Time, temperature, and strain effects on viscoelastic Poisson's ratio of epoxy resins

2008 ◽  
Vol 48 (7) ◽  
pp. 1434-1441 ◽  
Author(s):  
Stefano Pandini ◽  
Alessandro Pegoretti
Keyword(s):  
Poisson’S Ratio ◽  
Epoxy Resins ◽  
Poisson's Ratio ◽  
Strain Effects

scholarly journals United-Atom Molecular Dynamics Study of the Mechanical and Thermomechanical Properties of an Industrial Epoxy

Polymers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 3443
Author(s):  
Riki Maicas ◽  
Irena Yungerman ◽  
Yarden B. Weber ◽  
Simcha Srebnik
Keyword(s):  
Molecular Dynamics ◽  
Glass Transition ◽  
Elastic Modulus ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Poisson’S Ratio ◽  
Epoxy Resins ◽  
Molecular Simulations ◽  
Poisson's Ratio ◽  
Method Of Evaluation

Epoxy resins are the most commonly used adhesives in industry due to their versatility, low cost, low toxicity, low shrinkage, high strength, resistance to moisture, and effective electrical resistance. These diverse properties can be tailored based on the chemical structure of the curing agent and the conditions of the curing process. Molecular simulations of epoxy resins have gained attention in recent years as a means to navigate the vast choice of chemical agents and conditions that will give the required properties of the resin. This work examines the statistical uncertainty in predicting thermodynamic and mechanical properties of an industrial epoxy resin using united atom molecular dynamics simulation. The results are compared with experimental measurements of the elastic modulus, Poisson’s ratio, and the glass transition temperature obtained at different temperatures and degrees of curing. The decreasing trend of the elastic modulus with increasing temperature is accurately captured by the simulated model, though the uncertainty in the calculated average is large. The glass transition temperature is expectedly overpredicted due to the high rates accessible to molecular simulations. We find that Poisson’s ratio is particularly sensitive to sample anisotropy as well as the method of evaluation, which explains the lack of consistent trends previously observed with molecular simulation at different degrees of crosslinking and temperatures.

Tensile strain effects on C4N3H monolayer: Large Poisson's ratio and robust Dirac cone

2019 ◽  
Vol 114 (7) ◽  
pp. 073106 ◽  
Author(s):  
Hongzhe Pan ◽  
Hongyu Zhang ◽  
Jianfu Li ◽  
Qingfang Li ◽  
Yuanyuan Sun ◽  
...  
Keyword(s):  
Poisson’S Ratio ◽  
Tensile Strain ◽  
Poisson's Ratio ◽  
Dirac Cone ◽  
Strain Effects

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 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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