Construction of the Solutions of Non-stationary Dynamic Problems for Linear Viscoelastic Bodies with a Constant Poisson’s Ratio

2020 ◽  
pp. 89-96
Author(s):  
Leonid Igumnov ◽  
Ekaterina A. Korovaytseva ◽  
Sergey G. Pshenichnov
Keyword(s):  
Poisson’S Ratio ◽  
Poisson's Ratio ◽  
Dynamic Problems ◽  
Linear Viscoelastic ◽  
Viscoelastic Bodies

scholarly journals Mechanical properties of bituminous mixtures under sinusoidal cyclic loadings: Experiment and modelling

2021 ◽  
Vol 15 (2) ◽  
pp. 67-76
Author(s):  
Nguyen Quang Tuan
Keyword(s):  
Poisson’S Ratio ◽  
Complex Modulus ◽  
Thermal Sensitivity ◽  
Superposition Principle ◽  
Poisson's Ratio ◽  
Bituminous Mixture ◽  
Viscous Effects ◽  
Bituminous Mixtures ◽  
Complex Poisson’S Ratio ◽  
Linear Viscoelastic

Mechanical behaviour of bituminous mixtures is characterized by the great thermal sensitivity and the large viscous effects. This paper focuses on the linear viscoelastic (LVE) behaviour of bituminous mixtures that is considered for pavement design. The studied material is a GB3 mix (GB in French is “Grave Bitume”) which is often used for base course construction in France. Complex modulus tests are performed to determine the LVE properties of bituminous mix. Sinusoidal cyclic loadings in tension and compression for small strain amplitudes (up to 10-4 m/m) are applied on cylindrical samples at different temperatures (from -23.4°C to 39.1°C) and different frequencies (from 0.03 to 10Hz). The complex modulus E* and complex Poisson’s ratio ν* are obtained for these large ranges of temperature and frequency. From all these data, it is shown that within the linear viscoelastic domain and in the 3D case, the Time Temperature Superposition Principle (TTSP) is applicable and verified. A model with a continuum spectrum called 2S2P1D (2S2P1D means two Springs, two Parabolic elements, one Dashpot), developed at the Ecole Nationale des Travaux Publics de l’Etat (ENTPE), is used to simulate the 3D LVE behaviour of tested bituminous mixture. Keywords: linear viscoelasticity; bituminous mixture; modelling; complex modulus; complex Poisson’s ratio.

The Model for Calculating Elastic Modulus and Poisson's Ratio of Coal Body

2013 ◽  
Vol 6 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Ai Chi ◽  
Li Yuwei
Keyword(s):  
Elastic Modulus ◽  
Poisson’S Ratio ◽  
Fractured Rock ◽  
Rock Block ◽  
Poisson's Ratio ◽  
Linear Elastic ◽  
Study Results ◽  
The Face ◽  
Block Face ◽  
Coal Rock

Coal body is a type of fractured rock mass in which lots of cleat fractures developed. Its mechanical properties vary with the parametric variation of coal rock block, face cleat and butt cleat. Based on the linear elastic theory and displacement equivalent principle and simplifying the face cleat and butt cleat as multi-bank penetrating and intermittent cracks, the model was established to calculate the elastic modulus and Poisson's ratio of coal body combined with cleat. By analyzing the model, it also obtained the influence of the parameter variation of coal rock block, face cleat and butt cleat on the elastic modulus and Poisson's ratio of the coal body. Study results showed that the connectivity rate of butt cleat and the distance between face cleats had a weak influence on elastic modulus of coal body. When the inclination of face cleat was 90°, the elastic modulus of coal body reached the maximal value and it equaled to the elastic modulus of coal rock block. When the inclination of face cleat was 0°, the elastic modulus of coal body was exclusively dependent on the elastic modulus of coal rock block, the normal stiffness of face cleat and the distance between them. When the distance between butt cleats or the connectivity rate of butt cleat was fixed, the Poisson's ratio of the coal body initially increased and then decreased with increasing of the face cleat inclination.

scholarly journals Effects of Structural Parameters on the Poisson's Ratio and Compressive Modulus of 2D Pentamode Structures Fabricated by Selective Laser Melting

Engineering ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 56-67 ◽  
Author(s):  
Lei Zhang ◽  
Bo Song ◽  
Ruijie Liu ◽  
Aiguo Zhao ◽  
Jinliang Zhang ◽  
...  
Keyword(s):  
Selective Laser Melting ◽  
Poisson’S Ratio ◽  
Structural Parameters ◽  
Compressive Modulus ◽  
Poisson's Ratio ◽  
Laser Melting

scholarly journals A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks

2019 ◽  
Vol 11 (19) ◽  
pp. 5283 ◽  
Author(s):  
Gowida ◽  
Moussa ◽  
Elkatatny ◽  
Ali
Keyword(s):  
Poisson’S Ratio ◽  
Accurate Determination ◽  
Oil And Gas Industry ◽  
Poisson's Ratio ◽  
Elastic Behavior ◽  
Percentage Error ◽  
Ann Model ◽  
Field Development ◽  
Well Completion

Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young’s modulus and Poisson’s ratio. Accurate determination of the Poisson’s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson’s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson’s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson’s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution (SADE) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient (R) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson’s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson’s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson’s ratio without the need to run the ANN model.

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