open aircraft drag polar model - sesar joint undertaking · 2018. 12. 9. · open aircraft drag...

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Open Aircraft Drag Polar Model

Junzi Sun, Jacco M. Hoekstra, Joost EllerbroekDelft University of Technology

Sesar Innovation Days 2018Salzburg, Austria

Why are we talking about the Drag Polar now?

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BlueSky

https://github.com/ProfHoekstra/bluesky

What is a drag polar?

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1. It is an aircraft aerodynamic model

2. It describes the relationship between Lift and Drag

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Theory

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Aircraft dynamics

α: angle of attack

θ: pitch angle

γ: flight path angle

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Aircraft control surfaces

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Lift and drag coefficients

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The drag polar

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Current problems (Challenges)

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Challenge 1: Point-mass model

- “angle of attack” removed

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Point-mass model

α: angle of attack

θ: pitch angle

γ: flight path angle

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Challenge 2: Lack of openness

- Missing public data from manufacturer- The closed-source “go-to” model (BADA 3/BADA 4)

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Can we / How to estimate them?

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The drag polar

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Problem formulation

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/ V

/ V

/V

Problem formulation

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/ V

How to solve this using optimization?

10 Variable N Time steps

10 x N Dimensions

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Bayesian computationMarkov chain Monte Carlo with Metropolis algorithm

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Bayesian representation - the hierarchical model

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1 - Monte Carlo

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Monte Carlo

Source: Chuck Huberhttps://blog.stata.com/2016/11/15/introduction-to-bayesian-statistics-part-2-mcmc-and-the-metropolis-hastings-algorithm/

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Monte Carlo

Source: Chuck Huberhttps://blog.stata.com/2016/11/15/introduction-to-bayesian-statistics-part-2-mcmc-and-the-metropolis-hastings-algorithm/

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2 - Markov chain Monte Carlo (MCMC)

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MCMC

Source: Chuck Huberhttps://blog.stata.com/2016/11/15/introduction-to-bayesian-statistics-part-2-mcmc-and-the-metropolis-hastings-algorithm/

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MCMC

Source: Chuck Huberhttps://blog.stata.com/2016/11/15/introduction-to-bayesian-statistics-part-2-mcmc-and-the-metropolis-hastings-algorithm/

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2 - Markov chain Monte Carlo with Metropolis algorithm

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Source: Chuck Huberhttps://blog.stata.com/2016/11/15/introduction-to-bayesian-statistics-part-2-mcmc-and-the-metropolis-hastings-algorithm/ 29

Source: Chuck Huberhttps://blog.stata.com/2016/11/15/introduction-to-bayesian-statistics-part-2-mcmc-and-the-metropolis-hastings-algorithm/

MCMC with Metropolis

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Can this be solved?

10 Random variable N Time steps

10 x N Dimensions

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MCMC sampling of several states

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An easy way to ensure the convergence: Multiple MCMC chains

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Posterior distributions

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Verify with basic CFD results

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Verify with basic CFD results

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From

one AC type, one flight to Many AC types, many flights

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Flight data

- 20 aircraft types- FL30 to FL150, clean flaps configuration- High resolution (update rate) data.- Accurate real-time wind and temperature data (Meteo-Particle model *)

* Weather field reconstruction using aircraft surveillance data and a novel meteo-particle modelhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0205029

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Drag polar of multiple aircraft types (Clean configuration)

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Comparison with BADA 3

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Open Drag Polar

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Challenge 2: Lack of openness

- Missing public data from manufacture- Closed-source “go-to” model (BADA)

Challenge 2: Lack of openness

- Missing public data from manufacturer- Closed-source “go-to” model (BADA 3 / BADA 4)

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Conclusion

Limitations

- Mass uncertainty

- Thrust uncertainty

- Fixed aspect ratio

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- Better model for span efficiency e

- Incorporate fuel flow model

- Add more aircraft types

- Independent ways to validate and

refine the drag polar coefficients

Future work

Thank you for your attention!

Junzi Sunj.sun-1@tudelft.nl

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