Acoustic Inversion for Uncertainty Reduction in Reynolds-Averaged Navier-Stokes-Based Jet Noise Prediction

doi:10.2514/1.J060876

CORC > 力学研究所 > 中国科学院力学研究所 > 非线性力学国家重点实验室

	Acoustic Inversion for Uncertainty Reduction in Reynolds-Averaged Navier-Stokes-Based Jet Noise Prediction
	Zhang, Xin-Lei 2; Xiao, Heng 1; Wu, Ting 2; He, Guowei 2
刊名	AIAA JOURNAL
	2021-12-13
页码	16
ISSN号	0001-1452
DOI	10.2514/1.J060876
通讯作者	He, Guowei(hgw@lnm.imech.ac.cn)
英文摘要	The Reynolds-averaged Navier-Stokes (RANS)-based method is a practical tool to provide rapid assessment of jet noise-reduction concepts. However, the RANS-based method requires modeling assumptions to represent noise generation and propagation, which often reduces the predictive accuracy due to the model-form uncertainties. In this work, the ensemble Kalman filter-based acoustic inversion method is introduced to reduce uncertainties in the turbulent kinetic energy and dissipation rate based on the far-field noise and the axial centerline velocity data. The results show that jet noise data are more effective from which to infer turbulent kinetic energy and dissipation rate compared to velocity data. Moreover, the inferred noise source is able to improve the estimation of the turbulent flowfield and the far-field noise at unobserved locations. Further, the noise model parameters are also considered uncertain quantities, demonstrating the ability of the proposed framework to reduce uncertainties in both the RANS and noise models. Finally, one realistic case with experimental data is investigated to show the practicality of the proposed framework. The method opens up the possibility for the inverse modeling of jet noise sources by incorporating far-field noise data that are relatively straightforward to be measured compared to the velocity field.
资助项目	National Science Foundation of China Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics[11988102] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB22040104] ; Key Research Program of Frontier Sciences of the Chinese Academy of Sciences[QYZDJ-SSWSYS002]
WOS关键词	DATA ASSIMILATION ; MIXING NOISE ; MEAN-FLOW ; OPTIMIZATION ; ANALOGY ; SPACE ; MODEL ; SPEED
WOS研究方向	Engineering
语种	英语
WOS记录号	WOS:000742546900001
资助机构	National Science Foundation of China Basic Science Center Program for Multiscale Problems in Nonlinear Mechanics ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Key Research Program of Frontier Sciences of the Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/88375]
专题	力学研究所_非线性力学国家重点实验室
通讯作者	He, Guowei
作者单位	1.Virginia Tech, Kevin T Crofton Dept Aerosp & Ocean Engn, Blacksburg, VA 24060 USA 2.Chinese Acad Sci, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Xin-Lei,Xiao, Heng,Wu, Ting,et al. Acoustic Inversion for Uncertainty Reduction in Reynolds-Averaged Navier-Stokes-Based Jet Noise Prediction[J]. AIAA JOURNAL,2021:16.
APA	Zhang, Xin-Lei,Xiao, Heng,Wu, Ting,&He, Guowei.(2021).Acoustic Inversion for Uncertainty Reduction in Reynolds-Averaged Navier-Stokes-Based Jet Noise Prediction.AIAA JOURNAL,16.
MLA	Zhang, Xin-Lei,et al."Acoustic Inversion for Uncertainty Reduction in Reynolds-Averaged Navier-Stokes-Based Jet Noise Prediction".AIAA JOURNAL (2021):16.