Calculation model of unsteady temperature–pressure fields in wellbores and fractures of supercritical CO2 fracturing

In this study, we determined the factors that influence of the temperature on an asphalt pavement by developing a two-dimensional unsteady temperature numerical calculation model using the finite difference method and Matlab. Based on the temperatures obtained by a buried sensor in a construction project, we collected the temperatures at different depths in the pavement structure in real time, and we then compared and analyzed the calculated and measured data. The results showed that the temperature in the asphalt pavement structure was significantly correlated with meteorological factors, such as the air temperature, but it also exhibited obvious hysteresis. Compared with the measured data, the maximum deviation in the numerical model based on the variations in the atmospheric temperature and solar radiation was 3 °C. Thus, it is necessary to effectively optimize the selection of asphalt pavement materials by simulating the temperature conditions in the asphalt pavement structure.

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Simple and accurate calculation model of viscosity for supercritical CO2

Journal of Physics Conference Series ◽

10.1088/1742-6596/2076/1/012030 ◽

2021 ◽

Vol 2076 (1) ◽

pp. 012030

Author(s):

Jia Deng ◽

Guangjie Zhao ◽

Lan Zhang ◽

Huizhong Ma ◽

Fuquan Song ◽

...

Keyword(s):

Empirical Formula ◽

Supercritical Co2 ◽

Pressure Range ◽

Absolute Error ◽

Engineering Calculation ◽

High Accuracy ◽

Calculation Model ◽

Average Absolute Error ◽

Accurate Calculation ◽

Utilization Of Technology

Abstract The high-precision calculation method suitable for the viscosity of supercritical CO2 is reported. The modified LBC model, modified DS model and Ehsan empirical formula are selected to calculate viscosity. The error analysis result shows that the average absolute error of the modified DS model is the lowest, which can be applied to the engineering calculation. The LBC model is suitable at 7.5–25MPa and the Ehsan empirical formula has high accuracy at 25–60MPa. Then the viscosity plate is drawn at temperature range of 310–800K and pressure range of 7.5–60MPa, which can be applied in carbon dioxide resources recovery and utilization of technology.

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Preparation of nanometer dispersed polypropylene/polystyrene interpenetrating network using supercritical CO2 as a swelling agent

Engineering Structures ◽

10.1016/s0141-0296(02)00063-9 ◽

2002 ◽

Author(s):

D Li

Keyword(s):

Supercritical Co2 ◽

Interpenetrating Network ◽

Swelling Agent

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Nucleation and growth in microcellar materials: supercritical CO2 as foaming agent

International Journal of Multiphase Flow ◽

10.1016/s0301-9322(97)88130-x ◽

1996 ◽

Vol 22 ◽

pp. 93 ◽

Cited By ~ 3

Author(s):

S Goel

Keyword(s):

Supercritical Co2 ◽

Nucleation And Growth ◽

Foaming Agent

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Pressure fields investigation by LPS technology on the aerospacecraft model

Journal de Physique III ◽

10.1051/jp3:1994105 ◽

1994 ◽

Vol 4 (11) ◽

pp. 2317-2327 ◽

Cited By ~ 4

Author(s):

A. O. Andreev ◽

A. P. Bukov ◽

M. W. lpatov ◽

S. V. Kabin ◽

P. D. Nushtaev ◽

...

Keyword(s):

Pressure Fields

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A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

Matériaux & Techniques ◽

10.1051/mattech/2019002 ◽

2018 ◽

Vol 106 (6) ◽

pp. 603 ◽

Cited By ~ 2

Author(s):

Bendaoud Mebarek ◽

Mourad Keddam

Keyword(s):

Neural Network ◽

Experimental Data ◽

Fuzzy Neural Network ◽

Treatment Time ◽

Calculation Model ◽

Average Error ◽

Network Approach ◽

Neural Network Approach ◽

Fuzzy Neural ◽

Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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