The dynamic and static modulus of elasticity （MOE） between bluestained and non-bluestained lumber of Lodgepole pine were tested and analyzed by using three methods of Non-destructive testing （NDT）, Portable Ultras...The dynamic and static modulus of elasticity （MOE） between bluestained and non-bluestained lumber of Lodgepole pine were tested and analyzed by using three methods of Non-destructive testing （NDT）, Portable Ultrasonic Non-destructive Digital Indicating Testing （Pundit）, Metriguard and Fast Fourier Transform （FFT） and the normal bending method. Results showed that the dynamic and static MOE of bluestained wood were higher than those of non-bluestained wood. The significant differences in dynamic MOE and static MOE were found between bulestained and non-bluestained wood, of which, the difference in each of three dynamic MOE （Ep. the ultrasonic wave modulus of elasticity, Ems, the stress wave modulus of elasticity and El, the longitudinal wave modulus of elasticity） between bulestained and non-bluestained wood arrived at the 0.01 significance level, whereas that in the static MOE at the 0.05 significance level. The differences in MOE between bulestained and non-bluestained wood were induced by the variation between sapwood and heartwood and the different densities of bulestained and non-bluestained wood. The correlation between dynamic MOE and static MOE was statistically significant at the 0.01 significance level. Although the dynamic MOE values of Ep, Em, Er were significantly different, there exists a close relationship between them （arriving at the 0.01 correlation level）. Comparative analysis among the three techniques indicated that the accurateness of FFT was higher than that of Pundit and Metriguard. Effect of tree knots on MOE was also investigated. Result showed that the dynamic and static MOE gradually decreased with the increase of knot number, indicating that knot number had significant effect on MOE value.展开更多
The “Huang gua” melons were measured for their physical properties including firmness and static elastic modulus. The vibrational characteristics of fruits and vegetables are governed by their elastic modulus (firmn...The “Huang gua” melons were measured for their physical properties including firmness and static elastic modulus. The vibrational characteristics of fruits and vegetables are governed by their elastic modulus (firmness), mass, and geometry. Therefore, it is possible to evaluate firmness of fruits and vegetables based on their vibrational characteristics. Analysis of the vibration responses of a fruit is suggested for measuring elastic properties (Firmness) non-destructively. The impulse response method is often used to measure firmness of fruits. The fruit was excited using three types of balls (wooden, steel and rubber) and the vibration is detected by an accelerometer. The Instron device was used to measure the static elastic modulus of the inner, middle and outer portions of melon flesh. Finite element (FE) technique was used to determine the optimum excitation location of the chosen measurement sensor and to analyze the mode shape fruits. Four types of mode shapes (torsional or flexural mode shape, first-type, second-type spherical mode and breathing mode shape) were found. Finite element simulation results agreed well with experimental results. Correlation between the firmness and resonant frequency (r2=0.91) and between the resonant frequency and stiffness factor (r2=0.74) existed. The optimum location and suitable direction for excitation and response measurement on the fruit were suggested.展开更多
The processes of building dynamic and static relationships between secondary and primary variables are usually integrated in most of nonlinear dynamic soft sensor models. However, such integration limits the estimatio...The processes of building dynamic and static relationships between secondary and primary variables are usually integrated in most of nonlinear dynamic soft sensor models. However, such integration limits the estimation accuracy of soft sensor models. Wiener model effectively describes dynamic and static characteristics of a system with the structure of dynamic and static submodels in cascade. We propose a soft sensor model derived from Wiener model structure, which is an extension of Wiener model. Dynamic and static relationships between secondary and primary variables are built respectively to describe the dynamic and static characteristics of system. The feasibility of this model is verified. Then the expression of discrete model is derived for soft sensor system. Conjugate gradient algorithm is applied to identify the dynamic and static model parameters alternately. Corresponding update method for soft sensor system is also given. Case studies confirm the effectiveness of the proposed model, alternate identification algorithm, and update method.展开更多
基金This paper was supported by "Wood-inorganic Res-toration Material" in "Technique Introduction and Innovation of Bio-macromolecule New Material" of Introducing Overseas Advanced Forest Technology Innovation Program of China ("948" Innovation Pro-ject, Number: 2006-4-C03)
文摘The dynamic and static modulus of elasticity （MOE） between bluestained and non-bluestained lumber of Lodgepole pine were tested and analyzed by using three methods of Non-destructive testing （NDT）, Portable Ultrasonic Non-destructive Digital Indicating Testing （Pundit）, Metriguard and Fast Fourier Transform （FFT） and the normal bending method. Results showed that the dynamic and static MOE of bluestained wood were higher than those of non-bluestained wood. The significant differences in dynamic MOE and static MOE were found between bulestained and non-bluestained wood, of which, the difference in each of three dynamic MOE （Ep. the ultrasonic wave modulus of elasticity, Ems, the stress wave modulus of elasticity and El, the longitudinal wave modulus of elasticity） between bulestained and non-bluestained wood arrived at the 0.01 significance level, whereas that in the static MOE at the 0.05 significance level. The differences in MOE between bulestained and non-bluestained wood were induced by the variation between sapwood and heartwood and the different densities of bulestained and non-bluestained wood. The correlation between dynamic MOE and static MOE was statistically significant at the 0.01 significance level. Although the dynamic MOE values of Ep, Em, Er were significantly different, there exists a close relationship between them （arriving at the 0.01 correlation level）. Comparative analysis among the three techniques indicated that the accurateness of FFT was higher than that of Pundit and Metriguard. Effect of tree knots on MOE was also investigated. Result showed that the dynamic and static MOE gradually decreased with the increase of knot number, indicating that knot number had significant effect on MOE value.
基金Project supported by the National Natural Science Foundation of China (No. 30370371) and the Natural Science Foundation of Zheji-ang Province (No. 301267), China
文摘The “Huang gua” melons were measured for their physical properties including firmness and static elastic modulus. The vibrational characteristics of fruits and vegetables are governed by their elastic modulus (firmness), mass, and geometry. Therefore, it is possible to evaluate firmness of fruits and vegetables based on their vibrational characteristics. Analysis of the vibration responses of a fruit is suggested for measuring elastic properties (Firmness) non-destructively. The impulse response method is often used to measure firmness of fruits. The fruit was excited using three types of balls (wooden, steel and rubber) and the vibration is detected by an accelerometer. The Instron device was used to measure the static elastic modulus of the inner, middle and outer portions of melon flesh. Finite element (FE) technique was used to determine the optimum excitation location of the chosen measurement sensor and to analyze the mode shape fruits. Four types of mode shapes (torsional or flexural mode shape, first-type, second-type spherical mode and breathing mode shape) were found. Finite element simulation results agreed well with experimental results. Correlation between the firmness and resonant frequency (r2=0.91) and between the resonant frequency and stiffness factor (r2=0.74) existed. The optimum location and suitable direction for excitation and response measurement on the fruit were suggested.
基金Supported by the National Natural Science Foundation of China(61104218,21006127)the National Basic Research Program of China(2012CB720500)the Science Foundation of China University of Petroleum(YJRC-2013-12)
文摘The processes of building dynamic and static relationships between secondary and primary variables are usually integrated in most of nonlinear dynamic soft sensor models. However, such integration limits the estimation accuracy of soft sensor models. Wiener model effectively describes dynamic and static characteristics of a system with the structure of dynamic and static submodels in cascade. We propose a soft sensor model derived from Wiener model structure, which is an extension of Wiener model. Dynamic and static relationships between secondary and primary variables are built respectively to describe the dynamic and static characteristics of system. The feasibility of this model is verified. Then the expression of discrete model is derived for soft sensor system. Conjugate gradient algorithm is applied to identify the dynamic and static model parameters alternately. Corresponding update method for soft sensor system is also given. Case studies confirm the effectiveness of the proposed model, alternate identification algorithm, and update method.
