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Rachel Leuthold: Shape Optimization for Rigid Airfoils in Multiple-Kite AWE Systems
1. Shape Optimization for Rigid Airfoils in
Multiple-Kite AWE Systems
Rachel Leuthold
S´ebastien Gros, Moritz Diehl
Albert-Ludwigs-University, Freiburg, Germany
February 8, 2017
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 1
2. Hello...
Prof. Dr. Moritz Diehl
me
aerodynamics
background in:
& wind energy
(potential flow methods)
ALU-FR AWE group
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 2
3. Tether Drag and Multiple-Kite Systems
Consider an AWE system with
rigid kites, in pumping-cycle
operation...
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 3
4. Tether Drag and Multiple-Kite Systems
Tether drag proportional to
tether’s apparent velocity
squared and frontal area
Significant loss in system
efficiency.
What to do?
tether
apparent velocity
is significant over
majority of tether for
single-kite AWE systems
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 4
5. Tether Drag and Multiple-Kite Systems
Tether drag proportional to
tether’s apparent velocity
squared and frontal area
Significant loss in system
efficiency.
Multiple-Kite Systems
balance multiple
kites to keep
main-tether stationary
and tether drag small
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 5
6. Research Question
What does this look like?
For a multiple-kite
pumping-cycle system,
what kite shape, system
geometry and flight path
will maximize the mechanical
power output?
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 6
7. Research Question
What does this look like?
For a multiple-kite
pumping-cycle system,
what kite shape, system
geometry and flight path
will maximize the mechanical
power output?
argmin
parameters, states, controls, etc
− w1 power + w2 regularization
st.
modelled physics ⇒ forces and moments
forces and moments ⇒ dynamics
physical limits ⇒ variable bounds
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 7
8. A Note on Methodology
argmin
parameters, states, controls, etc
− w1 power + w2 regularization
st.
modelled physics ⇒ forces and moments
forces and moments ⇒ dynamics
physical limits ⇒ variable bounds
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 8
9. A Note on Methodology
argmin
parameters, states, controls, etc
− w1 power + w2 regularization
st.
modelled physics ⇒ forces and moments
forces and moments ⇒ dynamics
physical limits ⇒ variable bounds
very nonlinear problem
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 9
10. A Note on Methodology
argmin
parameters, states, controls, etc
− w1 power + w2 regularization
st.
modelled physics ⇒ forces and moments
forces and moments ⇒ dynamics
physical limits ⇒ variable bounds
very nonlinear problem
- solution approach: homotopy
e.g., F = (1 − ι)Ffictitious + ι (Lkite + Dkite + Dsec. tether) , 0 ≤ ι ≤ 1
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 10
11. A Note on Methodology
argmin
parameters, states, controls, etc
− w1 power + w2 regularization
st.
modelled physics ⇒ forces and moments
forces and moments ⇒ dynamics
physical limits ⇒ variable bounds
very nonlinear problem
- solution approach: homotopy
e.g., F = (1 − ι)Ffictitious + ι (Lkite + Dkite + Dsec. tether) , 0 ≤ ι ≤ 1
- reduce constraint nonlinearity by lifting variables
e.g., CL −
2πα
1 + 2c/b
= 0 ⇔ CL(b + 2c)ζ − 2πbξ = 0, ξα − ζ = 0
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 11
12. A Note on Methodology
argmin
parameters, states, controls, etc
− w1 power + w2 regularization
st.
modelled physics ⇒ forces and moments
forces and moments ⇒ dynamics
physical limits ⇒ variable bounds
very nonlinear problem
- solution approach: homotopy
e.g., F = (1 − ι)Ffictitious + ι (Lkite + Dkite + Dsec. tether) , 0 ≤ ι ≤ 1
- reduce constraint nonlinearity by lifting variables
e.g., CL −
2πα
1 + 2c/b
= 0 ⇔ CL(b + 2c)ζ − 2πbξ = 0, ξα − ζ = 0
- solve with open-source interior-point solver IPOPT via CasADi
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 12
13. What Needs to be Done?
External geometry and trajectory optimization
Internal structure optimization
Validation
Hopefully: prototype construction
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 13
14. What Needs to be Done?
External geometry and trajectory optimization
Model selection
Internal structure optimization
Validation
Hopefully: prototype construction
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 14
15. What Needs to be Done?
External geometry and trajectory optimization
Model selection
so far: aerodynamic model must include induction
[L., Gros, Diehl. Induction in Optimal Control of Multiple-Kite
Airborne Wind Energy Systems. submitted to IFAC 2017]
currently:
next:
does aero. model need to consider skewed-wake effects?
determine skewed-wake model for multi-kite systems
Internal structure optimization
Validation
Hopefully: prototype construction
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 15
16. What Needs to be Done?
External geometry and trajectory optimization
Model selection
Andrea Zanelli optimization algorithms
Elena Malz effect of wind data
Thomas Haas model validation
Internal structure optimization
Ashwin Candade structure optimization methods
Validation
Hopefully: prototype construction
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 16
17. What Needs to be Done?
External geometry and trajectory optimization
Model selection
Chalmers Uni. optimization in AWE problems 3 months
Uni. Victoria optimal design with potential flow
methods
2 months
Internal structure optimization
TU. Delft structure optimization (and more
potential flow methods)
2 months
Validation
Hopefully: prototype construction
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 17
18. Conclusion
there is a lot to do, but moving steadily forwards...
Thank you for your attention
Shape Optimization in Multi-Kite AWE Systems Rachel Leuthold (ALU-FR) 18