Accelerated Convergence Method for Flow Field Based on DMD-POD Combined Reduced-Order Optimization Model
This work presents a novel acceleration method that achieves more efficient convergence of steady-state flow fields. This method involves conducting dynamic mode decomposition (DMD) and proper orthogonal decomposition (POD) model reduction on the field snapshots. Subsequently, the residual of the re...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10835101/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832592970687184896 |
|---|---|
| author | Jianhui Li Jun Huang Yahui Sun Guoqiang Li |
| author_facet | Jianhui Li Jun Huang Yahui Sun Guoqiang Li |
| author_sort | Jianhui Li |
| collection | DOAJ |
| description | This work presents a novel acceleration method that achieves more efficient convergence of steady-state flow fields. This method involves conducting dynamic mode decomposition (DMD) and proper orthogonal decomposition (POD) model reduction on the field snapshots. Subsequently, the residual of the reduced-order model is optimized in the POD modal space to obtain a more accurate solution. This optimized solution is then used as the initial field, and the solver continues iterating until the residual converges. Taking full advantage of both DMD and POD, the proposed approach removes the interference of high-frequency oscillatory flow components and concentrates on the main energy components. This effectively overcomes the problems of slow convergence and residual jumps caused by system stiffness, thereby accelerating the convergence process. The results show that for linear equations, the proposed method achieves a significant acceleration, with a convergence speed five times faster than traditional numerical methods. For the nonlinear Burgers equation, the proposed method also reduces the number of convergence steps by nearly 70%. Additionally, the performance of the proposed accelerated convergence method was further validated through the complex flow around a high-dimensional dual ellipsoid. |
| format | Article |
| id | doaj-art-f5d6711b69594485897da9cf6a5a2844 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f5d6711b69594485897da9cf6a5a28442025-01-21T00:00:57ZengIEEEIEEE Access2169-35362025-01-0113103401035510.1109/ACCESS.2025.352763010835101Accelerated Convergence Method for Flow Field Based on DMD-POD Combined Reduced-Order Optimization ModelJianhui Li0https://orcid.org/0009-0000-0880-7536Jun Huang1https://orcid.org/0000-0002-4081-7897Yahui Sun2Guoqiang Li3School of Manufacturing Science and Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, ChinaSchool of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, Sichuan, ChinaSchool of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, Sichuan, ChinaSchool of Manufacturing Science and Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, ChinaThis work presents a novel acceleration method that achieves more efficient convergence of steady-state flow fields. This method involves conducting dynamic mode decomposition (DMD) and proper orthogonal decomposition (POD) model reduction on the field snapshots. Subsequently, the residual of the reduced-order model is optimized in the POD modal space to obtain a more accurate solution. This optimized solution is then used as the initial field, and the solver continues iterating until the residual converges. Taking full advantage of both DMD and POD, the proposed approach removes the interference of high-frequency oscillatory flow components and concentrates on the main energy components. This effectively overcomes the problems of slow convergence and residual jumps caused by system stiffness, thereby accelerating the convergence process. The results show that for linear equations, the proposed method achieves a significant acceleration, with a convergence speed five times faster than traditional numerical methods. For the nonlinear Burgers equation, the proposed method also reduces the number of convergence steps by nearly 70%. Additionally, the performance of the proposed accelerated convergence method was further validated through the complex flow around a high-dimensional dual ellipsoid.https://ieeexplore.ieee.org/document/10835101/Computational fluid dynamicscombined reduced order modelaccelerated convergence method |
| spellingShingle | Jianhui Li Jun Huang Yahui Sun Guoqiang Li Accelerated Convergence Method for Flow Field Based on DMD-POD Combined Reduced-Order Optimization Model IEEE Access Computational fluid dynamics combined reduced order model accelerated convergence method |
| title | Accelerated Convergence Method for Flow Field Based on DMD-POD Combined Reduced-Order Optimization Model |
| title_full | Accelerated Convergence Method for Flow Field Based on DMD-POD Combined Reduced-Order Optimization Model |
| title_fullStr | Accelerated Convergence Method for Flow Field Based on DMD-POD Combined Reduced-Order Optimization Model |
| title_full_unstemmed | Accelerated Convergence Method for Flow Field Based on DMD-POD Combined Reduced-Order Optimization Model |
| title_short | Accelerated Convergence Method for Flow Field Based on DMD-POD Combined Reduced-Order Optimization Model |
| title_sort | accelerated convergence method for flow field based on dmd pod combined reduced order optimization model |
| topic | Computational fluid dynamics combined reduced order model accelerated convergence method |
| url | https://ieeexplore.ieee.org/document/10835101/ |
| work_keys_str_mv | AT jianhuili acceleratedconvergencemethodforflowfieldbasedondmdpodcombinedreducedorderoptimizationmodel AT junhuang acceleratedconvergencemethodforflowfieldbasedondmdpodcombinedreducedorderoptimizationmodel AT yahuisun acceleratedconvergencemethodforflowfieldbasedondmdpodcombinedreducedorderoptimizationmodel AT guoqiangli acceleratedconvergencemethodforflowfieldbasedondmdpodcombinedreducedorderoptimizationmodel |