PSP Measurements in the Large-Scale Transonic Wind Tunnel and Associated Image Data Processing

2005 ◽  
Author(s):  
Hirofumi Ouchi ◽  
Tomoko Irikado ◽  
Kozo Fujii ◽  
Koichi Hayashi
Keyword(s):  
Wind Tunnel ◽  
Data Processing ◽  
Large Scale ◽  
Image Data ◽  
Transonic Wind Tunnel

Large-scale dynamical graph networks applied to brain cancer image data processing

2021 ◽  
Author(s):  
Amirhessam Tahmassebi ◽  
Gelareh Karbaschi ◽  
Uwe Meyer-Baese ◽  
Anke Meyer-Baese
Keyword(s):  
Data Processing ◽  
Brain Cancer ◽  
Large Scale ◽  
Image Data

Large-scale graph networks and AI applied to medical image data processing

2020 ◽  
Author(s):  
Anke Meyer-Baese ◽  
Simon Foo ◽  
Amirhessam Tahmassebi ◽  
Uwe Meyer-Baese ◽  
Ali Moradi Amani ◽  
...  
Keyword(s):  
Data Processing ◽  
Medical Image ◽  
Large Scale ◽  
Image Data ◽  
Medical Image Data

scholarly journals Experimental Research on Noise Reduction for Continuous Transonic Wind Tunnel Loop

2020 ◽  
Vol 38 (4) ◽  
pp. 855-861
Author(s):  
Jiming Chen ◽  
Shenghao Wu ◽  
Zhenhua Chen ◽  
Jinlei Lyu ◽  
Haitao Pei
Keyword(s):  
Wind Tunnel ◽  
Experimental Research ◽  
Test Section ◽  
High Speed ◽  
Large Scale ◽  
Glass Fibers ◽  
Pressure Coefficient ◽  
Wind Tunnel Test ◽  
Noise Sources ◽  
Transonic Wind Tunnel

The noise level of wind tunnel test section is respected as one of the most important performance specifications to represent the flow field quality, especially for large scale wind tunnel. According to the acoustic experimental research conducted in the 0.6 m continuous transonic wind tunnel of CARDC, main noise sources in the tunnel loop included the compressor, the high-speed diffuser and the test section. To reduce the noise in the test section, it is necessary to prevent the test section from the compressor noise propagated both forward and backward. In 0.6 m wind tunnel loop, acoustic treatments were installed on both the compressor rear cone and the fourth corner to prevent the noise emitted from the compressor from propagating forward. The vanes in the forth corner were filled with glass fibers and covered with perforated panels. And the compressor rear cone was covered with three layers of micro-perforated panels. With acoustic treatment in the tunnel loop and the second throat throttling, the fluctuation pressure coefficient (ΔCp) is lower than 0.8%, which is close to the international advanced level.

Identification of Wall Interference Parameters in Two-Dimensional Testing in Transonic Wind Tunnel Using the PSP Technique

2016 ◽  
Author(s):  
sergio oliveira ◽  
Ana Cristina Avelar ◽  
Henrique Leite ◽  
João Batista Pessoa Falcão Filho
Keyword(s):  
Wind Tunnel ◽  
Two Dimensional ◽  
Transonic Wind Tunnel

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

Sign in / Sign up

Export Citation Format

Share Document