scholarly journals Force performance of trawl system – III: mathematical modeling (part I)

2021 ◽  
Vol 0 (4) ◽  
pp. 63-72
Author(s):  
Aleksandr Nedostup ◽  
Alexey Olegovich Razhev ◽  
Pavel Nasenkov ◽  
Karina Konovalova ◽  
Alexey Aleksandrovich Bykov ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Physical Quantity ◽  
Electrostatic Field ◽  
Poisson’S Ratio ◽  
Relative Elongation ◽  
Poisson's Ratio ◽  
Thermodynamic Work ◽  
Direct Proportionality ◽  
Fiber Thickness ◽  
Moduli Of Elasticity

The force performance of trawl systems directly depends on the work performed by these forces.The mechanical work of a trawl system is a physical quantity that depends on the vectors of force (hydrodynamic, tension, compression, etc.) and displacement. Thermodynamic work is the amount of energy transmitted or received by the trawl system by changing its external parameters. There is also the work of the forces of the electrostatic field when the charge moves from one point of the field to another. Equations are obtained for ideal flexible steel ropes and rope-rope products, characterizing the direct proportionality of the ratio of the productivity of forces that are directed perpendicular to each other and depend on the Poisson's ratio and the constructive elongation of ideal flexible steel ropes and cordage products. The ratio of the capacities of the forces or the ratio of the moduli of elasticity in the transverse and longitudinal directions, which arise when steel ropes and rope-rope products are stretched, are inversely proportional to the square of the coefficient k connecting the elongation λ, Poisson's ratio μ and the relative elongation ε at a constant volume of the product and its mass. There has been described an example of stretching perfectly flexible steel ropes and cordage. Such constructive parameters as lay, the number of strands, wire and fiber thickness, type of weaving were not taken into account.

Effects of Mix Proportion on the Strength and Elasticity of Concrete Subjected to High Temperatures up to 800C

2016 ◽  
Vol 711 ◽  
pp. 472-479 ◽  
Author(s):  
Hironobu Nishi ◽  
Hideo Kasami ◽  
Takafumi Tayama
Keyword(s):  
Weight Loss ◽  
Compressive Strength ◽  
High Temperatures ◽  
Temperature Rise ◽  
Poisson’S Ratio ◽  
Reduction Rate ◽  
Strength Reduction ◽  
Poisson's Ratio ◽  
Exposure Temperature ◽  
Moduli Of Elasticity

This paper presents the results of an experimental study on the strength and elasticity of concrete subjected to high temperatures up to 800C carried out to determine the effects of exposure temperature and the effects of mixture proportion of concrete. Cylinders made of 4 mixture of normal-weight concrete with the W/C of 50 and 60% and slump of 50 and 210mm were subjected to 13 phases of temperatures from 20 to 800C without seal at the age of 91 days. Exposure term was 91 days for exposure up to 300C, 60 days for 400C and 24 hours for higher temperatures above 500C. After temperature exposure, cylinders were tested for weight loss, compressive strength, dynamic and static moduli of elasticity and Poisson's ratio at room temperature.Weight loss increased with exposure temperature, indicating greater loss below 110C and smaller loss above 300C. Compressive strength did not decline monotonously with temperature rise between 20 and 110C, but showed 10 % reduction at 35 to 50C and recovery at 80 to 110C, indicating the minimal and maximum points. The minimal point was associated with intermediate weight loss of 4 to 5 %. At temperatures higher than 400C, residual compressive strength showed greater reduction with temperature rise for smaller weight loss.As for the effects of mixture proportion, concrete with higher W/C and higher water content showed greater weight loss and greater strength reduction below 300C, while those with higher cement content showed greater strength reduction above 500C.Both dynamic and static modulus of elasticity declined monotonously with temperature rise, indicating higher reduction rate than compressive strength. And the relationship between the two moduli of elasticity was in good correlation. Poisson's ratio did not show monotonous change with temperature, but showed discontinuity between elevated and high temperatures, indicating downward peaks at about 80C which associated with 2 to 5 % weight loss, and an upward peak at 200 to 300C, which was associated with about 7 % weight loss, and a rapid increase at higher temperature of 700 to 800C.

scholarly journals Mathematical modeling of auxetic systems: bridging the gap between analytical models and observation

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
James N. Grima-Cornish ◽  
Joseph N. Grima ◽  
Daphne Attard
Keyword(s):  
Mathematical Modeling ◽  
Poisson’S Ratio ◽  
Scientific Literature ◽  
Applied Strain ◽  
Analytical Modelling ◽  
Poisson's Ratio ◽  
Analytical Models ◽  
Transverse Strain

AbstractThe Poisson’s ratio, a property which quantifies the changes in thickness when a material is stretched and compressed, can be determined as the negative of the transverse strain over the applied strain. In the scientific literature, there are various ways how strain may be defined and the actual definition used could result in a different Poisson’s ratio being computed. This paper will look in more detail at this by comparing the more commonly used forms of strain and the Poisson’s ratio that is computable from them. More specifically, an attempt is made to assess through examples on the usefulness of the various formulations to properly describe what can actually be observed, thus providing a clearer picture of which form of Poisson’s ratio should be used in analytical modelling.

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.

Sign in / Sign up

Export Citation Format

Share Document