Numerical simulation of gas-liquid transport in porous media using 3D color-gradient lattice Boltzmann method: trapped air and oxygen diffusion coefficient analysis

Cho, Jae Yong; Lee, Hee Min; Kim, Jun Hong; Lee, Wooju; Lee, Joon Sang

doi:10.1080/19942060.2021.2008012

Cited by 6 publications

(1 citation statement)

References 54 publications

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“…Once this database is built, the new constant is calculated based on this database: if the parameters are very close to those in the database, the constant is taken from the database, otherwise the new constant is calculated with a machine learning method [16]. So with this approach, the researchers estimate these parameters using a machine learning method and the couple using a physical numerical fluid simulation method [17][18][19][20].…”

Section: Related Workmentioning

confidence: 99%

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Amroun,

Hafid,

Ammi

2022

IJICS

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One of the applications that machine learning can offer to the world of Engineering and Fluid Mechanics in particular is the calibration of models making it possible to approximate the representation of a particular phenomenon. Indeed, the computational cost generated by some fluid mechanics models pushes scientists to use other models close to the original models but less computationally intensive in order to facilitate their handling. Among the different approaches used: machine learning coupled with some optimization methods and algorithms in order to reduce the computation cost induced. This paper proposes a new framework called OPTI-ENS: a new flexible, optimized and improved method, to calibrate a physical model, called the wake oscillator (WO), which simulates the vibratory behaviors of overhead line conductors. An approximation of a heavy and complex model called the strip theory (ST) model. OPTI-ENS is composed of an ensemble machine learning algorithm (ENS) and an optimization algorithm of the WO model so that the WO model can generate the adequate training data as input to the ENS model. ENS model will therefore take as input the data from the WO model and output the data from the ST model. As a benchmark, a series of Machine learning models have been implemented and tested. The OPTI-ENS algorithm was retained with a best Coefficient of determination (R2 Score) of almost 0.7 and a Root mean square error (RMSE) of 7.57e-09. In addition, this model is approximately 170 times faster (in terms of calculation time) than an ENS model without optimization of the generation of training data by the WO model. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

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