Time Series Prediction of Aerodynamic Noise Based on Variational Mode Decomposition and Echo State Network
Time series prediction of aerodynamic noise is critical for oscillatory instabilities analyses in fluid systems. Due to the significant dynamical and non-stationary characteristics of aerodynamic noise, it is challenging to precisely predict its temporal behavior. Here, we propose a method combining...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/14/7896 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849714513246420992 |
|---|---|
| author | Zhoufanxing Lei Haiyang Meng Jing Yang Bin Liang Jianchun Cheng |
| author_facet | Zhoufanxing Lei Haiyang Meng Jing Yang Bin Liang Jianchun Cheng |
| author_sort | Zhoufanxing Lei |
| collection | DOAJ |
| description | Time series prediction of aerodynamic noise is critical for oscillatory instabilities analyses in fluid systems. Due to the significant dynamical and non-stationary characteristics of aerodynamic noise, it is challenging to precisely predict its temporal behavior. Here, we propose a method combining variational mode decomposition (VMD) and echo state network (ESN) to accurately predict the time series of aerodynamic noise induced by flow around a cylinder. VMD adaptively decomposes the noise signal into multiple modes through a constrained variational optimization framework, effectively separating distinct frequency-scale features between vortex shedding and turbulent fluctuations. ESN then employs a randomly initialized reservoir to map each mode into a high-dimensional dynamical system, and learns their temporal evolution by leveraging the reservoir’s memory of past states to predict their future values. Aerodynamic noise data from cylinder flow at a Reynolds number of 90,000 is generated by numerical simulation and used for model validation. With a rolling prediction strategy, this VMD-ESN method achieves accurate prediction within 150 time steps with a root-mean-square-error of only 3.32 Pa, substantially reducing computational costs compared to conventional approaches. This work enables effective aerodynamic noise prediction and is valuable in fluid dynamics, aeroacoustics, and related areas. |
| format | Article |
| id | doaj-art-021314bab5da48b08e71196d7b0c61d2 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-021314bab5da48b08e71196d7b0c61d22025-08-20T03:13:41ZengMDPI AGApplied Sciences2076-34172025-07-011514789610.3390/app15147896Time Series Prediction of Aerodynamic Noise Based on Variational Mode Decomposition and Echo State NetworkZhoufanxing Lei0Haiyang Meng1Jing Yang2Bin Liang3Jianchun Cheng4Key Laboratory of Modern Acoustics, MOE, Institute of Acoustics, Department of Physics, Nanjing University, Nanjing 210093, ChinaKey Laboratory of Modern Acoustics, MOE, Institute of Acoustics, Department of Physics, Nanjing University, Nanjing 210093, ChinaKey Laboratory of Modern Acoustics, MOE, Institute of Acoustics, Department of Physics, Nanjing University, Nanjing 210093, ChinaKey Laboratory of Modern Acoustics, MOE, Institute of Acoustics, Department of Physics, Nanjing University, Nanjing 210093, ChinaKey Laboratory of Modern Acoustics, MOE, Institute of Acoustics, Department of Physics, Nanjing University, Nanjing 210093, ChinaTime series prediction of aerodynamic noise is critical for oscillatory instabilities analyses in fluid systems. Due to the significant dynamical and non-stationary characteristics of aerodynamic noise, it is challenging to precisely predict its temporal behavior. Here, we propose a method combining variational mode decomposition (VMD) and echo state network (ESN) to accurately predict the time series of aerodynamic noise induced by flow around a cylinder. VMD adaptively decomposes the noise signal into multiple modes through a constrained variational optimization framework, effectively separating distinct frequency-scale features between vortex shedding and turbulent fluctuations. ESN then employs a randomly initialized reservoir to map each mode into a high-dimensional dynamical system, and learns their temporal evolution by leveraging the reservoir’s memory of past states to predict their future values. Aerodynamic noise data from cylinder flow at a Reynolds number of 90,000 is generated by numerical simulation and used for model validation. With a rolling prediction strategy, this VMD-ESN method achieves accurate prediction within 150 time steps with a root-mean-square-error of only 3.32 Pa, substantially reducing computational costs compared to conventional approaches. This work enables effective aerodynamic noise prediction and is valuable in fluid dynamics, aeroacoustics, and related areas.https://www.mdpi.com/2076-3417/15/14/7896aerodynamic noisetime series predictionvariational mode decompositionecho state network |
| spellingShingle | Zhoufanxing Lei Haiyang Meng Jing Yang Bin Liang Jianchun Cheng Time Series Prediction of Aerodynamic Noise Based on Variational Mode Decomposition and Echo State Network Applied Sciences aerodynamic noise time series prediction variational mode decomposition echo state network |
| title | Time Series Prediction of Aerodynamic Noise Based on Variational Mode Decomposition and Echo State Network |
| title_full | Time Series Prediction of Aerodynamic Noise Based on Variational Mode Decomposition and Echo State Network |
| title_fullStr | Time Series Prediction of Aerodynamic Noise Based on Variational Mode Decomposition and Echo State Network |
| title_full_unstemmed | Time Series Prediction of Aerodynamic Noise Based on Variational Mode Decomposition and Echo State Network |
| title_short | Time Series Prediction of Aerodynamic Noise Based on Variational Mode Decomposition and Echo State Network |
| title_sort | time series prediction of aerodynamic noise based on variational mode decomposition and echo state network |
| topic | aerodynamic noise time series prediction variational mode decomposition echo state network |
| url | https://www.mdpi.com/2076-3417/15/14/7896 |
| work_keys_str_mv | AT zhoufanxinglei timeseriespredictionofaerodynamicnoisebasedonvariationalmodedecompositionandechostatenetwork AT haiyangmeng timeseriespredictionofaerodynamicnoisebasedonvariationalmodedecompositionandechostatenetwork AT jingyang timeseriespredictionofaerodynamicnoisebasedonvariationalmodedecompositionandechostatenetwork AT binliang timeseriespredictionofaerodynamicnoisebasedonvariationalmodedecompositionandechostatenetwork AT jianchuncheng timeseriespredictionofaerodynamicnoisebasedonvariationalmodedecompositionandechostatenetwork |