Aerodynamic Force Measurement on Caret and Delta Wings at High Incidence

1973 ◽  
Vol 10 (11) ◽  
pp. 750-751 ◽  
Author(s):  
G. T. COLEMAN
Keyword(s):  
Force Measurement ◽  
Aerodynamic Force ◽  
Delta Wings ◽  
High Incidence

Non-planar aerodynamics of delta wings at high incidence

2001 ◽  
Author(s):  
T. Lui ◽  
X. Huang ◽  
E. Hanff
Keyword(s):  
Delta Wings ◽  
High Incidence

605 Aerodynamic Force Measurement of a Flying Ball Using a Single Video Camera

2007 ◽  
Vol 2007.45 (0) ◽  
pp. 211-212
Author(s):  
Tatsuya NAKANISHI ◽  
Takashi OZAWA ◽  
Takashi MATSUNO ◽  
Hiromitsu KAWAZOE
Keyword(s):  
Force Measurement ◽  
Aerodynamic Force ◽  
Video Camera

scholarly journals Intelligent Force-Measurement System Use in Shock Tunnel

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6179
Author(s):  
Yunpeng Wang ◽  
Zonglin Jiang
Keyword(s):  
Deep Learning ◽  
Measurement System ◽  
Force Measurement ◽  
Aerodynamic Force ◽  
Low Frequency ◽  
Shock Tunnel ◽  
New Method ◽  
Test Results ◽  
Force Measurement System ◽  
The Impact

The inertial vibration of the force measurement system (FMS) has a large influence on the force measuring result of aircraft, especially on some tests carried out in high-enthalpy impulse facilities, such as in a shock tunnel. When force tests are conducted in a shock tunnel, the low-frequency vibrations of the FMS and its motion cannot be addressed through digital filtering because of the inertial forces, which are caused by the impact flow during the starting process of the shock tunnel. Therefore, this paper focuses on the dynamic characteristics of the performance of the FMS. A new method—i.e., deep-learning-based single-vector dynamic self-calibration (DL-based SV-DSC) of an impulse FMS, is proposed to increase the accuracy of aerodynamic force measurements in a shock tunnel. A deep-learning technique is used to train the dynamic model of the FMS in this study. Convolutional neural networks with a simple structure are applied to describe the dynamic modeling so that the low-frequency vibration signals are eliminated from the test results of the shock tunnel. By validation of the force test results measured in a shock tunnel, the current trained model can realize intelligent processing of the balance signals of the FMS. Based on this new method of dynamic calibration, the reliability and accuracy of force data processing are well verified.

A new method for force prediction in an accelerometer force balance system using support vector regression

2020 ◽  
Vol 42 (4) ◽  
pp. 880-889
Author(s):  
Sushmita Deka ◽  
Pallekonda Ramesh Babu ◽  
Maneswar Rahang
Keyword(s):  
Support Vector Regression ◽  
Force Measurement ◽  
Aerodynamic Force ◽  
Accurate Prediction ◽  
Force Balance ◽  
Support Vector ◽  
Inference System ◽  
Force Prediction ◽  
Balance System ◽  
Testing Data

The accurate prediction of force is very important in the present scenario of aerodynamic force measurement. The high accuracy of force prediction during calibration facilitates a better accuracy of force measurement in aerodynamic facilities like shock tunnels and wind tunnels. The present study describes the force prediction in an accelerometer force balance system using support vector regression (SVR). The comparison of SVR with the existing force prediction techniques namely, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) has also been carried out. The accelerometer force balance used in the current experimentation consists of a tri-axial accelerometer to measure the response on an aluminium hemispherical model on the application of force. The impulse forces were applied along the axial, normal and azimuthal directions. The forces were predicted using the accelerations obtained from the tri-axial accelerometer. SVR method was able to predict the forces quite accurately as compared to ANFIS and ANN. However, SVR has the advantage over ANFIS and ANN in that it is independent of the magnitude of the training and testing data. It is capable of an accurate prediction of forces with any magnitude of training and testing data, unlike ANFIS and ANN.

scholarly journals Fluid moment and force measurement based on control surface integration

2019 ◽  
Vol 61 (1) ◽  
Author(s):  
Diana D. Chin ◽  
David Lentink
Keyword(s):  
Particle Image ◽  
Force Measurement ◽  
Aerodynamic Force ◽  
Center Of Mass ◽  
Measurement Techniques ◽  
Control Surface ◽  
Fluid Forces ◽  
Surface Integration ◽  
Image Velocimetry ◽  
Unconstrained Motion

Abstract The moments and torques acting on a deforming body determine its stability and maneuverability. For animals, robots, vehicles, and other deforming objects locomoting in liquid or gaseous fluids, these fluid moments are challenging to accurately measure during unconstrained motion. Particle image velocimetry and aerodynamic force platforms have the potential to resolve this challenge through the use of control surface integration. These measurement techniques have previously been used to recover fluid forces. Here, we show how control surface integration can similarly be used to recover the 3D fluid moments generated about a deforming body’s center of mass. We first derive a general formulation that can be applied to any body locomoting in a fluid. We then show when and how this formulation can be greatly simplified without loss of accuracy for conditions commonly encountered during fluid experiments, such as for tests done in wind or water channels. Finally, we provide detailed formulations to show how measurements from an aerodynamic force platform can be used to determine the net instantaneous moments generated by a freely flying body. These formulations also apply more generally to other fluid applications, such as underwater swimming or locomotion over water surfaces. Graphic abstract

Effects of Reynolds number of the high-incidence flow over double-delta wings

1997 ◽  
Author(s):  
A. Fritzelas ◽  
M. Platzer ◽  
S. Hebbar ◽  
A. Fritzelas ◽  
M. Platzer ◽  
...  
Keyword(s):  
Reynolds Number ◽  
Delta Wings ◽  
High Incidence
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