and hy-brid supercapacitors.It investigates the difficulties that influence structural design,manufacturing(electrospinning,hydro-thermal/solvothermal,template-assisted synthesis,electrodeposition,electrospray,3D printing)techniques and the latest car-bon-based hybrid materials research offer practical solutions for producing high-performance,next-generation supercapacitors.
基金supported by the National Natural Science Foundation of China(No.52127816),the National Key Research and Development Program of China(No.2020YFA0715000)the National Natural Science and Hong Kong Research Grant Council Joint Research Funding Project of China(No.5181101182)the NSFC/RGC Joint Research Scheme sponsored by the Research Grants Council of Hong Kong and the National Natural Science Foundation of China(No.N_PolyU513/18).
文摘Latent heat thermal energy storage(TES)effectively reduces the mismatch between energy supply and demand of renewable energy sources by the utilization of phase change materials(PCMs).However,the low thermal conductivity and poor shape stability are the main drawbacks in realizing the large-scale application of PCMs.Promisingly,developing composite PCM(CPCM)based on porous supporting mate-rial provides a desirable solution to obtain performance-enhanced PCMs with improved effective thermal conductivity and shape stability.Among all the porous matrixes as supports for PCM,three-dimensional carbon-based porous supporting material has attracted considerable attention ascribing to its high ther-mal conductivity,desirable loading capacity of PCMs,and excellent chemical compatibility with various PCMs.Therefore,this work systemically reviews the CPCMs with three-dimensional carbon-based porous supporting materials.First,a concise rule for the fabrication of CPCMs is illustrated in detail.Next,the experimental and computational research of carbon nanotube-based support,graphene-based support,graphite-based support and amorphous carbon-based support are reviewed.Then,the applications of the shape-stabilized CPCMs including thermal management and thermal conversion are illustrated.Last but not least,the challenges and prospects of the CPCMs are discussed.To conclude,introducing carbon-based porous materials can solve the liquid leakage issue and essentially improve the thermal conductivity of PCMs.However,there is still a long way to further develop a desirable CPCM with higher latent heat capacity,higher thermal conductivity,and more excellent shape stability.
文摘Pitch produced by the lique-faction of coal was divided into two frac-tions:soluble in toluene(TS)and insol-uble in toluene but soluble in pyridine(TI-PS),and their differences in molecu-lar structure and oxidation activity were studied.Several different carbon materi-als were produced from them by oxida-tion in air(350℃,300 mL/min)fol-lowed by carbonization(1000℃ in Ar),and the effect of the cross-linked structure on their structure and sodium storage properties was investigated.The results showed that the two pitch fractions were obviously different after the air oxidation.The TS fraction with a low degree of condensation and abundant side chains had a stronger oxidation activity and thus introduced more cross-linked oxygen-containing functional groups C(O)―O which prevented carbon layer rearrangement during the carbonization.As a result,a disordered hard carbon with more defects was formed,which improved the electrochemical performance.Therefore,the carbon materials derived from TS(O-TS-1000)had an obvious disordered structure and a larger layer spacing,giving them better sodium storage perform-ance than those derived from the TI-PS fraction(O-TI-PS-1000).The specific capacity of O-TS-1000 was about 250 mAh/g at 20 mA/g,which was 1.67 times higher than that of O-TI-PS-1000(150 mAh/g).
基金the National Natural Science Foundation of China Key Program(No.U22A20420)Changzhou Leading Innovative Talents Introduction and Cultivation Project(No.CQ20230109)for supporting our work。
文摘In recent years,sodium-ion batteries(SIBs)have become one of the hot discussions and have gradually moved toward industrialization.However,there are still some shortcomings in their performance that have not been well addressed,including phase transition,structural degradation,and voltage platform.High entropy materials have recently gained significant attention from researchers due to their effects on thermodynamics,dynamics,structure,and performance.Researchers have attempted to use these materials in sodium-ion batteries to overcome their problems,making it a modification method.This paper aims to discuss the research status of high-entropy cathode materials for sodium-ion batteries and summarize their effects on sodium-ion batteries from three perspectives:Layered oxide,polyanion,and Prussian blue.The infiuence on material structure,the inhibition of phase transition,and the improvement of ion diffusivity are described.Finally,the advantages and disadvantages of high-entropy cathode materials for sodium-ion batteries are summarized,and their future development has prospected.
基金financially supported by the National Key Research and Development Program of China (No. 2021YFB4000604)the National Natural Science Foundation of China (No. 52271220)+2 种基金the 111 Project (No. B12015)the Fundamental Research Funds for the Central UniversitiesHaihe Laboratory of Sustainable Chemical Transformations, Guangxi Collaborative Innovation Centre of Structure and Property for New Energy and Materials, Science Research and Technology Development Project of Guilin (No. 20210102-4)
文摘Novel hydrogen storage materials have propelled progress in hydrogen storage technologies.Magnesium hydride(MgH_(2))is a highly promising candidate.Nevertheless,several drawbacks,including the need for elevated thermal conditions,sluggish dehydrogena-tion kinetics,and high thermodynamic stability,limit its practical application.One effective method of addressing these challenges is cata-lyst doping,which effectively boosts the hydrogen storage capability of Mg-based materials.Herein,we review recent advancements in catalyst-doped MgH_(2) composites,with particular focus on multicomponent and high-entropy catalysts.Structure-property relationships and catalytic mechanisms in these doping strategies are also summarized.Finally,based on existing challenges,we discuss future research directions for the development of Mg-based hydrogen storage systems.
基金funded by theNationalNatural Science Foundation of China(52061020)Major Science and Technology Projects in Yunnan Province(202302AG050009)Yunnan Fundamental Research Projects(202301AV070003).
文摘Finding materials with specific properties is a hot topic in materials science.Traditional materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high costs.With the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new materials.Especially through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties.This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse design.Then,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application scenarios.Finally,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are discussed.The authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.
基金supported by the National Key Research and Development Program(No.2022YFE0122000)National Natural Science Foundation of China under Grant Nos.52234009,52274383,52222409,and 52201113。
文摘Two sets of alloys,Mg-Zn-Ca-xNi(0≤x≤5),have been developed with tunable corrosion and mechanical properties,optimized for fracturing materials.High-zinc artificial aged(T6)Mg-12Zn-0.5Ca-x Ni(0≤x≤5)series,featuring a straightforward preparation method and the potential for manufacturing large-scale components,exhibit notable corrosion rates up to 29 mg cm^(-2)h^(-1)at 25℃ and 643 mg cm^(-2)h^(-1)at 93℃.The high corrosion rate is primary due to the Ni–containing second phases,which intensify the galvanic corrosion that overwhelms their corrosion barrier effect.Low-zinc rolled Mg-1.5Zn-0.2Ca-x Ni(0≤x≤5)series,characterizing excellent deformability with an elongation to failure of~26%,present accelerated corrosion rates up to 34 mg cm^(-2)h^(-1)at 25℃ and 942 mg cm^(-2)h^(-1)at 93℃.The elimination of corrosion barrier effect via deformation contributes to the further increase of corrosion rate compared to the T6 series.Additionally,Mg-Zn-Ca-xNi(0≤x≤5)alloys exhibit tunable ultimate tensile strengths ranging from~190 to~237 MPa,depending on their specific composition.The adjustable corrosion rate and mechanical properties render the Mg-Zn-Ca-x Ni(0≤x≤5)alloys suitable for fracturing materials.
基金supported by the National Key R&D Program of China(2021YFC2203502 and 2022YFF0711502)the National Natural Science Foundation of China(NSFC)(12173077)+4 种基金the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095 and 2023TSYCCX0112)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(PTYQ2022YZZD01)China National Astronomical Data Center(NADC)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences(CAS)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360)。
文摘As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.
基金supported by the National Key R&D Program of China(2022YFB4004000)National Natural Science Foundation of China(U24A20542,52472210,22279057)+3 种基金Natural Science Foundation of Jiangsu Province(BK20221312)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX23_1465)Cultivation Program for the Excellent Doctoral Dissertation of Nanjing Tech University(2023-09)the grant of Hydrogen Energy Laboratory(No.FEUZ-2024-0009)。
文摘As global energy demand increases and environmental standards tighten,the development of efficient,eco-friendly energy conversion and storage technologies becomes crucial.Solid oxide cells(SOCs)show great promise because of their high energy conversion efficiency and wide range of applications.Highentropy materials(HEMs),a novel class of materials comprising several principal elements,have attracted significant interest within the materials science and energy sectors.Their distinctive structural features and adaptable functional properties offer immense potential for innovation across various applications.This review systematically covers the basic concepts,crystal structures,element selection,and major synthesis strategies of HEMs,and explores in detail the specific applications of these materials in SOCs,including its potential as air electrodes,fuel electrodes,electrolytes,and interconnects(including barrier coatings).By analyzing existing studies,this review reveals the significant advantages of HEMs in enhancing the performance,anti-poisoning,and stability of SOCs;highlights the key areas and challenges for future research;and looks into possible future directions.
基金supported by the National Defense Fundamental Research Project(No.JCKY2022404C005)the Nuclear Energy Development Project(No.23ZG6106)+1 种基金the Sichuan Scientific and Technological Achievements Transfer and Transformation Demonstration Project(No.2023ZHCG0026)the Mianyang Applied Technology Research and Development Project(No.2021ZYZF1005)。
文摘In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.
基金support from the National Natural Science Foundation of China(Grant Nos:52379103 and 52279103)the Natural Science Foundation of Shandong Province(Grant No:ZR2023YQ049).
文摘Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.
基金financially supported by the National Natural Science Foundation of China(Nos.92160301,92060203,52175415,and 52205475)the Science Center for Gas Turbine Project(Nos.P2022-AB-IV-002-001 and P2023-B-IV-003-001)+2 种基金the Natural Science Foundation of Jiangsu Province(No.BK20210295)the Superior Postdoctoral Project of Jiangsu Province(No.2022ZB215)the National Key Laboratory of Science and Technology on Helicopter Transmission in NUAA(No.HTL-A-22G12).
文摘Minimum quantity lubrication(MQL),as a new sustainable and eco-friendly alternative cooling/lubrication technology that addresses the limitations of dry and wet machining,utilizes a small amount of lubricant or coolant to reduce friction,tool wear,and heat during cutting processes.MQL technique has witnessed significant developments in recent years,such as combining MQL with other sustainable techniques to achieve optimum results,using biodegradable lubricants,and innovations in nozzle designs and delivery methods.This review presents an in-depth analysis of machining characteristics(e.g.,cutting forces,temperature,tool wear,chip morphology and surface integrity,etc.)and sustainability characteristics(e.g.,energy consumption,carbon emissions,processing time,machining cost,etc.)of conventional MQL and hybrid MQL techniques like cryogenic MQL,Ranque-Hilsch vortex tube MQL,nanofluids MQL,hybrid nanofluid MQL and ultrasonic vibration assisted MQL in machining of aeronautical materials.Subsequently,the latest research and developments are analyzed and summarized in the field of MQL,and provide a detailed comparison between each technique,considering advantages,challenges,and limitations in practical implementation.In addition,this review serves as a valuable source for researchers and engineers to optimize machining processes while minimizing environmental impact and operational costs.Ultimately,the potential future aspects of MQL for research and industrial execution are discussed.
基金Supported by the National Natural Science Foundation of China(Nos.52222904 and 52309117)China Postdoctoral Science Foundation(Nos.2022TQ0168 and 2023M731895).
文摘Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.However,the widened hydraulic excitation frequency may satisfy the blade resonance due to the time variation in the velocity and angle of attack of the ocean current,even resulting in blade fatigue and destructively interfering with grid stability.A key parameter that determines the resonance amplitude of the blade is the hydrodynamic damping ratio(HDR).However,HDR is difficult to obtain due to the complex fluid-structure interaction(FSI).Therefore,a literature review was conducted on the hydrodynamic damping characteristics of blade-like structures.The experimental and simulation methods used to identify and obtain the HDR quantitatively were described,placing emphasis on the experimental processes and simulation setups.Moreover,the accuracy and efficiency of different simulation methods were compared,and the modal work approach was recommended.The effects of key typical parameters,including flow velocity,angle of attack,gap,rotational speed,and cavitation,on the HDR were then summarized,and the suggestions on operating conditions were presented from the perspective of increasing the HDR.Subsequently,considering multiple flow parameters,several theoretical derivations and semi-empirical prediction formulas for HDR were introduced,and the accuracy and application were discussed.Based on the shortcomings of the existing research,the direction of future research was finally determined.The current work offers a clear understanding of the HDR of blade-like structures,which could improve the evaluation accuracy of flow-induced vibration in the design stage.
基金support by the National Natural Science Foundation of China(Grant No.52402520)。
文摘Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from both academia and industry.However,the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation.In addition,previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time,especially when these are accompanied by sudden failures under dynamic conditions.This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors.The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation.The maximum error for each actuator is less than0.06%and 0.07%,with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases,respectively.These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions,even in the case of a sudden malfunction.The research results emonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.
基金supported by the National Natural Science Foundation of China(42122017,41821002)the Independent Innovation Research Program of China University of Petroleum(East China)(21CX06001A).
文摘It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in logging curves,this study establishes a grayscale-phase model based on high-resolution grayscale curves using clustering analysis algorithms for shale lithofacies identification,working with the Shahejie For-mation,Bohai Bay Basin,China.The grayscale phase is defined as the sum of absolute grayscale and relative amplitude as well as their features.The absolute grayscale is the absolute magnitude of the gray values and is utilized for evaluating the material composition(mineral composition+total organic carbon)of shale,while the relative amplitude is the difference between adjacent gray values and is used to identify the shale structure type.The research results show that the grayscale phase model can identify shale lithofacies well,and the accuracy and applicability of this model were verified by the fitting relationship between absolute grayscale and shale mineral composition,as well as corresponding re-lationships between relative amplitudes and laminae development in shales.Four lithofacies are iden-tified in the target layer of the study area:massive mixed shale,laminated mixed shale,massive calcareous shale and laminated calcareous shale.This method can not only effectively characterize the material composition of shale,but also numerically characterize the development degree of shale laminae,and solve the problem that difficult to identify millimeter-scale laminae based on logging curves,which can provide technical support for shale lithofacies identification,sweet spot evaluation and prediction of complex continental lacustrine basins.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2682024GF019)。
文摘Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials.Exploring the underlying factors that affect impact sensitivity and detonation performances as well as exploring how to obtain materials with desired properties remains a long-term challenge.Machine learning with its ability to solve complex tasks and perform robust data processing can reveal the relationship between performance and descriptive indicators,potentially accelerating the development process of energetic materials.In this background,impact sensitivity,detonation performances,and 28 physicochemical parameters for 222 energetic materials from density functional theory calculations and published literature were sorted out.Four machine learning algorithms were employed to predict various properties of energetic materials,including impact sensitivity,detonation velocity,detonation pressure,and Gurney energy.Analysis of Pearson coefficients and feature importance showed that the heat of explosion,oxygen balance,decomposition products,and HOMO energy levels have a strong correlation with the impact sensitivity of energetic materials.Oxygen balance,decomposition products,and density have a strong correlation with detonation performances.Utilizing impact sensitivity of 2,3,4-trinitrotoluene and the detonation performances of 2,4,6-trinitrobenzene-1,3,5-triamine as the benchmark,the analysis of feature importance rankings and statistical data revealed the optimal range of key features balancing impact sensitivity and detonation performances:oxygen balance values should be between-40%and-30%,density should range from 1.66 to 1.72 g/cm^(3),HOMO energy levels should be between-6.34 and-6.31 eV,and lipophilicity should be between-1.0 and 0.1,4.49 and 5.59.These findings not only offer important insights into the impact sensitivity and detonation performances of energetic materials,but also provide a theoretical guidance paradigm for the design and development of new energetic materials with optimal detonation performances and reduced sensitivity.
