scholarly journals Experimental data from the Benchmark SuperCritical Wing wind tunnel test on an oscillating turntable

Author(s):  
Jennifer Heeg ◽  
David J. Piatak
Aerospace ◽  
2020 ◽  
Vol 7 (12) ◽  
pp. 177
Author(s):  
Pooneh Aref ◽  
Mehdi Ghoreyshi ◽  
Adam Jirasek ◽  
Jürgen Seidel

This article presents the results of a computational investigation of an integrated propeller test case using the HPCMP CREATETM-AV Kestrel simulation tools. There is a renewed interest in propeller-driven aircraft for unmanned aerial vehicles, electric aircraft, and flying taxies. Computational resources can significantly accelerate the generation of aerodynamic models for these vehicles and reduce the development cost if the prediction tools can accurately predict the aircraft/propeller aerodynamic interactions. Unfortunately, limited propeller experimental data are available to validate computational methods. An American Institute of Aeronautics and Astronautics (AIAA) workshop was therefore established to address this problem. The objective of this workshop was to generate an open access-powered wind tunnel test database for computational validation of propeller effects on the wing aerodynamics, specifically for wing-tip-mounted propellers. The propeller selected for the workshop has four blades and a diameter of 16.2 in. The wing has a root and tip chord of 11.6 and 8.6 in, respectively. Two different simulation approaches were used: one using a single grid including wind tunnel walls and the second using a subset grid overset to an adaptive Cartesian grid that fills the space between the near-body grid and wind tunnel walls. The predictions of both approaches have been compared with available experimental data from the Lockheed Martin low-speed wind tunnel to investigate the grid resolution required for accurate prediction of flowfield data. The results show a good agreement for all tested conditions. The measured and predicted data show that wing aerodynamic performance is improved by the spinning tip-mounted propeller.


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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