Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution g...Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.展开更多
The constitutive model is essential for predicting the deformation and stability of rocksoil mass.The estimation of constitutive model parameters is a necessary and important task for the reliable characterization of ...The constitutive model is essential for predicting the deformation and stability of rocksoil mass.The estimation of constitutive model parameters is a necessary and important task for the reliable characterization of mechanical behaviors.However,constitutive model parameters cannot be evaluated accurately with a limited amount of test data,resulting in uncertainty in the prediction of stress-strain curves.This paper proposes a Bayesian analysis framework to address this issue.It combines the Bayesian updating with the structural reliability and adaptive conditional sampling methods to assess the equation parameter of constitutive models.Based on the triaxial and ring shear tests on shear zone soils from the Huangtupo landslide,a statistical damage constitutive model and a critical state hypoplastic constitutive model were used to demonstrate the effectiveness of the proposed framework.Moreover,the parameter uncertainty effects of the damage constitutive model on landslide stability were investigated.Results show that reasonable assessments of the constitutive model parameter can be well realized.The variability of stress-strain curves is strongly related to the model prediction performance.The estimation uncertainty of constitutive model parameters should not be ignored for the landslide stability calculation.Our study provides a reference for uncertainty analysis and parameter assessment of the constitutive model.展开更多
Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Pat...Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Patients diagnosed with essential hypertension and admitted to Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shang-hai Hospital of Traditional Chinese Medicine,and Shanghai Hospital of Integrated Tradition-al Chinese and Western Medicine from July 6th 2020 to June 16th 2021,and from August 11th 2023 to January 22nd 2024,were enrolled in this retrospective research.The baselines and clinical biochemical indicators of patients were collected.The SMART-I TCM pulse instru-ment was applied to gather pulse diagram parameters.Multivariate logistic regression was adopted to analyze the risk factors for HTH.RStudio was employed to construct the nomo-gram model,receiver operating characteristic(ROC)curve,and calibration curve(bootstrap self-sampling 200 times),and clinical decision curve were drawn to evaluate the model’s dis-crimination and clinical effectiveness.Results A total of 168 hospitalized patients with essential hypertension were selected and di-vided into non-HTH group(n=29)and HTH group(n=139).Compared with non-HTH group,HTH group had a lower body mass index(BMI),and higher proportions of male pa-tients and drinkers(P<0.05).The ventricular wall thickening(VWT)could not be deter-mined.The proportions of left common carotid intima-media wall thickness(LCCIMWT)and serum creatinine(SCR)were higher in HTH group(P<0.05).The pulse diagram parameter As was significantly higher,and H4/H1 and T1/T were lower in HTH group(P<0.05).Gender,al-cohol consumption,serum creatinine,and the pulse diagram parameter H4/H1 were identi-fied as independent risk factors for HTH(P<0.05).The nomogram’s area under the ROC curve(AUC)was 0.795[95%confidence interval(CI):(0.7066,0.8828)],with a specificity of 0.724 and sensitivity of 0.799.After 200 times repeated bootstrap self-samplings,the calibra-tion curve showed that the simulated curve fits well with the actual curve(x^(2)=9.5002,P=0.3019).The clinical decision curve indicated that the nomogram’s applicability was optimal when the threshold for predicting HTH was between 0.38 and 1.00.Conclusion The nomogram model could be valuable for predicting the onset risk of HTH and pulse diagram parameters can facilitate early screening and prevention of HTH.展开更多
The complex geometrical features of mechanical components significantly influence contact interactions and system dynamics.However,directly modeling contact forces on surfaces with intricate geometries presents consid...The complex geometrical features of mechanical components significantly influence contact interactions and system dynamics.However,directly modeling contact forces on surfaces with intricate geometries presents considerable challenges.This study focuses on the helically twisted wire rope-sheave contact and proposes a contact force model that incorporates complex geometric features through a parameter identification approach.The model's impact on contact forces and system dynamics is thoroughly investigated.Leveraging a point contact model and an elliptic integral approximation,a loss function is formulated using the finite element(FE)contact model results as the reference data.Geometric parameters are subsequently determined by optimizing this loss function via a genetic algorithm(GA).The findings reveal that the contact stiffness increases with the wire rope pitch length,the radius of principal curvature,and the elliptic eccentricity of the contact zone.The proposed contact force model is integrated into a rigid-flexible coupled dynamics model,developed by the absolute node coordinate formulation,to examine the effects of contact geometry on system dynamics.The results demonstrate that the variations in wire rope geometry alter the contact stiffness,which in turn affects dynamic rope tension through frictional energy dissipation.The enhanced model's predictions exhibit superior alignment with the experimental data,thereby validating the methodology.This approach provides new insights for deducing the contact geometry from kinetic parameters and monitoring the performance degradation of mechanical components.展开更多
Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in...Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency.To address these challenges,we propose an adaptive multi-learning cooperation search algorithm(AMLCSA)for efficient identification of unknown parameters in PV models.AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises.It enhances the original cooperation search algorithm in two key aspects:(i)an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights,allowing better individuals to focus on local exploitation while guiding poorer individuals toward global exploration;and(ii)a chaotic grouping reflection strategy that introduces chaotic sequences to enhance population diversity and improve search performance.The effectiveness of AMLCSA is demonstrated on single-diode,double-diode,and three PV-module models.Simulation results show that AMLCSA offers significant advantages in convergence,accuracy,and stability compared to existing state-of-the-art algorithms.展开更多
Recent discovery of high transition temperature superconductivity in La_(3)Ni_(2)O_(7) has sparked renewed theoretical and experimental interests in unconventional superconductivity. It is crucial to understand the in...Recent discovery of high transition temperature superconductivity in La_(3)Ni_(2)O_(7) has sparked renewed theoretical and experimental interests in unconventional superconductivity. It is crucial to understand the influence of various factors on its superconductivity. By refining the determinant quantum Monte Carlo algorithm, we characterize the parameter dependence of the superconducting transition temperature within a bilayer Hubbard model, which is sign-problem-free at arbitrary filling. A striking feature of this model is its similarity to the bilayer nickelate-based superconductor La_(3)Ni_(2)O_(7), where superconductivity emerges from the bilayer NiO_(2) planes.We find that interlayer spin-exchange J is critical to interlayer pairing, and that on-site interaction U contributes negatively to superconductivity at low doping levels but positively at high doping levels. Our findings can provide a reference for the next step in theoretical research on nickelate-based superconductors.展开更多
Accurate modeling and parameter estimation of sea clutter are fundamental for effective sea surface target detection.With the improvement of radar resolution,sea clutter exhibits a pronounced heavy-tailed characterist...Accurate modeling and parameter estimation of sea clutter are fundamental for effective sea surface target detection.With the improvement of radar resolution,sea clutter exhibits a pronounced heavy-tailed characteristic,rendering traditional distribution models and parameter estimation methods less effective.To address this,this paper proposes a dual compound-Gaussian model with inverse Gaussian texture(CG-IG)distribution model and combines it with an improved Adam algorithm to introduce a method for parameter correction.This method effectively fits sea clutter with heavy-tailed characteristics.Experiments with real measured sea clutter data show that the dual CGIG distribution model,after parameter correction,accurately describes the heavy-tailed phenomenon in sea clutter amplitude distribution,and the overall mean square error of the distribution is reduced.展开更多
This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and i...This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.展开更多
Aircraft wake turbulence is an inherent outcome of aircraft flight,presenting a substan-tial challenge to air traffic control,aviation safety and operational efficiency.Building upon data obtained from coherent Dopple...Aircraft wake turbulence is an inherent outcome of aircraft flight,presenting a substan-tial challenge to air traffic control,aviation safety and operational efficiency.Building upon data obtained from coherent Doppler Lidar detection,and combining Dynamic Bayesian Networks(DBN)with Genetic Algorithm-optimized Backpropagation Neural Networks(GA-BPNN),this paper proposes a model for the inversion of wake vortex parameters.During the wake vortex flow field simulation analysis,the wind and turbulent environment were initially superimposed onto the simulated wake velocity field.Subsequently,Lidar-detected echoes of the velocity field are simulated to obtain a data set similar to the actual situation for model training.In the case study validation,real measured data underwent preprocessing and were then input into the established model.This allowed us to construct the wake vortex characteristic parameter inversion model.The final results demonstrated that our model achieved parameter inversion with only minor errors.In a practical example,our model in this paper significantly reduced the mean square error of the inverted velocity field when compared to the traditional algorithm.This study holds significant promise for real-time monitoring of wake vortices at airports,and is proved a crucial step in developing wake vortex interval standards.展开更多
A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair.With its lightweight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well...A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair.With its lightweight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well suited for these robots.However,the coupled nature of the joint disrupts the direct linear relationship between the input and output torques,posing challenges for dynamic modeling and practical applications.This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics.Building on this foundation,the Newton-Euler method was used to develop a dynamic model for the entire robotic arm.A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to zero.An experimental method was designed to compensate for gravity,inertia,and modeling errors to identify the parameters of the friction model.This method establishes a mapping relationship between the friction force and motor current.In addition,a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm.Trajectory tracking experiments were conducted during the experimental validation phase,demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm.This study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms,thereby establishing a foundation for motion control in humanoid nursing robots.展开更多
According to a high-temperature compression test of rare earth magnesium alloy(WE43),a strain-compensated constitutive model of the Arrhenius equation based on Zener-Hollomon parameters was established,and the rheolog...According to a high-temperature compression test of rare earth magnesium alloy(WE43),a strain-compensated constitutive model of the Arrhenius equation based on Zener-Hollomon parameters was established,and the rheological behaviors were predicted.The model exhibited relatively serious prediction distortion in the low-temperature and high-strain rate parameter interval,and its accuracy was still unsatisfactory even after modification by a correction operator considering the coupling of temperature and strain rate.The microstructure characterization and statistical analysis showed that a large number of twinning occurred in the parameter intervals with prediction deviation.The occurrence of twinning complicated the local internal stress distribution by drastically changing the crystal orientation and led to significant fluctuations in the macroscopic strain-stress and hardening curves relative to the rheological processes dominated by the dislocation and softening mechanisms,making the logarithm of the strain rate and stress deviate from the linear relationship.This twinning phenomenon was greatly influenced by the temperature and strain rate.Herein,the influence mechanism on twinning behavior was analyzed from the perspective of the interaction of dislocation and twinning.展开更多
Coal has a highly complex chemical structure,similar to polymers,coal is a macromolecular structure composed of a large number of“similar compounds”,which is called the basic structural unit.Understanding coal struc...Coal has a highly complex chemical structure,similar to polymers,coal is a macromolecular structure composed of a large number of“similar compounds”,which is called the basic structural unit.Understanding coal structure is the basis of its transformation and utilization.Shendong(SD)coal was analyzed by FTIR,XRD,XPS,and NMR.The results show that SD coal normalized structure formula is C_(100)H_(68.5)O_(35.7)N_(1.2)S_(0.2)and the average number of aromatic rings is 1.98.-CH_(2)-content accounts for about 82%in aliphatic CeH region,and the ratio of ether bond CeO,aromatic ether C-O and C=O is about 2:1:11 in oxygen-containing functional group region.The d_(002),L_(C),L_(a)and N_(C)of S_(D)coal microcrystalline structure parameters are 0.1832 nm,1.4688 nm,2.0852 nm and 9.017,respectively.Aromatic carbon and aliphatic carbon ratios of SD coal are 55.67%and 29.97%,aromatic cluster size and average methylene chain length are 0.224 and 1.817.Based on these structural parameters,molecular model of SD coal was constructed with^(13)C SSNMR experimental spectra as a reference.The model was constructed with an atom composition of C_(214)H_(214)O_(49)N_(2)S.展开更多
The use of additive manufacturing techniques in the development of unconventional materials can help reduce the environmental impact of traditional construction materials.In this paper,the properties of a 3D-printed b...The use of additive manufacturing techniques in the development of unconventional materials can help reduce the environmental impact of traditional construction materials.In this paper,the properties of a 3D-printed biocomposite were evaluated.Biofilaments obtained by mixing pulverized bamboo fibers with polylactic acid(PLA)resin were extruded during the manufacturing process.To assess the effect of incorporating plant fibers,an analysis was conducted on the morphology,elemental chemical composition,crystallinity index,principal functional groups,thermal stability,surface roughness,microhardness,density,tensile strength,elastic modulus,and strain percentage of reinforced samples.The results were comparedwith those obtained from the characterization of standard PLAfilaments(unreinforced).The fused deposition modeling(FDM)technique was employed to print biocomposite specimens.Additionally,the influence of the printing parameters(infill density,build orientation,and layer thickness)on the physical,tribological,andmechanical properties of the biocomposites was analyzed.These results were compared with those obtained for specimens printed with pure PLA.The findings indicate that incorporating 10%vegetable filler into PLA filaments enhanced the strength and stiffness of the biocomposite under axial loads.Finally,the strength of the biocomposite subjected to axial loads was compared with the standardized values for wood-plastic composites,demonstrating the feasibility of its use for non-structural purposes in civil construction.展开更多
This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Pr...This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Previous research primarily focused on integrating reservoir,wellbore,and surface facility constraints,often resulting in broad constraint ranges and slow model convergence.To solve this problem,the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs,while considering extreme peak-shaving demands.This approach effectively narrows the constraint range.Subsequently,a collaborative optimization model with maximum gas production as the objective function is established,and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques.Finally,this methodology was applied to optimize operational parameters for Gas Storage T.The results demonstrate:(1)The convergence of the model was achieved after 6 iterations,which significantly improved the convergence speed of the model;(2)The maximum working gas volume reached 11.605×10^(8) m^(3),which increased by 13.78%compared with the traditional optimization method;(3)This method greatly improves the operation safety and the ultimate peak load balancing capability.The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability.展开更多
The hot spot temperature of a transformer is one of the critical indicators reflecting its operating status.Accurate and fast calculation of hot spot temperature is significant for the online monitoring of transformer...The hot spot temperature of a transformer is one of the critical indicators reflecting its operating status.Accurate and fast calculation of hot spot temperature is significant for the online monitoring of transformers.Considering the low computational efficiency of the transformer’s numerical full model(FM),this paper presents a model simplification method based on the equivalent thermal parameters of windings to expedite hot spot temperature computation.Initially,the representative volume element(RVE)reflecting the periodic structure of windings is selected to formulate a reduced model(RM)for the transformer.Subsequently,to achieve equivalence between the RM and the FM,the equivalent thermal parameters of the RVE are calculated,containing the equivalent thermal conductivity(ETC),the equivalent density(ED),and the equivalent specific heat capacity(ESHC).Finally,the validity of the RM is verified by the temperature rise test.The results show that,compared with the tested data,the maximum error of the hot spot temperature calculated by the RM is 2.56 K,demonstrating the accuracy of the hot spot temperature calculation by the RM.Compared with the FM,the computing time of the proposed RM is reduced to 1/189,which significantly improves the computational efficiency.展开更多
The Carter model is used to characterize the dynamic behaviors of fracture growth and fracturing fluid leakoff.A thermo-fluid coupling temperature response forward model is built considering the fluid flow and heat tr...The Carter model is used to characterize the dynamic behaviors of fracture growth and fracturing fluid leakoff.A thermo-fluid coupling temperature response forward model is built considering the fluid flow and heat transfer in wellbore,fracture and reservoir.The influences of fracturing parameters and fracture parameters on the responses of distributed temperature sensing(DTS)are analyzed,and a diagnosis method of fracture parameters is presented based on the simulated annealing algorithm.A field case study is introduced to verify the model’s reliability.Typical V-shaped characteristics can be observed from the DTS responses in the multi-cluster fracturing process,with locations corresponding to the hydraulic fractures.The V-shape depth is shallower for a higher injection rate and longer fracturing and shut-in time.Also,the V-shape is wider for a higher fracture-surface leakoff coefficient,longer fracturing time and smaller fracture width.Additionally,the cooling effect near the wellbore continues to spread into the reservoir during the shut-in period,causing the DTS temperature to decrease instead of rise.Real-time monitoring and interpretation of DTS temperature data can help understand the fracture propagation during fracturing operation,so that immediate measures can be taken to improve the fracturing performance.展开更多
Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache...Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.展开更多
Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock propert...Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock properties.Given the multiscale characteristics of rock pore structures,direct three-dimensional imaging at sub-micrometer and nanometer scales is typically infeasible.This study introduces a method for reconstructing porous media using multidimensional data,which combines one-dimensional pore structure parameters with two-dimensional images to reconstruct three-dimensional models.The pore network model(PNM)is stochastically reconstructed using one-dimensional parameters,and a generative adversarial network(GAN)is utilized to equip the PNM with pore morphologies derived from two-dimensional images.The digital rocks generated by this method possess excellent controllability.Using Berea sandstone and Grosmont carbonate samples,we performed digital rock reconstructions based on PNM extracted by the maximum ball algorithm and compared them with stochastically reconstructed PNM.Pore structure parameters,permeability,and formation factors were calculated.The results show that the generated samples exhibit good consistency with real samples in terms of pore morphology,pore structure,and physical properties.Furthermore,our method effectively supplements the micropores not captured in CT images,demonstrating its potential in multiscale carbonate samples.Thus,the proposed reconstruction method is promising for advancing porous media property research.展开更多
The tensile-shear interactive damage(TSID)model is a novel and powerful constitutive model for rock-like materials.This study proposes a methodology to calibrate the TSID model parameters to simulate sandstone.The bas...The tensile-shear interactive damage(TSID)model is a novel and powerful constitutive model for rock-like materials.This study proposes a methodology to calibrate the TSID model parameters to simulate sandstone.The basic parameters of sandstone are determined through a series of static and dynamic tests,including uniaxial compression,Brazilian disc,triaxial compression under varying confining pressures,hydrostatic compression,and dynamic compression and tensile tests with a split Hopkinson pressure bar.Based on the sandstone test results from this study and previous research,a step-by-step procedure for parameter calibration is outlined,which accounts for the categories of the strength surface,equation of state(EOS),strain rate effect,and damage.The calibrated parameters are verified through numerical tests that correspond to the experimental loading conditions.Consistency between numerical results and experimental data indicates the precision and reliability of the calibrated parameters.The methodology presented in this study is scientifically sound,straightforward,and essential for improving the TSID model.Furthermore,it has the potential to contribute to other rock constitutive models,particularly new user-defined models.展开更多
Foot-and-mouth disease(FMD)is a viral disease that affects cloven-hoofed animals including cattle,pigs,and sheep,hence causing export bans among others,causing high economic losses due to reduced productivity.The glob...Foot-and-mouth disease(FMD)is a viral disease that affects cloven-hoofed animals including cattle,pigs,and sheep,hence causing export bans among others,causing high economic losses due to reduced productivity.The global effect of FMD is most felt where livestock rearing forms an important source of income.It is therefore important to understand the modes of transmission of FMD to control its spread and prevent its occurrence.This work intends to address these dynamics by including the efficacy of active migrant animals transporting the disease from one area to another in a fuzzy mathematical modeling framework.Historical models of epidemics are determinable with a set of deterministic parameters and this does not reflect on real-life scenarios as observed in FMD.Fuzzy theory is used in this model as it permits the inclusion of uncertainties in the model;this makes the model more of a reality regarding disease transmission.A time lag,in this case,denotes the incubation period and other time-related factors affecting the spread of FMD and,therefore,is added to the current model for FMD.To that purpose,the analysis of steady states and the basic reproduction number are performed and,in addition,the stability checks are conveyed in the fuzzy environment.For the numerical solution of the model,we derive the Forward Euler Method and the fuzzy delayed non-standard finite difference(FDNSFD)method.Analytical studies of the FDNSFD scheme are performed for convergence,non-negativity,boundedness,and consistency analysis of the numerical projection to guarantee that the numerical model is an accurate discretization of the continuous dynamics of FMD transmission over time.In the following simulation study,we show that the FDNSFD method preserves the characteristics of the constant model and still works if relatively large time steps are employed;this is a bonus over the normal finite difference technique.The study shows how valuable it is to adopt fuzzy theory and time delays when simulating the transmission of the epidemic,especially for such diseases as FMD where uncertainty and migration have a defining role in transmission.This approach gives more sound and flexible grounds for analyzing and controlling the outbreak of FMD in various situations.展开更多
基金supported via funding from Prince Sattam Bin Abdulaziz University project number(PSAU/2025/R/1446).
文摘Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.
基金supported by the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(No.GLAB 2024ZR03)the National Natural Science Foundation of China(No.42407248)+2 种基金the Guizhou Provincial Basic Research Program(Natural Science)(No.QKHJC-[2023]-YB066)the Key Laboratory of Smart Earth(No.KF2023YB04-02)the Fundamental Research Funds for the Central Universities。
文摘The constitutive model is essential for predicting the deformation and stability of rocksoil mass.The estimation of constitutive model parameters is a necessary and important task for the reliable characterization of mechanical behaviors.However,constitutive model parameters cannot be evaluated accurately with a limited amount of test data,resulting in uncertainty in the prediction of stress-strain curves.This paper proposes a Bayesian analysis framework to address this issue.It combines the Bayesian updating with the structural reliability and adaptive conditional sampling methods to assess the equation parameter of constitutive models.Based on the triaxial and ring shear tests on shear zone soils from the Huangtupo landslide,a statistical damage constitutive model and a critical state hypoplastic constitutive model were used to demonstrate the effectiveness of the proposed framework.Moreover,the parameter uncertainty effects of the damage constitutive model on landslide stability were investigated.Results show that reasonable assessments of the constitutive model parameter can be well realized.The variability of stress-strain curves is strongly related to the model prediction performance.The estimation uncertainty of constitutive model parameters should not be ignored for the landslide stability calculation.Our study provides a reference for uncertainty analysis and parameter assessment of the constitutive model.
基金National Natural Science Foundation of China (81973749 and 8143594)State Administration of Traditional Chinese Medicine High-level Chinese Medicine Key Discipline Construction Project (zyyzdxk-2023069)。
文摘Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Patients diagnosed with essential hypertension and admitted to Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shang-hai Hospital of Traditional Chinese Medicine,and Shanghai Hospital of Integrated Tradition-al Chinese and Western Medicine from July 6th 2020 to June 16th 2021,and from August 11th 2023 to January 22nd 2024,were enrolled in this retrospective research.The baselines and clinical biochemical indicators of patients were collected.The SMART-I TCM pulse instru-ment was applied to gather pulse diagram parameters.Multivariate logistic regression was adopted to analyze the risk factors for HTH.RStudio was employed to construct the nomo-gram model,receiver operating characteristic(ROC)curve,and calibration curve(bootstrap self-sampling 200 times),and clinical decision curve were drawn to evaluate the model’s dis-crimination and clinical effectiveness.Results A total of 168 hospitalized patients with essential hypertension were selected and di-vided into non-HTH group(n=29)and HTH group(n=139).Compared with non-HTH group,HTH group had a lower body mass index(BMI),and higher proportions of male pa-tients and drinkers(P<0.05).The ventricular wall thickening(VWT)could not be deter-mined.The proportions of left common carotid intima-media wall thickness(LCCIMWT)and serum creatinine(SCR)were higher in HTH group(P<0.05).The pulse diagram parameter As was significantly higher,and H4/H1 and T1/T were lower in HTH group(P<0.05).Gender,al-cohol consumption,serum creatinine,and the pulse diagram parameter H4/H1 were identi-fied as independent risk factors for HTH(P<0.05).The nomogram’s area under the ROC curve(AUC)was 0.795[95%confidence interval(CI):(0.7066,0.8828)],with a specificity of 0.724 and sensitivity of 0.799.After 200 times repeated bootstrap self-samplings,the calibra-tion curve showed that the simulated curve fits well with the actual curve(x^(2)=9.5002,P=0.3019).The clinical decision curve indicated that the nomogram’s applicability was optimal when the threshold for predicting HTH was between 0.38 and 1.00.Conclusion The nomogram model could be valuable for predicting the onset risk of HTH and pulse diagram parameters can facilitate early screening and prevention of HTH.
基金supported by the National Key Research and Development Program of China(No.2023YFC3010400)。
文摘The complex geometrical features of mechanical components significantly influence contact interactions and system dynamics.However,directly modeling contact forces on surfaces with intricate geometries presents considerable challenges.This study focuses on the helically twisted wire rope-sheave contact and proposes a contact force model that incorporates complex geometric features through a parameter identification approach.The model's impact on contact forces and system dynamics is thoroughly investigated.Leveraging a point contact model and an elliptic integral approximation,a loss function is formulated using the finite element(FE)contact model results as the reference data.Geometric parameters are subsequently determined by optimizing this loss function via a genetic algorithm(GA).The findings reveal that the contact stiffness increases with the wire rope pitch length,the radius of principal curvature,and the elliptic eccentricity of the contact zone.The proposed contact force model is integrated into a rigid-flexible coupled dynamics model,developed by the absolute node coordinate formulation,to examine the effects of contact geometry on system dynamics.The results demonstrate that the variations in wire rope geometry alter the contact stiffness,which in turn affects dynamic rope tension through frictional energy dissipation.The enhanced model's predictions exhibit superior alignment with the experimental data,thereby validating the methodology.This approach provides new insights for deducing the contact geometry from kinetic parameters and monitoring the performance degradation of mechanical components.
基金supported by the National Natural Science Foundation of China(Grant Nos.62303197,62273214)the Natural Science Foundation of Shandong Province(ZR2024MFO18).
文摘Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency.To address these challenges,we propose an adaptive multi-learning cooperation search algorithm(AMLCSA)for efficient identification of unknown parameters in PV models.AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises.It enhances the original cooperation search algorithm in two key aspects:(i)an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights,allowing better individuals to focus on local exploitation while guiding poorer individuals toward global exploration;and(ii)a chaotic grouping reflection strategy that introduces chaotic sequences to enhance population diversity and improve search performance.The effectiveness of AMLCSA is demonstrated on single-diode,double-diode,and three PV-module models.Simulation results show that AMLCSA offers significant advantages in convergence,accuracy,and stability compared to existing state-of-the-art algorithms.
基金supported by the National Natural Science Foundation of China (Grant Nos.12234016,12174317 for C.Wu,and 12474218 for R.Ma,Z.Fan,and T.Ma)Beijing Natural Science Foundation (Grant No.1242022 for R.Ma,Z.Fan,and T.Ma)the New Cornerstone Science Foundation。
文摘Recent discovery of high transition temperature superconductivity in La_(3)Ni_(2)O_(7) has sparked renewed theoretical and experimental interests in unconventional superconductivity. It is crucial to understand the influence of various factors on its superconductivity. By refining the determinant quantum Monte Carlo algorithm, we characterize the parameter dependence of the superconducting transition temperature within a bilayer Hubbard model, which is sign-problem-free at arbitrary filling. A striking feature of this model is its similarity to the bilayer nickelate-based superconductor La_(3)Ni_(2)O_(7), where superconductivity emerges from the bilayer NiO_(2) planes.We find that interlayer spin-exchange J is critical to interlayer pairing, and that on-site interaction U contributes negatively to superconductivity at low doping levels but positively at high doping levels. Our findings can provide a reference for the next step in theoretical research on nickelate-based superconductors.
文摘Accurate modeling and parameter estimation of sea clutter are fundamental for effective sea surface target detection.With the improvement of radar resolution,sea clutter exhibits a pronounced heavy-tailed characteristic,rendering traditional distribution models and parameter estimation methods less effective.To address this,this paper proposes a dual compound-Gaussian model with inverse Gaussian texture(CG-IG)distribution model and combines it with an improved Adam algorithm to introduce a method for parameter correction.This method effectively fits sea clutter with heavy-tailed characteristics.Experiments with real measured sea clutter data show that the dual CGIG distribution model,after parameter correction,accurately describes the heavy-tailed phenomenon in sea clutter amplitude distribution,and the overall mean square error of the distribution is reduced.
文摘This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.
基金supported by the National Natural Science Foundation of China (No.U2133210).
文摘Aircraft wake turbulence is an inherent outcome of aircraft flight,presenting a substan-tial challenge to air traffic control,aviation safety and operational efficiency.Building upon data obtained from coherent Doppler Lidar detection,and combining Dynamic Bayesian Networks(DBN)with Genetic Algorithm-optimized Backpropagation Neural Networks(GA-BPNN),this paper proposes a model for the inversion of wake vortex parameters.During the wake vortex flow field simulation analysis,the wind and turbulent environment were initially superimposed onto the simulated wake velocity field.Subsequently,Lidar-detected echoes of the velocity field are simulated to obtain a data set similar to the actual situation for model training.In the case study validation,real measured data underwent preprocessing and were then input into the established model.This allowed us to construct the wake vortex characteristic parameter inversion model.The final results demonstrated that our model achieved parameter inversion with only minor errors.In a practical example,our model in this paper significantly reduced the mean square error of the inverted velocity field when compared to the traditional algorithm.This study holds significant promise for real-time monitoring of wake vortices at airports,and is proved a crucial step in developing wake vortex interval standards.
基金Supported by Shanghai Municipal Science and Technology Program (Grant No.21511101701)National Key Research and Development Program of China (Grant No.2021YFC0122704)。
文摘A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair.With its lightweight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well suited for these robots.However,the coupled nature of the joint disrupts the direct linear relationship between the input and output torques,posing challenges for dynamic modeling and practical applications.This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics.Building on this foundation,the Newton-Euler method was used to develop a dynamic model for the entire robotic arm.A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to zero.An experimental method was designed to compensate for gravity,inertia,and modeling errors to identify the parameters of the friction model.This method establishes a mapping relationship between the friction force and motor current.In addition,a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm.Trajectory tracking experiments were conducted during the experimental validation phase,demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm.This study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms,thereby establishing a foundation for motion control in humanoid nursing robots.
基金support of the Key Research and Development Program of Shandong Province of China(grant no.2021ZLGX01)Shandong Provincial Key Research and Development Program(Major Scientific and Technological Innovation Project),China(grant no.2021CXGC010206).
文摘According to a high-temperature compression test of rare earth magnesium alloy(WE43),a strain-compensated constitutive model of the Arrhenius equation based on Zener-Hollomon parameters was established,and the rheological behaviors were predicted.The model exhibited relatively serious prediction distortion in the low-temperature and high-strain rate parameter interval,and its accuracy was still unsatisfactory even after modification by a correction operator considering the coupling of temperature and strain rate.The microstructure characterization and statistical analysis showed that a large number of twinning occurred in the parameter intervals with prediction deviation.The occurrence of twinning complicated the local internal stress distribution by drastically changing the crystal orientation and led to significant fluctuations in the macroscopic strain-stress and hardening curves relative to the rheological processes dominated by the dislocation and softening mechanisms,making the logarithm of the strain rate and stress deviate from the linear relationship.This twinning phenomenon was greatly influenced by the temperature and strain rate.Herein,the influence mechanism on twinning behavior was analyzed from the perspective of the interaction of dislocation and twinning.
基金financed by the Department of education of Gansu Province:Young Doctor Fund Project(2022QB-029)the Fundamental Research Funds for the Central Universities(31920240125-06,31920240059)+1 种基金the Scientific Research Project of Introducing Talents of Northwest Minzu University(xbmuyjrc202215,xbmuyjrc202216)the National Natural Science Foundation of China(22178289).
文摘Coal has a highly complex chemical structure,similar to polymers,coal is a macromolecular structure composed of a large number of“similar compounds”,which is called the basic structural unit.Understanding coal structure is the basis of its transformation and utilization.Shendong(SD)coal was analyzed by FTIR,XRD,XPS,and NMR.The results show that SD coal normalized structure formula is C_(100)H_(68.5)O_(35.7)N_(1.2)S_(0.2)and the average number of aromatic rings is 1.98.-CH_(2)-content accounts for about 82%in aliphatic CeH region,and the ratio of ether bond CeO,aromatic ether C-O and C=O is about 2:1:11 in oxygen-containing functional group region.The d_(002),L_(C),L_(a)and N_(C)of S_(D)coal microcrystalline structure parameters are 0.1832 nm,1.4688 nm,2.0852 nm and 9.017,respectively.Aromatic carbon and aliphatic carbon ratios of SD coal are 55.67%and 29.97%,aromatic cluster size and average methylene chain length are 0.224 and 1.817.Based on these structural parameters,molecular model of SD coal was constructed with^(13)C SSNMR experimental spectra as a reference.The model was constructed with an atom composition of C_(214)H_(214)O_(49)N_(2)S.
基金a derivative product of the project INV-ING-3788 financed by the Vicerectory of Research of the Universidad Militar Nueva Granada,validity 2023.
文摘The use of additive manufacturing techniques in the development of unconventional materials can help reduce the environmental impact of traditional construction materials.In this paper,the properties of a 3D-printed biocomposite were evaluated.Biofilaments obtained by mixing pulverized bamboo fibers with polylactic acid(PLA)resin were extruded during the manufacturing process.To assess the effect of incorporating plant fibers,an analysis was conducted on the morphology,elemental chemical composition,crystallinity index,principal functional groups,thermal stability,surface roughness,microhardness,density,tensile strength,elastic modulus,and strain percentage of reinforced samples.The results were comparedwith those obtained from the characterization of standard PLAfilaments(unreinforced).The fused deposition modeling(FDM)technique was employed to print biocomposite specimens.Additionally,the influence of the printing parameters(infill density,build orientation,and layer thickness)on the physical,tribological,andmechanical properties of the biocomposites was analyzed.These results were compared with those obtained for specimens printed with pure PLA.The findings indicate that incorporating 10%vegetable filler into PLA filaments enhanced the strength and stiffness of the biocomposite under axial loads.Finally,the strength of the biocomposite subjected to axial loads was compared with the standardized values for wood-plastic composites,demonstrating the feasibility of its use for non-structural purposes in civil construction.
基金supported by the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202401501,KJZD-M202401501).
文摘This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Previous research primarily focused on integrating reservoir,wellbore,and surface facility constraints,often resulting in broad constraint ranges and slow model convergence.To solve this problem,the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs,while considering extreme peak-shaving demands.This approach effectively narrows the constraint range.Subsequently,a collaborative optimization model with maximum gas production as the objective function is established,and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques.Finally,this methodology was applied to optimize operational parameters for Gas Storage T.The results demonstrate:(1)The convergence of the model was achieved after 6 iterations,which significantly improved the convergence speed of the model;(2)The maximum working gas volume reached 11.605×10^(8) m^(3),which increased by 13.78%compared with the traditional optimization method;(3)This method greatly improves the operation safety and the ultimate peak load balancing capability.The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability.
基金supported by Hubei Technology Innovation Center for Smart Hydropower(SDCXZX-JJ-2023-03).
文摘The hot spot temperature of a transformer is one of the critical indicators reflecting its operating status.Accurate and fast calculation of hot spot temperature is significant for the online monitoring of transformers.Considering the low computational efficiency of the transformer’s numerical full model(FM),this paper presents a model simplification method based on the equivalent thermal parameters of windings to expedite hot spot temperature computation.Initially,the representative volume element(RVE)reflecting the periodic structure of windings is selected to formulate a reduced model(RM)for the transformer.Subsequently,to achieve equivalence between the RM and the FM,the equivalent thermal parameters of the RVE are calculated,containing the equivalent thermal conductivity(ETC),the equivalent density(ED),and the equivalent specific heat capacity(ESHC).Finally,the validity of the RM is verified by the temperature rise test.The results show that,compared with the tested data,the maximum error of the hot spot temperature calculated by the RM is 2.56 K,demonstrating the accuracy of the hot spot temperature calculation by the RM.Compared with the FM,the computing time of the proposed RM is reduced to 1/189,which significantly improves the computational efficiency.
基金Supported by the National High-Tech Research Project(GJSCB-HFGDY-2024-004)National Natural Science Foundation of China(12402305)+2 种基金Postdoctoral Fellowship Program of CPSF(GZC20232200)China Postdoctoral Science Foundation(2024M762703)Sichuan Science and Technology Program(2025ZNSFSC1352)。
文摘The Carter model is used to characterize the dynamic behaviors of fracture growth and fracturing fluid leakoff.A thermo-fluid coupling temperature response forward model is built considering the fluid flow and heat transfer in wellbore,fracture and reservoir.The influences of fracturing parameters and fracture parameters on the responses of distributed temperature sensing(DTS)are analyzed,and a diagnosis method of fracture parameters is presented based on the simulated annealing algorithm.A field case study is introduced to verify the model’s reliability.Typical V-shaped characteristics can be observed from the DTS responses in the multi-cluster fracturing process,with locations corresponding to the hydraulic fractures.The V-shape depth is shallower for a higher injection rate and longer fracturing and shut-in time.Also,the V-shape is wider for a higher fracture-surface leakoff coefficient,longer fracturing time and smaller fracture width.Additionally,the cooling effect near the wellbore continues to spread into the reservoir during the shut-in period,causing the DTS temperature to decrease instead of rise.Real-time monitoring and interpretation of DTS temperature data can help understand the fracture propagation during fracturing operation,so that immediate measures can be taken to improve the fracturing performance.
基金supported by the National Natural Science Foundation of China(Grant Nos.52090081,52079068)the State Key Laboratory of Hydroscience and Hydraulic Engineering(Grant No.2021-KY-04).
文摘Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.
基金supported by the Shandong Provincial Natural Science Foundation(ZR2024MD116)National Natural Science Foundation of China(Grant Nos.42174143,42004098)Technology Innovation Leading Program of Shaanxi(No.2024 ZC-YYDP-27).
文摘Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock properties.Given the multiscale characteristics of rock pore structures,direct three-dimensional imaging at sub-micrometer and nanometer scales is typically infeasible.This study introduces a method for reconstructing porous media using multidimensional data,which combines one-dimensional pore structure parameters with two-dimensional images to reconstruct three-dimensional models.The pore network model(PNM)is stochastically reconstructed using one-dimensional parameters,and a generative adversarial network(GAN)is utilized to equip the PNM with pore morphologies derived from two-dimensional images.The digital rocks generated by this method possess excellent controllability.Using Berea sandstone and Grosmont carbonate samples,we performed digital rock reconstructions based on PNM extracted by the maximum ball algorithm and compared them with stochastically reconstructed PNM.Pore structure parameters,permeability,and formation factors were calculated.The results show that the generated samples exhibit good consistency with real samples in terms of pore morphology,pore structure,and physical properties.Furthermore,our method effectively supplements the micropores not captured in CT images,demonstrating its potential in multiscale carbonate samples.Thus,the proposed reconstruction method is promising for advancing porous media property research.
基金funded by the National Natural Science Foundation of China(Grant No.12272247)National Key Project(Grant No.GJXM92579)Major Research and Development Project of Metallurgical Corporation of China Ltd.in the Non-Steel Field(Grant No.2021-5).
文摘The tensile-shear interactive damage(TSID)model is a novel and powerful constitutive model for rock-like materials.This study proposes a methodology to calibrate the TSID model parameters to simulate sandstone.The basic parameters of sandstone are determined through a series of static and dynamic tests,including uniaxial compression,Brazilian disc,triaxial compression under varying confining pressures,hydrostatic compression,and dynamic compression and tensile tests with a split Hopkinson pressure bar.Based on the sandstone test results from this study and previous research,a step-by-step procedure for parameter calibration is outlined,which accounts for the categories of the strength surface,equation of state(EOS),strain rate effect,and damage.The calibrated parameters are verified through numerical tests that correspond to the experimental loading conditions.Consistency between numerical results and experimental data indicates the precision and reliability of the calibrated parameters.The methodology presented in this study is scientifically sound,straightforward,and essential for improving the TSID model.Furthermore,it has the potential to contribute to other rock constitutive models,particularly new user-defined models.
文摘Foot-and-mouth disease(FMD)is a viral disease that affects cloven-hoofed animals including cattle,pigs,and sheep,hence causing export bans among others,causing high economic losses due to reduced productivity.The global effect of FMD is most felt where livestock rearing forms an important source of income.It is therefore important to understand the modes of transmission of FMD to control its spread and prevent its occurrence.This work intends to address these dynamics by including the efficacy of active migrant animals transporting the disease from one area to another in a fuzzy mathematical modeling framework.Historical models of epidemics are determinable with a set of deterministic parameters and this does not reflect on real-life scenarios as observed in FMD.Fuzzy theory is used in this model as it permits the inclusion of uncertainties in the model;this makes the model more of a reality regarding disease transmission.A time lag,in this case,denotes the incubation period and other time-related factors affecting the spread of FMD and,therefore,is added to the current model for FMD.To that purpose,the analysis of steady states and the basic reproduction number are performed and,in addition,the stability checks are conveyed in the fuzzy environment.For the numerical solution of the model,we derive the Forward Euler Method and the fuzzy delayed non-standard finite difference(FDNSFD)method.Analytical studies of the FDNSFD scheme are performed for convergence,non-negativity,boundedness,and consistency analysis of the numerical projection to guarantee that the numerical model is an accurate discretization of the continuous dynamics of FMD transmission over time.In the following simulation study,we show that the FDNSFD method preserves the characteristics of the constant model and still works if relatively large time steps are employed;this is a bonus over the normal finite difference technique.The study shows how valuable it is to adopt fuzzy theory and time delays when simulating the transmission of the epidemic,especially for such diseases as FMD where uncertainty and migration have a defining role in transmission.This approach gives more sound and flexible grounds for analyzing and controlling the outbreak of FMD in various situations.
