Sound pressure level spectrum analysis by combination of 4D PTV and ANFIS method around automotive side-view mirror models.

This paper proposes a data augmentation method based on artificial intelligence (AI) to obtain sound level spectrum as predicting the spatial and temporal data of time-resolved three-dimensional Particle Tracking Velocimetry (4D PTV) data. A 4D PTV has used to measure flow characteristics of three side mirror models adopting the Shake-The-Box (STB) algorithm with four high-speed cameras on a robotic arm for measuring industrial scale. Helium filled soap bubbles are used as tracers in the wind tunnel experiment to characterize flow structures around automobile side mirror models. Full volumetric velocity fields and evolution of vortex structures are obtained and analyzed. Instantaneous pressure fields are deduced by solving a Poisson equation based on the 4D PTV data. To predict spatial and temporal data of velocity field, artificial intelligence (AI)-based data prediction method has applied. Adaptive Neural Fuzzy Inference System (ANFIS) based machine learning algorithm works well to find 4D missing data behind the automobile side mirror model. Using the ANFIS model, power spectrum of velocity fluctuations and sound level spectrum of pressure fluctuations are successfully obtained to assess flow and noise characteristics of three different side mirror models.

Comments on 'Lattice Boltzmann simulation of alumina-water nanofluid in a square cavity' by Yurong He, Cong Qi, Yanwei Hu, Bin Qin, Fengchen Li and Yulong Ding.

This work presents some comments concerning the paper entitled 'Lattice Boltzmann simulation of alumina-water nanofluid in a square cavity' by Yurong He, Cong Qi, Yanwei Hu, Bin Qin, Fengchen Li and Yulong Ding which was published in Nanoscale Research Letters in 2011. The comments are related to the numerical parameters and the computed results of average Nusselt number.