Showing 3 results for Prediction
H. Arabi, M.t Salehi, B. Mirzakhani, M.r. Aboutalebi , S.h. Seyedein , S. Khoddam,
Volume 19, Issue 5 (7-2008)
Abstract
Hot torsion test (HTT) has extensively been used to analysis and physically model flow behavior and microstructure evolution of materials and alloys during hot deformation processes. In this test, the specimen geometry has a great influence in obtaining reliable test results. In this paper, the interaction of thermal-mechanical conditions and geometry of the HTT specimen was studied. The commercial finite element package ANSYS was utilized for prediction of temperature distribution during reheating treatment and a thermo-rigid viscoplastic FE code, THORAX.FOR, was used to predict thermo-mechanical parameters during the test for API-X70 micro alloyed steel. Simulation results show that no proper geometry and dimension selection result in non uniform temperature within specimen and predicted to have effects on the consequence assessment of material behavior during hot deformation. Recommendations on finding proper specimen geometry for reducing temperature gradient along the gauge part of specimen will be given to create homogeneous temperature as much as possible in order to avoid uncertainty in consequent results of HTT.
Hiwa Farughi, Ahmad Hakimi, Reza Kamranrad,
Volume 29, Issue 1 (3-2018)
Abstract
In this paper, one of the most important criterion in public services quality named availability is evaluated by using artificial neural network (ANN). In addition, the availability values are predicted for future periods by using exponential weighted moving average (EWMA) scheme and some time series models (TSM) including autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA). Results based on comparative studies between four methods based on ANN and by considering the several conditions for the effective parameters in ANN show that, the generalized regression method is the best method for predicting the availability. Furthermore, results of the EWMA and three mentioned TSM are also show the better performance of MA model for predicting the availability values in future periods.
Dr Chinedum Mgbemena, Dr Emmanuel Chinwuko,
Volume 31, Issue 1 (3-2020)
Abstract
Crude oil production output forecast is very important in the formulation of genuine and suitable production policies; it is pivotal in planning and decision making. This paper explores the use of forecasting techniques to assist the oil field manager in decision making. In this analysis, statistical models of projected trends which involves graphical, least squares, simple moving average and exponential smoothing methods were compared. The least squares method was found to be most suitable to capture the recent random nature of crude oil production output in the oilfield of the Niger Delta region of Nigeria. In addition, a multiple linear regression model was developed for predicting daily, weekly, monthly or even yearly volume of crude oil production output in the oilfield facility.
