RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performa...RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performances of existingtop RNA secondary structure prediction methods, including five deep-learning (DL) based methods and five minimum freeenergy (MFE) based methods. First, we made a brief overview of these RNA secondary structure prediction methods.Afterwards, we built two rigorous test datasets consisting of RNAs with non-redundant sequences and comprehensivelyexamined the performances of the RNA secondary structure prediction methods through classifying the RNAs into differentlength ranges and different types. Our examination shows that the DL-based methods generally perform better thanthe MFE-based methods for RNAs with long lengths and complex structures, while the MFE-based methods can achievegood performance for small RNAs and some specialized MFE-based methods can achieve good prediction accuracy forpseudoknots. Finally, we provided some insights and perspectives in modeling RNA secondary structures.展开更多
To tackle the difficulties of the point prediction in quantifying the reliability of landslide displacement prediction,a data-driven combination-interval prediction method(CIPM)based on copula and variational-mode-dec...To tackle the difficulties of the point prediction in quantifying the reliability of landslide displacement prediction,a data-driven combination-interval prediction method(CIPM)based on copula and variational-mode-decomposition associated with kernel-based-extreme-learningmachine optimized by the whale optimization algorithm(VMD-WOA-KELM)is proposed in this paper.Firstly,the displacement is decomposed by VMD to three IMF components and a residual component of different fluctuation characteristics.The key impact factors of each IMF component are selected according to Copula model,and the corresponding WOA-KELM is established to conduct point prediction.Subsequently,the parametric method(PM)and non-parametric method(NPM)are used to estimate the prediction error probability density distribution(PDF)of each component,whose prediction interval(PI)under the 95%confidence level is also obtained.By means of the differential evolution algorithm(DE),a weighted combination model based on the PIs is built to construct the combination-interval(CI).Finally,the CIs of each component are added to generate the total PI.A comparative case study shows that the CIPM performs better in constructing landslide displacement PI with high performance.展开更多
Wellbore instability is one of the significant challenges in the drilling engineering and during the development of carbonate reservoirs,especially with open-hole completion.The problems of wellbore instability such a...Wellbore instability is one of the significant challenges in the drilling engineering and during the development of carbonate reservoirs,especially with open-hole completion.The problems of wellbore instability such as downhole collapse and silt deposit in the fractured carbonate reservoir of Tarim Basin(Ordovician)are severe.Solid destabilization and production(SDP)was proposed to describe this engineering problem of carbonate reservoirs.To clarify the mechanism and mitigate potential borehole instability problems,we conducted particle size distribution(PSD)analysis,X-ray diffraction(XRD)analysis,triaxial compression tests,and micro-scale sand production tests based on data analysis.We found that the rock fragments and silt in the wellbore came from two sources:one from the wellbore collapse in the upper unplugged layers and the other from the production of sand particles carried by the fluid in the productive layers.Based on the experimental study,a novel method combining a geomechanical model and microscopic sand production model was proposed to predict wellbore instability and analyze its influencing factors.The critical condition and failure zone predicted by the prediction model fit well with the field observations.According to the prediction results,the management and prevention measures of wellbore instability in carbonate reservoirs were proposed.It is suggested to optimize the well track in new drilling wells while upgrading the production system in old wells.This study is of great guiding significance for the optimization of carbonate solid control and it improves the understanding of the sand production problems in carbonate reservoirs.展开更多
Accurate prediction of coal and gas outburst(CGO)hazards is paramount in gas disaster prevention and control.This paper endeavors to overcome the constraints posed by traditional prediction indexes when dealing with C...Accurate prediction of coal and gas outburst(CGO)hazards is paramount in gas disaster prevention and control.This paper endeavors to overcome the constraints posed by traditional prediction indexes when dealing with CGO incidents under low gas pressure conditions.In pursuit of this objective,we have studied and established a mechanical model of the working face under abnormal stress and the excitation energy conditions of CGO,and proposed a method for predicting the risk of CGO under abnormal stress.On site application verification shows that when a strong outburst hazard level prediction is issued,there is a high possibility of outburst disasters occurring.In one of the three locations where we predicted strong outburst hazards,a small outburst occurred,and the accuracy of the prediction was higher than the traditional drilling cuttings index S and drilling cuttings gas desorption index q.Finally,we discuss the mechanism of CGO under the action of stress anomalies.Based on the analysis of stress distribution changes and energy accumulation characteristics of coal under abnormal stress,this article believes that the increase in outburst risk caused by high stress abnormal gradient is mainly due to two reasons:(1)The high stress abnormal gradient leads to an increase in the plastic zone of the coal seam.After the working face advances,it indirectly leads to an increase in the gas expansion energy that can be released from the coal seam before reaching a new stress equilibrium.(2)Abnormal stress leads to increased peak stress of coal body in front of working face.When coal body in elastic area transforms to plastic area,its failure speed is accelerated,which induces accelerated gas desorption and aggravates the risk of outburst.展开更多
The mathematical method of ZTD(zenith tropospheric delay)spatial prediction is important for precise ZTD derivation and real-time precise point positioning(PPP)augmentation.This paper analyses the performance of the p...The mathematical method of ZTD(zenith tropospheric delay)spatial prediction is important for precise ZTD derivation and real-time precise point positioning(PPP)augmentation.This paper analyses the performance of the popular optimal function coefficient(OFC),sphere cap harmonic analysis(SCHA),kriging and inverse distance weighting(IDW)interpolation in ZTD spatial prediction and Beidou satellite navigation system(BDS)-PPP augmentation over China.For ZTD spatial prediction,the average time consumption of the OFC,kriging,and IDW methods is less than 0.1 s,which is significantly better than that of the SCHA method(63.157 s).The overall ZTD precision of the OFC is 3.44 cm,which outperforms those of the SCHA(9.65 cm),Kriging(10.6 cm),and IDW(11.8 cm)methods.We confirmed that the low performance of kriging and IDW is caused by their weakness in modelling ZTD variation in the vertical direction.To mitigate such deficiencies,an elevation normalization factor(ENF)is introduced into the kriging and IDW models(kriging-ENF and IDW-ENF).The overall ZTD spatial prediction accuracies of IDW-ENF and kriging-ENF are 2.80 cm and 2.01 cm,respectively,which are both superior to those of the OFC and the widely used empirical model GPT3(4.92 cm).For BDS-PPP enhancement,the ZTD provided by the kriging-ENF,IDW-ENF and OFC as prior constraints can effectively reduce the convergence time.Compared with unconstrained BDS-PPP,our proposed kriging-ENF outperforms IDW-ENF and OFC by reducing the horizontal and vertical convergence times by approximately 13.2%and 5.8%in Ningxia and 30.4%and 7.84%in Guangdong,respectively.These results indicate that kriging-ENF is a promising method for ZTD spatial prediction and BDS-PPP enhancement over China.展开更多
In this paper, on the basis of the heat conduction equation without consideration of the advection and turbulence effects, one-dimensional model for describing surface sea temperature ( T1), bottom sea temperature ( T...In this paper, on the basis of the heat conduction equation without consideration of the advection and turbulence effects, one-dimensional model for describing surface sea temperature ( T1), bottom sea temperature ( Tt ) and the thickness of the upper homogeneous layer ( h ) is developed in terms of the dimensionless temperature θT and depth η and self-simulation function θT - f(η) of vertical temperature profile by means of historical temperature data.The results of trial prediction with our one-dimensional model on T, Th, h , the thickness and gradient of thermocline are satisfactory to some extent.展开更多
In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken a...In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.展开更多
Aiming at the problem of insufficient prediction accuracy of strip flatness at the outlet of cold tandem rolling,the prediction performance of strip flatness based on different ensemble methods was studied and a high-...Aiming at the problem of insufficient prediction accuracy of strip flatness at the outlet of cold tandem rolling,the prediction performance of strip flatness based on different ensemble methods was studied and a high-precision prediction ensemble model of strip flatness at the outlet was established.Firstly,based on linear regression(LR),K nearest neighbors(KNN),support vector regression,regression trees(RT),and backpropagation neural network(BPN),bagging,boosting,and stacking ensemble methods were used for ensemble experiments.Secondly,three existing ensemble models,i.e.,random forest,extreme random tree(ET)and extreme gradient boosting,were used to conduct experiments and compare the results.The research shows that bagging,boosting,and stacking three ensemble methods have the most significant improvement in the prediction accuracy of the regression trees model,which is increased by 5.28%,6.51%,and 5.32%,respectively.At the same time,the stacking ensemble method improves both the simple model and the complex model,and the improvement effect on the simple base model is the greatest,which is 4.69%higher than that of the base model KNN.Comparing all of the ensemble models,the stacking ensemble model of level-1(ET,AdaBoost-RT,LR,BPN)paired with level-2(LR)was discovered to be the best model(EALB-LR)and can be further studied for industrial applications.展开更多
Two-dimensional(2D)transition metal sulfides(TMDs)are emerging and highly well received 2D materials,which are considered as an ideal 2D platform for studying various electronic properties and potential applications d...Two-dimensional(2D)transition metal sulfides(TMDs)are emerging and highly well received 2D materials,which are considered as an ideal 2D platform for studying various electronic properties and potential applications due to their chemical diversity.Converting 2D TMDs into one-dimensional(1D)TMDs nanotubes can not only retain some advantages of 2D nanosheets but also providing a unique direction to explore the novel properties of TMDs materials in the 1D limit.However,the controllable preparation of high-quality nanotubes remains a major challenge.It is very necessary to review the advanced development of one-dimensional transition metal dichalcogenide nanotubes from preparation to application.Here,we first summarize a series of bottom-up synthesis methods of 1D TMDs,such as template growth and metal catalyzed method.Then,top-down synthesis methods are summarized,which included selfcuring and stacking of TMDs nanosheets.In addition,we discuss some key applications that utilize the properties of 1D-TMDs nanotubes in the areas of catalyst preparation,energy storage,and electronic devices.Last but not least,we prospect the preparation methods of high-quality 1D-TMDs nanotubes,which will lay a foundation for the synthesis of high-performance optoelectronic devices,catalysts,and energy storage components.展开更多
Computer simulation permits answering theoretical and applied questions in animal and plant breeding.Blib is a novel multi-module simulation platform,which is able to handle more complicated genetic effects and models...Computer simulation permits answering theoretical and applied questions in animal and plant breeding.Blib is a novel multi-module simulation platform,which is able to handle more complicated genetic effects and models than most existing tools.In this study,we describe one major and unified application module of Blib,i.e.,ISB(abbreviated from in silico breeding),for simulating the three categories of breeding programs for developing clonal,pure-line and hybrid cultivars in plants.Genetic models on environments and breeding-targeted traits,one or several parental populations,and a number of breeding methods are key elements to run simulation experiments in ISB,which are arranged in three external input files by given formats.Applications of ISB are illustrated by three case studies,representing the three categories of plant breeding programs.Under the condition that 5000 F1 progenies were generated and tested from 50 heterozygous parents,Case study I showed that 50 crosses,each of 100 progenies,made the best balance between genetic achievement and field cost.In Case study II,one optimum breeding method was identified by which the pure lines with high yield and medium maturity could be developed.Case study III investigated the genetic consequence in hybrid breeding from five testers.One tester was identified for the simultaneous improvement in F1 hybrids and inbred lines.In summary,ISB identified a balanced crossing scheme,an optimum pure-line selection method,and an optimized tester in three case studies which are relevant to plant breeding.We believe the prediction by simulation would be highly required in front of the next generation of breeding to be driven by informatics and intelligence.展开更多
In contemporary geotechnical projects,various approaches are employed for forecasting the settlement of shallow foundations(S_(m)).However,achieving precise modeling of foundation behavior using certain techniques(suc...In contemporary geotechnical projects,various approaches are employed for forecasting the settlement of shallow foundations(S_(m)).However,achieving precise modeling of foundation behavior using certain techniques(such as analytical,numerical,and regression)is challenging and sometimes unattainable.This is primarily due to the inherent nonlinearity of the model,the intricate nature of geotechnical materials,the complex interaction between soil and foundation,and the inherent uncertainty in soil parameters.Therefore,thesemethods often introduce assumptions and simplifications,resulting in relationships that deviate from the actual problem’s reality.In addition,many of these methods demand significant investments of time and resources but neglect to account for the uncertainty inherent in soil/rock parameters.This study explores the application of innovative intelligent techniques to predict S_(m) to address these shortcomings.Specifically,two optimization algorithms,namely teaching-learning-based optimization(TLBO)and harmony search(HS),are harnessed for this purpose.The modeling process involves utilizing input parameters,such as thewidth of the footing(B),the pressure exerted on the footing(q),the count of SPT(Standard Penetration Test)blows(N),the ratio of footing embedment(Df/B),and the footing’s geometry(L/B),during the training phase with a dataset comprising 151 data points.Then,the models’accuracy is assessed during the testing phase using statistical metrics,including the coefficient of determination(R^(2)),mean square error(MSE),and rootmean square error(RMSE),based on a dataset of 38 data points.The findings of this investigation underscore the substantial efficacy of intelligent optimization algorithms as valuable tools for geotechnical engineers when estimating S_(m).In addition,a sensitivity analysis of the input parameters in S_(m) estimation is conducted using@RISK software,revealing that among the various input parameters,the N exerts the most pronounced influence on S_(m).展开更多
We study the thermodynamic properties of the classical one-dimensional generalized nonlinear Klein-Gordon lattice model(n≥2)by using the cluster variation method with linear response theory.The results of this method...We study the thermodynamic properties of the classical one-dimensional generalized nonlinear Klein-Gordon lattice model(n≥2)by using the cluster variation method with linear response theory.The results of this method are exact in the thermodynamic limit.We present the single-site reduced densityρ^((1))(z),averages such as(z^(2)),<|z^(n)|>,and<(z_(1)-z_(2))^(2)>,the specific heat C_(v),and the static correlation functions.We analyze the scaling behavior of these quantities and obtain the exact scaling powers at the low and high temperatures.Using these results,we gauge the accuracy of the projective truncation approximation for theφ^(4)lattice model.展开更多
Since the 1975 M_(S)7.3 Haicheng earthquake,spatio-temporal variations in the gravity field have attracted much attention as potential earthquake precursors.Recent technical advances in terrestrial gravity observation...Since the 1975 M_(S)7.3 Haicheng earthquake,spatio-temporal variations in the gravity field have attracted much attention as potential earthquake precursors.Recent technical advances in terrestrial gravity observation,along with the construction of a high-precision mobile gravity network covering Chinese mainland,have positioned temporal gravity variations(GVs)as an important tool for clarifying the signal characteristics and dynamic mechanisms of crustal sources.Reportedly,crustal mass transfer,which is affected by stress state and structural environment,alters the characteristics of the regional gravity field,thus serving as an indicator for locations of moderate to strong earthquakes and a seismology-independent predictor for regions at risk for strong earthquakes.Therefore,quantitatively tracking time-varying gravity is of paramount importance to enhance the effectiveness of earthquake prediction.In this study,we divided the areas effectively covered by the terrestrial mobile gravity network in the Sichuan-Yunnan region into small grids based on the latest observational data(since 2018)from the network.Next,we calculated the 1-and 3-year GVs and gravity gradient indicators(amplitude of analytic signal,AAS;total horizontal derivative,THD;and amplitude of vertical gradient,AVG)to quantitatively characterize variations in regional time-varying gravity field.Next,we assessed the effectiveness of gravity field variations in predicting earthquakes in the Sichuan-Yunnan region using Molchan diagrams constructed for gravity signals of 13 earthquakes(M≥5.0;occurred between 2021 and 2024)within the terrestrial mobile gravity network.The results reveal a certain correspondence between gravity field variations and the locations of moderate and strong earthquakes in the Sichuan-Yunnan region.Furthermore,the 3-year AAS and AVG outperform the 3-year THD in predicting subsequent seismic events.Notably,the AAS and AVG showed large probability gains prior to the M_(S)6.8 Luding earthquake,indicating their potential for earthquake prediction.展开更多
This research introduces a spectrum-based physics-informed neural network(SP-PINN)model to significantly improve the accuracy of calculation of two-phase flow parameters,surpassing existing methods that have limitatio...This research introduces a spectrum-based physics-informed neural network(SP-PINN)model to significantly improve the accuracy of calculation of two-phase flow parameters,surpassing existing methods that have limitations in global and continuous data sampling.SP-PINNs address the challenges of traditional methods in terms of continuous sampling by integrating the spectral analysis and pressure correction into the Navier-Stokes(N-S)equations,enhancing the predictive accuracy especially in critical regions like gas-phase boundaries and velocity peaks.The novel introduction of a pressure-correction module within SP-PINNs mitigates prediction errors,achieving a substantial reduction to 1‰compared with the conventional physics-informed neural network(PINN)approaches.Experimental applications validate the model’s ability to accurately and rapidly predict flow parameters with different sampling time intervals,with the computation time of predicting unsampled data less than 0.01 s.Such advancements signify a 100-fold improvement over traditional DNS calculations,underscoring the model’s potential in the real-time calculation and analysis of multiphase flow dynamics.展开更多
As one of the most serious geological disasters in deep underground engineering,rockburst has caused a large number of casualties.However,because of the complex relationship between the inducing factors and rockburst ...As one of the most serious geological disasters in deep underground engineering,rockburst has caused a large number of casualties.However,because of the complex relationship between the inducing factors and rockburst intensity,the problem of rockburst intensity prediction has not been well solved until now.In this study,we collect 292 sets of rockburst data including eight parameters,such as the maximum tangential stress of the surrounding rock σ_(θ),the uniaxial compressive strength of the rockσc,the uniaxial tensile strength of the rock σ_(t),and the strain energy storage index W_(et),etc.from more than 20 underground projects as training sets and establish two new rockburst prediction models based on the kernel extreme learning machine(KELM)combined with the genetic algorithm(KELM-GA)and cross-entropy method(KELM-CEM).To further verify the effect of the two models,ten sets of rockburst data from Shuangjiangkou Hydropower Station are selected for analysis and the results show that new models are more accurate compared with five traditional empirical criteria,especially the model based on KELM-CEM which has the accuracy rate of 90%.Meanwhile,the results of 10 consecutive runs of the model based on KELM-CEM are almost the same,meaning that the model has good stability and reliability for engineering applications.展开更多
This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was...This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specifi c tunnel segments.Additionally,the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method.Subsequently,a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions.Meanwhile,risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information.Finally,model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study,yielding favorable risk prediction results and enabling effi cient information fusion and support for construction decision-making.展开更多
Due to global warming and diminishing ice cover in Arctic regions,the northern sea route(NSR)has attracted increasing attention in recent years.Extreme cold temperatures and high wind speeds in Arctic regions present ...Due to global warming and diminishing ice cover in Arctic regions,the northern sea route(NSR)has attracted increasing attention in recent years.Extreme cold temperatures and high wind speeds in Arctic regions present substantial risks to vessels operating along the NSR.Consequently,analyzing extreme temperature and wind speed values along the NSR is essential for ensuring maritime operational safety in the region.This study analyzes wind and temperature data spanning 40 years,from 1981 to 2020,at four representative sites along the NSR for extreme value analysis.The average conditional exceedance rate(ACER)method and the Gumbel method are employed to estimate extreme wind speed and air temperature at these sites.Comparative analysis reveals that the ACER method provides higher accuracy and lower uncertainty in estimations.The predicted extreme wind speed for a 100-year return period is 30.36 m/s,with a minimum temperature of-56.66°C,varying across the four sites.Furthermore,the study presents extreme values corresponding to each return period,providing temperature extremes as a basis for guiding steel thickness specifications.These findings provide valuable reference for designing polar vessels and offshore structures,contributing to enhanced engineering standards for Arctic conditions.展开更多
Based on a normalized difference vegetation index(NDVI)dataset for 1982-2021,this work investigates the principal modes of interannual variability in summer NDVI over eastern Siberia using the year-to-year increment m...Based on a normalized difference vegetation index(NDVI)dataset for 1982-2021,this work investigates the principal modes of interannual variability in summer NDVI over eastern Siberia using the year-to-year increment method and empirical orthogonal function(EOF)analysis.The first three principal modes(EOF1-3)of the year-to-year increment of summer NDVI(NDVI_DY)exhibit a regionally consistent mode,a western-eastern dipole mode,and a northern-southern dipole mode,respectively.Further analysis shows that sea surface temperature(SST)in the tropical Indian Ocean in February-March and western Siberian soil moisture in April could influence EOF1.EOF2 is modulated by April Northwest Pacific SST and western Siberian soil moisture in May.May North Atlantic SST and sea ice in the Kara Sea in the preceding October significantly affect EOF3.Using the year-to-year increment method and multiple linear regression analysis,prediction schemes for EOF1-3 are developed based on these predictors.To assess the predictive skill of these schemes,one-year-out cross-validation and independent hindcast methods are employed.The temporal correlation coefficients between observed EOF1-3 and the cross-validation results are 0.62,0.46,and 0.37,respectively,exceeding the 95%confidence level.In addition,reconstructed schemes for summer NDVI are developed using predicted NDVI_DY and the observed principal modes of NDVI_DY.Independent hindcasts of NDVI anomalies during 2019-2021 also present consistent distributions with the observed results.展开更多
Rotor blade is one of the most significant components of helicopters. But due to its highspeed rotation characteristics, it is difficult to collect the vibration signals during the flight stage.Moreover, sensors are h...Rotor blade is one of the most significant components of helicopters. But due to its highspeed rotation characteristics, it is difficult to collect the vibration signals during the flight stage.Moreover, sensors are highly susceptible to damage resulting in the failure of the measurement.In order to make signal predictions for the damaged sensors, an operational modal analysis(OMA) together with the virtual sensing(VS) technology is proposed in this paper. This paper discusses two situations, i.e., mode shapes measured by all sensors(both normal and damaged) can be obtained using OMA, and mode shapes measured by some sensors(only including normal) can be obtained using OMA. For the second situation, it is necessary to use finite element(FE) analysis to supplement the missing mode shapes of damaged sensor. In order to improve the correlation between the FE model and the real structure, the FE mode shapes are corrected using the local correspondence(LC) principle and mode shapes measured by some sensors(only including normal).Then, based on the VS technology, the vibration signals of the damaged sensors during the flight stage can be accurately predicted using the identified mode shapes(obtained based on OMA and FE analysis) and the normal sensors signals. Given the high degrees of freedom(DOFs) in the FE mode shapes, this approach can also be used to predict vibration data at locations without sensors. The effectiveness and robustness of the proposed method is verified through finite element simulation, experiment as well as the actual flight test. The present work can be further used in the fault diagnosis and damage identification for rotor blade of helicopters.展开更多
This paper aims to experimentally and numerically probe fatigue behaviours and lifetimes of 3D4D(three-dimensional four-directional)braided composite I-beam under four-point flexure spectrum loading.New fatigue damage...This paper aims to experimentally and numerically probe fatigue behaviours and lifetimes of 3D4D(three-dimensional four-directional)braided composite I-beam under four-point flexure spectrum loading.New fatigue damage models of fibre yarn,matrix and fibre–matrix interface are proposed,and fatigue failure criteria and PFDA(Progressive Fatigue Damage Algorithm)are thus presented for meso-scale fatigue damage modelling of 3D4D braided composite I-beam.To validate the aforementioned model and algorithm,fatigue tests are conducted on the 3D4D braided composite I-beam under four-point flexure spectrum loading,and fatigue failure mechanisms are analyzed and discussed.Novel global–local FE(Finite Element)model based on the PFDA is generated for modelling progressive fatigue failure process and predicting fatigue life of 3D4D braided composite I-beam under four-point flexure spectrum loading.Good agreement has been achieved between experimental results and predictions,demonstrating the effective usage of new model.It is shown that matrix cracking and interfacial debonding initially initiates on top surface of top flange of I-beam,and then gradually propagates from the side surface of top flange to the intermediate web along the braiding angle,and considerable fiber breakage finally causes final fatigue failure of I-beam.展开更多
基金supported by grants from the National Science Foundation of China(Grant Nos.12375038 and 12075171 to ZJT,and 12205223 to YLT).
文摘RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performances of existingtop RNA secondary structure prediction methods, including five deep-learning (DL) based methods and five minimum freeenergy (MFE) based methods. First, we made a brief overview of these RNA secondary structure prediction methods.Afterwards, we built two rigorous test datasets consisting of RNAs with non-redundant sequences and comprehensivelyexamined the performances of the RNA secondary structure prediction methods through classifying the RNAs into differentlength ranges and different types. Our examination shows that the DL-based methods generally perform better thanthe MFE-based methods for RNAs with long lengths and complex structures, while the MFE-based methods can achievegood performance for small RNAs and some specialized MFE-based methods can achieve good prediction accuracy forpseudoknots. Finally, we provided some insights and perspectives in modeling RNA secondary structures.
基金financially supported by the National Natural Science Foundation of China(Nos.42277149,41502299,41372306)the Research Planning of Sichuan Education Department,China(No.16ZB0105)+3 种基金the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project(Nos.SKLGP2016Z007,SKLGP2018Z017,SKLGP2020Z009)Chengdu University of Technology Young and Middle Aged Backbone Program(No.KYGG201720)Sichuan Provincial Science and Technology Department Program(No.19YYJC2087)China Scholarship Council。
文摘To tackle the difficulties of the point prediction in quantifying the reliability of landslide displacement prediction,a data-driven combination-interval prediction method(CIPM)based on copula and variational-mode-decomposition associated with kernel-based-extreme-learningmachine optimized by the whale optimization algorithm(VMD-WOA-KELM)is proposed in this paper.Firstly,the displacement is decomposed by VMD to three IMF components and a residual component of different fluctuation characteristics.The key impact factors of each IMF component are selected according to Copula model,and the corresponding WOA-KELM is established to conduct point prediction.Subsequently,the parametric method(PM)and non-parametric method(NPM)are used to estimate the prediction error probability density distribution(PDF)of each component,whose prediction interval(PI)under the 95%confidence level is also obtained.By means of the differential evolution algorithm(DE),a weighted combination model based on the PIs is built to construct the combination-interval(CI).Finally,the CIs of each component are added to generate the total PI.A comparative case study shows that the CIPM performs better in constructing landslide displacement PI with high performance.
基金financially supported by the National Natural Science Foundation of China(Grant No.52074331).
文摘Wellbore instability is one of the significant challenges in the drilling engineering and during the development of carbonate reservoirs,especially with open-hole completion.The problems of wellbore instability such as downhole collapse and silt deposit in the fractured carbonate reservoir of Tarim Basin(Ordovician)are severe.Solid destabilization and production(SDP)was proposed to describe this engineering problem of carbonate reservoirs.To clarify the mechanism and mitigate potential borehole instability problems,we conducted particle size distribution(PSD)analysis,X-ray diffraction(XRD)analysis,triaxial compression tests,and micro-scale sand production tests based on data analysis.We found that the rock fragments and silt in the wellbore came from two sources:one from the wellbore collapse in the upper unplugged layers and the other from the production of sand particles carried by the fluid in the productive layers.Based on the experimental study,a novel method combining a geomechanical model and microscopic sand production model was proposed to predict wellbore instability and analyze its influencing factors.The critical condition and failure zone predicted by the prediction model fit well with the field observations.According to the prediction results,the management and prevention measures of wellbore instability in carbonate reservoirs were proposed.It is suggested to optimize the well track in new drilling wells while upgrading the production system in old wells.This study is of great guiding significance for the optimization of carbonate solid control and it improves the understanding of the sand production problems in carbonate reservoirs.
基金supported by the National Natural Science Foundation of China(52174162)the Fundamental Research Funds for the Central Universities(FRF-TP-20-002A3).
文摘Accurate prediction of coal and gas outburst(CGO)hazards is paramount in gas disaster prevention and control.This paper endeavors to overcome the constraints posed by traditional prediction indexes when dealing with CGO incidents under low gas pressure conditions.In pursuit of this objective,we have studied and established a mechanical model of the working face under abnormal stress and the excitation energy conditions of CGO,and proposed a method for predicting the risk of CGO under abnormal stress.On site application verification shows that when a strong outburst hazard level prediction is issued,there is a high possibility of outburst disasters occurring.In one of the three locations where we predicted strong outburst hazards,a small outburst occurred,and the accuracy of the prediction was higher than the traditional drilling cuttings index S and drilling cuttings gas desorption index q.Finally,we discuss the mechanism of CGO under the action of stress anomalies.Based on the analysis of stress distribution changes and energy accumulation characteristics of coal under abnormal stress,this article believes that the increase in outburst risk caused by high stress abnormal gradient is mainly due to two reasons:(1)The high stress abnormal gradient leads to an increase in the plastic zone of the coal seam.After the working face advances,it indirectly leads to an increase in the gas expansion energy that can be released from the coal seam before reaching a new stress equilibrium.(2)Abnormal stress leads to increased peak stress of coal body in front of working face.When coal body in elastic area transforms to plastic area,its failure speed is accelerated,which induces accelerated gas desorption and aggravates the risk of outburst.
基金co-supported by the National Nature Science Foundation of China(No.12303071)the Shanghai Science and Technology Plan Project,China(No.23YF1455500)+1 种基金the China Postdoctoral Science Foundation(No.2023M743653)Ministry of Industry and Information Technology of China through the High Precision Timing Service Project(No.TC220A04A-80)。
文摘The mathematical method of ZTD(zenith tropospheric delay)spatial prediction is important for precise ZTD derivation and real-time precise point positioning(PPP)augmentation.This paper analyses the performance of the popular optimal function coefficient(OFC),sphere cap harmonic analysis(SCHA),kriging and inverse distance weighting(IDW)interpolation in ZTD spatial prediction and Beidou satellite navigation system(BDS)-PPP augmentation over China.For ZTD spatial prediction,the average time consumption of the OFC,kriging,and IDW methods is less than 0.1 s,which is significantly better than that of the SCHA method(63.157 s).The overall ZTD precision of the OFC is 3.44 cm,which outperforms those of the SCHA(9.65 cm),Kriging(10.6 cm),and IDW(11.8 cm)methods.We confirmed that the low performance of kriging and IDW is caused by their weakness in modelling ZTD variation in the vertical direction.To mitigate such deficiencies,an elevation normalization factor(ENF)is introduced into the kriging and IDW models(kriging-ENF and IDW-ENF).The overall ZTD spatial prediction accuracies of IDW-ENF and kriging-ENF are 2.80 cm and 2.01 cm,respectively,which are both superior to those of the OFC and the widely used empirical model GPT3(4.92 cm).For BDS-PPP enhancement,the ZTD provided by the kriging-ENF,IDW-ENF and OFC as prior constraints can effectively reduce the convergence time.Compared with unconstrained BDS-PPP,our proposed kriging-ENF outperforms IDW-ENF and OFC by reducing the horizontal and vertical convergence times by approximately 13.2%and 5.8%in Ningxia and 30.4%and 7.84%in Guangdong,respectively.These results indicate that kriging-ENF is a promising method for ZTD spatial prediction and BDS-PPP enhancement over China.
文摘In this paper, on the basis of the heat conduction equation without consideration of the advection and turbulence effects, one-dimensional model for describing surface sea temperature ( T1), bottom sea temperature ( Tt ) and the thickness of the upper homogeneous layer ( h ) is developed in terms of the dimensionless temperature θT and depth η and self-simulation function θT - f(η) of vertical temperature profile by means of historical temperature data.The results of trial prediction with our one-dimensional model on T, Th, h , the thickness and gradient of thermocline are satisfactory to some extent.
基金This work is funded by the National Natural Science Foundation of China(Grant Nos.42377164 and 52079062)the National Science Fund for Distinguished Young Scholars of China(Grant No.52222905).
文摘In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.
基金This study was supported by the National Key Research and Development Program of China(No.2017YFB0304100)Key Projects of the National Natural Science Foundation of China(No.51634002).
文摘Aiming at the problem of insufficient prediction accuracy of strip flatness at the outlet of cold tandem rolling,the prediction performance of strip flatness based on different ensemble methods was studied and a high-precision prediction ensemble model of strip flatness at the outlet was established.Firstly,based on linear regression(LR),K nearest neighbors(KNN),support vector regression,regression trees(RT),and backpropagation neural network(BPN),bagging,boosting,and stacking ensemble methods were used for ensemble experiments.Secondly,three existing ensemble models,i.e.,random forest,extreme random tree(ET)and extreme gradient boosting,were used to conduct experiments and compare the results.The research shows that bagging,boosting,and stacking three ensemble methods have the most significant improvement in the prediction accuracy of the regression trees model,which is increased by 5.28%,6.51%,and 5.32%,respectively.At the same time,the stacking ensemble method improves both the simple model and the complex model,and the improvement effect on the simple base model is the greatest,which is 4.69%higher than that of the base model KNN.Comparing all of the ensemble models,the stacking ensemble model of level-1(ET,AdaBoost-RT,LR,BPN)paired with level-2(LR)was discovered to be the best model(EALB-LR)and can be further studied for industrial applications.
基金supported by the National Natural Science Foundation of China(No.22202065).
文摘Two-dimensional(2D)transition metal sulfides(TMDs)are emerging and highly well received 2D materials,which are considered as an ideal 2D platform for studying various electronic properties and potential applications due to their chemical diversity.Converting 2D TMDs into one-dimensional(1D)TMDs nanotubes can not only retain some advantages of 2D nanosheets but also providing a unique direction to explore the novel properties of TMDs materials in the 1D limit.However,the controllable preparation of high-quality nanotubes remains a major challenge.It is very necessary to review the advanced development of one-dimensional transition metal dichalcogenide nanotubes from preparation to application.Here,we first summarize a series of bottom-up synthesis methods of 1D TMDs,such as template growth and metal catalyzed method.Then,top-down synthesis methods are summarized,which included selfcuring and stacking of TMDs nanosheets.In addition,we discuss some key applications that utilize the properties of 1D-TMDs nanotubes in the areas of catalyst preparation,energy storage,and electronic devices.Last but not least,we prospect the preparation methods of high-quality 1D-TMDs nanotubes,which will lay a foundation for the synthesis of high-performance optoelectronic devices,catalysts,and energy storage components.
基金supported by Biological Breeding-National Science and Technology Major Project(2023ZD0407501)National Natural Science Foundation of China(31861143003)Innovation Program of Chinese Academy of Agricultural Sciences.
文摘Computer simulation permits answering theoretical and applied questions in animal and plant breeding.Blib is a novel multi-module simulation platform,which is able to handle more complicated genetic effects and models than most existing tools.In this study,we describe one major and unified application module of Blib,i.e.,ISB(abbreviated from in silico breeding),for simulating the three categories of breeding programs for developing clonal,pure-line and hybrid cultivars in plants.Genetic models on environments and breeding-targeted traits,one or several parental populations,and a number of breeding methods are key elements to run simulation experiments in ISB,which are arranged in three external input files by given formats.Applications of ISB are illustrated by three case studies,representing the three categories of plant breeding programs.Under the condition that 5000 F1 progenies were generated and tested from 50 heterozygous parents,Case study I showed that 50 crosses,each of 100 progenies,made the best balance between genetic achievement and field cost.In Case study II,one optimum breeding method was identified by which the pure lines with high yield and medium maturity could be developed.Case study III investigated the genetic consequence in hybrid breeding from five testers.One tester was identified for the simultaneous improvement in F1 hybrids and inbred lines.In summary,ISB identified a balanced crossing scheme,an optimum pure-line selection method,and an optimized tester in three case studies which are relevant to plant breeding.We believe the prediction by simulation would be highly required in front of the next generation of breeding to be driven by informatics and intelligence.
文摘In contemporary geotechnical projects,various approaches are employed for forecasting the settlement of shallow foundations(S_(m)).However,achieving precise modeling of foundation behavior using certain techniques(such as analytical,numerical,and regression)is challenging and sometimes unattainable.This is primarily due to the inherent nonlinearity of the model,the intricate nature of geotechnical materials,the complex interaction between soil and foundation,and the inherent uncertainty in soil parameters.Therefore,thesemethods often introduce assumptions and simplifications,resulting in relationships that deviate from the actual problem’s reality.In addition,many of these methods demand significant investments of time and resources but neglect to account for the uncertainty inherent in soil/rock parameters.This study explores the application of innovative intelligent techniques to predict S_(m) to address these shortcomings.Specifically,two optimization algorithms,namely teaching-learning-based optimization(TLBO)and harmony search(HS),are harnessed for this purpose.The modeling process involves utilizing input parameters,such as thewidth of the footing(B),the pressure exerted on the footing(q),the count of SPT(Standard Penetration Test)blows(N),the ratio of footing embedment(Df/B),and the footing’s geometry(L/B),during the training phase with a dataset comprising 151 data points.Then,the models’accuracy is assessed during the testing phase using statistical metrics,including the coefficient of determination(R^(2)),mean square error(MSE),and rootmean square error(RMSE),based on a dataset of 38 data points.The findings of this investigation underscore the substantial efficacy of intelligent optimization algorithms as valuable tools for geotechnical engineers when estimating S_(m).In addition,a sensitivity analysis of the input parameters in S_(m) estimation is conducted using@RISK software,revealing that among the various input parameters,the N exerts the most pronounced influence on S_(m).
基金supported by the National Natural Science Foundation of China(Grant No.11974420).
文摘We study the thermodynamic properties of the classical one-dimensional generalized nonlinear Klein-Gordon lattice model(n≥2)by using the cluster variation method with linear response theory.The results of this method are exact in the thermodynamic limit.We present the single-site reduced densityρ^((1))(z),averages such as(z^(2)),<|z^(n)|>,and<(z_(1)-z_(2))^(2)>,the specific heat C_(v),and the static correlation functions.We analyze the scaling behavior of these quantities and obtain the exact scaling powers at the low and high temperatures.Using these results,we gauge the accuracy of the projective truncation approximation for theφ^(4)lattice model.
基金funded by the National Key R&D Program of China(Nos.2023YFE0101800 and 2023YFC 3007305)National Natural Science Foundation of China(Nos.42004069 and 42204093)Special Fund of the Institute of Geophysics,China Earthquake Administration(No.DQJB24X24).
文摘Since the 1975 M_(S)7.3 Haicheng earthquake,spatio-temporal variations in the gravity field have attracted much attention as potential earthquake precursors.Recent technical advances in terrestrial gravity observation,along with the construction of a high-precision mobile gravity network covering Chinese mainland,have positioned temporal gravity variations(GVs)as an important tool for clarifying the signal characteristics and dynamic mechanisms of crustal sources.Reportedly,crustal mass transfer,which is affected by stress state and structural environment,alters the characteristics of the regional gravity field,thus serving as an indicator for locations of moderate to strong earthquakes and a seismology-independent predictor for regions at risk for strong earthquakes.Therefore,quantitatively tracking time-varying gravity is of paramount importance to enhance the effectiveness of earthquake prediction.In this study,we divided the areas effectively covered by the terrestrial mobile gravity network in the Sichuan-Yunnan region into small grids based on the latest observational data(since 2018)from the network.Next,we calculated the 1-and 3-year GVs and gravity gradient indicators(amplitude of analytic signal,AAS;total horizontal derivative,THD;and amplitude of vertical gradient,AVG)to quantitatively characterize variations in regional time-varying gravity field.Next,we assessed the effectiveness of gravity field variations in predicting earthquakes in the Sichuan-Yunnan region using Molchan diagrams constructed for gravity signals of 13 earthquakes(M≥5.0;occurred between 2021 and 2024)within the terrestrial mobile gravity network.The results reveal a certain correspondence between gravity field variations and the locations of moderate and strong earthquakes in the Sichuan-Yunnan region.Furthermore,the 3-year AAS and AVG outperform the 3-year THD in predicting subsequent seismic events.Notably,the AAS and AVG showed large probability gains prior to the M_(S)6.8 Luding earthquake,indicating their potential for earthquake prediction.
基金Supported by the National Natural Science Foundation of China(No.62304022)。
文摘This research introduces a spectrum-based physics-informed neural network(SP-PINN)model to significantly improve the accuracy of calculation of two-phase flow parameters,surpassing existing methods that have limitations in global and continuous data sampling.SP-PINNs address the challenges of traditional methods in terms of continuous sampling by integrating the spectral analysis and pressure correction into the Navier-Stokes(N-S)equations,enhancing the predictive accuracy especially in critical regions like gas-phase boundaries and velocity peaks.The novel introduction of a pressure-correction module within SP-PINNs mitigates prediction errors,achieving a substantial reduction to 1‰compared with the conventional physics-informed neural network(PINN)approaches.Experimental applications validate the model’s ability to accurately and rapidly predict flow parameters with different sampling time intervals,with the computation time of predicting unsampled data less than 0.01 s.Such advancements signify a 100-fold improvement over traditional DNS calculations,underscoring the model’s potential in the real-time calculation and analysis of multiphase flow dynamics.
基金funded by National Natural Science Foundation of China(Grants Nos.41825018 and 42141009)the Second Tibetan Plateau Scientific Expedition and Research Program(Grants No.2019QZKK0904)。
文摘As one of the most serious geological disasters in deep underground engineering,rockburst has caused a large number of casualties.However,because of the complex relationship between the inducing factors and rockburst intensity,the problem of rockburst intensity prediction has not been well solved until now.In this study,we collect 292 sets of rockburst data including eight parameters,such as the maximum tangential stress of the surrounding rock σ_(θ),the uniaxial compressive strength of the rockσc,the uniaxial tensile strength of the rock σ_(t),and the strain energy storage index W_(et),etc.from more than 20 underground projects as training sets and establish two new rockburst prediction models based on the kernel extreme learning machine(KELM)combined with the genetic algorithm(KELM-GA)and cross-entropy method(KELM-CEM).To further verify the effect of the two models,ten sets of rockburst data from Shuangjiangkou Hydropower Station are selected for analysis and the results show that new models are more accurate compared with five traditional empirical criteria,especially the model based on KELM-CEM which has the accuracy rate of 90%.Meanwhile,the results of 10 consecutive runs of the model based on KELM-CEM are almost the same,meaning that the model has good stability and reliability for engineering applications.
基金supported by the National Natural Science Foundation of China (grant numbers 42293351, and U2468221)。
文摘This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specifi c tunnel segments.Additionally,the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method.Subsequently,a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions.Meanwhile,risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information.Finally,model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study,yielding favorable risk prediction results and enabling effi cient information fusion and support for construction decision-making.
基金supported by the National Natural Science Foundation of China(Grant No.52201379)the Fundamental Research Funds for the Central Universities(Grant No.WUT:3120622898)+2 种基金State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment,Dalian University of Technology(Grant No.GZ 231088)Shanghai Key Laboratory of Naval Architecture Engineering(Grant No.SE202305)funded by European Research Council project under the European Union’s Horizon 2020 research and innovation program(Grant No.TRUST CoG 2019864724).
文摘Due to global warming and diminishing ice cover in Arctic regions,the northern sea route(NSR)has attracted increasing attention in recent years.Extreme cold temperatures and high wind speeds in Arctic regions present substantial risks to vessels operating along the NSR.Consequently,analyzing extreme temperature and wind speed values along the NSR is essential for ensuring maritime operational safety in the region.This study analyzes wind and temperature data spanning 40 years,from 1981 to 2020,at four representative sites along the NSR for extreme value analysis.The average conditional exceedance rate(ACER)method and the Gumbel method are employed to estimate extreme wind speed and air temperature at these sites.Comparative analysis reveals that the ACER method provides higher accuracy and lower uncertainty in estimations.The predicted extreme wind speed for a 100-year return period is 30.36 m/s,with a minimum temperature of-56.66°C,varying across the four sites.Furthermore,the study presents extreme values corresponding to each return period,providing temperature extremes as a basis for guiding steel thickness specifications.These findings provide valuable reference for designing polar vessels and offshore structures,contributing to enhanced engineering standards for Arctic conditions.
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]the National Natural Science Foundation of China[grant number 42230603]+1 种基金the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]supported by Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209].
文摘Based on a normalized difference vegetation index(NDVI)dataset for 1982-2021,this work investigates the principal modes of interannual variability in summer NDVI over eastern Siberia using the year-to-year increment method and empirical orthogonal function(EOF)analysis.The first three principal modes(EOF1-3)of the year-to-year increment of summer NDVI(NDVI_DY)exhibit a regionally consistent mode,a western-eastern dipole mode,and a northern-southern dipole mode,respectively.Further analysis shows that sea surface temperature(SST)in the tropical Indian Ocean in February-March and western Siberian soil moisture in April could influence EOF1.EOF2 is modulated by April Northwest Pacific SST and western Siberian soil moisture in May.May North Atlantic SST and sea ice in the Kara Sea in the preceding October significantly affect EOF3.Using the year-to-year increment method and multiple linear regression analysis,prediction schemes for EOF1-3 are developed based on these predictors.To assess the predictive skill of these schemes,one-year-out cross-validation and independent hindcast methods are employed.The temporal correlation coefficients between observed EOF1-3 and the cross-validation results are 0.62,0.46,and 0.37,respectively,exceeding the 95%confidence level.In addition,reconstructed schemes for summer NDVI are developed using predicted NDVI_DY and the observed principal modes of NDVI_DY.Independent hindcasts of NDVI anomalies during 2019-2021 also present consistent distributions with the observed results.
基金supported by grants from the High-Level Oversea Talent Introduction Plan,Chinathe Special Fund for Basic Scientific Research in Central Universities of China-Doctoral Research and Innovation Fund Project,China(No.3072023CFJ0206).
文摘Rotor blade is one of the most significant components of helicopters. But due to its highspeed rotation characteristics, it is difficult to collect the vibration signals during the flight stage.Moreover, sensors are highly susceptible to damage resulting in the failure of the measurement.In order to make signal predictions for the damaged sensors, an operational modal analysis(OMA) together with the virtual sensing(VS) technology is proposed in this paper. This paper discusses two situations, i.e., mode shapes measured by all sensors(both normal and damaged) can be obtained using OMA, and mode shapes measured by some sensors(only including normal) can be obtained using OMA. For the second situation, it is necessary to use finite element(FE) analysis to supplement the missing mode shapes of damaged sensor. In order to improve the correlation between the FE model and the real structure, the FE mode shapes are corrected using the local correspondence(LC) principle and mode shapes measured by some sensors(only including normal).Then, based on the VS technology, the vibration signals of the damaged sensors during the flight stage can be accurately predicted using the identified mode shapes(obtained based on OMA and FE analysis) and the normal sensors signals. Given the high degrees of freedom(DOFs) in the FE mode shapes, this approach can also be used to predict vibration data at locations without sensors. The effectiveness and robustness of the proposed method is verified through finite element simulation, experiment as well as the actual flight test. The present work can be further used in the fault diagnosis and damage identification for rotor blade of helicopters.
基金supported by the National Natural Science Foundation of China(No.12472340).
文摘This paper aims to experimentally and numerically probe fatigue behaviours and lifetimes of 3D4D(three-dimensional four-directional)braided composite I-beam under four-point flexure spectrum loading.New fatigue damage models of fibre yarn,matrix and fibre–matrix interface are proposed,and fatigue failure criteria and PFDA(Progressive Fatigue Damage Algorithm)are thus presented for meso-scale fatigue damage modelling of 3D4D braided composite I-beam.To validate the aforementioned model and algorithm,fatigue tests are conducted on the 3D4D braided composite I-beam under four-point flexure spectrum loading,and fatigue failure mechanisms are analyzed and discussed.Novel global–local FE(Finite Element)model based on the PFDA is generated for modelling progressive fatigue failure process and predicting fatigue life of 3D4D braided composite I-beam under four-point flexure spectrum loading.Good agreement has been achieved between experimental results and predictions,demonstrating the effective usage of new model.It is shown that matrix cracking and interfacial debonding initially initiates on top surface of top flange of I-beam,and then gradually propagates from the side surface of top flange to the intermediate web along the braiding angle,and considerable fiber breakage finally causes final fatigue failure of I-beam.
