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
Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated opt...Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated optimization strategy to reduce wheel–rail contact fatigue damage.Design/methodology/approach–Taking a small-radius curve of a high-speed railway as the research object,field measurements were conducted to obtain track parameters and wheel–rail profiles.A coupled vehicle-track dynamics model was established.Multiple numerical experiments were designed using the Latin Hypercube Sampling method to extract wheel-rail creepage indicators and construct a parameter-creepage response surface model.Findings–Key service parameters affecting wheel–rail creepage were identified,including the matching relationship between curve geometry and vehicle speed and rail profile parameters.The influence patterns of various parameters on wheel–rail creepage were revealed through response surface analysis,leading to the establishment of parameter optimization criteria.Originality/value–This study presents the systematic investigation of wheel–rail creepage characteristics under multi-parameter coupling in high-speed railway curves.A response surface-based parameter-creepage relationship model was established,and a multi-parameter coordinated optimization strategy was proposed.The research findings provide theoretical guidance for controlling wheel–rail contact fatigue damage and optimizing wheel–rail profiles in high-speed railway curves.展开更多
In this study,an improved integrated radial basis function with nonuniform shape parameter is introduced.The proposed shape parameter varies in each support domain and is defined byθ=1/d_(max),where d_(max)is the max...In this study,an improved integrated radial basis function with nonuniform shape parameter is introduced.The proposed shape parameter varies in each support domain and is defined byθ=1/d_(max),where d_(max)is the maximum distance of any pair of nodes in the support domain.The proposed method is verified and shows good performance.The results are stable and accurate with any number of nodes and an arbitrary nodal distribution.Notably,the support domain should be large enough to obtain accurate results.This method is then applied for transient analysis of curved shell structures made from functionally graded materials with complex geometries.Through several numerical examples,the accuracy of the proposed approach is demonstrated and discussed.Additionally,the influence of various factors on the dynamic behavior of the structures,including the power-law index,different materials,loading conditions,and geometrical parameters of the structures,was investigated.展开更多
A novel porous shock absorption layer is put forward in this study, and the shock absorption performance of the porous shock absorption layer is evaluated based on three-dimensional pseudo-static analysis. The modifie...A novel porous shock absorption layer is put forward in this study, and the shock absorption performance of the porous shock absorption layer is evaluated based on three-dimensional pseudo-static analysis. The modified reaction acceleration method is adopted and validated in the three-dimensional model. Seven ground motions are selected and the peak ground acceleration is adjusted to 0.2 g, 0.4 g and 0.6 g. The impact of the void ratio and thickness of the porous shock absorption layer is studied, while the surrounding rock grade and tunnel depth are also investigated. The numerical results show that the porous shock absorption layer has good shock absorption performance and can effectively reduce the maximum internal force of the secondary lining, but it cannot reduce the maximum horizontal relative displacement of the secondary lining. The circumferential rubber strip in the porous shock absorption layer will reduce shock absorption performance. The results of parameter analysis indicate that the shock absorption performance of the porous shock absorption layer increases with the increase of the void ratio and thickness, and it has good shock absorption performance under different surrounding rock grades and tunnel depths.展开更多
Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache...Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.展开更多
In order to improve the performance and service life of the Leningrader seal of the Stirling engine piston rod,interference,pre-load and friction coefficient were taken as influencing factors,and the curved surface re...In order to improve the performance and service life of the Leningrader seal of the Stirling engine piston rod,interference,pre-load and friction coefficient were taken as influencing factors,and the curved surface response method was adopted to reduce the contact stress of sealing surface and von Mises stress of the sealing sleeve as the response index,with the optimization goal of reducing wear and extending life.The above three key parameters are analyzed and optimized,the influence of each parameter on the sealing performance and service life is obtained,and the best combination scheme of the three is determined.The results show that the interaction between pre-tightening force and interference fit has the greatest impact on contact stress.The interaction between interference fit and friction coeffi-cient has the most significant effect on von Mises stress.The optimized parameters can reduce the maximum contact stress and maximum von Mises stress of the sealing sleeve by 26.3%and 20.6%,respectively,under a media pressure of 5-9 MPa.Test bench verification shows that the leakage of the optimized sealing device in 12 h is reduced by 0.44 cc·min^(-1)(1 cc=1 cm^(3)).The wear rate of the sealing sleeve is 1.08%before optimization and 0.45%after optimization,indicating that the optimized parameters in this paper are effective.展开更多
A rigorous back analysis of shear strength parameters of landslide slip was presented. Kinematical element method was adopted to determine factor of safety and critical failure surface, which overcomes the disadvantag...A rigorous back analysis of shear strength parameters of landslide slip was presented. Kinematical element method was adopted to determine factor of safety and critical failure surface, which overcomes the disadvantage of limit equilibrium method. The theoretical relationship between the combination of shear strength parameters and stability state was studied. The results show that the location of critical slip surface, F/tan f and F/c depend only on the value of c/tan f. The failure surface moves towards the inside of slope as c/tan f increases. According to the information involving factor of safety and critical failure surface in a specific cross-section, strength parameters can be back calculated based on the above findings. Three examples were given for demonstrating the validity of the present method. The shear strength parameters obtained by back analysis are almost consistent with their correct solutions or test results.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 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
The neutron capture resonance parameters for 159Tb are crucial for validating nuclear models,nucleosynthesis during the neutron capture process,and nuclear technology applications.In this study,resonance analyses were...The neutron capture resonance parameters for 159Tb are crucial for validating nuclear models,nucleosynthesis during the neutron capture process,and nuclear technology applications.In this study,resonance analyses were performed for the neutron capture cross sections of 159Tb measured at the China Spallation Neutron Source(CSNS)backscattering white neutron beamline(Back-n)facility.The resonance parameters were extracted from the R-Matrix code SAMMY and fitted to the experimental capture yield up to the 1.2 keV resolved resonance region(RRR).The average resonance parameters were determined by performing statistical analysis on the set of the resonance parameters in the RRR.These results were used to fit the measured average capture cross sections using the FITACS code in the unresolved resonance region from 2 keV to 1 MeV.The contributions of partial waves l=0,1,2 to the average capture cross sections are reported.展开更多
The current investigation focuses on intertwined relationships of ecology and aquaculture for the benefit of farmers involved in fish farming practices.The study evaluated glucosinolate reduction in black,brown,and wh...The current investigation focuses on intertwined relationships of ecology and aquaculture for the benefit of farmers involved in fish farming practices.The study evaluated glucosinolate reduction in black,brown,and white mustard meals as fish feed ingredients for Indian Major Carps.Fish were fed with 10%mustard meal-supplemented diets in three forms:Raw(R),Anti-nutritional Rich(AR),and Anti-nutritional Lowered(AL),alongside a control group using floating feed.The three-month indoor experiment(September-November 2023)was conducted in FRP tanks with triplicate treatments.Blood analysis revealed compromised health in AR-fed carps,with reduced hemoglobin levels in rohu,catla and mrigal and elevated total leukocyte counts indicating inflammation in all the three carps studied here.Liver function was impaired in AR-fed fish,shown by increased alanine transaminase levels,highest in rohu followed by mrigal and catla.Histopathological examination of AR-fed carps liver tissue revealed necrotic spots,deformed hepatocytes,and significant vacuolation.In contrast,AL-fed fish demonstrated improved health parameters through Complete Blood Count analysis,liver function tests,and histo-pathological observations,suggesting successful reduction of anti-nutritional factors in the processed mustard meals.In near future,replacement of unprocessed seed meal with processed seed meal will lead to economic gains in fish farming.展开更多
Disaster mitigation necessitates scientifi c and accurate aftershock forecasting during the critical 2 h after an earthquake. However, this action faces immense challenges due to the lack of early postearthquake data ...Disaster mitigation necessitates scientifi c and accurate aftershock forecasting during the critical 2 h after an earthquake. However, this action faces immense challenges due to the lack of early postearthquake data and the unreliability of forecasts. To obtain foundational data for sequence parameters of the land-sea adjacent zone and establish a reliable and operational aftershock forecasting framework, we combined the initial sequence parameters extracted from envelope functions and incorporated small-earthquake information into our model to construct a Bayesian algorithm for the early postearthquake stage. We performed parameter fitting and early postearthquake aftershock occurrence rate forecasting and effectiveness evaluation for 36 earthquake sequences with M ≥ 4.0 in the Bohai Rim region since 2010. According to the results, during the early stage after the mainshock, earthquake sequence parameters exhibited relatively drastic fl uctuations with signifi cant errors. The integration of prior information can mitigate the intensity of these changes and reduce errors. The initial and stable sequence parameters generally display advantageous distribution characteristics, with each parameter’s distribution being relatively concentrated and showing good symmetry and remarkable consistency. The sequence parameter p-values were relatively small, which indicates the comparatively slow attenuation of signifi cant earthquake events in the Bohai Rim region. A certain positive correlation was observed between earthquake sequence parameters b and p. However, sequence parameters are unrelated to the mainshock magnitude, which implies that their statistical characteristics and trends are universal. The Bayesian algorithm revealed a good forecasting capability for aftershocks in the early postearthquake period (2 h) in the Bohai Rim region, with an overall forecasting effi cacy rate of 76.39%. The proportion of “too low” failures exceeded that of “too high” failures, and the number of forecasting failures for the next three days was greater than that for the next day.展开更多
Rate of penetration(ROP)is the key factor affecting the drilling cycle and cost,and it directly reflects the drilling efficiency.With the increasingly complex field data,the original drilling parameter optimization me...Rate of penetration(ROP)is the key factor affecting the drilling cycle and cost,and it directly reflects the drilling efficiency.With the increasingly complex field data,the original drilling parameter optimization method can't meet the needs of drilling parameter optimization in the era of big data and artificial intelligence.This paper presents a drilling parameter optimization method based on big data of drilling,which takes machine learning algorithms as a tool.First,field data is pre-processed according to the characteristics of big data of drilling.Then a formation clustering model based on unsupervised learning is established,which takes sonic logging,gamma logging,and density logging data as input.Formation clusters with similar stratum characteristics are decided.Aiming at improving ROP,the formation clusters are input into the ROP model,and the mechanical parameters(weight on bit,revolution per minute)and hydraulic parameters(standpipe pressure,flow rate)are optimized.Taking the Southern Margin block of Xinjiang as an example,the MAPE of prediction of ROP after clustering is decreased from 18.72%to 10.56%.The results of this paper provide a new method to improve drilling efficiency based on big data of drilling.展开更多
With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing hig...With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods,this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification.To resolve the scalability issue of coupled transmission and distribution power systems,clustering is first carried out based on the distribution system states.As the data and models of the transmission system and distribution systems are not shared.For the transmission system,equating the power transmitted from the transmission system to the distribution system is the same as equating the distribution system.Further,the power transmitted from the transmission system to different types of distribution systems is equivalent to different polynomial equivalent load models.Then,a parameter identification method is proposed to obtain the parameters of the equivalent load model.Finally,a transmission and distribution collaborative state estimation model is constructed based on the equivalent load model.The results of the numerical analysis show that compared with the traditional master-slave splitting method,the proposed method significantly enhances computational efficiency while maintaining high estimation accuracy.展开更多
The constitutive model is essential for predicting the deformation and stability of rocksoil mass.The estimation of constitutive model parameters is a necessary and important task for the reliable characterization of ...The constitutive model is essential for predicting the deformation and stability of rocksoil mass.The estimation of constitutive model parameters is a necessary and important task for the reliable characterization of mechanical behaviors.However,constitutive model parameters cannot be evaluated accurately with a limited amount of test data,resulting in uncertainty in the prediction of stress-strain curves.This paper proposes a Bayesian analysis framework to address this issue.It combines the Bayesian updating with the structural reliability and adaptive conditional sampling methods to assess the equation parameter of constitutive models.Based on the triaxial and ring shear tests on shear zone soils from the Huangtupo landslide,a statistical damage constitutive model and a critical state hypoplastic constitutive model were used to demonstrate the effectiveness of the proposed framework.Moreover,the parameter uncertainty effects of the damage constitutive model on landslide stability were investigated.Results show that reasonable assessments of the constitutive model parameter can be well realized.The variability of stress-strain curves is strongly related to the model prediction performance.The estimation uncertainty of constitutive model parameters should not be ignored for the landslide stability calculation.Our study provides a reference for uncertainty analysis and parameter assessment of the constitutive model.展开更多
Bifurcation properties of dynamical systems with two parameters are investigated in this paper. The definition of transition set is proposed, and the approach developed is used to investigate the dynamic characteristi...Bifurcation properties of dynamical systems with two parameters are investigated in this paper. The definition of transition set is proposed, and the approach developed is used to investigate the dynamic characteristic of the nonlin- ear forced Duffing system with nonlinear feedback controller. The whole parametric plane is divided into several persistent regions by the transition set, and then the bifurcation dia- grams in different persistent regions are obtained.展开更多
Mastering the influence laws of parameters on the solution structure of nonlinear systems is the basis of carrying out vibration isolation and control.Many researches on solution structure and bifurcation phenomenon i...Mastering the influence laws of parameters on the solution structure of nonlinear systems is the basis of carrying out vibration isolation and control.Many researches on solution structure and bifurcation phenomenon in parameter spaces are carried out broadly in many fields,and the research on nonlinear gear systems has attracted the attention of many scholars.But there is little study on the solution domain boundary of nonlinear gear systems.For a periodic non-autonomous nonlinear dynamic system with several control parameters,a solution domain boundary analysis method of nonlinear systems in parameter spaces is proposed,which combines the cell mapping method based on Poincaré point mapping in phase spaces with the domain decomposition technique of parameter spaces.The cell mapping is known as a global analysis method to analyze the global behavior of a nonlinear dynamic system with finite dimensions,and the basic idea of domain decomposition techniques is to divide and rule.The method is applied to analyze the solution domain boundaries in parameter spaces of a nonlinear gear system.The distribution of different period domains,chaos domain and the domain boundaries between different period domains and chaotic domain are obtained in control parameter spaces constituted by meshing damping ratio with excitation frequency,fluctuation coefficient of meshing stiffness and average exciting force respectively by calculation.The calculation results show that as the meshing damping increases,the responses of the system change towards a single motion,while the variations of the excitation frequency,meshing stiffness and exciting force make the solution domain presenting diversity.The proposed research contribution provides evidence for vibration control and parameter design of the gear system,and confirms the validity of the solution domain boundary analysis method.展开更多
Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity ana...Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.展开更多
The present study was designed to determine the relationships between the performance of ethanol precipitation and seven process parameters in the ethanol precipitation process of Re Du Ning Injections,including conce...The present study was designed to determine the relationships between the performance of ethanol precipitation and seven process parameters in the ethanol precipitation process of Re Du Ning Injections,including concentrate density,concentrate temperature,ethanol content,flow rate and stir rate in the addition of ethanol,precipitation time,and precipitation temperature.Under the experimental and simulated production conditions,a series of precipitated resultants were prepared by changing these variables one by one,and then examined by HPLC fingerprint analyses.Different from the traditional evaluation model based on single or a few constituents,the fingerprint data of every parameter fluctuation test was processed with Principal Component Analysis(PCA)to comprehensively assess the performance of ethanol precipitation.Our results showed that concentrate density,ethanol content,and precipitation time were the most important parameters that influence the recovery of active compounds in precipitation resultants.The present study would provide some reference for pharmaceutical scientists engaged in research on pharmaceutical process optimization and help pharmaceutical enterprises adapt a scientific and reasonable cost-effective approach to ensure the batch-to-batch quality consistency of the final products.展开更多
The parameter sensitivities affecting the flutter speed of the NREL （National Renewable Energy Laboratory） 5-MW baseline HAWT （horizontal axis wind turbine） blades are analyzed. An aeroelastic model, which compris...The parameter sensitivities affecting the flutter speed of the NREL （National Renewable Energy Laboratory） 5-MW baseline HAWT （horizontal axis wind turbine） blades are analyzed. An aeroelastic model, which comprises an aerodynamic part to calculate the aerodynamic loads and a structural part to determine the structural dynamic responses, is established to describe the classical flutter of the blades. For the aerodynamic part, Theodorsen unsteady aerodynamics model is used. For the structural part, Lagrange’s equation is employed. The flutter speed is determined by introducing “V–g” method to the aeroelastic model, which converts the issue of classical flutter speed determination into an eigenvalue problem. Furthermore, the time domain aeroelastic response of the wind turbine blade section is obtained with employing Runge-Kutta method. The results show that four cases （i.e., reducing the blade torsional stiffness, moving the center of gravity or the elastic axis towards the trailing edge of the section, and placing the turbine in high air density area） will decrease the flutter speed. Therefore, the judicious selection of the four parameters （the torsional stiffness, the chordwise position of the center of gravity, the elastic axis position and air density） can increase the relative inflow speed at the blade section associated with the onset of flutter.展开更多
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sponsored by the National Natural Science Foundation of China(Grant No.52405443)the Technology Research and Development Plan of China Railway(Grant No.N2023G063)the Fund of China Academy of Railway Sciences Corporation Limited(Grant No.2023YJ054).
文摘Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated optimization strategy to reduce wheel–rail contact fatigue damage.Design/methodology/approach–Taking a small-radius curve of a high-speed railway as the research object,field measurements were conducted to obtain track parameters and wheel–rail profiles.A coupled vehicle-track dynamics model was established.Multiple numerical experiments were designed using the Latin Hypercube Sampling method to extract wheel-rail creepage indicators and construct a parameter-creepage response surface model.Findings–Key service parameters affecting wheel–rail creepage were identified,including the matching relationship between curve geometry and vehicle speed and rail profile parameters.The influence patterns of various parameters on wheel–rail creepage were revealed through response surface analysis,leading to the establishment of parameter optimization criteria.Originality/value–This study presents the systematic investigation of wheel–rail creepage characteristics under multi-parameter coupling in high-speed railway curves.A response surface-based parameter-creepage relationship model was established,and a multi-parameter coordinated optimization strategy was proposed.The research findings provide theoretical guidance for controlling wheel–rail contact fatigue damage and optimizing wheel–rail profiles in high-speed railway curves.
基金Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study
文摘In this study,an improved integrated radial basis function with nonuniform shape parameter is introduced.The proposed shape parameter varies in each support domain and is defined byθ=1/d_(max),where d_(max)is the maximum distance of any pair of nodes in the support domain.The proposed method is verified and shows good performance.The results are stable and accurate with any number of nodes and an arbitrary nodal distribution.Notably,the support domain should be large enough to obtain accurate results.This method is then applied for transient analysis of curved shell structures made from functionally graded materials with complex geometries.Through several numerical examples,the accuracy of the proposed approach is demonstrated and discussed.Additionally,the influence of various factors on the dynamic behavior of the structures,including the power-law index,different materials,loading conditions,and geometrical parameters of the structures,was investigated.
基金Science and Technology Plan Project of Xizang Autonomous Region,China under Grant No.XZ202501YD0007。
文摘A novel porous shock absorption layer is put forward in this study, and the shock absorption performance of the porous shock absorption layer is evaluated based on three-dimensional pseudo-static analysis. The modified reaction acceleration method is adopted and validated in the three-dimensional model. Seven ground motions are selected and the peak ground acceleration is adjusted to 0.2 g, 0.4 g and 0.6 g. The impact of the void ratio and thickness of the porous shock absorption layer is studied, while the surrounding rock grade and tunnel depth are also investigated. The numerical results show that the porous shock absorption layer has good shock absorption performance and can effectively reduce the maximum internal force of the secondary lining, but it cannot reduce the maximum horizontal relative displacement of the secondary lining. The circumferential rubber strip in the porous shock absorption layer will reduce shock absorption performance. The results of parameter analysis indicate that the shock absorption performance of the porous shock absorption layer increases with the increase of the void ratio and thickness, and it has good shock absorption performance under different surrounding rock grades and tunnel depths.
基金supported by the National Natural Science Foundation of China(Grant Nos.52090081,52079068)the State Key Laboratory of Hydroscience and Hydraulic Engineering(Grant No.2021-KY-04).
文摘Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.
基金Supported by the National Natural Science Foundation of China (51675509)Wenzhou Public Welfare Industrial Technology Project (G20170026).
文摘In order to improve the performance and service life of the Leningrader seal of the Stirling engine piston rod,interference,pre-load and friction coefficient were taken as influencing factors,and the curved surface response method was adopted to reduce the contact stress of sealing surface and von Mises stress of the sealing sleeve as the response index,with the optimization goal of reducing wear and extending life.The above three key parameters are analyzed and optimized,the influence of each parameter on the sealing performance and service life is obtained,and the best combination scheme of the three is determined.The results show that the interaction between pre-tightening force and interference fit has the greatest impact on contact stress.The interaction between interference fit and friction coeffi-cient has the most significant effect on von Mises stress.The optimized parameters can reduce the maximum contact stress and maximum von Mises stress of the sealing sleeve by 26.3%and 20.6%,respectively,under a media pressure of 5-9 MPa.Test bench verification shows that the leakage of the optimized sealing device in 12 h is reduced by 0.44 cc·min^(-1)(1 cc=1 cm^(3)).The wear rate of the sealing sleeve is 1.08%before optimization and 0.45%after optimization,indicating that the optimized parameters in this paper are effective.
基金Project(51174228)supported by the National Natural Science Foundation of ChinaProject(CX2012B069)supported by Hunan Provincial Innovation Foundation for PostgraduateProject(201003)supported by Transportation Science and Technology Projects of Hunan Province,China
文摘A rigorous back analysis of shear strength parameters of landslide slip was presented. Kinematical element method was adopted to determine factor of safety and critical failure surface, which overcomes the disadvantage of limit equilibrium method. The theoretical relationship between the combination of shear strength parameters and stability state was studied. The results show that the location of critical slip surface, F/tan f and F/c depend only on the value of c/tan f. The failure surface moves towards the inside of slope as c/tan f increases. According to the information involving factor of safety and critical failure surface in a specific cross-section, strength parameters can be back calculated based on the above findings. Three examples were given for demonstrating the validity of the present method. The shear strength parameters obtained by back analysis are almost consistent with their correct solutions or test results.
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 the National Natural Science Foundation of China(Nos.12365018,U2032146,12465024)Natural Science Foundation of Inner Mongolia(Nos.2023MS01005,2024ZD23,2024FX30)the program of Innovative Research Team and Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region(Nos.NMGIRT2217,NJYT23109)。
文摘The neutron capture resonance parameters for 159Tb are crucial for validating nuclear models,nucleosynthesis during the neutron capture process,and nuclear technology applications.In this study,resonance analyses were performed for the neutron capture cross sections of 159Tb measured at the China Spallation Neutron Source(CSNS)backscattering white neutron beamline(Back-n)facility.The resonance parameters were extracted from the R-Matrix code SAMMY and fitted to the experimental capture yield up to the 1.2 keV resolved resonance region(RRR).The average resonance parameters were determined by performing statistical analysis on the set of the resonance parameters in the RRR.These results were used to fit the measured average capture cross sections using the FITACS code in the unresolved resonance region from 2 keV to 1 MeV.The contributions of partial waves l=0,1,2 to the average capture cross sections are reported.
文摘The current investigation focuses on intertwined relationships of ecology and aquaculture for the benefit of farmers involved in fish farming practices.The study evaluated glucosinolate reduction in black,brown,and white mustard meals as fish feed ingredients for Indian Major Carps.Fish were fed with 10%mustard meal-supplemented diets in three forms:Raw(R),Anti-nutritional Rich(AR),and Anti-nutritional Lowered(AL),alongside a control group using floating feed.The three-month indoor experiment(September-November 2023)was conducted in FRP tanks with triplicate treatments.Blood analysis revealed compromised health in AR-fed carps,with reduced hemoglobin levels in rohu,catla and mrigal and elevated total leukocyte counts indicating inflammation in all the three carps studied here.Liver function was impaired in AR-fed fish,shown by increased alanine transaminase levels,highest in rohu followed by mrigal and catla.Histopathological examination of AR-fed carps liver tissue revealed necrotic spots,deformed hepatocytes,and significant vacuolation.In contrast,AL-fed fish demonstrated improved health parameters through Complete Blood Count analysis,liver function tests,and histo-pathological observations,suggesting successful reduction of anti-nutritional factors in the processed mustard meals.In near future,replacement of unprocessed seed meal with processed seed meal will lead to economic gains in fish farming.
基金supported by the Natural Science Foundation of Tianjin (No. 22JCQNJC01070)the National Natural Science Foundation of China (No. 42404079)the Key Project of Tianjin Earthquake Agency (No. Zd202402)。
文摘Disaster mitigation necessitates scientifi c and accurate aftershock forecasting during the critical 2 h after an earthquake. However, this action faces immense challenges due to the lack of early postearthquake data and the unreliability of forecasts. To obtain foundational data for sequence parameters of the land-sea adjacent zone and establish a reliable and operational aftershock forecasting framework, we combined the initial sequence parameters extracted from envelope functions and incorporated small-earthquake information into our model to construct a Bayesian algorithm for the early postearthquake stage. We performed parameter fitting and early postearthquake aftershock occurrence rate forecasting and effectiveness evaluation for 36 earthquake sequences with M ≥ 4.0 in the Bohai Rim region since 2010. According to the results, during the early stage after the mainshock, earthquake sequence parameters exhibited relatively drastic fl uctuations with signifi cant errors. The integration of prior information can mitigate the intensity of these changes and reduce errors. The initial and stable sequence parameters generally display advantageous distribution characteristics, with each parameter’s distribution being relatively concentrated and showing good symmetry and remarkable consistency. The sequence parameter p-values were relatively small, which indicates the comparatively slow attenuation of signifi cant earthquake events in the Bohai Rim region. A certain positive correlation was observed between earthquake sequence parameters b and p. However, sequence parameters are unrelated to the mainshock magnitude, which implies that their statistical characteristics and trends are universal. The Bayesian algorithm revealed a good forecasting capability for aftershocks in the early postearthquake period (2 h) in the Bohai Rim region, with an overall forecasting effi cacy rate of 76.39%. The proportion of “too low” failures exceeded that of “too high” failures, and the number of forecasting failures for the next three days was greater than that for the next day.
基金financially supported by Sichuan Science and Technology Program(No.2025ZNSFSC0373)National Natural Science foundation of China(Grant No.52104006)Science and Technology Cooperation Project of the CNPC-SWPU Innovation Alliance(Grant No.2020CX040202)。
文摘Rate of penetration(ROP)is the key factor affecting the drilling cycle and cost,and it directly reflects the drilling efficiency.With the increasingly complex field data,the original drilling parameter optimization method can't meet the needs of drilling parameter optimization in the era of big data and artificial intelligence.This paper presents a drilling parameter optimization method based on big data of drilling,which takes machine learning algorithms as a tool.First,field data is pre-processed according to the characteristics of big data of drilling.Then a formation clustering model based on unsupervised learning is established,which takes sonic logging,gamma logging,and density logging data as input.Formation clusters with similar stratum characteristics are decided.Aiming at improving ROP,the formation clusters are input into the ROP model,and the mechanical parameters(weight on bit,revolution per minute)and hydraulic parameters(standpipe pressure,flow rate)are optimized.Taking the Southern Margin block of Xinjiang as an example,the MAPE of prediction of ROP after clustering is decreased from 18.72%to 10.56%.The results of this paper provide a new method to improve drilling efficiency based on big data of drilling.
基金State Grid Jiangsu Electric Power Co.,Ltd.Technology Project(J2023121).
文摘With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods,this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification.To resolve the scalability issue of coupled transmission and distribution power systems,clustering is first carried out based on the distribution system states.As the data and models of the transmission system and distribution systems are not shared.For the transmission system,equating the power transmitted from the transmission system to the distribution system is the same as equating the distribution system.Further,the power transmitted from the transmission system to different types of distribution systems is equivalent to different polynomial equivalent load models.Then,a parameter identification method is proposed to obtain the parameters of the equivalent load model.Finally,a transmission and distribution collaborative state estimation model is constructed based on the equivalent load model.The results of the numerical analysis show that compared with the traditional master-slave splitting method,the proposed method significantly enhances computational efficiency while maintaining high estimation accuracy.
基金supported by the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(No.GLAB 2024ZR03)the National Natural Science Foundation of China(No.42407248)+2 种基金the Guizhou Provincial Basic Research Program(Natural Science)(No.QKHJC-[2023]-YB066)the Key Laboratory of Smart Earth(No.KF2023YB04-02)the Fundamental Research Funds for the Central Universities。
文摘The constitutive model is essential for predicting the deformation and stability of rocksoil mass.The estimation of constitutive model parameters is a necessary and important task for the reliable characterization of mechanical behaviors.However,constitutive model parameters cannot be evaluated accurately with a limited amount of test data,resulting in uncertainty in the prediction of stress-strain curves.This paper proposes a Bayesian analysis framework to address this issue.It combines the Bayesian updating with the structural reliability and adaptive conditional sampling methods to assess the equation parameter of constitutive models.Based on the triaxial and ring shear tests on shear zone soils from the Huangtupo landslide,a statistical damage constitutive model and a critical state hypoplastic constitutive model were used to demonstrate the effectiveness of the proposed framework.Moreover,the parameter uncertainty effects of the damage constitutive model on landslide stability were investigated.Results show that reasonable assessments of the constitutive model parameter can be well realized.The variability of stress-strain curves is strongly related to the model prediction performance.The estimation uncertainty of constitutive model parameters should not be ignored for the landslide stability calculation.Our study provides a reference for uncertainty analysis and parameter assessment of the constitutive model.
基金supported by the National Natural Science Foundation of China(10632040)
文摘Bifurcation properties of dynamical systems with two parameters are investigated in this paper. The definition of transition set is proposed, and the approach developed is used to investigate the dynamic characteristic of the nonlin- ear forced Duffing system with nonlinear feedback controller. The whole parametric plane is divided into several persistent regions by the transition set, and then the bifurcation dia- grams in different persistent regions are obtained.
基金supported by National Hi-tech Research and Development Program of China (863 Program,Grant No.2009AA04Z404)
文摘Mastering the influence laws of parameters on the solution structure of nonlinear systems is the basis of carrying out vibration isolation and control.Many researches on solution structure and bifurcation phenomenon in parameter spaces are carried out broadly in many fields,and the research on nonlinear gear systems has attracted the attention of many scholars.But there is little study on the solution domain boundary of nonlinear gear systems.For a periodic non-autonomous nonlinear dynamic system with several control parameters,a solution domain boundary analysis method of nonlinear systems in parameter spaces is proposed,which combines the cell mapping method based on Poincaré point mapping in phase spaces with the domain decomposition technique of parameter spaces.The cell mapping is known as a global analysis method to analyze the global behavior of a nonlinear dynamic system with finite dimensions,and the basic idea of domain decomposition techniques is to divide and rule.The method is applied to analyze the solution domain boundaries in parameter spaces of a nonlinear gear system.The distribution of different period domains,chaos domain and the domain boundaries between different period domains and chaotic domain are obtained in control parameter spaces constituted by meshing damping ratio with excitation frequency,fluctuation coefficient of meshing stiffness and average exciting force respectively by calculation.The calculation results show that as the meshing damping increases,the responses of the system change towards a single motion,while the variations of the excitation frequency,meshing stiffness and exciting force make the solution domain presenting diversity.The proposed research contribution provides evidence for vibration control and parameter design of the gear system,and confirms the validity of the solution domain boundary analysis method.
基金supported by the National Natural Science Foundation of China (Grant No. 41271003)the National Basic Research Program of China (Grants No. 2010CB428403 and 2010CB951103)
文摘Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.
基金supported by 973 Project from the Ministry of Science and Technology of China(No.2010 CB735604)
文摘The present study was designed to determine the relationships between the performance of ethanol precipitation and seven process parameters in the ethanol precipitation process of Re Du Ning Injections,including concentrate density,concentrate temperature,ethanol content,flow rate and stir rate in the addition of ethanol,precipitation time,and precipitation temperature.Under the experimental and simulated production conditions,a series of precipitated resultants were prepared by changing these variables one by one,and then examined by HPLC fingerprint analyses.Different from the traditional evaluation model based on single or a few constituents,the fingerprint data of every parameter fluctuation test was processed with Principal Component Analysis(PCA)to comprehensively assess the performance of ethanol precipitation.Our results showed that concentrate density,ethanol content,and precipitation time were the most important parameters that influence the recovery of active compounds in precipitation resultants.The present study would provide some reference for pharmaceutical scientists engaged in research on pharmaceutical process optimization and help pharmaceutical enterprises adapt a scientific and reasonable cost-effective approach to ensure the batch-to-batch quality consistency of the final products.
基金Project(2015B37714)supported by the Fundamental Research Funds for the Central Universities of ChinaProject(51605005)supported by the National Natural Science Foundation of China+1 种基金Project(ZK16-03-03)supported by the Open Foundation of Jiangsu Wind Technology Center,ChinaProject([2013]56)supported by the First Group of 2011 Plan of Jiangsu Province,China
文摘The parameter sensitivities affecting the flutter speed of the NREL （National Renewable Energy Laboratory） 5-MW baseline HAWT （horizontal axis wind turbine） blades are analyzed. An aeroelastic model, which comprises an aerodynamic part to calculate the aerodynamic loads and a structural part to determine the structural dynamic responses, is established to describe the classical flutter of the blades. For the aerodynamic part, Theodorsen unsteady aerodynamics model is used. For the structural part, Lagrange’s equation is employed. The flutter speed is determined by introducing “V–g” method to the aeroelastic model, which converts the issue of classical flutter speed determination into an eigenvalue problem. Furthermore, the time domain aeroelastic response of the wind turbine blade section is obtained with employing Runge-Kutta method. The results show that four cases （i.e., reducing the blade torsional stiffness, moving the center of gravity or the elastic axis towards the trailing edge of the section, and placing the turbine in high air density area） will decrease the flutter speed. Therefore, the judicious selection of the four parameters （the torsional stiffness, the chordwise position of the center of gravity, the elastic axis position and air density） can increase the relative inflow speed at the blade section associated with the onset of flutter.
