Surrogate Based Optimization of Aerodynamic Noise for Streamlined Shape of High Speed Trains

Sun, Zhenxu; Zhang, Ye; Yang, Guowei

doi:10.3390/app7020196

Cited by 16 publications

(3 citation statements)

References 26 publications

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“…In [22], an aerodynamic noise optimization was performed by a non-linear acoustics solver (NLAS) approach for acoustic calculation. With the use of Kriging surrogate model, a multi-objective optimization of the streamlined shape of high-speed trains was obtained, which takes the noise level in the far field and the drag of the whole train as the objectives.…”

Section: The Present Issuementioning

confidence: 99%

Advances in Vibroacoustics and Aeroacustics of Aerospace and Automotive Systems

Citarella

Federico

2018

Applied Sciences

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Section: The Present Issuementioning

confidence: 99%

Advances in Vibroacoustics and Aeroacustics of Aerospace and Automotive Systems

Citarella

Federico

2018

Applied Sciences

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“…The head and tail cars of high-speed trains have the same streamlined nose. However, their sound mechanisms are different [12,13]. When high-speed air flows over the streamlined nose of the head car, it firstly stagnates at the tip of the nose, after which, the flow accelerates.…”

Section: Introductionmentioning

confidence: 99%

A Fast Approach for Predicting Aerodynamic Noise Sources of High-Speed Train Running in Tunnel

Qin¹,

Li²,

Wang³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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The aerodynamic noise of high-speed trains passing through a tunnel has gradually become an important issue. Numerical approaches for predicting the aerodynamic noise sources of high-speed trains running in tunnels are the key to alleviating aerodynamic noise issues. In this paper, two typical numerical methods are used to calculate the aerodynamic noise of high-speed trains. These are the static method combined with non-reflective boundary conditions and the dynamic mesh method combined with adaptive mesh. The fluctuating pressure, flow field and aerodynamic noise source are numerically simulated using the above methods. The results show that the fluctuating pressure, flow field structure and noise source characteristics obtained using different methods, are basically consistent. Compared to the dynamic mesh method, the pressure, vortex size and noise source radiation intensity, obtained by the static method, are larger. The differences are in the tail car and its wake. The two calculation methods show that the spectral characteristics of the surface noise source are consistent. The maximum difference in the sound pressure level is 1.9 dBA. The static method is more efficient and more suitable for engineering applications.

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“…Recently, a surrogate modeling method, namely meta-modeling method, has been widely studied in data-driven modeling, mainly including Polynomial Response Surface (PRS) [5], Kriging [6,7], Radial Basis Function (RBF) [8], Support Vector Regression (SVR) [9], etc. In the work of Song et al [10], the performance of PRS, RBF, Kriging, and SVR are compared in the design optimization of foam-filled tapered structures, and the results show that no single model is the best for approximating all objective functions in the considered problems.…”

Section: Introductionmentioning

confidence: 99%

An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction

Zhang

Yao³

et al. 2019

Applied Sciences

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The satellite constellation network is a powerful tool to provide ground traffic business services for continuous global coverage. For the resource-limited satellite network, it is necessary to predict satellite coverage traffic volume (SCTV) in advance to properly allocate onboard resources for better task fulfillment. Traditionally, a global SCTV distribution data table is first statistically constructed on the ground according to historical data and uploaded to the satellite. Then SCTV is predicted onboard by a data table lookup. However, the cost of the large data transmission and storage is expensive and prohibitive for satellites. To solve these problems, this paper proposes to distill the data into a surrogate model to be uploaded to the satellite, which can both save the valuable communication link resource and improve the SCTV prediction accuracy compared to the table lookup. An effective surrogate ensemble modeling method is proposed in this paper for better prediction. First, according to prior geographical knowledge of the SCTV distribution, the global earth surface domain is split into multiple sub-domains. Second, on each sub-domain, multiple candidate surrogates are built. To fully exploit these surrogates and combine them into a more accurate ensemble, a partial weighted aggregation method (PWTA) is developed. For each sub-domain, PWTA adaptively selects the candidate surrogates with higher accuracy as the contributing models, based on which the ultimate ensemble is constructed for each sub-domain SCTV prediction. The proposed method is demonstrated and testified with an air traffic SCTV engineering problem. The results demonstrate the effectiveness of PWTA regarding good local and global prediction accuracy and modeling robustness.

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Surrogate Based Optimization of Aerodynamic Noise for Streamlined Shape of High Speed Trains

Cited by 16 publications

References 26 publications

Advances in Vibroacoustics and Aeroacustics of Aerospace and Automotive Systems

Advances in Vibroacoustics and Aeroacustics of Aerospace and Automotive Systems

A Fast Approach for Predicting Aerodynamic Noise Sources of High-Speed Train Running in Tunnel

An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction

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