This paper presents a comprehensive treatment of the parametric sensitivity and runaway in fixed bed reactors with one dimensional pseudo homogeneous dispersion model (ODDM). In this case, we find the existence of m...This paper presents a comprehensive treatment of the parametric sensitivity and runaway in fixed bed reactors with one dimensional pseudo homogeneous dispersion model (ODDM). In this case, we find the existence of multiplicity and determine the runaway criterion through the critical isodisper sion curve. The calculated results indicate when the axial dispersion is relatively small, the impact of the axial dispersion on the parametric sensitivity may be neglected; but when the axial dispersion is large, this impact must be considered.展开更多
Shot and step response measurements were carried out with inert bed and adsorption bed both under iso-thermal conditions.Parameter values were determined from a time domain analysis of the measured inputand response s...Shot and step response measurements were carried out with inert bed and adsorption bed both under iso-thermal conditions.Parameter values were determined from a time domain analysis of the measured inputand response signal.Sensitivity test in the parameter values showed that shot response measurements maygive more reliable parameter values than step measurements.Since Kubin[1]and Kucera[2]proposed a parameter estimation technique based on a moment methodfor adsorption system,attention has been focused on dynamic input-output measurements with variouspacked bed systems for the parameter estimation.The object of this work is to compare shot and step re-sponse measurements and see which measurement gives more reliable parameter values.展开更多
A probabilistic framework for durability assessment of concrete structures in marine environments was proposed in terms of reliability and sensitivity analysis, which takes into account the uncertainties under the env...A probabilistic framework for durability assessment of concrete structures in marine environments was proposed in terms of reliability and sensitivity analysis, which takes into account the uncertainties under the environmental,material, structural and executional conditions. A time-dependent probabilistic model of chloride ingress was established first to consider the variations in various governing parameters, such as the chloride concentration,chloride diffusion coefficient, and age factor. Then the Nataf transformation was adopted to transform the nonnormal random variables from the original physical space into the independent standard Normal space. After that the durability limit state function and its gradient vector with respect to the original physical parameters were derived analytically, based on which the first-order reliability method was adopted to analyze the time-dependent reliability and parametric sensitivity of concrete structures in marine environments. The accuracy of the proposed method was verified by comparing with the second-order reliability method and the Monte Carlo simulation. Finally, the influences of environmental conditions, material properties, structural parameters and execution conditions on the time-dependent reliability of concrete structures in marine environments were also investigated. The proposed probabilistic framework can be implemented in the decision-making algorithm for the maintenance and repair of deteriorating concrete structures in marine environments.展开更多
Vertical height growth of hydraulic fractures(HFs)can unexpectedly penetrate a stratigraphic interface and propagate into neighboring layers,thereby resulting in low gas-production efficiency and high risk of groundwa...Vertical height growth of hydraulic fractures(HFs)can unexpectedly penetrate a stratigraphic interface and propagate into neighboring layers,thereby resulting in low gas-production efficiency and high risk of groundwater contamination or fault reactivation.Understanding of hydraulic fracture behavior at the interface is of pivotal importance for the successful development of layered reservoirs.In this paper,a twodimensional analytical model was developed to examine HF penetration and termination behavior at an orthogonal interface between two dissimilar materials.This model involves changes in the stress singularity ahead of the HF tip,which may alter at the formation interface due to material heterogeneity.Three critical stress conditions were considered to assess possible fracture behavior(i.e.,crossing,slippage,and opening)at the interface.Then,this model was verified by comparing its theoretical predictions to numerical simulations and three independent experiments.Good agreement with the simulation results and experimental data was observed,which shows the validity and reliability of this model.Finally,a parametric study was conducted to investigate the effects of key formation parameters(elastic modulus,Poisson’s ratio,and fracture toughness)between adjacent layers.These results indicate that the variation in the introduced parameters can limit or promote vertical HF growth by redistributing the induced normal and shear stresses at the interface.Among the three studied parameters,Poisson’s ratio has the least influence on the formation interface.When the fracture toughness and elastic modulus of the bounding layer are larger than those of the pay zone layer,the influence of fracture toughness will dominate the HF behavior at the interface;otherwise,the HF behavior will more likely be influenced by elastic modulus.展开更多
Kinetics model was developed for the mixed （steam and dry） reforming of methane, which is useful for the control of H2/CO ratio. The equilibrium constants of reaction rate were determined using the experimental equi...Kinetics model was developed for the mixed （steam and dry） reforming of methane, which is useful for the control of H2/CO ratio. The equilibrium constants of reaction rate were determined using the experimental equilibrium data at different reaction temperatures, while the forward reaction rate constants were estimated using the experimental data under non-equilibrium （high inert fraction and high space velocity） conditions. The comparison between calculated and experimental data clearly showed that the developed model described satisfactorily, and further analysis using the parametric sensitivity determined the wall temperature and CO2 fraction in the feed gas as effective parameters for the manipulation of CH4 conversion and H2/CO ratio of synthesis gas under the equilibrium condition. Meanwhile, the inert fraction, rather than the residence time, was selected as additional parameter under non-equilibrium condition.展开更多
Microwave precondition has been highlighted as a promising technology for softening the rock mass prior to rock breakage by machine to reduce drill bit/cutter wear as well as inverse production rate.To numerically exp...Microwave precondition has been highlighted as a promising technology for softening the rock mass prior to rock breakage by machine to reduce drill bit/cutter wear as well as inverse production rate.To numerically explore the effect of numerical parameters on rock static strength simulation,and determine the numerical mechanical parameters of microwave-treated basalts for future drilling and cutting simulations,numerical models of uniaxial compression strength(UCS)and Brazilian tensile strength(BTS)were established with the coupling of smoothed particle hydrodynamics and finite element method(SPH-FEM).To eliminate the large rock strength errors caused by microwave-induced damage,the cohesion and internal friction angle of microwave-treated basalt specimens with the same microwave treatment parameters were calibrated based on a linear Mohr-Coulomb theory.Based on parametric sensitivity analysis of SPH simulation of UCS and BTS,experimental UCS and BTS values were simultaneously captured according to the same set of calibrated cohesion and internal friction angle data,and the UCS modeling results are in good agreement with experimental tests.Furthermore,the effect of microwave irradiation parameter on the basalt mechanical behaviors was evaluated.展开更多
Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading blastholes.This condition,known as hot-hole blasting,is dangerous due to the inc...Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading blastholes.This condition,known as hot-hole blasting,is dangerous due to the increased possibility of premature explosions in loaded blastholes.Thus,it is crucial to load the blastholes with an appropriate amount of explosives within a short period to avoid premature detonation caused by high temperatures of blastholes.Additionally,it will help achieve the desired fragment size.This study tried to ascertain the most influencial variables of mean fragment size and their optimum values adopted for blasting in a fiery seam.Data on blast design,rock mass,and fragmentation of 100 blasts in fiery seams of a coal mine were collected and used to develop mean fragmentation prediction models using soft computational techniques.The coefficient of determination(R^(2)),root mean square error(RMSE),mean absolute error(MAE),mean square error(MSE),variance account for(VAF)and coefficient of efficiency in percentage(CE)were calculated to validate the results.It indicates that the random forest algorithm(RFA)outperforms the artificial neural network(ANN),response surface method(RSM),and decision tree(DT).The values of R^(2),RMSE,MAE,MSE,VAF,and CE for RFA are 0.94,0.034,0.027,0.001,93.58,and 93.01,respectively.Multiple parametric sensitivity analyses(MPSAs)of the input variables showed that the Schmidt hammer rebound number and spacing-to-burden ratio are the most influencial variables for the blast fragment size.The analysis was finally used to define the best blast design variables to achieve optimum fragment size from blasting.The optimum factor values for RFA of S/B,ld/B and ls/ld are 1.03,1.85 and 0.7,respectively.展开更多
The discrepancy between pseudo-homogeneous one-dimensional model and pseudo-homogeneous two-dimensional model is studied. It is found that there are great differences between two models. This paper compares the maximu...The discrepancy between pseudo-homogeneous one-dimensional model and pseudo-homogeneous two-dimensional model is studied. It is found that there are great differences between two models. This paper compares the maximum and minimum values of the radial temperature in the hot spot in case that a single exothermic reaction is carried out, a correlation is obtained with pseudo-homogeneous one-dimensional model to describe the entire reactor behavior. A new runaway criterion, based on the occurrence of inflection in the hot spot locus, is developed for the case of pseudo-homogeneous two-dimensional model. This criterion predicts the maximum allowable temperature for safe operation and the regions of runaway, respectively. The calculated results show that, compared with the results based on pseudo-homogeneous one-dimensional model, runaway will easily occur when the radial temperature gradient has to be considered.展开更多
By integrating advanced digital technologies such as cloud computing and the Internet of Things in sensor measurement,information communication,and other fields,the digital DC distribution network can efficiently and ...By integrating advanced digital technologies such as cloud computing and the Internet of Things in sensor measurement,information communication,and other fields,the digital DC distribution network can efficiently and reliably access DistributedGenerator(DG)and Energy Storage Systems(ESS),exhibiting significant advantages in terms of controllability and meeting requirements of Plug-and-Play(PnP)operations.However,during device plug-in and-out processes,improper systemparametersmay lead to small-signal stability issues.Therefore,before executing PnP operations,conducting stability analysis and adjusting parameters swiftly is crucial.This study introduces a four-stage strategy for parameter optimization to enhance systemstability efficiently.In the first stage,state-of-the-art technologies in measurement and communication are utilized to correct model parameters.Then,a novel indicator is adopted to identify the key parameters that influence stability in the second stage.Moreover,in the third stage,a local-parameter-tuning strategy,which leverages rapid parameter boundary calculations as a more efficient alternative to plotting root loci,is used to tune the selected parameters.Considering that the local-parameter-tuning strategy may fail due to some operating parameters being limited in adjustment,a multiparameter-tuning strategy based on the particle swarm optimization(PSO)is proposed to comprehensively adjust the dominant parameters to improve the stability margin of the system.Lastly,system stability is reassessed in the fourth stage.The proposed parameter-optimization strategy’s effectiveness has been validated through eigenvalue analysis and nonlinear time-domain simulations.展开更多
Machine learning has started to be used in engine research to optimize combustion and predict fuel spray characteristics.This paper presents the development of a machine learning model using a Genetic Algorithm-Backpr...Machine learning has started to be used in engine research to optimize combustion and predict fuel spray characteristics.This paper presents the development of a machine learning model using a Genetic Algorithm-Backpropagation(GA-BP)neural network to predict spray penetration.The GA-BP neural network was selected for its ability to optimize neural network weights and thresholds,thereby improving model convergence and avoiding local minima,which are common challenges in complex,non-linear problems such as spray prediction.The model was trained using experimental data from diesel injector spray tests,and its accuracy was evaluated through parametric sensitivity analysis,examining the influence of various input factors.A comparison between the machine learning model and the traditional empirical formulas of spray penetration revealed that the machine learning model achieved greater accuracy.In terms of the sensitivity to inputs,it is interesting to find that the cognition of machines is different from that of humans.When an input parameter does not have any functional relationship with other input parameters,the absence of this input parameter will lead to a significant decrease in the accuracy of the output result.The results demonstrate that the machine learning approach offers higher accuracy and better generalizability compared to traditional empirical methods.This study recommends the ways to get better results of penetration prediction with BP neural networks,which is efficient in training and utilizing Artificial Neural Networks(ANNs).展开更多
文摘This paper presents a comprehensive treatment of the parametric sensitivity and runaway in fixed bed reactors with one dimensional pseudo homogeneous dispersion model (ODDM). In this case, we find the existence of multiplicity and determine the runaway criterion through the critical isodisper sion curve. The calculated results indicate when the axial dispersion is relatively small, the impact of the axial dispersion on the parametric sensitivity may be neglected; but when the axial dispersion is large, this impact must be considered.
文摘Shot and step response measurements were carried out with inert bed and adsorption bed both under iso-thermal conditions.Parameter values were determined from a time domain analysis of the measured inputand response signal.Sensitivity test in the parameter values showed that shot response measurements maygive more reliable parameter values than step measurements.Since Kubin[1]and Kucera[2]proposed a parameter estimation technique based on a moment methodfor adsorption system,attention has been focused on dynamic input-output measurements with variouspacked bed systems for the parameter estimation.The object of this work is to compare shot and step re-sponse measurements and see which measurement gives more reliable parameter values.
基金financially supported by National Natural Science Foundation of China(Grant Nos.51168003,51368006 and51478125)the Major Project of Guangxi Natural Science Foundation(Grant No.2012GXNSFEA053002)+1 种基金Program for Distinguished Scholars and High-Level Innovative Research Team of Guangxi Higher Education(Grant No.GJR-2013-38)the Guangxi Science and Technology Development Program(Grant No.1377001-11)
文摘A probabilistic framework for durability assessment of concrete structures in marine environments was proposed in terms of reliability and sensitivity analysis, which takes into account the uncertainties under the environmental,material, structural and executional conditions. A time-dependent probabilistic model of chloride ingress was established first to consider the variations in various governing parameters, such as the chloride concentration,chloride diffusion coefficient, and age factor. Then the Nataf transformation was adopted to transform the nonnormal random variables from the original physical space into the independent standard Normal space. After that the durability limit state function and its gradient vector with respect to the original physical parameters were derived analytically, based on which the first-order reliability method was adopted to analyze the time-dependent reliability and parametric sensitivity of concrete structures in marine environments. The accuracy of the proposed method was verified by comparing with the second-order reliability method and the Monte Carlo simulation. Finally, the influences of environmental conditions, material properties, structural parameters and execution conditions on the time-dependent reliability of concrete structures in marine environments were also investigated. The proposed probabilistic framework can be implemented in the decision-making algorithm for the maintenance and repair of deteriorating concrete structures in marine environments.
基金supported by the National Natural Science Foundation of China(No.52064006,52164001 and 52004072)the Guizhou Provincial Science and Technology Foundation(No.[2020]4Y044,No.[2021]292,No.GCC[2022]005 and[2021]N404)the China Scholarship Council program(202006050112)
文摘Vertical height growth of hydraulic fractures(HFs)can unexpectedly penetrate a stratigraphic interface and propagate into neighboring layers,thereby resulting in low gas-production efficiency and high risk of groundwater contamination or fault reactivation.Understanding of hydraulic fracture behavior at the interface is of pivotal importance for the successful development of layered reservoirs.In this paper,a twodimensional analytical model was developed to examine HF penetration and termination behavior at an orthogonal interface between two dissimilar materials.This model involves changes in the stress singularity ahead of the HF tip,which may alter at the formation interface due to material heterogeneity.Three critical stress conditions were considered to assess possible fracture behavior(i.e.,crossing,slippage,and opening)at the interface.Then,this model was verified by comparing its theoretical predictions to numerical simulations and three independent experiments.Good agreement with the simulation results and experimental data was observed,which shows the validity and reliability of this model.Finally,a parametric study was conducted to investigate the effects of key formation parameters(elastic modulus,Poisson’s ratio,and fracture toughness)between adjacent layers.These results indicate that the variation in the introduced parameters can limit or promote vertical HF growth by redistributing the induced normal and shear stresses at the interface.Among the three studied parameters,Poisson’s ratio has the least influence on the formation interface.When the fracture toughness and elastic modulus of the bounding layer are larger than those of the pay zone layer,the influence of fracture toughness will dominate the HF behavior at the interface;otherwise,the HF behavior will more likely be influenced by elastic modulus.
基金supported by the Energy Efficiency & Resources Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea Government Ministry of Knowledge Economy (No. 2006CCC11P011B-21-2-100)Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2010-0003380)
文摘Kinetics model was developed for the mixed （steam and dry） reforming of methane, which is useful for the control of H2/CO ratio. The equilibrium constants of reaction rate were determined using the experimental equilibrium data at different reaction temperatures, while the forward reaction rate constants were estimated using the experimental data under non-equilibrium （high inert fraction and high space velocity） conditions. The comparison between calculated and experimental data clearly showed that the developed model described satisfactorily, and further analysis using the parametric sensitivity determined the wall temperature and CO2 fraction in the feed gas as effective parameters for the manipulation of CH4 conversion and H2/CO ratio of synthesis gas under the equilibrium condition. Meanwhile, the inert fraction, rather than the residence time, was selected as additional parameter under non-equilibrium condition.
基金the National Natural Science Foundation of China (No. 51774323)the Natural Science Foundation of Hunan Province, China (No. 2020JJ4704)+1 种基金the Fundamental Research Funds for the Central Universities of Central South University, China (No. 2018zzts216) the financial support from the China Scholarship Councilthe support of the high-performance computer from Compute Canada
文摘Microwave precondition has been highlighted as a promising technology for softening the rock mass prior to rock breakage by machine to reduce drill bit/cutter wear as well as inverse production rate.To numerically explore the effect of numerical parameters on rock static strength simulation,and determine the numerical mechanical parameters of microwave-treated basalts for future drilling and cutting simulations,numerical models of uniaxial compression strength(UCS)and Brazilian tensile strength(BTS)were established with the coupling of smoothed particle hydrodynamics and finite element method(SPH-FEM).To eliminate the large rock strength errors caused by microwave-induced damage,the cohesion and internal friction angle of microwave-treated basalt specimens with the same microwave treatment parameters were calibrated based on a linear Mohr-Coulomb theory.Based on parametric sensitivity analysis of SPH simulation of UCS and BTS,experimental UCS and BTS values were simultaneously captured according to the same set of calibrated cohesion and internal friction angle data,and the UCS modeling results are in good agreement with experimental tests.Furthermore,the effect of microwave irradiation parameter on the basalt mechanical behaviors was evaluated.
文摘Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading blastholes.This condition,known as hot-hole blasting,is dangerous due to the increased possibility of premature explosions in loaded blastholes.Thus,it is crucial to load the blastholes with an appropriate amount of explosives within a short period to avoid premature detonation caused by high temperatures of blastholes.Additionally,it will help achieve the desired fragment size.This study tried to ascertain the most influencial variables of mean fragment size and their optimum values adopted for blasting in a fiery seam.Data on blast design,rock mass,and fragmentation of 100 blasts in fiery seams of a coal mine were collected and used to develop mean fragmentation prediction models using soft computational techniques.The coefficient of determination(R^(2)),root mean square error(RMSE),mean absolute error(MAE),mean square error(MSE),variance account for(VAF)and coefficient of efficiency in percentage(CE)were calculated to validate the results.It indicates that the random forest algorithm(RFA)outperforms the artificial neural network(ANN),response surface method(RSM),and decision tree(DT).The values of R^(2),RMSE,MAE,MSE,VAF,and CE for RFA are 0.94,0.034,0.027,0.001,93.58,and 93.01,respectively.Multiple parametric sensitivity analyses(MPSAs)of the input variables showed that the Schmidt hammer rebound number and spacing-to-burden ratio are the most influencial variables for the blast fragment size.The analysis was finally used to define the best blast design variables to achieve optimum fragment size from blasting.The optimum factor values for RFA of S/B,ld/B and ls/ld are 1.03,1.85 and 0.7,respectively.
基金Supported by the Tianjin Natural Science Foundation.
文摘The discrepancy between pseudo-homogeneous one-dimensional model and pseudo-homogeneous two-dimensional model is studied. It is found that there are great differences between two models. This paper compares the maximum and minimum values of the radial temperature in the hot spot in case that a single exothermic reaction is carried out, a correlation is obtained with pseudo-homogeneous one-dimensional model to describe the entire reactor behavior. A new runaway criterion, based on the occurrence of inflection in the hot spot locus, is developed for the case of pseudo-homogeneous two-dimensional model. This criterion predicts the maximum allowable temperature for safe operation and the regions of runaway, respectively. The calculated results show that, compared with the results based on pseudo-homogeneous one-dimensional model, runaway will easily occur when the radial temperature gradient has to be considered.
基金supported by State Grid Information and Telecommunication Group Scientific and Technological Innovation Project“Research on Power Digital Space Technology System and Key Technologies”(Program No.SGIT0000XMJS2310456).
文摘By integrating advanced digital technologies such as cloud computing and the Internet of Things in sensor measurement,information communication,and other fields,the digital DC distribution network can efficiently and reliably access DistributedGenerator(DG)and Energy Storage Systems(ESS),exhibiting significant advantages in terms of controllability and meeting requirements of Plug-and-Play(PnP)operations.However,during device plug-in and-out processes,improper systemparametersmay lead to small-signal stability issues.Therefore,before executing PnP operations,conducting stability analysis and adjusting parameters swiftly is crucial.This study introduces a four-stage strategy for parameter optimization to enhance systemstability efficiently.In the first stage,state-of-the-art technologies in measurement and communication are utilized to correct model parameters.Then,a novel indicator is adopted to identify the key parameters that influence stability in the second stage.Moreover,in the third stage,a local-parameter-tuning strategy,which leverages rapid parameter boundary calculations as a more efficient alternative to plotting root loci,is used to tune the selected parameters.Considering that the local-parameter-tuning strategy may fail due to some operating parameters being limited in adjustment,a multiparameter-tuning strategy based on the particle swarm optimization(PSO)is proposed to comprehensively adjust the dominant parameters to improve the stability margin of the system.Lastly,system stability is reassessed in the fourth stage.The proposed parameter-optimization strategy’s effectiveness has been validated through eigenvalue analysis and nonlinear time-domain simulations.
基金supported by EPSRC(Engineering and Physical Sciences Research Council,United Kingdom)(Grant numbers:EP/W002299/1,EP/Y024605/1).
文摘Machine learning has started to be used in engine research to optimize combustion and predict fuel spray characteristics.This paper presents the development of a machine learning model using a Genetic Algorithm-Backpropagation(GA-BP)neural network to predict spray penetration.The GA-BP neural network was selected for its ability to optimize neural network weights and thresholds,thereby improving model convergence and avoiding local minima,which are common challenges in complex,non-linear problems such as spray prediction.The model was trained using experimental data from diesel injector spray tests,and its accuracy was evaluated through parametric sensitivity analysis,examining the influence of various input factors.A comparison between the machine learning model and the traditional empirical formulas of spray penetration revealed that the machine learning model achieved greater accuracy.In terms of the sensitivity to inputs,it is interesting to find that the cognition of machines is different from that of humans.When an input parameter does not have any functional relationship with other input parameters,the absence of this input parameter will lead to a significant decrease in the accuracy of the output result.The results demonstrate that the machine learning approach offers higher accuracy and better generalizability compared to traditional empirical methods.This study recommends the ways to get better results of penetration prediction with BP neural networks,which is efficient in training and utilizing Artificial Neural Networks(ANNs).
