Size Dependent Debye Temperature and Total Mean Square Relative Atomic Displacement in Nanosolids under High Pressure and High Temperature

2010 ◽  
Vol 114 (4) ◽  
pp. 1805-1808 ◽  
Author(s):  
G. Ouyang ◽  
Z. M. Zhu ◽  
W. G. Zhu ◽  
C. Q. Sun
2019 ◽  
Author(s):  
Uma Pachauri ◽  
Deepika P. Joshi ◽  
Neha Arora

2019 ◽  
Vol 10 (3) ◽  
pp. 1081-1095 ◽  
Author(s):  
Okorie E. Agwu ◽  
Julius U. Akpabio ◽  
Adewale Dosunmu

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.


Author(s):  
E. F. Koch

Because of the extremely rigid lattice structure of diamond, generating new dislocations or moving existing dislocations in diamond by applying mechanical stress at ambient temperature is very difficult. Analysis of portions of diamonds deformed under bending stress at elevated temperature has shown that diamond deforms plastically under suitable conditions and that its primary slip systems are on the ﹛111﹜ planes. Plastic deformation in diamond is more commonly observed during the high temperature - high pressure sintering process used to make diamond compacts. The pressure and temperature conditions in the sintering presses are sufficiently high that many diamond grains in the sintered compact show deformed microtructures.In this report commercially available polycrystalline diamond discs for rock cutting applications were analyzed to study the deformation substructures in the diamond grains using transmission electron microscopy. An individual diamond particle can be plastically deformed in a high pressure apparatus at high temperature, but it is nearly impossible to prepare such a particle for TEM observation, since any medium in which the diamond is mounted wears away faster than the diamond during ion milling and the diamond is lost.


Alloy Digest ◽  
2019 ◽  
Vol 68 (11) ◽  

Abstract YSS YXM4 is a cobalt-alloyed molybdenum high-speed tool steel with resistance to abrasion, seizure, and deformation under high pressure. This datasheet provides information on composition, physical properties, and hardness. It also includes information on high temperature performance. Filing Code: TS-780. Producer or source: Hitachi Metals America, Ltd.


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