Ordinary refractory ceramics are multi-phase materials,and their inhomogeneous microstructures induce the scatter of properties.The definition of a reasonable number of samples is important to obtain representative re...Ordinary refractory ceramics are multi-phase materials,and their inhomogeneous microstructures induce the scatter of properties.The definition of a reasonable number of samples is important to obtain representative results from experiments and simulations,and this reasonable number might be property or microstructure relevant.Stochastic discrete element(DE)simulations of cold crushing tests with homogeneous interface properties were performed.Three minimum DE size ranges were used to represent matrix variation at different levels.Statistical methods,i.e.,Kolmogorov–Smirnov(K–S)test,t-test,and correlation analysis,were utilized to study the influences of minimal number of samples on mechanical properties and crack density.It revealed that a relatively small number of samples are sufficient to obtain representative cold crushing strength(CCS)and Young’s modulus,whilst a large number of samples are favourable when the fracture energy and crack density under cold crushing conditions are of interest.The analysis also showed that the fracture energy under cold crushing condition generally correlates positively with CCS,and the static Young’s moduli determined from the stress–piston displacement curves with different definitions are divergent,caused by contact compliance and premature cracking.The data from the stress–strain curves recorded directly on the sample are required for the accurate static Young’s modulus calculation.展开更多
The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume...The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates.In this study,we analyzed a database of thinning trees from a forest management plan in the Contendas de SincoráNational Forest,southwestern Bahia State,Brazil.The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm.Adjustments,validation and statistical selection of four volumetric models were performed.Due to the difference in height values for the same diameter and the low correlation between both variables,we do not suggest models which only use the diameter at breast height(DBH)variable as a predictor because they accommodate the largest estimation errors.In comparing the best single entry model(Hohenald-Krenn)with the Spurr model(best fit model),it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion(AIC)and adjusted determination coefficient(R2 adj),which are poorer than the second best model(Schumacher-Hall).Regarding the minimum sample size,errors in estimation(root mean square error(RMSE)and bias)of the best model decrease as the sample size increases,especially when a larger number of trees with DBH≥15.0 cm are randomly sampled.Stratified sampling by diameter class produces smaller volume prediction errors than random sampling,especially when considering all trees.In summary,the Spurr and Schumacher-Hall models perform better.These models suggest that the total variance explained in the estimates is not less than 95%,producing reliable forecasts of the total volume with shell.Our estimates indicate that the bias around the average is not greater than 7%.Our results support the decision to use regression methods to build models and estimate their parameters,seeking stratification strategies in diameter classes for the sample trees.Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest.Stratified sampling of the data set for model adjustment and selection is necessary,since we find significant results with mean error square root values and bias of up to 70%of the total database.展开更多
Laser-ablation laser-induced breakdown spectroscopy （LA-LIBS） based on single Nd：YAG laser is used to analyze copper impurity in silver jewellery with enhanced sensitivity and minimal sample ablation. 6-30 folds si...Laser-ablation laser-induced breakdown spectroscopy （LA-LIBS） based on single Nd：YAG laser is used to analyze copper impurity in silver jewellery with enhanced sensitivity and minimal sample ablation. 6-30 folds signal enhancement can be achieved under the re-excitation of the breakdown laser and the spatial resolution is only determined by the ablation laser. 50 ppm limit of detection of copper is achieved when the crater diameter is 17.2 μm under current experimental condition. This technique gives higher analysis sensitivity under the same sample ablation in comparison with single pulse （SP） LIBS. It is useful for high sensitive element mieroanalvsis of precious samples.展开更多
In order to investigate sample minimization for classification of supercritical and subcritical patterns in supersonic inlet, three optimization methods, namely, opposite one towards nearest method, closest one toward...In order to investigate sample minimization for classification of supercritical and subcritical patterns in supersonic inlet, three optimization methods, namely, opposite one towards nearest method, closest one towards the byper-plane method and random selection method, are proposed for investigation on minimization of classification samples for supercritical and subcritical patterns of supersonic inlet. The study has been carried out to analyze wind tunnel test data and to compare the classification accuracy based on those three methods with or without priori knowledge. Those three methods are different from each other by different selecting methods for samples. The results show that one of the optimization methods needs the minimization samples to get the highest classification accuracy without priori knowledge. Meanwhile, the number of minimization samples needed to get highest classification accuracy can be further reduced by introducing priori knowledge. Furthermore, it demonstrates that the best optimization method has been found by comparing all cases studied with or without introducing priori knowledge. This method can be applied to reduce the number of wind tunnel tests to obtain the inlet performance and to identify the supercritical/subcritical modes for supersonic inlet.展开更多
文摘Ordinary refractory ceramics are multi-phase materials,and their inhomogeneous microstructures induce the scatter of properties.The definition of a reasonable number of samples is important to obtain representative results from experiments and simulations,and this reasonable number might be property or microstructure relevant.Stochastic discrete element(DE)simulations of cold crushing tests with homogeneous interface properties were performed.Three minimum DE size ranges were used to represent matrix variation at different levels.Statistical methods,i.e.,Kolmogorov–Smirnov(K–S)test,t-test,and correlation analysis,were utilized to study the influences of minimal number of samples on mechanical properties and crack density.It revealed that a relatively small number of samples are sufficient to obtain representative cold crushing strength(CCS)and Young’s modulus,whilst a large number of samples are favourable when the fracture energy and crack density under cold crushing conditions are of interest.The analysis also showed that the fracture energy under cold crushing condition generally correlates positively with CCS,and the static Young’s moduli determined from the stress–piston displacement curves with different definitions are divergent,caused by contact compliance and premature cracking.The data from the stress–strain curves recorded directly on the sample are required for the accurate static Young’s modulus calculation.
基金the National Council for Scientific and Technological Development-CNPq for granting financial support to the project(484260/2013-8).
文摘The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates.In this study,we analyzed a database of thinning trees from a forest management plan in the Contendas de SincoráNational Forest,southwestern Bahia State,Brazil.The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm.Adjustments,validation and statistical selection of four volumetric models were performed.Due to the difference in height values for the same diameter and the low correlation between both variables,we do not suggest models which only use the diameter at breast height(DBH)variable as a predictor because they accommodate the largest estimation errors.In comparing the best single entry model(Hohenald-Krenn)with the Spurr model(best fit model),it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion(AIC)and adjusted determination coefficient(R2 adj),which are poorer than the second best model(Schumacher-Hall).Regarding the minimum sample size,errors in estimation(root mean square error(RMSE)and bias)of the best model decrease as the sample size increases,especially when a larger number of trees with DBH≥15.0 cm are randomly sampled.Stratified sampling by diameter class produces smaller volume prediction errors than random sampling,especially when considering all trees.In summary,the Spurr and Schumacher-Hall models perform better.These models suggest that the total variance explained in the estimates is not less than 95%,producing reliable forecasts of the total volume with shell.Our estimates indicate that the bias around the average is not greater than 7%.Our results support the decision to use regression methods to build models and estimate their parameters,seeking stratification strategies in diameter classes for the sample trees.Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest.Stratified sampling of the data set for model adjustment and selection is necessary,since we find significant results with mean error square root values and bias of up to 70%of the total database.
基金the National"973"Program of China(No.2012CB921900)the National Natural Science Foundation of China(Nos.11274123 and 11304100)the Basic Scientific Research Program of South China University of Technology(No.2014ZZ0066)
文摘Laser-ablation laser-induced breakdown spectroscopy （LA-LIBS） based on single Nd：YAG laser is used to analyze copper impurity in silver jewellery with enhanced sensitivity and minimal sample ablation. 6-30 folds signal enhancement can be achieved under the re-excitation of the breakdown laser and the spatial resolution is only determined by the ablation laser. 50 ppm limit of detection of copper is achieved when the crater diameter is 17.2 μm under current experimental condition. This technique gives higher analysis sensitivity under the same sample ablation in comparison with single pulse （SP） LIBS. It is useful for high sensitive element mieroanalvsis of precious samples.
基金Academy of Fundamental and Interdisciplinary Sciences,Harbin Institute of Technology
文摘In order to investigate sample minimization for classification of supercritical and subcritical patterns in supersonic inlet, three optimization methods, namely, opposite one towards nearest method, closest one towards the byper-plane method and random selection method, are proposed for investigation on minimization of classification samples for supercritical and subcritical patterns of supersonic inlet. The study has been carried out to analyze wind tunnel test data and to compare the classification accuracy based on those three methods with or without priori knowledge. Those three methods are different from each other by different selecting methods for samples. The results show that one of the optimization methods needs the minimization samples to get the highest classification accuracy without priori knowledge. Meanwhile, the number of minimization samples needed to get highest classification accuracy can be further reduced by introducing priori knowledge. Furthermore, it demonstrates that the best optimization method has been found by comparing all cases studied with or without introducing priori knowledge. This method can be applied to reduce the number of wind tunnel tests to obtain the inlet performance and to identify the supercritical/subcritical modes for supersonic inlet.
