Acceleration-Based In Situ Eddy Dissipation Rate Estimation with Flight Data
Abstract
:1. Introduction
2. Turbulence Severity Estimation Based on In-Situ Flight Data
2.1. Derived Vertical Wind From QAR Flight Data
2.2. The von Karman Turbulence Model
2.3. Vertical Wind-Based EDR Estimation
2.4. A New Vertical Acceleration-Based EDR Estimation Algorithm
2.4.1. Non-Planar Unsteady Vortex Lattice Method
2.4.2. Acceleration-Based EDR Estimation
3. Experiments and Discussion
3.1. Experiments on Non-Planar UVLM
3.1.1. Analysis of Grid Convergence
3.1.2. Computation of Pitching Moment Coefficient
3.2. Acceleration Response Analysis
3.2.1. Comparison of Acceleration Response
3.2.2. Spectrum Analysis at Different Locations
3.3. EDR Estimation Comparison
3.3.1. Application to EDR Estimation
3.3.2. Comparison of EDR Estimation
4. Conclusions
- 1.
- Based on wing and horizontal tail combination of target aircraft, the non-planar Unsteady Vortex Lattice Method (UVLM) is proposed to obtain the aerodynamic force change in turbulent flight. To improve the computing accuracy of UVLM, vortex rings are assigned on the mean camber surface, and the semi-circle division method is adopted to refine the lattices neighboring to wingtip, wing root, and leading edge. Another major improvement is that the lattices neighboring to the control surfaces, such as spoilers and elevators, are divided as close as possible to the structural edge, which is beneficial for further EDR estimation.
- 2.
- A complete algorithm flow for estimating vertical acceleration and in situ EDR value from QAR flight data is proposed. Experiments on acceleration response show that compared with a linear model, there are improvements in both response accuracy and tracking performance in moderate and severe bumpiness. The vertical accelerations at different locations of the fuselage are computed accurately. Compared with wind-based EDR estimation, the new acceleration-based EDR algorithm shows better accuracy and stability because the adverse influence of aircraft maneuvers on EDR estimation is eliminated effectively.
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Flight Parameter | Symbol | Wind-Based | Acceleration-Based |
---|---|---|---|
True airspeed | √ | √ | |
Angle of attack | √ | √ | |
Pitching angle | √ | √ | |
Roll angle | √ | √ | |
Ground speed | √ | √ | |
Pitching angular rate | √ | ||
Rolling angular rate | √ | ||
Yawing angular rate | √ |
Wind Fields | Turbulence Severity | Max/Min Acceleration |
---|---|---|
WF1 | Light | +1.130 g/+0.980 g |
WF2 | Light | +1.232 g/+0.972 g |
WF3 | Moderate | +1.602 g/+0.655 g |
WF4 | Moderate | +1.677 g/+0.672 g |
WF5 | Severe | +2.004 g/+0.169 g |
WF6 | Severe | +2.124 g/+0.175 g |
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Gao, Z.; Wang, H.; Qi, K.; Xiang, Z.; Wang, D. Acceleration-Based In Situ Eddy Dissipation Rate Estimation with Flight Data. Atmosphere 2020, 11, 1247. https://doi.org/10.3390/atmos11111247
Gao Z, Wang H, Qi K, Xiang Z, Wang D. Acceleration-Based In Situ Eddy Dissipation Rate Estimation with Flight Data. Atmosphere. 2020; 11(11):1247. https://doi.org/10.3390/atmos11111247
Chicago/Turabian StyleGao, Zhenxing, Haofeng Wang, Kai Qi, Zhiwei Xiang, and Debao Wang. 2020. "Acceleration-Based In Situ Eddy Dissipation Rate Estimation with Flight Data" Atmosphere 11, no. 11: 1247. https://doi.org/10.3390/atmos11111247