Numerical simulation of hydrogen-fueled supersonic combustion using the linear-eddy model based on IDDES method

2018 ◽  
Author(s):  
Jian Niu
Keyword(s):  
Numerical Simulation ◽  
Supersonic Combustion ◽  
Model Based ◽  
Linear Eddy Model ◽  
Iddes Method

Numerical Simulation Study of Metal Transfer in MIG Welding with Magnetic Control

2014 ◽  
Vol 532 ◽  
pp. 545-548 ◽  
Author(s):  
Chao Yang ◽  
Shu Yuan Jiang ◽  
Hai Bo Bi
Keyword(s):  
Fluid Dynamics ◽  
Numerical Simulation ◽  
Mathematical Model ◽  
Simulation Study ◽  
Electromagnetic Theory ◽  
Metal Transfer ◽  
Experimental Result ◽  
Magnetic Control ◽  
Mig Welding ◽  
Model Based

This paper simulate the mode of metal transfer in MIG magnetic control welding by using CFD software FLUENT, establishing mathematical model based on fluid dynamics and electromagnetic theory, and simulate the form, grow and drop process of metal transfer with and without magnetic. Meanwhile, do experiments to confirm the simulate result, and it is well consistent with the experimental result.

Prediction and Optimization of WAG Flooding by Using LSTM Neural Network Model in Middle East Carbonate Reservoir

2021 ◽  
Author(s):  
Ruijie Huang ◽  
Chenji Wei ◽  
Baozhu Li ◽  
Jian Yang ◽  
Suwei Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Numerical Simulation ◽  
Short Term Memory ◽  
Oil Production ◽  
Simulation Method ◽  
Analysis Data ◽  
Carbonate Reservoir ◽  
Parameters Optimization ◽  
Model Based

Abstract Production prediction continues to play an increasingly significant role in reservoir development adjustment and optimization, especially in water-alternating-gas (WAG) flooding. As artificial intelligence continues to develop, data-driven machine learning method can establish a robust model based on massive data to clarify development risks and challenges, predict development dynamic characteristics in advance. This study gathers over 15 years actual data from targeted carbonate reservoir and establishes a robust Long Short-Term Memory (LSTM) neural network prediction model based on correlation analysis, data cleaning, feature variables selection, hyper-parameters optimization and model evaluation to forecast oil production, gas-oil ratio (GOR), and water cut (WC) of WAG flooding. In comparison to traditional reservoir numerical simulation (RNS), LSTM neural networks have a huge advantage in terms of computational efficiency and prediction accuracy. The calculation time of LSTM method is 864% less than reservoir numerical simulation method, while prediction error of LSTM method is 261% less than RNS method. We classify producers into three types based on the prediction results and propose optimization measures aimed at the risks and challenges they faced. Field implementation indicates promising outcome with better reservoir support, lower GOR, lower WC, and stabler oil production. This study provides a novel direction for application of artificial intelligence in WAG flooding development and optimization.

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