Pseudoelasticity of Excised Visceral Pleura

1987 ◽  
Vol 109 (2) ◽  
pp. 115-120 ◽  
Author(s):  
J. D. Humphrey ◽  
D. L. Vawter ◽  
R. P. Vito
Keyword(s):  
Least Squares ◽  
Energy Function ◽  
Nonlinear Least Squares ◽  
Visceral Pleura ◽  
Material Constants ◽  
Least Squares Algorithm ◽  
Theory And Experiment

A pseudostrain-energy function is proposed for describing the behavior of excised sheets of canine visceral pleura. Pseudoelastic material constants are determined from experimental biaxial data by employing a nonlinear least-squares algorithm. The agreement between theory and experiment is shown to be quite good. Furthermore, the visceral pleura studied appears to be inelastic and to exhibit in-plane isotropy. Comparison with previous works is discussed.

scholarly journals Quantifying soil hydraulic properties and their uncertainties by modified GLUE method

2017 ◽  
Vol 31 (3) ◽  
pp. 433-445
Author(s):  
Yifan Yan ◽  
Jianli Liu ◽  
Jiabao Zhang ◽  
Xiaopeng Li ◽  
Yongchao Zhao
Keyword(s):  
Confidence Interval ◽  
Least Squares ◽  
Water Content ◽  
Soil Water ◽  
Nonlinear Least Squares ◽  
Uncertainty Estimation ◽  
Model Parameters ◽  
Retention Curve ◽  
Least Squares Algorithm ◽  
Soil Hydraulic

AbstractNonlinear least squares algorithm is commonly used to fit the evaporation experiment data and to obtain the ‘optimal’ soil hydraulic model parameters. But the major defects of nonlinear least squares algorithm include non-uniqueness of the solution to inverse problems and its inability to quantify uncertainties associated with the simulation model. In this study, it is clarified by applying retention curve and a modified generalised likelihood uncertainty estimation method to model calibration. Results show that nonlinear least squares gives good fits to soil water retention curve and unsaturated water conductivity based on data observed by Wind method. And meanwhile, the application of generalised likelihood uncertainty estimation clearly demonstrates that a much wider range of parameters can fit the observations well. Using the ‘optimal’ solution to predict soil water content and conductivity is very risky. Whereas, 95% confidence interval generated by generalised likelihood uncertainty estimation quantifies well the uncertainty of the observed data. With a decrease of water content, the maximum of nash and sutcliffe value generated by generalised likelihood uncertainty estimation performs better and better than the counterpart of nonlinear least squares. 95% confidence interval quantifies well the uncertainties and provides preliminary sensitivities of parameters.

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