Sound Pressure Level Spectra of Automotive Side-View Mirror Models Deduced From Time-Resolved Three-Dimensional Particle Tracking Velocimetry Data With Artificial Intelligence Based Data Assimilation Method

[1]  Constantin Jux,et al.  Robotic Volumetric Particle Tracking Velocimetry by Coaxial Imaging and Illumination , 2017 .

[2]  Diego Baresch,et al.  WavenumberFrequency Analysis of the Wall Pressure Fluctuations in the Wake of a Car Side Mirror , 2011 .

[3]  Yuji Hattori,et al.  Searching for turbulence models by artificial neural network , 2016, 1607.01042.

[4]  Lei Ma,et al.  Numerical investigation and experimental test on aerodynamic noises of the bionic rear view mirror in vehicles , 2017 .

[5]  B. W. Oudheusden,et al.  PIV-based pressure measurement , 2013 .

[6]  A. Schröder,et al.  Shake-The-Box: Lagrangian particle tracking at high particle image densities , 2016, Experiments in Fluids.

[7]  S. Watanabe,et al.  Orthogonal wavelet decomposition of turbulent structures behind a vehicle external mirror , 2010 .

[8]  J. Rabault,et al.  Performing particle image velocimetry using artificial neural networks: a proof-of-concept , 2017 .

[9]  L. Davidson,et al.  Generation of interior cavity noise due to window vibration excited by turbulent flows past a generic side-view mirror , 2018 .

[10]  J. Templeton,et al.  Reynolds averaged turbulence modelling using deep neural networks with embedded invariance , 2016, Journal of Fluid Mechanics.

[11]  Chao Yu.  Automotive Wind Noise Prediction using Deterministic Aero-Vibro-Acoustics Method , 2017 .

[12]  Arman Safdari,et al.  Visualization of nanofluid flow field by adaptive-network-based fuzzy inference system (ANFIS) with cubic interpolation particle approach , 2020, J. Vis..

[13]  F. Scarano,et al.  Robotic PTV study of the flow around automotive side-view mirror models , 2020 .

[14]  M Gregory Forest,et al.  Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D , 2017, Proceedings of the National Academy of Sciences.

[15]  Hua Yang,et al.  PIV-DCNN: cascaded deep convolutional neural networks for particle image velocimetry , 2017, Experiments in Fluids.

[16]  Heng Xiao,et al.  Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields using Convolutional Neural Networks , 2018, Communications in Computational Physics.

[17]  Edward G. Duell,et al.  Prediction of Flow-Induced Vibration of Vehicle Side-View Mirrors by CFD Simulation , 2015 .

[18]  Tino Ebbers,et al.  Improving computation of cardiovascular relative pressure fields from velocity MRI , 2009, Journal of magnetic resonance imaging : JMRI.

[19]  Michael S. Triantafyllou,et al.  Deep learning of vortex-induced vibrations , 2018, Journal of Fluid Mechanics.

[20]  Bayram Akdemir,et al.  Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset , 2015, International Conference on Graphic and Image Processing.

[21]  Matteo Bernardini,et al.  On the estimation of wall pressure coherence using time-resolved tomographic PIV , 2013 .

[22]  Hooman Yarmand,et al.  Flow visualization and analysis of thermal distribution for the nanofluid by the integration of fuzzy c-means clustering ANFIS structure and CFD methods , 2019, J. Vis..

[23]  Zhengqi Gu,et al.  Evaluation of Aerodynamic Noise Generation by a Generic Side Mirror , 2010 .