Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s.The prediction of sea ice is highly important,but accurate simulation of sea ice v...Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s.The prediction of sea ice is highly important,but accurate simulation of sea ice variations remains highly challenging.For improving model performance,sensitivity experiments were conducted using the coupled ocean and sea ice model(NEMO-LIM),and the simulation results were compared against satellite observations.Moreover,the contribution ratios of dynamic and thermodynamic processes to sea ice variations were analyzed.The results show that the performance of the model in reconstructing the spatial distribution of Arctic sea ice is highly sensitive to ice strength decay constant(C^(rhg)).By reducing the C^(rhg) constant,the sea ice compressive strength increases,leading to improved simulated sea ice states.The contribution of thermodynamic processes to sea ice melting was reduced due to less deformation and fracture of sea ice with increased compressive strength.Meanwhile,dynamic processes constrained more sea ice to the central Arctic Ocean and contributed to the increases in ice concentration,reducing the simulation bias in the central Arctic Ocean in summer.The root mean square error(RMSE)between modeled and the CryoSat-2/SMOS satellite observed ice thickness was reduced in the compressive strength-enhanced model solution.The ice thickness,especially of multiyear thick ice,was also reduced and matched with the satellite observation better in the freezing season.These provide an essential foundation on exploring the response of the marine ecosystem and biogeochemical cycling to sea ice changes.展开更多
In order to investigate the effect o f some factors on the unconfined compressive strength(UCS)for composite soil stabilizer-stabilized gravel soil(CSSSGS),the orthogonal test is adopted to set up the experimental sch...In order to investigate the effect o f some factors on the unconfined compressive strength(UCS)for composite soil stabilizer-stabilized gravel soil(CSSSGS),the orthogonal test is adopted to set up the experimental scheme.Three levels o f each factor armconsidered to obtain the change laws o f UCS,in which the binder dosages are8%,10%,and12%;the curing times ae7,14and21d;the gradation nae0.3,0.35and0.4;and the degrees of compaction are95%,97%,and99%.The range analysis clearly indicates that the influence degree o f the four factors on UCS is in such an order:dosage,age,gradation,and degree o f compaction.The variance analysis shows that only the composite soil stabilizer dosage can significantly affect UCS.In road construction,the examination o f composite soil stabilizer dosage and base-course maintenance should be given much more attention to obtain satisfactory base-course strength,compared w ith gradation floating and the change of degree o f compaction.展开更多
A three-dimensional model for the numerical simulation of casing-cement behavior is used to investigate residual strength in the perforated casing of ultra deep wells.The influence of the hole diameter,hole density an...A three-dimensional model for the numerical simulation of casing-cement behavior is used to investigate residual strength in the perforated casing of ultra deep wells.The influence of the hole diameter,hole density and phase angle on the residual strength of the casing under non-uniform stress and fracturing conditions is revealed through the consideration of different perforation parameters.It is shown that the residual strength of the casing increases with the hole diameter and periodically changes with the hole density;the phase angle is the main factor that affects the residual strength of the perforated casing,and the perforation should be avoided in the direction of the minimum principal stress to reduce stress concentration at the perforation hole.Moreover,as shown by a companion orthogonal experiment,the descending order of influence of the different influential parameters is:phase angle,hole diameter,hole density and the thickness of casing.展开更多
The analysis of variance(ANOVA), multiple quadratic regression and radial basis function artificial neural network(RBFANN) methods were used to study the springback and tensile strength in age forming of 2A97 aluminum...The analysis of variance(ANOVA), multiple quadratic regression and radial basis function artificial neural network(RBFANN) methods were used to study the springback and tensile strength in age forming of 2A97 aluminum alloy based on orthogonal array. The ANOVA analysis indicates that the springback reaches the minimum value when age forming is performed at 210 °C for 20 h using a single-curvature die with a radius of 400 mm, and the tensile strength reaches the maximum value when age forming is performed at 180 °C for 15 h using a single-curvature die with a radius of 1000 mm. The orders of the importance for the three factors of pre-deformation radius, aging temperature and aging time on the springback and tensile strength were determined. By analyzing the predicted results of the multiple quadratic regression and RBFANN methods, the prediction accuracy of the RBFANN model is higher than that of the regression model.展开更多
基金Supported by the National Natural Science Foundation of China(Nos.41630969,41941013,41806225)the Tianjin Municipal Natural Science Foundation(No.20JCQNJC01290)。
文摘Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s.The prediction of sea ice is highly important,but accurate simulation of sea ice variations remains highly challenging.For improving model performance,sensitivity experiments were conducted using the coupled ocean and sea ice model(NEMO-LIM),and the simulation results were compared against satellite observations.Moreover,the contribution ratios of dynamic and thermodynamic processes to sea ice variations were analyzed.The results show that the performance of the model in reconstructing the spatial distribution of Arctic sea ice is highly sensitive to ice strength decay constant(C^(rhg)).By reducing the C^(rhg) constant,the sea ice compressive strength increases,leading to improved simulated sea ice states.The contribution of thermodynamic processes to sea ice melting was reduced due to less deformation and fracture of sea ice with increased compressive strength.Meanwhile,dynamic processes constrained more sea ice to the central Arctic Ocean and contributed to the increases in ice concentration,reducing the simulation bias in the central Arctic Ocean in summer.The root mean square error(RMSE)between modeled and the CryoSat-2/SMOS satellite observed ice thickness was reduced in the compressive strength-enhanced model solution.The ice thickness,especially of multiyear thick ice,was also reduced and matched with the satellite observation better in the freezing season.These provide an essential foundation on exploring the response of the marine ecosystem and biogeochemical cycling to sea ice changes.
基金The National Natural Science Foundation of China(No.51108081)
文摘In order to investigate the effect o f some factors on the unconfined compressive strength(UCS)for composite soil stabilizer-stabilized gravel soil(CSSSGS),the orthogonal test is adopted to set up the experimental scheme.Three levels o f each factor armconsidered to obtain the change laws o f UCS,in which the binder dosages are8%,10%,and12%;the curing times ae7,14and21d;the gradation nae0.3,0.35and0.4;and the degrees of compaction are95%,97%,and99%.The range analysis clearly indicates that the influence degree o f the four factors on UCS is in such an order:dosage,age,gradation,and degree o f compaction.The variance analysis shows that only the composite soil stabilizer dosage can significantly affect UCS.In road construction,the examination o f composite soil stabilizer dosage and base-course maintenance should be given much more attention to obtain satisfactory base-course strength,compared w ith gradation floating and the change of degree o f compaction.
基金supported by the National Natural Science Foundation of China[52074326].
文摘A three-dimensional model for the numerical simulation of casing-cement behavior is used to investigate residual strength in the perforated casing of ultra deep wells.The influence of the hole diameter,hole density and phase angle on the residual strength of the casing under non-uniform stress and fracturing conditions is revealed through the consideration of different perforation parameters.It is shown that the residual strength of the casing increases with the hole diameter and periodically changes with the hole density;the phase angle is the main factor that affects the residual strength of the perforated casing,and the perforation should be avoided in the direction of the minimum principal stress to reduce stress concentration at the perforation hole.Moreover,as shown by a companion orthogonal experiment,the descending order of influence of the different influential parameters is:phase angle,hole diameter,hole density and the thickness of casing.
文摘The analysis of variance(ANOVA), multiple quadratic regression and radial basis function artificial neural network(RBFANN) methods were used to study the springback and tensile strength in age forming of 2A97 aluminum alloy based on orthogonal array. The ANOVA analysis indicates that the springback reaches the minimum value when age forming is performed at 210 °C for 20 h using a single-curvature die with a radius of 400 mm, and the tensile strength reaches the maximum value when age forming is performed at 180 °C for 15 h using a single-curvature die with a radius of 1000 mm. The orders of the importance for the three factors of pre-deformation radius, aging temperature and aging time on the springback and tensile strength were determined. By analyzing the predicted results of the multiple quadratic regression and RBFANN methods, the prediction accuracy of the RBFANN model is higher than that of the regression model.
