A Coupled Machine Learning and Lattice Boltzmann Method Approach for Immiscible Two-Phase Flows
An innovative coupling numerical algorithm is proposed in the current paper, the front-tracking method–lattice Boltzmann method–machine learning (FTM-LBM-ML) method, to precisely capture fluid flow phase interfaces at the mesoscale and accurately simulate dynamic processes. This method combines the...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/12/1/109 |
| _version_ | 1797358502862127104 |
|---|---|
| author | Peisheng Li Hongsheng Zhou Zhaoqing Ke Shuting Zhao Ying Zhang Jiansheng Liu Yuan Tian |
| author_facet | Peisheng Li Hongsheng Zhou Zhaoqing Ke Shuting Zhao Ying Zhang Jiansheng Liu Yuan Tian |
| author_sort | Peisheng Li |
| collection | DOAJ |
| description | An innovative coupling numerical algorithm is proposed in the current paper, the front-tracking method–lattice Boltzmann method–machine learning (FTM-LBM-ML) method, to precisely capture fluid flow phase interfaces at the mesoscale and accurately simulate dynamic processes. This method combines the distinctive abilities of the FTM to accurately capture phase interfaces and the advantages of the LBM for easy handling of mesoscopic multi-component flow fields. Taking a single vacuole rising as an example, the input and output sets of the machine learning model are constructed using the FTM’s flow field, such as the velocity and position data from phase interface markers. Such datasets are used to train the Bayesian-Regularized Back Propagation Neural Network (BRBPNN) machine learning model to establish the corresponding relationship between the phase interface velocity and the position. Finally, the trained BRBPNN neural network is utilized within the multi-relaxation LBM pseudo potential model flow field to predict the phase interface position, which is compared with the FTM simulation. It was observed that the BRBPNN-predicted interface within the LBM exhibits a high degree of consistency with the FTM-predicted interface position, showing that the BRBPNN model is feasible and satisfies the accuracy requirements of the FT-LB coupling model. |
| first_indexed | 2024-03-08T15:02:57Z |
| format | Article |
| id | doaj.art-f9ff4431a05e4de3babc3e383f4130e4 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-08T15:02:57Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-f9ff4431a05e4de3babc3e383f4130e42024-01-10T15:03:38ZengMDPI AGMathematics2227-73902023-12-0112110910.3390/math12010109A Coupled Machine Learning and Lattice Boltzmann Method Approach for Immiscible Two-Phase FlowsPeisheng Li0Hongsheng Zhou1Zhaoqing Ke2Shuting Zhao3Ying Zhang4Jiansheng Liu5Yuan Tian6School of Advanced Manufacturing, Nanchang University, Nanchang 330031, ChinaSchool of Advanced Manufacturing, Nanchang University, Nanchang 330031, ChinaSchool of Advanced Manufacturing, Nanchang University, Nanchang 330031, ChinaSchool of Advanced Manufacturing, Nanchang University, Nanchang 330031, ChinaSchool of Advanced Manufacturing, Nanchang University, Nanchang 330031, ChinaSchool of Advanced Manufacturing, Nanchang University, Nanchang 330031, ChinaSchool of Advanced Manufacturing, Nanchang University, Nanchang 330031, ChinaAn innovative coupling numerical algorithm is proposed in the current paper, the front-tracking method–lattice Boltzmann method–machine learning (FTM-LBM-ML) method, to precisely capture fluid flow phase interfaces at the mesoscale and accurately simulate dynamic processes. This method combines the distinctive abilities of the FTM to accurately capture phase interfaces and the advantages of the LBM for easy handling of mesoscopic multi-component flow fields. Taking a single vacuole rising as an example, the input and output sets of the machine learning model are constructed using the FTM’s flow field, such as the velocity and position data from phase interface markers. Such datasets are used to train the Bayesian-Regularized Back Propagation Neural Network (BRBPNN) machine learning model to establish the corresponding relationship between the phase interface velocity and the position. Finally, the trained BRBPNN neural network is utilized within the multi-relaxation LBM pseudo potential model flow field to predict the phase interface position, which is compared with the FTM simulation. It was observed that the BRBPNN-predicted interface within the LBM exhibits a high degree of consistency with the FTM-predicted interface position, showing that the BRBPNN model is feasible and satisfies the accuracy requirements of the FT-LB coupling model.https://www.mdpi.com/2227-7390/12/1/109front-tracking methodlattice Boltzmann methodmachine learning |
| spellingShingle | Peisheng Li Hongsheng Zhou Zhaoqing Ke Shuting Zhao Ying Zhang Jiansheng Liu Yuan Tian A Coupled Machine Learning and Lattice Boltzmann Method Approach for Immiscible Two-Phase Flows Mathematics front-tracking method lattice Boltzmann method machine learning |
| title | A Coupled Machine Learning and Lattice Boltzmann Method Approach for Immiscible Two-Phase Flows |
| title_full | A Coupled Machine Learning and Lattice Boltzmann Method Approach for Immiscible Two-Phase Flows |
| title_fullStr | A Coupled Machine Learning and Lattice Boltzmann Method Approach for Immiscible Two-Phase Flows |
| title_full_unstemmed | A Coupled Machine Learning and Lattice Boltzmann Method Approach for Immiscible Two-Phase Flows |
| title_short | A Coupled Machine Learning and Lattice Boltzmann Method Approach for Immiscible Two-Phase Flows |
| title_sort | coupled machine learning and lattice boltzmann method approach for immiscible two phase flows |
| topic | front-tracking method lattice Boltzmann method machine learning |
| url | https://www.mdpi.com/2227-7390/12/1/109 |
| work_keys_str_mv | AT peishengli acoupledmachinelearningandlatticeboltzmannmethodapproachforimmiscibletwophaseflows AT hongshengzhou acoupledmachinelearningandlatticeboltzmannmethodapproachforimmiscibletwophaseflows AT zhaoqingke acoupledmachinelearningandlatticeboltzmannmethodapproachforimmiscibletwophaseflows AT shutingzhao acoupledmachinelearningandlatticeboltzmannmethodapproachforimmiscibletwophaseflows AT yingzhang acoupledmachinelearningandlatticeboltzmannmethodapproachforimmiscibletwophaseflows AT jianshengliu acoupledmachinelearningandlatticeboltzmannmethodapproachforimmiscibletwophaseflows AT yuantian acoupledmachinelearningandlatticeboltzmannmethodapproachforimmiscibletwophaseflows AT peishengli coupledmachinelearningandlatticeboltzmannmethodapproachforimmiscibletwophaseflows AT hongshengzhou coupledmachinelearningandlatticeboltzmannmethodapproachforimmiscibletwophaseflows AT zhaoqingke coupledmachinelearningandlatticeboltzmannmethodapproachforimmiscibletwophaseflows AT shutingzhao coupledmachinelearningandlatticeboltzmannmethodapproachforimmiscibletwophaseflows AT yingzhang coupledmachinelearningandlatticeboltzmannmethodapproachforimmiscibletwophaseflows AT jianshengliu coupledmachinelearningandlatticeboltzmannmethodapproachforimmiscibletwophaseflows AT yuantian coupledmachinelearningandlatticeboltzmannmethodapproachforimmiscibletwophaseflows |