A Study on Flow Noise Simulation Using the SUBOFF Model Based on Bayesian Sparse Grids

LIU Jiayu; LIU Peng; JIN Hui; YIN Yibing; LIU Jiang; LIU Guijie; WEN Zhenhua

doi:10.11993/j.issn.2096-3920.2026-0033

Article Contents

Article Navigation > Journal of Unmanned Undersea Systems > 2026 > Accepted Manuscript

LIU Jiayu, LIU Peng, JIN Hui, YIN Yibing, LIU Jiang, LIU Guijie, WEN Zhenhua. A Study on Flow Noise Simulation Using the SUBOFF Model Based on Bayesian Sparse Grids[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0033

Citation:

LIU Jiayu, LIU Peng, JIN Hui, YIN Yibing, LIU Jiang, LIU Guijie, WEN Zhenhua. A Study on Flow Noise Simulation Using the SUBOFF Model Based on Bayesian Sparse Grids[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0033

Citation:

PDF( 2322 KB)

A Study on Flow Noise Simulation Using the SUBOFF Model Based on Bayesian Sparse Grids

doi: 10.11993/j.issn.2096-3920.2026-0033

1.
School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525, China
2.
College of Engineering, Ocean University of China, Qingdao 266404, China
3.
School of Aero Engine, Zhengzhou university of Aeronautics, Zhengzhou 450046, China

Received Date: 2026-01-29
Accepted Date: 2026-03-11
Rev Recd Date: 2026-03-10

Available Online: 2026-03-30

Abstract

Abstract

This paper proposes a Bayesian-based sparse grid correction method to address the degradation in flow-induced noise prediction accuracy arising from mismatched spatial scales between finite element model grid dimensions and turbulent boundary layer pressure in large-scale sparse grids. Using the DARPA SUBOFF 5470 submarine model as a case study, numerical simulations were conducted to investigate its applicability in predicting far-field acoustic radiation from submarine appendages. The study employs Corcos's self-spectrum and normalised cross-power spectral density function computational model, utilising virtual mesh refinement to compensate for the spatial correlation characteristics of low-frequency turbulent pulsation pressures. By integrating engineering characteristics and operational environments, the correlation between pulsation pressure influencing factors is extracted, ultimately constructing a Bayesian network model for flow-induced noise excitation forces. Employing wall-resolved LES (WRLES) coupled with the Ffowcs Williams-Hawkings (FW-H) acoustic analogy method, the flow-induced noise obtained from the modified simulation calculations and the results of the refined mesh fluid-structure interaction simulation. Acoustic characteristics are contrasted between the SUBOFF 5470 configuration with appendages and the bare hull. Simulation experiments demonstrate that the Bayesian sparse grid correction method effectively compensates for prediction errors arising from mismatches between grid scales and turbulence-relevant scales, validating the method's validity and applicability.
- SUBOFF model,
- Bayesian,
- hydrodynamic noise,
- spatial correlation

FullText(HTML)

References(23)

References

[1]	Tyler G D. The Emergence of low-frequency active acoustics as a aritical antisubmarine warfare technology[J]. Johns Hopkins APL Technical Digest, 1992, 13(1): 145-159.
[2]	Gilli A, Gilli M, Ricchi A, et al. Climate change and military power: Hunting for submarines in the warming ocean[J]. Texas National Security Review, 2024, 7(2): 16-41.
[3]	Li W, Song Z W, He Z Y, et al. Advancements in ship acoustic stealth technology based on acoustic absorbing and insulating metamaterials[J]. Chinese Journal of Ship Research, 2025, 20(5): 30.
[4]	Finnveden S, Birgersson F, Ross U, et al. A model of wall pressure correlation for prediction of turbulence-induced vibration[J]. Journal of fluids and structures, 2005, 20(8): 1127-1143. doi: 10.1016/j.jfluidstructs.2005.05.012
[5]	Ichchou M N, Bareille O, Troclet B, et al. Vibroacoustics Under Aerodynamic Excitations: Flinovia-Flow Induced Noise and Vibration Issues and Aspects[C]// First International Workshop on Flow Induced Noise and Vibration (FLINOVIA), 2014: 227-247.
[6]	Karimi M, Maxit L, Croaker P, et al. Analytical and numerical prediction of acoustic radiation from a panel under turbulent boundary layer excitation[J]. Journal of Sound and Vibration, 2020, 479: 115372. doi: 10.1016/j.jsv.2020.115372
[7]	李祖荟, 陈美霞. 湍流边界层激励下平板辐射噪声数值计算方法[J]. 中国舰船研究, 2017, 12(4): 76-82. doi: 10.3969/j.issn.1673-3185.2017.04.012 Li Z H, Chen M X. Numerical method for calculating sound radiation characteristics of plate structure excited by turbulent boundary layer[J]. Chinese Journal of Ship Research, 2017, 12(4): 76-82. doi: 10.3969/j.issn.1673-3185.2017.04.012
[8]	高岩, 沈琪, 俞孟萨, 等. 大尺度回转体模型边界层壁面脉动压力测量及相似性分析[J]. 声学学报, 2025, 50(2): 299-307. doi: 10.12395/0371-0025.2024019 Gao Y, Shen Q, Yu M S, et al. Measurement and similarity analysis of boundary layer wall pressure fluctuation in large-scale revolution body models[J]. Acta Acustica, 2025, 50(2): 299-307. doi: 10.12395/0371-0025.2024019
[9]	Maxit L. Simulation of the pressure field beneath a turbulent boundary layer using realizations of uncorrelated wall plane waves[J]. The Journal of the Acoustical Society of America, 2016, 140(2): 1268-1285. doi: 10.1121/1.4960516
[10]	沈琪, 刘进, 高岩, 等. 稀疏网格条件下的水下航行体低频水动力噪声计算[J]. 声学学报, 2023, 48(5): 989-995. doi: 10.12395/0371-0025.2022055 Shen Q, Liu J, Gao Y, et al. Low-frequency hydrodynamic noise calculation of underwater vehicle with sparse grid conditions[J]. Acta Acustica, 2023, 48(5): 989-995. doi: 10.12395/0371-0025.2022055
[11]	Liu G, Hao Z R, Bie H Y, et al. Control mechanism of a vortex control baffle for the horseshoe vortex around the sail of a DARPA SUBOFF model[J]. Ocean Engineering, 2023, 275: 114166. doi: 10.1016/j.oceaneng.2023.114166
[12]	Posa A, Balaras E. A numerical investigation of the wake of an axisymmetric body with appendages[J]. Journal of fluid mechanics, 2016, 792(1): 470-498. doi: 10.1017/jfm.2016.47
[13]	Zhou Z T, Xu Z Y, Wang S Z, et al. Wall-modeled large-eddy simulation of noise generated by turbulence around an appended axisymmetric body of revolution[J]. Journal of Hydrodynamics, 2022, 34(4): 533-554. doi: 10.1007/s42241-022-0062-z
[14]	周杰, 王良明, 傅健, 等. 基于Hyperband-贝叶斯优化-LSTM网络的高旋尾控修正弹修正能力研究[J]. 兵工学报, 2025, 46(7): 250-260. doi: 10.12382/bgxb.2024.0651 Zhou J, Wang L M, Fu J, et al. Research on the Correction Capability of High-spin and Tail-controlled Correction Projectile Based on HBBO-LSTM Network[J]. Acta Armamentarii, 2025, 46(7): 250-260. doi: 10.12382/bgxb.2024.0651
[15]	唐贤琪, 施玉群, 杨海云, 等. 基于贝叶斯网络推理的大坝风险评估[J]. 武汉大学学报(工学版), 2024, 57(2): 152-158. doi: 10.14188/j.1671-8844.2024-02-003 Tang X Q, Shi Y Q, Yang H Y, et al. Dam risk assessment based on Bayesian network inference[J]. Engineering Journal of Wuhan University, 2024, 57(2): 152-158. doi: 10.14188/j.1671-8844.2024-02-003
[16]	卢鑫月, 许成顺, 侯本伟, 等. 基于动态贝叶斯网络的地铁隧道施工风险评估[J]. 岩土工程学报, 2022, 44(3): 492-501. doi: 10.11779/CJGE202203011 Lu X Y, Xu C S, Hou B W, et al. Risk assessment of metro construction based on dynamic Bayesian network[J]. Chinese Journal of Geotechnical Engineering, 2022, 44(3): 492-501. doi: 10.11779/CJGE202203011
[17]	谭凯中, 高琬晴, 刘健. 基于连续型贝叶斯概率估计器预测原子核质量[J]. 核技术, 2025, 48(5): 100-110. doi: 10.11889/j.0253-3219.2025.hjs.48.250104 Tan K Z, Gao W Q, Liu J. Continuous Bayesian probability estimator in predictions of nuclear masses[J]. Nuclear Techniques, 2025, 48(5): 100-110. doi: 10.11889/j.0253-3219.2025.hjs.48.250104
[18]	张孟石, 王昕, 张亮, 等. 激波边界层干扰流动中SST模型的贝叶斯修正[J]. 力学学报, 2025, 57(9): 2122-2133. doi: 10.6052/0459-1879-24-038 Zhang M S, Wang X, Zhang L, et al. Bayesian correction of sst model for shock wave-boundary layer interaction flows[J]. Chinese Journal of Theoretical and Applied Mechanics, 2025, 57(9): 2122-2133. doi: 10.6052/0459-1879-24-038
[19]	Corcos G M. Resolution of pressure in turbulence[J]. The Journal of the Acoustical Society of America, 1963, 35(2): 192-199.
[20]	Corcos G M. The structure of the turbulent pressure field in boundary-layer flows[J]. Journal of Fluid Mechanics, 1964, 18(3): 353-378. doi: 10.1017/S002211206400026X
[21]	刘鹏. 深水全电采油树设计及可靠性评估关键技术研究[D]. 青岛: 中国石油大学(华东), 2020.
[22]	Jiang P, Liao S, Liu L, et al. Effects of appendages on the turbulence and flow noise of a submarine model using high-order scheme[PP]. arXiv(2025-03-25)[2026-02-02]. https://arxiv.org/abs/2503.19674.
[23]	Guo C, Wang X, Chen C, et al. Numerical investigation of self-propulsion performance and noise level of DARPA suboff model[J]. Journal of Marine Science and Engineering, 2023, 11(6): 1206. doi: 10.3390/jmse11061206