Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achievin...Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.展开更多
Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters accordi...Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.展开更多
Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
High-temperature microwave absorbing materials(MAMs)and structures are increasingly appealing due to their critical role in stealth applications under harsh environments.However,the impedance mismatch caused by increa...High-temperature microwave absorbing materials(MAMs)and structures are increasingly appealing due to their critical role in stealth applications under harsh environments.However,the impedance mismatch caused by increased conduction loss often leads to a significant decline in electromagnetic wave absorp-tion(EMWA)performance at elevated temperatures,which severely restricts their practical application.In this study,we propose a novel approach for efficient electromagnetic wave absorption across a wide temperature range using reduced graphene oxide(RGO)/epoxy resin(EP)metacomposites that integrate both electromagnetic parameters and metamaterial design concepts.Due to the discrete distribution of the units,electromagnetic waves can more easily penetrate the interior of materials,thereby exhibiting stable microwave absorption(MA)performance and impedance-matching characteristics suitable across a wide temperature range.Consequently,exceptional MA properties can be achieved within the tem-perature range from 298 to 473 K.Furthermore,by carefully controlling the structural parameters in RGO metacomposites,both the resonant frequency and effective absorption bandwidth(EAB)can be optimized based on precise manipulation of equivalent electromagnetic parameters.This study not only provides an effective approach for the rational design of MA performance but also offers novel insights into achieving super metamaterials with outstanding performance across a wide temperature spectrum.展开更多
Na-ion batteries are considered a promising next-generation battery alternative to Li-ion batteries,due to the abundant Na resources and low cost.Most efforts focus on developing new materials to enhance energy densit...Na-ion batteries are considered a promising next-generation battery alternative to Li-ion batteries,due to the abundant Na resources and low cost.Most efforts focus on developing new materials to enhance energy density and electrochemical performance to enable it comparable to Li-ion batteries,without considering thermal hazard of Na-ion batteries and comparison with Li-ion batteries.To address this issue,our work comprehensively compares commercial prismatic lithium iron phosphate(LFP) battery,lithium nickel cobalt manganese oxide(NCM523) battery and Na-ion battery of the same size from thermal hazard perspective using Accelerating Rate Calorimeter.The thermal hazard of the three cells is then qualitatively assessed from thermal stability,early warning and thermal runaway severity perspectives by integrating eight characteristic parameters.The Na-ion cell displays comparable thermal stability with LFP while LFP exhibits the lowest thermal runaway hazard and severity.However,the Na-ion cell displays the lowest safety venting temperature and the longest time interval between safety venting and thermal runaway,allowing the generated gas to be released as early as possible and detected in a timely manner,providing sufficient time for early warning.Finally,a database of thermal runaway characteristic temperature for Li-ion and Na-ion cells is collected and processed to delineate four thermal hazard levels for quantitative assessment.Overall,LFP cells exhibit the lowest thermal hazard,followed by the Na-ion cells and NCM523 cells.This work clarifies the thermal hazard discrepancy between the Na-ion cell and prevalent Li-ion cells,providing crucial guidance for development and application of Na-ion cell.展开更多
Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the s...Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.展开更多
Friction stir welding(FSW)is a relatively new welding technique that has significant advantages compared to the fusion welding techniques in joining non weld able alloys by fusion,such as aluminum alloys.Three FSW sea...Friction stir welding(FSW)is a relatively new welding technique that has significant advantages compared to the fusion welding techniques in joining non weld able alloys by fusion,such as aluminum alloys.Three FSW seams of AA6061-T6 plates were made us-ing different FSW parameters.The structure of the FSW seams was investigated using X-ray diffraction(XRD),scanning electron mi-croscope(SEM)and non destructive testing(NDT)techniques and their hardness was also measured.The dominated phase in the AA6061-T6 alloy and the FSW seams was theα-Al.The FSW seam had lower content of the secondary phases than the AA6061-T6 al-loy.The hardness of the FSW seams was decreased by about 30%compared to the AA6061-T6 alloy.The temperature distributions in the weld seams were also studied experimentally and numerically modeled and the results were in a good agreement.展开更多
This study primarily aimed to investigate the prevalence of human papillomavirus(HPV)and other common pathogens of sexually transmitted infections(STIs)in spermatozoa of infertile men and their effects on semen parame...This study primarily aimed to investigate the prevalence of human papillomavirus(HPV)and other common pathogens of sexually transmitted infections(STIs)in spermatozoa of infertile men and their effects on semen parameters.These pathogens included Ureaplasma urealyticum,Ureaplasma parvum,Chlamydia trachomatis,Mycoplasma genitalium,herpes simplex virus 2,Neisseria gonorrhoeae,Enterococcus faecalis,Streptococcus agalactiae,Pseudomonas aeruginosa,and Staphylococcus aureus.A total of 1951 men of infertile couples were recruited between 23 March 2023,and 17 May 2023,at the Department of Reproductive Medicine of The First People’s Hospital of Yunnan Province(Kunming,China).Multiplex polymerase chain reaction and capillary electrophoresis were used for HPV genotyping.Polymerase chain reaction and electrophoresis were also used to detect the presence of other STIs.The overall prevalence of HPV infection was 12.4%.The top five prevalent HPV subtypes were types 56,52,43,16,and 53 among those tested positive for HPV.Other common infections with high prevalence rates were Ureaplasma urealyticum(28.3%),Ureaplasma parvum(20.4%),and Enterococcus faecalis(9.5%).The prevalence rates of HPV coinfection with Ureaplasma urealyticum,Ureaplasma parvum,Chlamydia trachomatis,Mycoplasma genitalium,herpes simplex virus 2,Neisseria gonorrhoeae,Enterococcus faecalis,Streptococcus agalactiae,and Staphylococcus aureus were 24.8%,25.4%,10.6%,6.4%,2.4%,7.9%,5.9%,0.9%,and 1.3%,respectively.The semen volume and total sperm count were greatly decreased by HPV infection alone.Coinfection with HPV and Ureaplasma urealyticum significantly reduced sperm motility and viability.Our study shows that coinfection with STIs is highly prevalent in the semen of infertile men and that coinfection with pathogens can seriously affect semen parameters,emphasizing the necessity of semen screening for STIs.展开更多
Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in speci...Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.展开更多
The seismic data of the Laoshan Uplift in the South Yellow Sea Basin reveal a low signal-tonoise ratio and low refl ection signal energy in the deep Mesozoic–Paleozoic strata.The main reason is that the Mesozoic-Pale...The seismic data of the Laoshan Uplift in the South Yellow Sea Basin reveal a low signal-tonoise ratio and low refl ection signal energy in the deep Mesozoic–Paleozoic strata.The main reason is that the Mesozoic-Paleozoic marine carbonate rock strata are directly covered by the Cenozoic terrestrial clastic rock strata,which form a strong shielding layer.To obtain the reflection signals of the strata below the strong shielding layer,a one-way wave equation bidirectional illumination analysis of the main observation system parameters was conducted by analyzing the mechanism of the strong shielding layer.Low-frequency seismic sources are assumed to have a high illumination intensity on the reflection layer below the strong shielding layer.Accordingly,optimized acquisition parameter suggestions were proposed,and reacquisition was performed at the existing survey line locations in the Laoshan Uplift area.The imaging of the newly acquired data in the middle and deep layers was drastically improved.It revealed the unconformity between the Sinian and Cambrian under the strong shielding layer.The study yielded new insights into the tectonic and sedimentary evolution of the Lower Paleozoic in the South Yellow Sea.展开更多
To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions tha...To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current methods.Firstly,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic environments.With a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic environments.Particularly,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.展开更多
The reaction rate constant is a crucial kinetic parameter that governs the charge and discharge performance of batteries,particularly in high-rate and thick-electrode applications.However,conventional estimation or fi...The reaction rate constant is a crucial kinetic parameter that governs the charge and discharge performance of batteries,particularly in high-rate and thick-electrode applications.However,conventional estimation or fitting methods often overestimate the charge transfer overpotential,leading to substantial errors in reaction rate constant measurements.These inaccuracies hinder the accurate prediction of voltage profiles and overall cell performance.In this study,we propose the characteristic time-decomposed overpotential(CTDO)method,which employs a single-layer particle electrode(SLPE)structure to eliminate interference overpotentials.By leveraging the distribution of relaxation times(DRT),our method effectively isolates the characteristic time of the charge transfer process,enabling a more precise determination of the reaction rate constant.Simulation results indicate that our approach reduces measurement errors to below 2%,closely aligning with theoretical values.Furthermore,experimental validation demonstrates an 80% reduction in error compared to the conventional galvanostatic intermittent titration technique(GITT)method.Overall,this study provides a novel voltage-based approach for determining the reaction rate constant,enhancing the applicability of theoretical analysis in electrode structural design and facilitating rapid battery optimization.展开更多
To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is deve...To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is developed to identify the geometric parameters.The study utilizes a common precast element for highway bridges as the research subject.First,edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology.Subsequently,a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output.A dataset is generated by varying the control parameters and noise levels for model training.Finally,field measurements are conducted to validate the accuracy of the developed method.The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components,with an error rate maintained within 5%.展开更多
Prepulse combined hydraulic fracturing facilitates the development of fracture networks by integrating prepulse hydraulic loading with conventional hydraulic fracturing.The formation mechanisms of fracture networks be...Prepulse combined hydraulic fracturing facilitates the development of fracture networks by integrating prepulse hydraulic loading with conventional hydraulic fracturing.The formation mechanisms of fracture networks between hydraulic and pre-existing fractures under different prepulse loading parameters remain unclear.This research investigates the impact of prepulse loading parameters,including the prepulse loading number ratio(C),prepulse loading stress ratio(S),and prepulse loading frequency(f),on the formation of fracture networks between hydraulic and pre-existing fractures,using both experimental and numerical methods.The results suggest that low prepulse loading stress ratios and high prepulse loading number ratios are advantageous loading modes.Multiple hydraulic fractures are generated in the specimen under the advantageous loading modes,facilitating the development of a complex fracture network.Fatigue damage occurs in the specimen at the prepulse loading stage.The high water pressure at the secondary conventional hydraulic fracturing promotes the growth of hydraulic fractures along the damage zones.This allows the hydraulic fractures to propagate deeply and interact with pre-existing fractures.Under advantageous loading conditions,multiple hydraulic fractures can extend to pre-existing fractures,and these hydraulic fractures penetrate or propagate along pre-existing fractures.Especially when the approach angle is large,the damage range in the specimen during the prepulse loading stage increases,resulting in the formation of more hydraulic fractures.展开更多
A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that th...A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that the loading parameters(initial normal stress,normal stiffness,and shear velocity)determine propagation paths of the wing and secondary cracks in rock bridges during the initial shear cycle,creating different morphologies of macroscopic step-path rupture surfaces and asperities on them.The differences in stress state and rupture surface induce different cyclic shear responses.It shows that high initial normal stress accelerates asperity degradation,raises shear resistance,and promotes compression of intermittent joints.In addition,high normal stiffness provides higher normal stress and shear resistance during the initial cycles and inhibits the dilation and compression of intermittent joints.High shear velocity results in a higher shear resistance,greater dilation,and greater compression.Finally,shear strength is most sensitive to initial normal stress,followed by shear velocity and normal stiffness.Moreover,average dilation angle is most sensitive to initial normal stress,followed by normal stiffness and shear velocity.During the shear cycles,frictional coefficient is affected by asperity degradation,backfilling of rock debris,and frictional area,exhibiting a non-monotonic behavior.展开更多
The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.Howev...The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.However,most scholars currently focus on modifying methods to enhance model accuracy,while overlooking the extent to which input parameters influence accuracy.To address this issue,in this study,a prediction model for the endpoint carbon content in the converter was developed using factor analysis(FA)and support vector machine(SVM)optimized by improved particle swarm optimization(IPSO).Analysis of the factors influencing the endpoint carbon content during the converter smelting process led to the identification of 21 input parameters.Subsequently,FA was used to reduce the dimensionality of the data and applied to the prediction model.The results demonstrate that the performance of the FA-IPSO-SVM model surpasses several existing methods,such as twin support vector regression and support vector machine.The model achieves hit rates of 89.59%,96.21%,and 98.74%within error ranges of±0.01%,±0.015%,and±0.02%,respectively.Finally,based on the prediction results obtained by sequentially removing input parameters,the parameters were classified into high influence(5%-7%),medium influence(2%-5%),and low influence(0-2%)categories according to their varying degrees of impact on prediction accuracy.This classi-fication provides a reference for selecting input parameters in future prediction models for endpoint carbon content.展开更多
Transcutaneous electrical acupoint stimulation(TEAS)is a kind of physical therapy that use electric cur-rent through the electrodes placed on the surface of acupoints to produce clinical effects in the human body,whic...Transcutaneous electrical acupoint stimulation(TEAS)is a kind of physical therapy that use electric cur-rent through the electrodes placed on the surface of acupoints to produce clinical effects in the human body,which is characterized by less adverse reaction and convenient operation.It has been widely used in the treatment of various diseases.This review introduces six major clinical applications of TEAS,named analgesia,regulation of gastrointestinal function,improvement of reproductive function,enhancement of cognitive function,promotion of limb function recovery and relief of fatigue.Besides,TEAS has been ap-plied to the treatment of other chronic diseases such as hypertension and diabetes,achieving satisfactory clinical effects.However,two crucial challenges are encountered in the development of TEAS.One is the lack of standardization in the selection of parameters such as waveform,frequency,intensity and stimula-tion duration.The other is the limitation on the flexibility in the acupoint selection.This review analyzes key issues that need to be addressed in the current clinical application of TEAS,such as the selection of parameters and acupoints,and this review provides a certain reference value for optimizing regimens of TEAS and promoting its development and application.展开更多
Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in c...Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate models.Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations.In this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse.Through a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s variability.The traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC.The results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter slowly.However,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.展开更多
The available test methods for optimal moisture content of cold recycled mixture(CRM)as well as its bulk specific gravity,and theoretical maximum relative density were analyzed in this work.Some test improvements were...The available test methods for optimal moisture content of cold recycled mixture(CRM)as well as its bulk specific gravity,and theoretical maximum relative density were analyzed in this work.Some test improvements were suggested to improve test control of the CRM road performance based on the discovered flaws.Besides,the properties of reclaimed asphalt pavement(RAP),including the content of old asphalt,penetration index,passing rate of 4.75 mm sieve,and gradation change rate after extraction,were examined.The effects of RAP characteristics on splitting tensile strength,water stability,the high-and low-temperature performance of emulsified asphalt CRM were studied.The results show that the optimum moisture content of CRM should be determined when the compaction work matches the specimen’s molding work.Among the analyzed methods of bulk specific gravity assessment,the dry-surface and CoreLok methods provide more robust and accurate results than the wax-sealing method,while the dry-surface method is the most cost-efficient.The modified theoretical maximum relative density test method is proposed,which can reduce the systematic error of the vacuum test method.The following RAP-CRM trends can be observed.The lower the content of old asphalt and the smaller the change rate of gradation,the smaller the voids and the better the water stability of CRM.The greater the penetration of old asphalt,the higher the fracture work and low-temperature splitting strength.The greater the penetration,the higher the passing rate of 4.75 mm sieve after extraction,and the worse the high-temperature performance of CRM.展开更多
Steel catenary riser represents the pioneering riser technology implemented in China’s deep-sea oil and gas opera-tions.Given the complex mechanical conditions of the riser,extensive research has been conducted on it...Steel catenary riser represents the pioneering riser technology implemented in China’s deep-sea oil and gas opera-tions.Given the complex mechanical conditions of the riser,extensive research has been conducted on its dynamic analysis and structural design.This study investigates a deep-sea oil and gas field by developing a coupled model of a semi-submersible platform and steel catenary riser to analyze it mechanical behavior under extreme marine condi-tions.Through multi-objective optimization methodology,the study compares and analyzes suspension point tension and touchdown point stress under various conditions by modifying the suspension position,suspension angle,and catenary length.The optimal configuration parameters were determined:a suspension angle of 12°,suspension position in the southwest direction of the column,and a catenary length of approximately 2000 m.These findings elucidate the impact of configuration parameters on riser dynamic response and establish reasonable parameter layout ranges for adverse sea conditions,offering valuable optimization strategies for steel catenary riser deployment in domestic deep-sea oil and gas fields.展开更多
基金supported by Key Program of National Natural Science Foundation of China(U2368215)the Science and Technology Research and Development Program Project of China Railway Group Co.,Ltd.(N2023J056).
文摘Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.
基金supported by the Innovation Foundation of Provincial Education Department of Gansu(2024B-005)the Gansu Province National Science Foundation(22YF7GA182)the Fundamental Research Funds for the Central Universities(No.lzujbky2022-kb01)。
文摘Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
基金supported by the National Nature Science Foundation of China(Nos.22305066 and 52372041).
文摘High-temperature microwave absorbing materials(MAMs)and structures are increasingly appealing due to their critical role in stealth applications under harsh environments.However,the impedance mismatch caused by increased conduction loss often leads to a significant decline in electromagnetic wave absorp-tion(EMWA)performance at elevated temperatures,which severely restricts their practical application.In this study,we propose a novel approach for efficient electromagnetic wave absorption across a wide temperature range using reduced graphene oxide(RGO)/epoxy resin(EP)metacomposites that integrate both electromagnetic parameters and metamaterial design concepts.Due to the discrete distribution of the units,electromagnetic waves can more easily penetrate the interior of materials,thereby exhibiting stable microwave absorption(MA)performance and impedance-matching characteristics suitable across a wide temperature range.Consequently,exceptional MA properties can be achieved within the tem-perature range from 298 to 473 K.Furthermore,by carefully controlling the structural parameters in RGO metacomposites,both the resonant frequency and effective absorption bandwidth(EAB)can be optimized based on precise manipulation of equivalent electromagnetic parameters.This study not only provides an effective approach for the rational design of MA performance but also offers novel insights into achieving super metamaterials with outstanding performance across a wide temperature spectrum.
基金supported by the National Key R&D Program of China(No.2022YFE0207400)supported by the Xiaomi Young Talents Programsupported by the Youth Innovation Promotion Association CAS(No.Y201768)。
文摘Na-ion batteries are considered a promising next-generation battery alternative to Li-ion batteries,due to the abundant Na resources and low cost.Most efforts focus on developing new materials to enhance energy density and electrochemical performance to enable it comparable to Li-ion batteries,without considering thermal hazard of Na-ion batteries and comparison with Li-ion batteries.To address this issue,our work comprehensively compares commercial prismatic lithium iron phosphate(LFP) battery,lithium nickel cobalt manganese oxide(NCM523) battery and Na-ion battery of the same size from thermal hazard perspective using Accelerating Rate Calorimeter.The thermal hazard of the three cells is then qualitatively assessed from thermal stability,early warning and thermal runaway severity perspectives by integrating eight characteristic parameters.The Na-ion cell displays comparable thermal stability with LFP while LFP exhibits the lowest thermal runaway hazard and severity.However,the Na-ion cell displays the lowest safety venting temperature and the longest time interval between safety venting and thermal runaway,allowing the generated gas to be released as early as possible and detected in a timely manner,providing sufficient time for early warning.Finally,a database of thermal runaway characteristic temperature for Li-ion and Na-ion cells is collected and processed to delineate four thermal hazard levels for quantitative assessment.Overall,LFP cells exhibit the lowest thermal hazard,followed by the Na-ion cells and NCM523 cells.This work clarifies the thermal hazard discrepancy between the Na-ion cell and prevalent Li-ion cells,providing crucial guidance for development and application of Na-ion cell.
基金supported in part by the National Natural Science Foundation of China under Grant 52077002。
文摘Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.
文摘Friction stir welding(FSW)is a relatively new welding technique that has significant advantages compared to the fusion welding techniques in joining non weld able alloys by fusion,such as aluminum alloys.Three FSW seams of AA6061-T6 plates were made us-ing different FSW parameters.The structure of the FSW seams was investigated using X-ray diffraction(XRD),scanning electron mi-croscope(SEM)and non destructive testing(NDT)techniques and their hardness was also measured.The dominated phase in the AA6061-T6 alloy and the FSW seams was theα-Al.The FSW seam had lower content of the secondary phases than the AA6061-T6 al-loy.The hardness of the FSW seams was decreased by about 30%compared to the AA6061-T6 alloy.The temperature distributions in the weld seams were also studied experimentally and numerically modeled and the results were in a good agreement.
基金supported by the Yunnan Provincial Key Laboratory of Clinical Virology(No.202002AG070062)the Key Projects of Yunnan Province Science and Technology Department(No.202302AA310044)the National Natural Science Foundation of China(No.82060664).
文摘This study primarily aimed to investigate the prevalence of human papillomavirus(HPV)and other common pathogens of sexually transmitted infections(STIs)in spermatozoa of infertile men and their effects on semen parameters.These pathogens included Ureaplasma urealyticum,Ureaplasma parvum,Chlamydia trachomatis,Mycoplasma genitalium,herpes simplex virus 2,Neisseria gonorrhoeae,Enterococcus faecalis,Streptococcus agalactiae,Pseudomonas aeruginosa,and Staphylococcus aureus.A total of 1951 men of infertile couples were recruited between 23 March 2023,and 17 May 2023,at the Department of Reproductive Medicine of The First People’s Hospital of Yunnan Province(Kunming,China).Multiplex polymerase chain reaction and capillary electrophoresis were used for HPV genotyping.Polymerase chain reaction and electrophoresis were also used to detect the presence of other STIs.The overall prevalence of HPV infection was 12.4%.The top five prevalent HPV subtypes were types 56,52,43,16,and 53 among those tested positive for HPV.Other common infections with high prevalence rates were Ureaplasma urealyticum(28.3%),Ureaplasma parvum(20.4%),and Enterococcus faecalis(9.5%).The prevalence rates of HPV coinfection with Ureaplasma urealyticum,Ureaplasma parvum,Chlamydia trachomatis,Mycoplasma genitalium,herpes simplex virus 2,Neisseria gonorrhoeae,Enterococcus faecalis,Streptococcus agalactiae,and Staphylococcus aureus were 24.8%,25.4%,10.6%,6.4%,2.4%,7.9%,5.9%,0.9%,and 1.3%,respectively.The semen volume and total sperm count were greatly decreased by HPV infection alone.Coinfection with HPV and Ureaplasma urealyticum significantly reduced sperm motility and viability.Our study shows that coinfection with STIs is highly prevalent in the semen of infertile men and that coinfection with pathogens can seriously affect semen parameters,emphasizing the necessity of semen screening for STIs.
基金supported by the National Key R&D Program of China(No.2021YFB0301200)National Natural Science Foundation of China(No.62025208).
文摘Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.
基金“High precision prestack reverse time depth migration imaging of long array seismic data in the East China Sea Shelf Basin”of the National Natural Science Foundation of China(No.42106207)“Seismic acquisition technology for deep strata under strong shielding layers in the sea and rugged seabed”of Laoshan Laboratory Science and Technology Innovation Project(No.LSKJ202203404)“Research on the compensation methods of the middledeep weak seismic reflections in the South Yellow Sea based on multi-resolution HHT time-frequency analysis”of the National Natural Science Foundation of China(No.42106208).
文摘The seismic data of the Laoshan Uplift in the South Yellow Sea Basin reveal a low signal-tonoise ratio and low refl ection signal energy in the deep Mesozoic–Paleozoic strata.The main reason is that the Mesozoic-Paleozoic marine carbonate rock strata are directly covered by the Cenozoic terrestrial clastic rock strata,which form a strong shielding layer.To obtain the reflection signals of the strata below the strong shielding layer,a one-way wave equation bidirectional illumination analysis of the main observation system parameters was conducted by analyzing the mechanism of the strong shielding layer.Low-frequency seismic sources are assumed to have a high illumination intensity on the reflection layer below the strong shielding layer.Accordingly,optimized acquisition parameter suggestions were proposed,and reacquisition was performed at the existing survey line locations in the Laoshan Uplift area.The imaging of the newly acquired data in the middle and deep layers was drastically improved.It revealed the unconformity between the Sinian and Cambrian under the strong shielding layer.The study yielded new insights into the tectonic and sedimentary evolution of the Lower Paleozoic in the South Yellow Sea.
基金supported by the Beijing Natural Science Foundation(No.L212029)the National Natural Science Foundation of China(No.62271043).
文摘To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current methods.Firstly,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic environments.With a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic environments.Particularly,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.
基金supported by the National Key R&D Program of China 2022YFB2404300the National Natural Science Foundation of China U22B2069the China Postdoctoral Science Foundation 2024M761006。
文摘The reaction rate constant is a crucial kinetic parameter that governs the charge and discharge performance of batteries,particularly in high-rate and thick-electrode applications.However,conventional estimation or fitting methods often overestimate the charge transfer overpotential,leading to substantial errors in reaction rate constant measurements.These inaccuracies hinder the accurate prediction of voltage profiles and overall cell performance.In this study,we propose the characteristic time-decomposed overpotential(CTDO)method,which employs a single-layer particle electrode(SLPE)structure to eliminate interference overpotentials.By leveraging the distribution of relaxation times(DRT),our method effectively isolates the characteristic time of the charge transfer process,enabling a more precise determination of the reaction rate constant.Simulation results indicate that our approach reduces measurement errors to below 2%,closely aligning with theoretical values.Furthermore,experimental validation demonstrates an 80% reduction in error compared to the conventional galvanostatic intermittent titration technique(GITT)method.Overall,this study provides a novel voltage-based approach for determining the reaction rate constant,enhancing the applicability of theoretical analysis in electrode structural design and facilitating rapid battery optimization.
基金The National Natural Science Foundation of China(No.52338011,52378291)Young Elite Scientists Sponsorship Program by CAST(No.2022-2024QNRC0101).
文摘To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is developed to identify the geometric parameters.The study utilizes a common precast element for highway bridges as the research subject.First,edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology.Subsequently,a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output.A dataset is generated by varying the control parameters and noise levels for model training.Finally,field measurements are conducted to validate the accuracy of the developed method.The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components,with an error rate maintained within 5%.
基金financially supported by,the Fundamental Research Funds for the Central Universities(Grant No.2023QN1064)the China Postdoctoral Science Foundation(Grant No.2023M733772)Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant No.2023ZB847)。
文摘Prepulse combined hydraulic fracturing facilitates the development of fracture networks by integrating prepulse hydraulic loading with conventional hydraulic fracturing.The formation mechanisms of fracture networks between hydraulic and pre-existing fractures under different prepulse loading parameters remain unclear.This research investigates the impact of prepulse loading parameters,including the prepulse loading number ratio(C),prepulse loading stress ratio(S),and prepulse loading frequency(f),on the formation of fracture networks between hydraulic and pre-existing fractures,using both experimental and numerical methods.The results suggest that low prepulse loading stress ratios and high prepulse loading number ratios are advantageous loading modes.Multiple hydraulic fractures are generated in the specimen under the advantageous loading modes,facilitating the development of a complex fracture network.Fatigue damage occurs in the specimen at the prepulse loading stage.The high water pressure at the secondary conventional hydraulic fracturing promotes the growth of hydraulic fractures along the damage zones.This allows the hydraulic fractures to propagate deeply and interact with pre-existing fractures.Under advantageous loading conditions,multiple hydraulic fractures can extend to pre-existing fractures,and these hydraulic fractures penetrate or propagate along pre-existing fractures.Especially when the approach angle is large,the damage range in the specimen during the prepulse loading stage increases,resulting in the formation of more hydraulic fractures.
基金financially supported by the National Natural Science Foundation of China(Grant No.42172292)Taishan Scholars Project Special Funding,and Shandong Energy Group(Grant No.SNKJ 2022A01-R26).
文摘A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that the loading parameters(initial normal stress,normal stiffness,and shear velocity)determine propagation paths of the wing and secondary cracks in rock bridges during the initial shear cycle,creating different morphologies of macroscopic step-path rupture surfaces and asperities on them.The differences in stress state and rupture surface induce different cyclic shear responses.It shows that high initial normal stress accelerates asperity degradation,raises shear resistance,and promotes compression of intermittent joints.In addition,high normal stiffness provides higher normal stress and shear resistance during the initial cycles and inhibits the dilation and compression of intermittent joints.High shear velocity results in a higher shear resistance,greater dilation,and greater compression.Finally,shear strength is most sensitive to initial normal stress,followed by shear velocity and normal stiffness.Moreover,average dilation angle is most sensitive to initial normal stress,followed by normal stiffness and shear velocity.During the shear cycles,frictional coefficient is affected by asperity degradation,backfilling of rock debris,and frictional area,exhibiting a non-monotonic behavior.
基金financially supported by the National Natural Science Foundation of China(No.52174297).
文摘The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.However,most scholars currently focus on modifying methods to enhance model accuracy,while overlooking the extent to which input parameters influence accuracy.To address this issue,in this study,a prediction model for the endpoint carbon content in the converter was developed using factor analysis(FA)and support vector machine(SVM)optimized by improved particle swarm optimization(IPSO).Analysis of the factors influencing the endpoint carbon content during the converter smelting process led to the identification of 21 input parameters.Subsequently,FA was used to reduce the dimensionality of the data and applied to the prediction model.The results demonstrate that the performance of the FA-IPSO-SVM model surpasses several existing methods,such as twin support vector regression and support vector machine.The model achieves hit rates of 89.59%,96.21%,and 98.74%within error ranges of±0.01%,±0.015%,and±0.02%,respectively.Finally,based on the prediction results obtained by sequentially removing input parameters,the parameters were classified into high influence(5%-7%),medium influence(2%-5%),and low influence(0-2%)categories according to their varying degrees of impact on prediction accuracy.This classi-fication provides a reference for selecting input parameters in future prediction models for endpoint carbon content.
基金Supported by Shanghai 2020“Science and Technology Innovation Action Plan”Medical Innovation Research Special Program:20Y21902800Shanghai Municipal Health Commission Shanghai Three-Year Action Plan to Further Accelerate the Development of Traditional Chinese Medicine Inheritance and Innovation:ZY(2021-2023)−0302)+1 种基金Shanghai Key Specialty(Acupuncture)Construction Project:shslczdzk04701Shanghai 2024"Science and Technology Innovation Action Plan"star cultivation(Sail special):24YF2740600.
文摘Transcutaneous electrical acupoint stimulation(TEAS)is a kind of physical therapy that use electric cur-rent through the electrodes placed on the surface of acupoints to produce clinical effects in the human body,which is characterized by less adverse reaction and convenient operation.It has been widely used in the treatment of various diseases.This review introduces six major clinical applications of TEAS,named analgesia,regulation of gastrointestinal function,improvement of reproductive function,enhancement of cognitive function,promotion of limb function recovery and relief of fatigue.Besides,TEAS has been ap-plied to the treatment of other chronic diseases such as hypertension and diabetes,achieving satisfactory clinical effects.However,two crucial challenges are encountered in the development of TEAS.One is the lack of standardization in the selection of parameters such as waveform,frequency,intensity and stimula-tion duration.The other is the limitation on the flexibility in the acupoint selection.This review analyzes key issues that need to be addressed in the current clinical application of TEAS,such as the selection of parameters and acupoints,and this review provides a certain reference value for optimizing regimens of TEAS and promoting its development and application.
基金supported by the National Key R&D Program of China [grant number 2023YFF0805202]the National Natural Science Foun-dation of China [grant number 42175045]the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDB42000000]。
文摘Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate models.Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations.In this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse.Through a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s variability.The traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC.The results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter slowly.However,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.
文摘The available test methods for optimal moisture content of cold recycled mixture(CRM)as well as its bulk specific gravity,and theoretical maximum relative density were analyzed in this work.Some test improvements were suggested to improve test control of the CRM road performance based on the discovered flaws.Besides,the properties of reclaimed asphalt pavement(RAP),including the content of old asphalt,penetration index,passing rate of 4.75 mm sieve,and gradation change rate after extraction,were examined.The effects of RAP characteristics on splitting tensile strength,water stability,the high-and low-temperature performance of emulsified asphalt CRM were studied.The results show that the optimum moisture content of CRM should be determined when the compaction work matches the specimen’s molding work.Among the analyzed methods of bulk specific gravity assessment,the dry-surface and CoreLok methods provide more robust and accurate results than the wax-sealing method,while the dry-surface method is the most cost-efficient.The modified theoretical maximum relative density test method is proposed,which can reduce the systematic error of the vacuum test method.The following RAP-CRM trends can be observed.The lower the content of old asphalt and the smaller the change rate of gradation,the smaller the voids and the better the water stability of CRM.The greater the penetration of old asphalt,the higher the fracture work and low-temperature splitting strength.The greater the penetration,the higher the passing rate of 4.75 mm sieve after extraction,and the worse the high-temperature performance of CRM.
基金financially supported by the National Key Research and Development Program of China(Grant No.2022YFC2806100)the National Natural Science Foundation of China(Grant Nos.U22B20126 and 52374020)+1 种基金Science Foundation of China University of Petroleum,Beijing(Grant No.2462025QNXZ009)Beijing Nova Program(Grant No.20250484913).
文摘Steel catenary riser represents the pioneering riser technology implemented in China’s deep-sea oil and gas opera-tions.Given the complex mechanical conditions of the riser,extensive research has been conducted on its dynamic analysis and structural design.This study investigates a deep-sea oil and gas field by developing a coupled model of a semi-submersible platform and steel catenary riser to analyze it mechanical behavior under extreme marine condi-tions.Through multi-objective optimization methodology,the study compares and analyzes suspension point tension and touchdown point stress under various conditions by modifying the suspension position,suspension angle,and catenary length.The optimal configuration parameters were determined:a suspension angle of 12°,suspension position in the southwest direction of the column,and a catenary length of approximately 2000 m.These findings elucidate the impact of configuration parameters on riser dynamic response and establish reasonable parameter layout ranges for adverse sea conditions,offering valuable optimization strategies for steel catenary riser deployment in domestic deep-sea oil and gas fields.
