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
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
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
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
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
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).
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 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.
