A comparative study on constitutive equations and artificial neural network model to predict high-temperature deformation behavior in Nitinol 60 shape memory alloy

The artificial neural network and support vector machine were used to estimate the compressive strength and flexural strength of carbon fiber-reinforced lightweight concrete with the silica fume exposed to the high temperature. Cement was replaced with three percentages of silica fumes (0%, 10%, and 20%). The carbon fibers were used in four different proportions (0, 2, 4, and 8 kg/m3). The specimens of each concrete mixture were heated at 20°C, 400°C, 600°C, and 800°C. After this process, the specimens were subjected to the strength tests. The amount of cement, the amount of silica fumes, the amount of carbon fiber, the amount of aggregates, and temperature were selected as the input variables for the prediction models. The compressive and flexural strengths of the lightweight concrete were determined as the output variables. The model results were compared with the experimental results. The best results were achieved from the artificial neural network model. The accuracy of the artificial neural network model was found at 99.02% and 96.80%.

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High temperature deformation behavior and processing map for a nickel-titanium shape memory alloy

Metals and Materials International ◽

10.1007/s12540-017-6736-3 ◽

2017 ◽

Vol 23 (5) ◽

pp. 1002-1011 ◽

Cited By ~ 3

Author(s):

Xiang-Qian Yin ◽

Sang-Won Lee ◽

Yan-Feng Li ◽

Chan-Hee Park ◽

Xu-Jun Mi ◽

...

Keyword(s):

High Temperature ◽

Shape Memory Alloy ◽

Shape Memory ◽

Deformation Behavior ◽

Processing Map ◽

Nickel Titanium ◽

High Temperature Deformation ◽

Temperature Deformation

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Development of an artificial neural network model for the prediction of hydrocarbon density at high-pressure, high-temperature conditions

Thermochimica Acta ◽

10.1016/j.tca.2012.10.022 ◽

2013 ◽

Vol 551 ◽

pp. 124-130 ◽

Cited By ~ 35

Author(s):

Reza Haghbakhsh ◽

Hooman Adib ◽

Peyman Keshavarz ◽

Mehdi Koolivand ◽

Simin Keshtkari

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

High Pressure ◽

High Temperature ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Temperature Conditions ◽

High Pressure High Temperature ◽

Artificial Neural

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Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells

Journal of Petroleum Exploration and Production Technology ◽

10.1007/s13202-019-00802-6 ◽

2019 ◽

Vol 10 (3) ◽

pp. 1081-1095 ◽

Cited By ~ 2

Author(s):

Okorie E. Agwu ◽

Julius U. Akpabio ◽

Adewale Dosunmu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

High Pressure ◽

High Temperature ◽

Mean Square Error ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Mean Square ◽

High Temperature High Pressure

AbstractIn this paper, an artificial neural network model was developed to predict the downhole density of oil-based muds under high-temperature, high-pressure conditions. Six performance metrics, namely goodness of fit (R2), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), sum of squares error (SSE) and root mean square error (RMSE), were used to assess the performance of the developed model. From the results, the model had an overall MSE of 0.000477 with an MAE of 0.017 and an R2 of 0.9999, MAPE of 0.127, RMSE of 0.022 and SSE of 0.056. All the model predictions were in excellent agreement with the measured results. Consequently, in assessing the generalization capability of the developed model for the oil-based mud, a new set of data that was not part of the training process of the model comprising 34 data points was used. In this regard, the model was able to predict 99% of the unfamiliar data with an MSE of 0.0159, MAE of 0.101, RMSE of 0.126, SSE of 0.54 and a MAPE of 0.7. In comparison with existing models, the ANN model developed in this study performed better. The sensitivity analysis performed shows that the initial mud density has the greatest impact on the final mud density downhole. This unique modelling technique and the model it evolved represents a huge step in the trajectory of achieving full automation of downhole mud density estimation. Furthermore, this method eliminates the need for surface measurement equipment, while at the same time, representing more accurately the downhole mud density at any given pressure and temperature.

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