Identification of Sharp Edge Non-Slender Delta Wing Aerodynamic Coefficient Using Neural Network

Abstract Delta wing formed a vortical flow on its surface which produced higher lift compared to conventional wing. The vortical flow is complex and non-linear which requires more studies to understand its flow physics. However, conventional flow analysis (wind tunnel test and computational flow dynamic) comes with several significant drawbacks. In recent times, application of neural network as alternative to conventional flow analysis has increased. This study is about utilization of Multi-Layer Perceptron (MLP) neural network to predict the coefficient of pressure (Cp ) on a delta wing model. The physical model that was used is a sharp edge non-slender delta wing. The training data was taken from wind tunnel tests. 70% of data is used as training, 15% is used as validation and another 15% is used as test set. The wind tunnel test was done at angle of attack from 0°-18° with increment of 3°. The flow velocity was set at 25m/s which correspond to 800,000 Reynolds number. The inputs are angle of attack and location of pressure tube (y/cr) while the output is Cp . The MLP models were fitted with 3 different transfer functions (linear, sigmoid, and tanh) and trained with Lavenberg-Marquadt backpropagation algorithm. The results of the models were compared to determine the best performing model. Results show that large amount of data is required to produce accurate prediction model because the model suffer from condition called overfitting.

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Low speed wind tunnel investigation of the flow about delta wing, oscillating in pitch to very high angle of attack

10.2514/6.1989-295 ◽

1989 ◽

Cited By ~ 18

Author(s):

MOHAMMAD-AMEEN JARRAH

Keyword(s):

Wind Tunnel ◽

Delta Wing ◽

Angle Of Attack ◽

High Angle ◽

High Angle Of Attack ◽

Low Speed ◽

Low Speed Wind Tunnel ◽

Very High

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Two optical techniques appropriate for visualizing the steady/unsteady vortical flow past a highly swept delta-wing equipped with double vortex-flaps in low and high speed wind-tunnel

Communications in Nonlinear Science and Numerical Simulation ◽

10.1016/s1007-5704(98)90009-0 ◽

1998 ◽

Vol 3 (3) ◽

pp. 167-172

Author(s):

Gong Ni ◽

Jieke Lu ◽

Dingding Xin

Keyword(s):

Wind Tunnel ◽

High Speed ◽

Delta Wing ◽

Vortical Flow ◽

Optical Techniques ◽

Flow Past

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Design of a Wind Tunnel Test Section for Single Airfoil Configuration With Real-Time Lift Force Control

Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Control; Adaptive and Intelligent Systems Control; Advances in Wind Energy Systems; Advances in Robotics; Assistive and Rehabilitation Robotics; Biomedical and Neural Systems Modeling, Diagnostics, and Control; Bio-Mechatronics and Physical Human Robot; Advanced Driver Assistance Systems and Autonomous Vehicles; Automotive Systems ◽

10.1115/dscc2017-5087 ◽

2017 ◽

Cited By ~ 1

Author(s):

Aline Aguiar da Franca ◽

Dirk Abel

Keyword(s):

Wind Tunnel ◽

Wind Turbine ◽

Real Time ◽

Force Control ◽

Test Section ◽

Lift Force ◽

Angle Of Attack ◽

Wind Tunnel Test ◽

Wiener Model ◽

Dynamic Matrix

This article presents a concept of test section for a closed-return wind tunnel, where the lift force of an airfoil, which depends on the angle of attack, is controlled in real-time. This airfoil is used to represent a wind turbine blade. The lift force of the blades is what produces the rotor torque of the wind turbine. This torque determines the amount of energy that will be captured by the wind turbine. The linear dynamics of the motor used to change the angle of attack and the static non-linearity of the airfoil are modeled as a Wiener model. The Quadratic Dynamic Matrix Controller based on Wiener model with linearizing pre-compensation is implemented to keep the lift force constant, which is desirable to avoid mechanical loads for wind turbine applications.

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The Aerodynamic Analysis for Iced Four Bundled Transmission Lines with Typical Crescent

The Open Electrical & Electronic Engineering Journal ◽

10.2174/1874129001408010084 ◽

2014 ◽

Vol 8 (1) ◽

pp. 84-89 ◽

Cited By ~ 1

Author(s):

Liu Yuejun ◽

Tang Ai P. ◽

Liu Ke T. ◽

Tu Jie W.

Keyword(s):

Numerical Simulation ◽

Wind Tunnel ◽

Transmission Lines ◽

Angle Of Attack ◽

Wind Tunnel Test ◽

Simulation Method ◽

Single Wire ◽

Wind Tunnel Tests ◽

Absolute Value ◽

Aerodynamic Test

Despite the fact that the wind tunnel tests have been carried out on iced transmission lines subjected to wind load, it is not practical to do wind tunnel tests due to its high cost. This paper describes a detailed numerical simulation method that can be used to instead of wind tunnel tests. Based on the galloping mechanism of iced transmission lines, the aerodynamic test was simulated with the typical crescent super-large thickness iced four bundled conductors. One of the results highlighted in this study is that the wind angle of attack had significant influence on the aerodynamics of iced conductors. The Den-Hartog and O.Nigol coefficient were calculated to determine galloping of iced transmission lines, comparing with the reference of wind tunnel test in the Zhejiang university, the range of the wind angle of attack to the bundled conductor which can lead to gallop is larger than single wire, but the absolute value of amplitude is less than the single conductor, split conductor is more likely to gallop than single conductor.

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Wing Load and Angle of Attack Identification by Integrating Optical Fiber Sensing and Neural Network Approach in Wind Tunnel Test

Applied Sciences ◽

10.3390/app9071461 ◽

2019 ◽

Vol 9 (7) ◽

pp. 1461 ◽

Cited By ~ 2

Author(s):

Daichi Wada ◽

Masato Tamayama

Keyword(s):

Neural Network ◽

Standard Deviation ◽

Wind Tunnel ◽

Wind Tunnel Test ◽

Network Approach ◽

Neural Network Approach ◽

Fiber Sensing ◽

Load Distributions ◽

Wing Load ◽

Optical Fiber Sensing

The load and angle of attack (AoA) for wing structures are critical parameters to be monitored for efficient operation of an aircraft. This study presents wing load and AoA identification techniques by integrating an optical fiber sensing technique and a neural network approach. We developed a 3.6-m semi-spanned wing model with eight flaps and bonded two optical fibers with 30 fiber Bragg gratings (FBGs) each along the main and aft spars. Using this model in a wind tunnel test, we demonstrate load and AoA identification through a neural network approach. We input the FBG data and the eight flap angles to a neural network and output estimated load distributions on the eight wing segments. Thereafter, we identify the AoA by using the estimated load distributions and the flap angles through another neural network. This multi-neural-network process requires only the FBG and flap angle data to be measured. We successfully identified the load distributions with an error range of −1.5–1.4 N and a standard deviation of 0.57 N. The AoA was also successfully identified with error ranges of −1.03–0.46° and a standard deviation of 0.38°.

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Neural Network Model Predictive Control of Wind Tunnel Test Conditions

54th AIAA Aerospace Sciences Meeting ◽

10.2514/6.2016-1150 ◽

2016 ◽

Cited By ~ 2

Author(s):

Peter Sutcliffe ◽

Mark R. Rennie

Keyword(s):

Neural Network ◽

Wind Tunnel ◽

Model Predictive Control ◽

Network Model ◽

Predictive Control ◽

Neural Network Model ◽

Wind Tunnel Test ◽

Test Conditions

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Wind tunnel test and numerical simulation of wind characteristics at a bridge site in mountainous terrain

Advances in Structural Engineering ◽

10.1177/1369433216673377 ◽

2016 ◽

Vol 20 (8) ◽

pp. 1223-1231 ◽

Cited By ~ 14

Author(s):

Yongle Li ◽

Xinyu Xu ◽

Mingjin Zhang ◽

Youlin Xu

Keyword(s):

Wind Speed ◽

Wind Tunnel ◽

Bridge Deck ◽

Angle Of Attack ◽

Wind Tunnel Test ◽

Vertical Direction ◽

Steep Slope ◽

Mountainous Terrain ◽

Bridge Site ◽

Wind Characteristics

Wind tunnel test and computational fluid dynamics simulation were conducted to study the wind characteristics at a bridge site in mountainous terrain. The upstream terrains were classified into three types: open terrain, open terrain with a steep slope close to the bridge, and open terrain with a ridge close to the bridge. Results obtained from the two methods were compared, including mean speed profiles in the vertical direction and variations of wind speed and angle of attack along the bridge deck. In addition, turbulence intensities at the bridge site obtained from wind tunnel test were discussed. For mean speed profiles in the vertical direction, two methods are reasonably close for open terrain, while mountain shielding effects are evident for open terrain with a steep slope for both the methods, but the extents of effects appear different. Wind speed and angle of attack along the bridge deck are mainly influenced by the local terrain. Strong downslope wind is generated at the lee slope for the case of wind normal to top of the ridge. The comparative results are expected to provide useful references for the study of wind characteristics in mountainous terrain in the future.

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NEURAL NETWORK-BASED INTERPOLATION AND EXTRAPOLATION OF WIND TUNNEL TEST DATA

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026809002564 ◽

2009 ◽

Vol 08 (02) ◽

pp. 225-235 ◽

Cited By ~ 2

Author(s):

S. THAMARAI SELVI ◽

E. MAHENDRAN ◽

S. RAMA

Keyword(s):

Neural Network ◽

Wind Tunnel ◽

Test Data ◽

Wind Tunnel Test ◽

Knowledge Based ◽

Network Training ◽

General Regression Neural Networks ◽

Training Methodologies ◽

The Cost ◽

General Regression

There is an increasing need to apply emerging technologies in knowledge-based management system. This proposed system captures the knowledge of experts and the knowledge acquired for designing a new systems. Wind Tunnel Test data of missiles has been taken into consideration for knowledge management. Interpolation and Extrapolation of missile test data has been attempted using neural network technique General Regression Neural Networks training algorithm. The results produced by neural network training methodologies are validated with the existing test data. The knowledge extraction using neural network is found suitable for interpolation and to some extent for extrapolation, thereby reducing the cost as well as number of test runs in Wind Tunnel Test.

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