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

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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