Hybrid mechanism and data driven approach for high-precision modeling of gas flow regulation systems of VFDR
Abstract The variable flow ducted rocket (VFDR) poses significant challenges for high-precision modeling due to its complex nonlinear dynamics, harsh operational conditions, and integration of multiple physical fields. To address this challenge, this paper introduces a hybrid mechanism and data-driv...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-05-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01899-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850272764006498304 |
|---|---|
| author | Zongyu Zhang Huan Wang Meng Tang Jie Zhang Xinhan Hu |
| author_facet | Zongyu Zhang Huan Wang Meng Tang Jie Zhang Xinhan Hu |
| author_sort | Zongyu Zhang |
| collection | DOAJ |
| description | Abstract The variable flow ducted rocket (VFDR) poses significant challenges for high-precision modeling due to its complex nonlinear dynamics, harsh operational conditions, and integration of multiple physical fields. To address this challenge, this paper introduces a hybrid mechanism and data-driven modeling approach. Initially, the parameter perturbation method was employed to elucidate the interdependencies between system parameters and the VFDR's dynamic and steady-state responses. Entropy weight method (EWM) and technique for order preference by similarity to ideal solution (TOPSIS) were utilized for ranking the compensation parameters of the dynamic-state and steady-state models of the VFDR. Additionally, the throat area of the regulation valve was chosen as a compensatory parameter for the steady-state model. A data-driven residual compensation model was developed using the nonlinear autoregressive neural networks with external inputs (NARX) algorithm to enhance the steady-state mechanistic VFDR model, addressing its time-varying and high uncertainty characteristics. To mitigate dynamic response errors in the mechanistic model, a compensation strategy integrating error and similarity evolution with extreme learning machine (ELM) was implemented to generate compensation value. Simulation and ground experiment results validate the efficacy of the proposed algorithm, the experimental results indicate that, after compensation using the proposed strategy, the maximum error in a single test is reduced by 24.19%, and the average error is decreased by 17.81%. |
| format | Article |
| id | doaj-art-8e40799e5c794a2da137cf8f1b7c4d0b |
| institution | OA Journals |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-8e40799e5c794a2da137cf8f1b7c4d0b2025-08-20T01:51:41ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-05-0111612310.1007/s40747-025-01899-5Hybrid mechanism and data driven approach for high-precision modeling of gas flow regulation systems of VFDRZongyu Zhang0Huan Wang1Meng Tang2Jie Zhang3Xinhan Hu4School of Artificial Intelligence, Dalian Maritime UniversitySchool of Advanced Manufacturing, Sun Yat-Sen UniversitySchool of Aeronautics and Astronautics, Sun Yat-Sen UniversitySchool of Mechanics and Aerospace Engineering, Liaoning Provincial Key Laboratory of Aerospace Advanced Technology, Dalian University of TechnologySchool of Mechanics and Aerospace Engineering, Liaoning Provincial Key Laboratory of Aerospace Advanced Technology, Dalian University of TechnologyAbstract The variable flow ducted rocket (VFDR) poses significant challenges for high-precision modeling due to its complex nonlinear dynamics, harsh operational conditions, and integration of multiple physical fields. To address this challenge, this paper introduces a hybrid mechanism and data-driven modeling approach. Initially, the parameter perturbation method was employed to elucidate the interdependencies between system parameters and the VFDR's dynamic and steady-state responses. Entropy weight method (EWM) and technique for order preference by similarity to ideal solution (TOPSIS) were utilized for ranking the compensation parameters of the dynamic-state and steady-state models of the VFDR. Additionally, the throat area of the regulation valve was chosen as a compensatory parameter for the steady-state model. A data-driven residual compensation model was developed using the nonlinear autoregressive neural networks with external inputs (NARX) algorithm to enhance the steady-state mechanistic VFDR model, addressing its time-varying and high uncertainty characteristics. To mitigate dynamic response errors in the mechanistic model, a compensation strategy integrating error and similarity evolution with extreme learning machine (ELM) was implemented to generate compensation value. Simulation and ground experiment results validate the efficacy of the proposed algorithm, the experimental results indicate that, after compensation using the proposed strategy, the maximum error in a single test is reduced by 24.19%, and the average error is decreased by 17.81%.https://doi.org/10.1007/s40747-025-01899-5Data drivenModel correctionPrecise modelingGas flow regulationVariable flow ducted rocket |
| spellingShingle | Zongyu Zhang Huan Wang Meng Tang Jie Zhang Xinhan Hu Hybrid mechanism and data driven approach for high-precision modeling of gas flow regulation systems of VFDR Complex & Intelligent Systems Data driven Model correction Precise modeling Gas flow regulation Variable flow ducted rocket |
| title | Hybrid mechanism and data driven approach for high-precision modeling of gas flow regulation systems of VFDR |
| title_full | Hybrid mechanism and data driven approach for high-precision modeling of gas flow regulation systems of VFDR |
| title_fullStr | Hybrid mechanism and data driven approach for high-precision modeling of gas flow regulation systems of VFDR |
| title_full_unstemmed | Hybrid mechanism and data driven approach for high-precision modeling of gas flow regulation systems of VFDR |
| title_short | Hybrid mechanism and data driven approach for high-precision modeling of gas flow regulation systems of VFDR |
| title_sort | hybrid mechanism and data driven approach for high precision modeling of gas flow regulation systems of vfdr |
| topic | Data driven Model correction Precise modeling Gas flow regulation Variable flow ducted rocket |
| url | https://doi.org/10.1007/s40747-025-01899-5 |
| work_keys_str_mv | AT zongyuzhang hybridmechanismanddatadrivenapproachforhighprecisionmodelingofgasflowregulationsystemsofvfdr AT huanwang hybridmechanismanddatadrivenapproachforhighprecisionmodelingofgasflowregulationsystemsofvfdr AT mengtang hybridmechanismanddatadrivenapproachforhighprecisionmodelingofgasflowregulationsystemsofvfdr AT jiezhang hybridmechanismanddatadrivenapproachforhighprecisionmodelingofgasflowregulationsystemsofvfdr AT xinhanhu hybridmechanismanddatadrivenapproachforhighprecisionmodelingofgasflowregulationsystemsofvfdr |