KBM METHOD OF ANALYZING LIMIT CYCLE FLUTTER OF A WING WITH AN EXTERNAL STORE AND COMPARISON WITH A WIND-TUNNEL TEST

1995 ◽  
Vol 187 (2) ◽  
pp. 271-280 ◽  
Author(s):  
Y.-R. Yang
1998 ◽  
Author(s):  
Hiroshi Matsushita ◽  
Kenichi Saitoh ◽  
Peter Granasy

2021 ◽  
Author(s):  
David F. Castillo Zuñiga ◽  
Alain Giacobini Souza ◽  
Roberto G. da Silva ◽  
Luiz Carlos Sandoval Góes

Author(s):  
Bruno Ricardo Massucatto Padilha ◽  
Guilherme Barufaldi ◽  
ROBERTO GIL ANNES DA SILVA

2016 ◽  
Vol 7 (2) ◽  
pp. 131-138
Author(s):  
Ivransa Zuhdi Pane

Data post-processing plays important roles in a wind tunnel test, especially in supporting the validation of the test results and further data analysis related to the design activities of the test objects. One effective solution to carry out the data post-processing in an automated productive manner, and thus eliminate the cumbersome conventional manual way, is building a software which is able to execute calculations and have abilities in presenting and analyzing the data in accordance with the post-processing requirement. Through several prototype development cycles, this work attempts to engineer and realize such software to enhance the overall wind tunnel test activities. Index Terms—software engineering, wind tunnel test, data post-processing, prototype, pseudocode


2021 ◽  
Vol 11 (8) ◽  
pp. 3315
Author(s):  
Fabio Rizzo

Experimental wind tunnel test results are affected by acquisition times because extreme pressure peak statistics depend on the length of acquisition records. This is also true for dynamic tests on aeroelastic models where the structural response of the scale model is affected by aerodynamic damping and by random vortex shedding. This paper investigates the acquisition time dependence of linear transformation through singular value decomposition (SVD) and its correlation with floor accelerometric signals acquired during wind tunnel aeroelastic testing of a scale model high-rise building. Particular attention was given to the variability of eigenvectors, singular values and the correlation coefficient for two wind angles and thirteen different wind velocities. The cumulative distribution function of empirical magnitudes was fitted with numerical cumulative density function (CDF). Kolmogorov–Smirnov test results are also discussed.


2019 ◽  
Vol 52 (12) ◽  
pp. 128-133
Author(s):  
Yoshiro Hamada ◽  
Kenichi Saitoh ◽  
Noboru Kobiki

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