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A data-driven methodology to predict ice-induced aerodynamic degradation applied to aircraft tailplane design 认领 引用
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作者 Salvatore CORCIONE Agostino DE MARCO Vincenzo CUSATI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第8期328-346,共19页
This study presents a data-driven approach to predict tailplane aerodynamics in icing conditions,supporting the ice-tolerant design of aircraft horizontal stabilizers.The core of this work is a low-cost predictive mod... This study presents a data-driven approach to predict tailplane aerodynamics in icing conditions,supporting the ice-tolerant design of aircraft horizontal stabilizers.The core of this work is a low-cost predictive model for analyzing icing effects on swept tailplanes.The method relies on a multi-fidelity data gathering campaign,enabling seamless integration into multidisciplinary aircraft design workflows.A dataset of iced airfoil shapes was generated using 2D inviscid methods across various flight conditions.High-fidelity CFD simulations were conducted on both clean and iced geometries,forming a multidimensional aerodynamic database.This 2D database feeds a nonlinear vortex lattice method to estimate 3D aerodynamic characteristics,following a'quasi-3D'approach.The resulting reduced-order model delivers fast aerodynamic performance estimates of iced tailplanes.To demonstrate its effectiveness,optimal ice-tolerant tailplane designs were selected from a range of feasible shapes based on a reference transport aircraft.The analysis validates the model's reliability,accuracy,and limitations concerning 3D ice shapes and aerodynamic characteristics.Most notably,the model offers near-zero computational cost compared to high-fidelity simulations,making it a valuable tool for efficient aircraft design. 展开更多
关键词 Data-driven aerodynamics Forward swept tailplane Gaussian process regression Ice accretion prediction Machine learning for icing analysis
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Data-driven Methods to Predict the Burst Strength of Corroded Line Pipelines Subjected to Internal Pressure 认领 引用 被引量：4
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作者 Jie Cai Xiaoli Jiang +2 位作者 Yazhou Yang Gabriel Lodewijks Minchang Wang 《Journal of Marine Science and Application》 CSCD 2022年第2期115-132,共18页
A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines,especially those served for a long time.Finite-element method and empirical formulas are thereby used for the strength p... A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines,especially those served for a long time.Finite-element method and empirical formulas are thereby used for the strength prediction of such pipes with corrosion.However,it is time-consuming for finite-element method and there is a limited application range by using empirical formulas.In order to improve the prediction of strength,this paper investigates the burst pressure of line pipelines with a single corrosion defect subjected to internal pressure based on data-driven methods.Three supervised ML(machine learning)algorithms,including the ANN(artificial neural network),the SVM(support vector machine)and the LR(linear regression),are deployed to train models based on experimental data.Data analysis is first conducted to determine proper pipe features for training.Hyperparameter tuning to control the learning process is then performed to fit the best strength models for corroded pipelines.Among all the proposed data-driven models,the ANN model with three neural layers has the highest training accuracy,but also presents the largest variance.The SVM model provides both high training accuracy and high validation accuracy.The LR model has the best performance in terms of generalization ability.These models can be served as surrogate models by transfer learning with new coming data in future research,facilitating a sustainable and intelligent decision-making of corroded pipelines. 展开更多
关键词 Pipelines Corrosion Burst strength Internal pressure Data-driven method Machine learning
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Data-driven methods for predicting the representative temperature of bridge cable based on limited measured data 认领 引用 被引量：2
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作者 WANG Fen DAI Gong-lian +2 位作者 HE Chang-lin GE Hao RAO Hui-ming 《Journal of Central South University》 SCIE EI CAS CSCD 2024年第9期3168-3186,共19页
Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and mai... Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and maintenance of cable-stayed bridges.However,the representative temperatures of stayed cables are not specified in the existing design codes.To address this issue,this study investigates the distribution of the cable temperature and determinates its representative temperature.First,an experimental investigation,spanning over a period of one year,was carried out near the bridge site to obtain the temperature data.According to the statistical analysis of the measured data,it reveals that the temperature distribution is generally uniform along the cable cross-section without significant temperature gradient.Then,based on the limited data,the Monte Carlo,the gradient boosted regression trees(GBRT),and univariate linear regression(ULR)methods are employed to predict the cable’s representative temperature throughout the service life.These methods effectively overcome the limitations of insufficient monitoring data and accurately predict the representative temperature of the cables.However,each method has its own advantages and limitations in terms of applicability and accuracy.A comprehensive evaluation of the performance of these methods is conducted,and practical recommendations are provided for their application.The proposed methods and representative temperatures provide a good basis for the operation and maintenance of in-service long-span cable-stayed bridges. 展开更多
关键词 cable-stayed bridges representative temperature gradient boosted regression trees(GBRT)method field test limited measured data
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Data-driven iterative calibration method for prior knowledge of earth-rockfilldam wetting model parameters 认领 引用
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作者 Shaolin Ding Jiajun Pan +4 位作者 Yanli Wang Lin Wang Han Xu Yiwei Lu Xudong Zhao 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1621-1632,共12页
Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations a... Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments. 展开更多
关键词 Earth-rockfilldam Wetting deformation Prior knowledge Data-driven Bayesian inversion
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Erratum:“Data-driven Simulations of Magnetic Field Evolution in Active Region 11429:Magneto-frictional Method Using PENCIL CODE”(2024,RAA,24,025007) 认领 引用
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作者 P.Vemareddy Jön Warnecke Ph.A.Bourdin 《Research in Astronomy and Astrophysics》 SCIE CAS CSCD 2026年第3期293-293,共1页
On page 6,Figure 3 is a duplicate of Figure 4.We publish below the correct figure,sincerely apologizing to the authors.
关键词 magnetic field evolution magneto frictional method pencil code erratum figure duplication apology active region
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A data-driven integrated optimization method of macroscopic topology and microscopic configuration for the graded functional cellular structures 认领 引用
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作者 Yu Guo Yijie Lu +2 位作者 Zhengwei Zhang Yanguo Zhou Hui Liu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期473-495,共23页
In the topology optimization of the multiscale structure,how to ensure the connectivity between adjacent microstructures,how to control the design space of microstructures,and how to reduce the amount of calculation a... In the topology optimization of the multiscale structure,how to ensure the connectivity between adjacent microstructures,how to control the design space of microstructures,and how to reduce the amount of calculation and improve calculation efficiency are three basic challenging issues currently faced.To this end,this paper proposes a data-driven approach to achieve the integrated optimization of macroscopic topology and microscopic configuration of the graded functional cellular structures.At the macro level,a topological description function is introduced to realize the topological control of the macrostructure.At the micro level,several cutting functions are used to realize the control of the configuration and size of the microstructure.The integrated optimization design of macro and micro cellular structures can be realized.Based on the computational homogenization method and numerical integration technology,an optimization problem independent offline microstructure database is established at the microscopic scale,where the relationship between the equivalent elastic parameters,relative pseudo-density,and design variables of the microstructure is stored.Based on this offline database,the entire topology optimization process is completed only on a macro scale,which greatly reduces the amount of calculation and improves calculation efficiency.In addition,implicit geometric modeling of full-scale cellular structures can be achieved using the reconstruction technique introduced in this work,which ensures smooth connection between adjacent microstructures.Finally,numerical examples are used to verify the effectiveness of the algorithm and the superiority of gradient cellular structures compared with single-scale structures. 展开更多
关键词 Graded cellular structure Integrated optimization Data-driven Computational homogenization M-VCUT Level set method
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Multimodal and data-driven approaches in acupuncture research:methods,applications,and challenges 认领 引用
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作者 Dehui Nie Puchen Huang +10 位作者 Dan Jin Yiming Chen Baochao Fan Shanshan Huang Yuqing Zhang Yu Zhou Bin Han Jianlong Huang Gaolei Yao Liming Lu Peijing Rong 《Acupuncture and Herbal Medicine》 CAS CSCD 2026年第2期141-154,共14页
Acupuncture research increasingly involves heterogeneous and multimodal data that are difficult to analyze using conventional methods.This review summarizes data-driven approaches in acupuncture research within a fram... Acupuncture research increasingly involves heterogeneous and multimodal data that are difficult to analyze using conventional methods.This review summarizes data-driven approaches in acupuncture research within a framework encompassing intervention,response,and contextual data.We discuss causal inference,artificial intelligence,text mining,and integrative analysis,along with their applications in efficacy evaluation,outcome prediction,mechanistic investigation,and clinical decision support.These approaches shift the focus of acupuncture research from population-level average effects toward individualized clinical decision-making by enabling the analysis of treatment heterogeneity and underlying mechanisms.However,current research remains limited by inadequate data standardization,insufficient external validation,and limited model interpretability.Despite these challenges,data-driven approaches offer substantial promise for advancing more rigorous and personalized acupuncture research. 展开更多
关键词 Acupuncture Causal inference Clinical decision support Data-driven Deep learning Machine learning Mechanistic studies Multimodal data
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A Data-Driven Fault Prediction Method for Bearing Ring CNC Grinding Machines 认领 引用
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作者 Yanan Wang Xiaoying Yang +2 位作者 Zhijie Pei Xin Yang Bo Li 《Computers, Materials & Continua》 SCIE EI 2026年第10期1815-1843,共29页
Sudden faults in bearing ring computer numerical control(CNC)grinding machines significantly impact product processing quality and production efficiency,making precise state prediction urgent to avoid downtime risks.H... Sudden faults in bearing ring computer numerical control(CNC)grinding machines significantly impact product processing quality and production efficiency,making precise state prediction urgent to avoid downtime risks.However,the numerous operational parameters collected on-site and the focus of existing methods on outputting fault labels without analyzing the evolution trends of the equipment’s operational state lead to unclear fault discrimination criteria and weak traceability,making it difficult to provide effective early-warning support during the incipient stages of a fault.To address these issues,this paper constructs a data-driven integrated algorithm adopting a“predict-then-classify”approach.First,the Pearson-ReliefF algorithm is utilized to eliminate redundant features and retain sensitive parameters.Second,the BiGRU-Attention algorithm is employed to capture bidirectional dependencies in time-series data,realizing the prediction of the equipment’s operational state trends.Finally,the Sparrow Search Algorithm(SSA)is introduced to optimize core Support Vector Machine(SVM)parameters,achieving precise identification of fault types in bearing ring CNC grinding machines.Experimental results indicate that the proposed algorithm exhibits robust performance in both the prediction and classification stages.The prediction metrics MAE,RMSE,and R2 are 0.0124,0.0152,and 0.975,respectively,and the average multi-fault identification accuracy based on 10 repeated experiments with random seeds reaches 98.25%.This verifies the effectiveness of the method,which is of significant importance for ensuring the processing quality of bearing rings,reducing operation and maintenance costs,achieving intelligent online predictive diagnosis,and supporting the preventive maintenance of equipment. 展开更多
关键词 Bearing CNC grinding machine data-driven fault prediction
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Optimal design strategy of traditional courtyard based on performance and data-driven method--A case study of Yanshen ancient town, China 认领 引用
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作者 Zhixin Xu Xin Zheng Xiangfeng Li 《Frontiers of Architectural Research》 EI CSCD 2025年第5期1168-1195,共28页
To address the insufficient integration of performance evaluation and contextual analysis in traditional architectural design,this paper proposes a design workflow that combines data-driven and performance-driven appr... To address the insufficient integration of performance evaluation and contextual analysis in traditional architectural design,this paper proposes a design workflow that combines data-driven and performance-driven approaches,establishing a comprehensive operational pathway from typology selection and design generation to performance assessment.Using Yanshen Ancient Town,a cold region,as the study area,the research evaluates 18 traditional courtyard types and 8 brick kiln courtyard types.Benchmark models are selected based on the combined performance of PET(Physiological Equivalent Temperature)and MRT(Mean Radiant Temperature)indices.Subsequently,multiple performance indicators,including indoor and outdoor thermal comfort,indoor illuminance,and building energy consumption,are integrated into the analysis.Using a genetic algorithm,Pareto optimal solutions that meet performance requirements are iteratively optimized and filtered.Based on the learning rates and various evaluation indicators,XGBoost is ultimately selected to classify and predict the overall building performance.Results indicate that the model achieves an average prediction accuracy of 83.6%.Additionally,SHAP analysis of the independent variables in the algorithm reveals distinct influencing trends under different performance labels.The workflow demonstrates the feasibility of incorporating performance prediction in the early design stage of village courtyards,significantly enhancing the efficiency of feedback and follow-up between design decision-making and performance evaluation. 展开更多
关键词 Traditional courtyard Performance and data-driven method Generative design Machine learning
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Leveraging Bayesian methods for addressing multi-uncertainty in data-driven seismic liquefaction assessment 认领 引用 被引量：1
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作者 Zhihui Wang Roberto Cudmani +2 位作者 Andrés Alfonso Peña Olarte Chaozhe Zhang Pan Zhou 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第4期2474-2491,共18页
When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding bia... When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding biased data selection,ameliorating overconfident models,and being flexible to varying practical objectives,especially when the training and testing data are not identically distributed.A workflow characterized by leveraging Bayesian methodology was proposed to address these issues.Employing a Multi-Layer Perceptron(MLP)as the foundational model,this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity,accuracy,and resistance to overfitting.The analysis revealed that,while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios,Bayesian neural networks showed great potential for preventing overfitting.Additionally,integrating decision thresholds through various evaluative principles offers insights for challenging decisions.Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data,employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics.Overall,the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation,showing improved robustness against overfitting and greater versatility in addressing practical challenges.This research contributes to the seismic liquefaction assessment field by providing a structured,adaptable methodology for accurate and reliable analysis. 展开更多
关键词 Data-driven method Bayes analysis Seismic liquefaction Uncertainty Neural network
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A data-driven PCA-RF-VIM method to identify key factors driving post-fracturing gas production of tight reservoirs 认领 引用
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作者 Yifan Zhao Xiaofan Li +5 位作者 Lei Zuo Zhongtai Hu Liangbin Dou Huagui Yu Tiantai Li Jun Lu 《Energy Geoscience》 EI CAS CSCD 2025年第2期436-450,共15页
Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysi... Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysis methods have limitations in dealing with these complex and interrelated factors,and it is difficult to fully reveal the actual contribution of each factor to the production.Machine learning-based methods explore the complex mapping relationships between large amounts of data to provide datadriven insights into the key factors driving production.In this study,a data-driven PCA-RF-VIM(Principal Component Analysis-Random Forest-Variable Importance Measures)approach of analyzing the importance of features is proposed to identify the key factors driving post-fracturing production.Four types of parameters,including log parameters,geological and reservoir physical parameters,hydraulic fracturing design parameters,and reservoir stimulation parameters,were inputted into the PCA-RF-VIM model.The model was trained using 6-fold cross-validation and grid search,and the relative importance ranking of each factor was finally obtained.In order to verify the validity of the PCA-RF-VIM model,a consolidation model that uses three other independent data-driven methods(Pearson correlation coefficient,RF feature significance analysis method,and XGboost feature significance analysis method)are applied to compare with the PCA-RF-VIM model.A comparison the two models shows that they contain almost the same parameters in the top ten,with only minor differences in one parameter.In combination with the reservoir characteristics,the reasonableness of the PCA-RF-VIM model is verified,and the importance ranking of the parameters by this method is more consistent with the reservoir characteristics of the study area.Ultimately,the ten parameters are selected as the controlling factors that have the potential to influence post-fracturing gas production,as the combined importance of these top ten parameters is 91.95%on driving natural gas production.Analyzing and obtaining these ten controlling factors provides engineers with a new insight into the reservoir selection for fracturing stimulation and fracturing parameter optimization to improve fracturing efficiency and productivity. 展开更多
关键词 Data-driven method Controlling factor Hydraulic fracturing Gas production
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Data-Driven Combination-Interval Prediction for Landslide Displacement Based on Copula and VMD-WOA-KELM Method 认领 引用 被引量：2
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作者 Longqi Li Yunhuang Yang +1 位作者 Tianzhi Zhou Mengyun Wang 《Journal of Earth Science》 SCIE CAS CSCD 2025年第1期291-306,共16页
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
关键词 landslide displacement interval prediction combination method copula Landslides VMD-WOA-KELM
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Experimental data-driven deep neural network modeling and prediction for the streaks of turbulent separated shear flow 认领 引用 被引量：1
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作者 Xingyu Ma Jiateng Pan +1 位作者 Yihong Liu Nan Jiang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期52-58,共7页
In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a... In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a plane backward-facing step,spanwise-aligned high-and low-speed streaks were generated within the separated shear layer behind the step.The velocity profiles of the shear flow were measured by single-probe hot-wire anemometer in both the streamwise-vertical and the streamwise-spanwise planes in the wind tunnel.Deep neural network models are trained and verified based on the experimental datasets.The input parameter sets include the vortex generator height,spanwise spacing,and the spatial coordinates within the measurement domain,while the output parameter sets are mean and root-mean-square velocities of the shear flow.Mean squared errors between the model-predicted and experimentally measured data are used for quality evaluation of different deep neural network model designs,among which the minimum error of the optimal design descends less than 1%.For other vortex generator parameters,which are not measured in the wind tunnel or used in the training,the model prediction provides reasonable mean velocity contours with streak structures.Thus,we find that the experimental data-driven modeling approach shows reliable robustness for nonlinear fitting of complex datasets as well as considerable generalization for turbulent coherent structures. 展开更多
关键词 Streak Experimental data-driven Deep neural network Vortex generator Backward-facing step
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Adaptive-length data-driven predictive control for post-operation of space robot non-cooperative target capture with disturbances 认领 引用 被引量：1
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作者 Peiji WANG Bicheng CAI +2 位作者 Chengfei YUE Yong ZHAO Weiren WU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期485-498,共14页
This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi... This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering. 展开更多
关键词 Combined control Data-driven predictive control Post operation Predictive control systems Space non-cooperative target capture
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Big data-driven analysis of shale gas enrichment patterns:A case study of the Wufeng–Longmaxi Formation in the Sichuan Basin and its periphery 认领 引用 被引量：1
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作者 Zongquan Hu Jin Meng +10 位作者 Wei Du Yitian Xiao Chuanxiang Sun Guanping Wang Baojian Shen Tianrui Ye Dongjun Feng Zengqin Liu Longfei Lu Ruyue Wang Qianru Wang 《Energy Geoscience》 EI CAS CSCD 2026年第1期166-178,共13页
The Wufeng–Longmaxi Formation derives its name from the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation,found in sequence in the Sichuan Basin.This formation hosts rich shale gas reservoir... The Wufeng–Longmaxi Formation derives its name from the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation,found in sequence in the Sichuan Basin.This formation hosts rich shale gas reservoirs,and its shale gas enrichment patterns are examined in this study using data from 1197 shale samples collected from 14 wells.Five basic and three key parameters,eight in all,are assessed for each sample.The five basic parameters include burial depth and the contents of four mineral types—quartz,clay,carbonate,and other minerals;the three key parameters,representing shale gas enrichment,are total organic carbon(TOC)content,porosity,and gas content.The SHapley Additive exPlanations(SHAP)analysis originated in game theory is used here in an interpretable machine learning framework,to address issues of heterogeneous data structure,noisy relationships,and multi-objective optimization.An evaluation of the ranking,contribution values,and conditions of changes for these parameters offers new quantitative insights into shale gas enrichment patterns.A quantitative analysis of the relationship between data-sets identifies the primary factors controlling TOC,porosity,and gas content of shale gas reservoirs.The results show that TOC and porosity jointly influence gas content;mineral content has a significant impact on both,TOC and porosity;and the burial depth governs porosity which,in turn,affects the conditions under which shale gas is preserved.Input parameter thresholds are also determined and provide a basis for the establishment of quantitative criteria to evaluate shale gas enrichment.The predictive accuracy of the model used in this study is significantly improved by the step-wise addition of two input parameters,namely TOC and porosity,separately and together.Thus,the game theory method in big data-driven analysis uses a combination of TOC and porosity to evaluate the gas content with encouraging results—suggesting that these are the key parameters that indicate source rock and reservoir properties. 展开更多
关键词 Big data-driven analysis Primary controlling factor Shale gas enrichment pattern Wufeng–Longmaxi Formation Sichuan basin
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Broadband ground motion simulation and analysis of a near-fault 3D basin-mountain coupling site based on the hybrid method 认领 引用 被引量：1
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作者 Liu Zhongxian Tang Kang +2 位作者 Li Chengcheng Yuan Xiaoming Zhang Hai 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2026年第1期87-110,共24页
This study presents an effective hybrid simulation approach for simulating broadband ground motion in complex near-fault locations.The approach utilizes a deterministic approach based on the spectral element method(SE... This study presents an effective hybrid simulation approach for simulating broadband ground motion in complex near-fault locations.The approach utilizes a deterministic approach based on the spectral element method(SEM),which is used to simulate low-frequency ground motion(f1 Hz).A fourth-order Butterworth filter with zero phase shift is employed for time-domain filtering of low-and high-frequency time series at a crossover frequency of 1 Hz,merging the low and high-frequency ground motions into a broadband time series.Taking an Ms 6.8 Luding earthquake,as an example,this hybrid method was used for a rapid and efficient simulation analysis of broadband ground motion in the region.The accuracy and efficiency of this hybrid method were verified through comparisons with actually observed station data and empirical attenuation curves.Deterministic method simulation results revealed the effects of mountainous topography,basin effects,nonlinear effects within the basin’s sedimentary layers,and a coupling interaction between the basin and the mountains.The findings are consistent with similar studies,showing that near-fault sedimentary basins significantly focus and amplify strong ground motion,and the soil’s nonlinear behavior in the basin influences ground motion to varying extents at different distances from the fault.The mountainous topography impacts the basin’s response to ground motion,leading to barrier effects.This research provides a scientific foundation for seismic zoning,urban planning,and seismic design in nearfault mountain basin regions. 展开更多
关键词 hybrid ground motion simulation method spectral element method three-dimensional stochastic finite fault method near-fault basin-mountain coupling effect basin effect nonlinear effect
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Methods of preparation,structural characteristics of resistant starch and its relation to human health:a review 认领 引用 被引量：1
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作者 Weiyi Zhang Yang Yang +6 位作者 Chunmin Ma Bing Wang Xiaofei Liu Yan Wang Xin Bian Guang Zhang Na Zhang 《Food Science and Human Wellness》 SCIE CAS CSCD 2026年第4期1503-1519,共17页
Resistant starch(RS)comprises starch fractions that resist digestion in the small intestine and reach the colon,where they are fermented by the microbiota.Resistant starch harbors functional properties and healthpromo... Resistant starch(RS)comprises starch fractions that resist digestion in the small intestine and reach the colon,where they are fermented by the microbiota.Resistant starch harbors functional properties and healthpromoting ingredients that can regulate blood glucose and lipid levels,prevent cancer,and enhance the quality of life.Consequently,new technologies for the preparation of RS are continually being developed to support its industrial production.This review describes the structural and nutritional properties of RS and examines recent advancements in RS preparation methods.Emphasis is placed on how RS structure influences its properties and the physiological mechanisms in vivo.This review aims to stimulate further research into the preparation methods,functional characteristics,and utilization of RS,thereby supporting ongoing developments in the food industry. 展开更多
关键词 Resistant starch Preparation methods Characterization Function Human health
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Development of nontarget method based on GC-QTOF-HRMS for analyzing organic pollutants in human serum 认领 引用 被引量：1
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作者 Congcong Yue Chang He +5 位作者 Hailing Li Zhiquan Yuan Guiying Li Shengtao Ma Xin Zhang Taicheng An 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第7期379-386,共8页
Traditional targeted analyses often overlook unknown or emerging contaminants,highlighting the significance of nontarget and suspect screening approaches.A novel and high-sensitivity methodology for nontarget analysis... Traditional targeted analyses often overlook unknown or emerging contaminants,highlighting the significance of nontarget and suspect screening approaches.A novel and high-sensitivity methodology for nontarget analysis of organic pollutants in human serum was newly-developed based on gas chromatography coupled with quadrupole time-of-flight high-resolution mass spectrometry.The extraction protocol employing an acetonitrile-ethyl acetate(9:1,V:V)mixture significantly improved the extraction efficiency while minimizing matrix effect.A hybridized analytical strategy integrating nontarget and suspect screening was developed to achieve comprehensive identification and classification of pollutants,employing the National Institute of Standards and Technology(NIST)20 library and Agilent Technologies Personal Compound Database and Library(PCDL).This approach successfully characterized 273 organic contaminants spanning 12 categories,including polycyclic aromatic hydrocarbons(PAHs)and their derivatives,esters,and phenolic compounds in human serum,with a significant increase in detection specificity compared to conventional workflows.The methodology used serum samples of the workers from coking industry,revealing widespread contamination dominated by PAHs and PAH derivatives.Among the target analytes,three were identified solely by NIST and six solely by PCDL,indicating the complementary benefits of combining these different databases.Notably,this work reported the first confirmed detection of 2-naphthalenamine in human serum.This optimized approach demonstrates enhanced sensitivity and reliability in serum analysis,advancing biomonitoring capabilities and providing a deep understanding of human exposure to environmental pollutants. 展开更多
关键词 Nontarget analysis Biomonitoring method Human serum sample Organic pollutants GC-QTOF-HRMS
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Implicit unified gas kinetic particle method for steady-state solution of multiscale phonon transport 认领 引用 被引量：2
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作者 Hongyu Liu Xiaojian Yang +2 位作者 Chuang Zhang Xing Ji Kun Xu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期496-510,共15页
This paper presents a highly efficient implicit unified gas-kinetic particle(IUGKP)method for obtaining steady-state solutions of multi-scale phonon transport.The method adapts and reinterprets the integral solution o... This paper presents a highly efficient implicit unified gas-kinetic particle(IUGKP)method for obtaining steady-state solutions of multi-scale phonon transport.The method adapts and reinterprets the integral solution of the Bhatnagar-Gross-Krook(BGK)equation for time-independent solutions.The distribution function at a given point is determined solely by the surrounding equilibrium states,where the corresponding macroscopic quantities are computed through a weighted sum of equilibrium distribution functions from neighboring spatial positions.From a particle perspective,changes in macroscopic quantities within a cell result from particle transport across cell interfaces.These particles are sampled according to the equilibrium state of their original cells,accounting for their mean free path as the traveling distance.The IUGKP method evolves the solution according to the physical relaxation time scale,achieving high efficiency in large Knudsen number regimes.To accelerate convergence for small Knudsen numbers,an inexact Newton iteration method is implemented,incorporating macroscopic equations for convergence acceleration in the near-diffusive limit.The method also addresses spatial-temporal inconsistency caused by relaxation time variations in physical space through the null-collision concept.Numerical tests demonstrate the method’s excellent performance in accelerating multi-scale phonon transport solutions,achieving speedups of one to two orders of magnitude.The IUGKP method proves to be an efficient and accurate computational tool for simulating multiscale non-equilibrium heat transfer,offering significant advantages over traditional methods in both numerical performance and physical applicability. 展开更多
关键词 Implicit gas kinetic particle method Phonon Boltzmann transport equation Multi-scale heat conduction Steady-state
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Prediction method for stationary crossflow instability transition in supersonic boundary layers based on multi-layer perceptron 认领 引用 被引量：2
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作者 Lei Qiao Xi Jiang +5 位作者 Jiakun Fan Yutian Wang Lu Xie Ningjuan Dong Jiakuan Xu Junqiang Bai 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期36-51,共16页
Crossflow vortices induced transition is one of the most important instability types in supersonic aircraft boundary layers.While the traditional linear stability theory(LST)-based eN method demonstrates satisfactory ... Crossflow vortices induced transition is one of the most important instability types in supersonic aircraft boundary layers.While the traditional linear stability theory(LST)-based eN method demonstrates satisfactory predictive capabilities for this kind of transition,its practical implementation faces inherent limitations:the requirement of first-and second-order wallnormal derivatives of boundary layer velocityemperature profiles,the need for initial eigenvalue guesses,and the computational burden of solving eigenvalue problems.To address these challenges,this study develops a multi-layer perceptron(MLP)model tailored for linear stability analysis of three-dimensional compressible boundary layers based on the artificially defined quasi-three-dimensional non-similar boundary layer solutions.The boundary layer edge flow parameters and perturbation characteristics are mapped to eigenvalues or local growth rates of the envelop curves through fully connected layers.This architecture eliminates the need for computing wall-normal derivatives of velocityemperature profiles,initial eigenvalue estimation,and direct eigenvalue problem solving.Extensive validation across varying operational conditions and geometries(airfoils and swept wings)demonstrates exceptional agreement between the MLP’s predictions(eigenvalues and disturbance amplification factors)and traditional LST results.Furthermore,the model’s transition prediction capability is rigorously verified using National Aeronautics and Space Administration’s supersonic swept-wing crossflow-dominated transition benchmark,incorporating both stability analysis and flight test data.Results confirm the model is an efficient and reliable computational framework for transition prediction in three-dimensional finite-span wings. 展开更多
关键词 Supersonic swept wing Boundary layer transition Crossflow instability Linear stability theory eN method Multi-layer perceptron
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