Aerodynamic parameter optimization during wind tunnel testing

1982 ◽  
Author(s):  
R. PALKO ◽  
M. CRAWFORD
Keyword(s):  
Wind Tunnel ◽  
Parameter Optimization ◽  
Wind Tunnel Testing ◽  
Aerodynamic Parameter ◽  
Tunnel Testing

Automated wind tunnel testing

1998 ◽  
Author(s):  
William Schoenfeld ◽  
Francis Priolo
Keyword(s):  
Wind Tunnel ◽  
Wind Tunnel Testing ◽  
Tunnel Testing

scholarly journals Wind Tunnel Testing of an Optimal Feedback/feedfoward Control Law for Wind Turbines

2020 ◽  
Vol 53 (2) ◽  
pp. 12638-12643
Author(s):  
Michael Sinner ◽  
Vlaho Petrović ◽  
Frederik Berger ◽  
Lars Neuhaus ◽  
Martin Kühn ◽  
...  
Keyword(s):  
Wind Tunnel ◽  
Wind Turbines ◽  
Control Law ◽  
Wind Tunnel Testing ◽  
Optimal Feedback ◽  
Tunnel Testing

scholarly journals 3D Acoustic Mapping in Automotive Wind Tunnel: Algorithm and Problem Analysis on Simulated Data

2021 ◽  
Vol 11 (7) ◽  
pp. 3241
Author(s):  
Gianmarco Battista ◽  
Paolo Chiariotti ◽  
Milena Martarelli ◽  
Paolo Castellini ◽  
Claudio Colangeli ◽  
...  
Keyword(s):  
Wind Tunnel ◽  
Problem Definition ◽  
Aerodynamic Noise ◽  
Accurate Information ◽  
Problem Analysis ◽  
Wind Tunnel Testing ◽  
Experimental Conditions ◽  
Automotive Manufacturing ◽  
Acoustic Mapping ◽  
Tunnel Testing

Localization and quantification of noise sources are important to fulfill customer and regulation requirements in a such competitive sector like automotive manufacturing. Wind tunnel testing and acoustic mapping techniques based on microphone arrays can provide accurate information on these aspects. However, it is not straightforward to get source positions and strengths in these testing conditions. In fact, the car is a 3D object that radiates noise from different parts simultaneously, involving different noise generation mechanisms such as tire noise and aerodynamic noise. Commonly, acoustic maps are produced on a 3D surface that envelopes the objects. However, this practice produces misleading and/or incomplete results, as acoustic sources can be generated outside the surface. When the hypothesis of sources on the model surface is removed, additional issues arise. In this paper, we propose exploiting an inverse method tailored to a volumetric approach. The aim of this paper is to investigate the issues to face when the method is applied to automotive wind tunnel testing. Two different kinds of problem must be considered: On the one hand, the results of inverse methods are strongly influenced by the problem definition, while, on the other hand, experimental conditions must be taken into account to get accurate results. These aspects have been studied making use of simulated experiments. Such a controlled simulation environment, by contrast to a purely experimental case, enables accurate assessment of both the localization and quantification performance of the proposed method. Finally, a set of scores is defined to evaluate the resulting maps with objective metrics.

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