AnalysisoftheBladeElementMomentum … · 2019-03-10 · 1 Combine "Momentum Theory" and "Blade...
Transcript of AnalysisoftheBladeElementMomentum … · 2019-03-10 · 1 Combine "Momentum Theory" and "Blade...
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Analysis of the Blade Element MomentumTheory, application to river current power
extraction
Jérémy Ledoux, Sebastian Reyes-Riffo, Julien Salomon(*)
(*) Lab. J-L. Lions & INRIA-Paris
50 ans du LJLL - Roscoff, 4.3.19
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Evaluation and design of propeller, Seminal models :1865 : "Momentum Theory" : William J. M. Rankine 1, alsoGreenhill and Froude.1878 : "Blade Element Theory" : William Froude 2, also Taylorand Drzewiecki.1919 : The 1D-model of Betz and the Betz limit
Cp,Betz = 16/27 ≈ 0.5926
1926 : "Blade Element Momentum Theory" : Glauert’sbreakthrough.
1 Combine "Momentum Theory" and"Blade Element Theory",
2 Take into account the wakemomentum.
1. W. J. M. Rankine. On the mechanical principles of the action of propellers.Transactions, Institute of Naval Architects, 6 :13-30, 1865.2. W. Froude. On the elementary relation between pitch, slip and propulsive
efficiency. Trans. Roy. Inst. Naval Arch., 19(47) :47-57, 1878.
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Outline
1 The Glauert’s modelLocal/Macro decompositionReformulationCorrection of the modelExistence of solutions
2 Classical solverUsual algorithmConvergence issues
3 OptimizationFunctionalUsual algorithmWith correction ?
4 Conclusion
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Outline
1 The Glauert’s modelLocal/Macro decompositionReformulationCorrection of the modelExistence of solutions
2 Classical solverUsual algorithmConvergence issues
3 OptimizationFunctionalUsual algorithmWith correction ?
4 Conclusion
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The Glauert’s modelLocal/Macro decomposition
Hermann Glauert,
1892-1934.
"The Elements of Aerofoil and Airscrew
Theory" - 1926
Ideas :Decompose the blade into elements,considered to be independent.
Coupling of two models :
1 Local 2D model, describing thelift and drag forces on a 2Dprofile
2 Macroscopic model, describingthe evolution of a fluid ringcrossing the propeler
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The Glauert’s modelLocal/Macro decomposition
Local 2D model :Using windtunnel, orComputational Fluid Mechanics,one use a 2D prototype or modelto assess the Drag and Liftforces on a profile, assumingthey are on the form :
dL = CL(α)12ρW 2c(r)dr
dD = CD(α)12ρW 2c(r)dr.
with :α = angle of incidence,W = macroscopic velocityin x = −∞,c = is the chorddistribution.
http://www.pilotwings.org/
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The Glauert’s modelLocal/Macro decomposition
Local 2D model :Using windtunnel, orComputational Fluid Mechanics,one use a 2D prototype or modelto assess the Drag and Liftforces on a profile, assumingthey are on the form :
dL = CL(α)12ρW 2c(r)dr
dD = CD(α)12ρW 2c(r)dr.
with :α = angle of incidence,W = macroscopic velocityin x = −∞,c = is the chorddistribution.
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
0 10 20 30 40
CL o
r CD
incidence / [degrees]
CL
CD
"Wind Turbine Blade Analysis using the BladeElement Momentum Method", Notes by G. Ingram.
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The Glauert’s modelLocal/Macro decomposition
Macroscopic model :axial and rotational interferencefactors
a = U−∞ − Ux=0
U−∞
a′ = ωx=0+
2Ω"Aerodynamics for students",
http://www-mdp.eng.cam.ac.uk
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The Glauert’s modelLocal/Macro decomposition
Macroscopic model :axial and rotational interferencefactors
a = U−∞ − Ux=0
U−∞
a′ = ωx=0+
2Ω
Wilson, Lissaman, "Applied Aerodynamics of
wind power machines", 1974, p.51.
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The Glauert’s modelLocal/Macro decomposition
Macroscopic model :Angular relations
θ = Blade angleα = Incidence angleϕ = Relative angle deviation
"The element will work at
α = θ − ϕ.”
"The Elements of Aerofoil and Airscrew Theory", 1926, p.212.
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The Glauert’s modelLocal/Macro decomposition
Macroscopic model :Angular relationsGlauert motivation :aeronautics
⇒aGlauert → −a,a′Glauert → −a′
θ → γλ.
tan−1 ϕ = λ1 + a′
1− a
λ = rΩU−∞
"The Elements of Aerofoil and Airscrew Theory", 1926, p.212.
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The Glauert’s modelLocal/Macro decomposition
Using Bernouilli’s relation, one can find the elementary force andtorque :
dFx = ρU2x=0(4a(1− a))πrdr,
dT = 4a′(1− a)ρUx=0r3Ωπdr.
But the lift and drag coefficients definitions imply
dFx = σ(r)πρU2x=0(1− a)2
sin2 ϕ(CL(ϕ− γλ) cosϕ+ CD(ϕ− γλ) sinϕ)rdr,
dT = σ(r)πρU2x=0(1− a)2
sin2 ϕ(CL(ϕ− γλ) sinϕ− CD(ϕ− γλ) cosϕ)r2dr,
where we have introduced the local solidity, defined by :
σ(r) = Bc(r)2πr .
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The Glauert’s modelLocal/Macro decomposition
We end up with the Glauert’s system :
tan−1 ϕ = λ1 + a′
1− a ,
a
1− a = σ(r)4 sin2 ϕ
(CL(ϕ− γλ) cosϕ+ CD(ϕ− γλ) sinϕ),
a′
1− a = σ(r)4λ sin2 ϕ
(CL(ϕ− γλ) sinϕ− CD(ϕ− γλ) cosϕ).
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Outline
1 The Glauert’s modelLocal/Macro decompositionReformulationCorrection of the modelExistence of solutions
2 Classical solverUsual algorithmConvergence issues
3 OptimizationFunctionalUsual algorithmWith correction ?
4 Conclusion
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The Glauert’s modelReformulation
Simplification : we assume CD = 0 → fits with the practical cases 3
tan−1 ϕ = λ1 + a′
1− a ,
a
1− a = σ(r)4 sin2 ϕ
(CL(ϕ− γλ) cosϕ),
a′
1− a = σ(r)4λ sin2 ϕ
(CL(ϕ− γλ) sinϕ).
3. "In the calculation of induction factors,[...] accepted practice is to set CD
equal to zero [...]. For airfoils with low drag coefficients, this simplificationintroduces negligible errors.", Manwell et al. p.125.
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Reformulation
tan−1 ϕ = λ1 + a′
1− a ,
a
1− a = σ(r)4 sin2 ϕ
(CL(ϕ− γλ) cosϕ),
a′
1− a = σ(r)4λ sin2 ϕ
(CL(ϕ− γλ) sinϕ).
Remarks :Also for practical cases, we are interested in solution such thatCL(ϕ− γλ) > 0⇔ γλ > ϕ,(a, a′, ϕ) = (1, a′, π2 ) is always a (non-interesting) solution of thissystem.
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The Glauert’s modelReformulation
Set µCL = µCL(ϕ) := σ(r)CL(ϕ− γλ)4 :
tan−1 ϕ = λ1 + a′
1− a ,
a
1− a = σ(r)4 sin2 ϕ
(CL(ϕ− γλ) cosϕ),
a′
1− a = σ(r)4λ sin2 ϕ
(CL(ϕ− γλ) sinϕ).
m
tan−1 ϕ = λ1 + a′
1− a ,a
1− a = µCLsin2 ϕ
cosϕ,
a′
1− a = µCLλ sinϕ.
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The Glauert’s modelReformulation
tan−1 ϕ = λ1 + a′
1− a ,a
1− a = µCLsin2 ϕ
cosϕ,
a′
1− a = µCLλ sinϕ.
m
tan−1 ϕ = λ
(1 + µCL
sin2 ϕcosϕ
)+ µCL
sinϕ.
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The Glauert’s modelReformulation
To study the solution(s) of Glauert’s system, we rewrite it :
tan−1 ϕ = λ
(1 + µCL
sin2 ϕcosϕ
)+ µCL
sinϕ
⇔µCL = sinϕcosϕ− λ sinϕsinϕ+ λ cosϕ
⇔µCL = sinϕ tan(θλ − ϕ) =: µG.
Solving Glauert’s approachconsists in solving :
σ(r)CL(ϕ− γλ)4 = sinϕ tan(θλ − ϕ)
mµCL(ϕ) = µG(ϕ)
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The Glauert’s modelReformulation
Example : river current power, ’Hydrolienne H3’
→ A.N.R HyFloEF lu
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The Glauert’s modelReformulation
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Outline
1 The Glauert’s modelLocal/Macro decompositionReformulationCorrection of the modelExistence of solutions
2 Classical solverUsual algorithmConvergence issues
3 OptimizationFunctionalUsual algorithmWith correction ?
4 Conclusion
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The Glauert’s modelCorrection of the model
Recall that :
dFx = 4a(1− a)U2−∞ρπrdr.
The quantity
CT = dFx12U
2−∞ρ2πrdr
,
is called local thrust coefficient.Manwell et al, "Wind Energy Explained
Theory, Design and Application", 2nd Ed.,
p.130
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The Glauert’s modelCorrection of the model
dFx = 4a(1− a)U2−∞ρπrdr
⇓dFx = 4χ(a, ac)U2
−∞ρπrdr
= 4 (a(1− a) + ψ ((a− ac)+))U2−∞ρπrdr
Order Author ac ψ
((a − ac)+
)3 Glauert 1/3
(a − ac)+4
((a − ac)2
+ac
+ 2(a − ac)+ + ac
)2 Glauert emp. 2/5 ac(1 − ac) +
(a − ac)+[Fλ(ϕ)(a − ac)+ + 2Fλ(ϕ)ac − 0.286]
2.5708Fλ(ϕ)
2 Buhl 2/51
2Fλ(ϕ)
((a − ac)+
1 − ac
)2
1 Wilson et al. 1/3 (a − ac)2+
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Outline
1 The Glauert’s modelLocal/Macro decompositionReformulationCorrection of the modelExistence of solutions
2 Classical solverUsual algorithmConvergence issues
3 OptimizationFunctionalUsual algorithmWith correction ?
4 Conclusion
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The Glauert’s modelExistence of solutions
LemmaSuppose that (ϕ, a, a′) satisfies Glauert’s model.
1 There exists τ : ϕ 7→ a = τ(ϕ) satisfying :
a
1− a +(
1− cos θλ cosϕcos(θλ − ϕ)
)ψ ((a− ac)+)
(1− a)2 = g(ϕ),
with
g(ϕ) := tan−1 ϕ tan(θλ − ϕ) + µCDsinϕ
(1 + tan−1 ϕ tan(θλ − ϕ)
),
2 The unknown ϕ satisfies
µCL(ϕ)− tan(θλ − ϕ)µCD (ϕ) = µcG(ϕ),
where
µcG(ϕ) := µG(ϕ) + cos θλsin2 ϕ
cos(θλ − ϕ)ψ ((τ(ϕ)− ac)+)
(1− τ(ϕ))2 .
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The Glauert’s modelExistence of solutions
Expanding further, one finds
a = 1−
√ψ(1− ac)µCD (0) ϕ3/2
LemmaThe function µcG satisfies
µcG(ϕ) ≈ϕ=0+µCD (0)ϕ
.
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Outline
1 The Glauert’s modelLocal/Macro decompositionReformulationCorrection of the modelExistence of solutions
2 Classical solverUsual algorithmConvergence issues
3 OptimizationFunctionalUsual algorithmWith correction ?
4 Conclusion
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Outline
1 The Glauert’s modelLocal/Macro decompositionReformulationCorrection of the modelExistence of solutions
2 Classical solverUsual algorithmConvergence issues
3 OptimizationFunctionalUsual algorithmWith correction ?
4 Conclusion
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Classical solverUsual algorithm
Usual way to solve this system :
"General Momentum Theory for Horizontal Axis Wind Turbines", J. N. Sorensen
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Classical solverUsual algorithm
Usual way to solve this system :
tan−1 ϕk+1 = λ1 + a′
k
1− ak ,
ak
1− ak = σ(r)4 sin2 ϕk
(CL(ϕk − γλ) cosϕk + CD(ϕk − γλ) sinϕk),
a′k
1− ak = σ(r)4λ sin2 ϕk
(CL(ϕk − γλ) sinϕk − CD(ϕk − γλ) cosϕk).
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Classical solverUsual algorithm
TheoremLet
µCL(θλ) ≤ µG(γλ).
and
‖µ′CL‖∞h(γλ)1 + λ2 ≤ 1
‖µCL‖∞|h′(γλ)|1 + λ2 ≤ 1.
Then, the sequence (ϕk)k∈N defined by the classical solver converges toa solution of Glauert’s model.
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Outline
1 The Glauert’s modelLocal/Macro decompositionReformulationCorrection of the modelExistence of solutions
2 Classical solverUsual algorithmConvergence issues
3 OptimizationFunctionalUsual algorithmWith correction ?
4 Conclusion
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Classical solverConvergence issues
All the variant of the model may give rise to multiple solutions. Morepreciselly :
1 With the simplification CD = 0 and CL approximately lineararound 0 : two solutions.
2 Stall : possible other solution after the critical angle.3 Corrected model : µcG may change of concavity.→ possible problem of convergence..⇒ Bisection method will always work...
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Outline
1 The Glauert’s modelLocal/Macro decompositionReformulationCorrection of the modelExistence of solutions
2 Classical solverUsual algorithmConvergence issues
3 OptimizationFunctionalUsual algorithmWith correction ?
4 Conclusion
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Outline
1 The Glauert’s modelLocal/Macro decompositionReformulationCorrection of the modelExistence of solutions
2 Classical solverUsual algorithmConvergence issues
3 OptimizationFunctionalUsual algorithmWith correction ?
4 Conclusion
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OptimizationFunctional
Quantity to maximize :
CP = 8λ2R
∫ λR
λr
λ3a′(1− a)(
1− CD(ϕ− γλ)CL(ϕ− γλ) tanϕ
)dλ.
Design parameters : c(r), γλ(r)⇒ (µCL , µCD ).
Indeed :
µCL = σ(r)CL(γλ(r)− ϕ)4
µCD = σ(r)CD(γλ(r)− ϕ)4
σ(r) = Bc(r)2πr .
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OptimizationFunctional
Mathematical formulation, for fixed λ :
min J(µCL , µCD ) = a′(1− a)(
1− µCDµCL
tan−1 ϕ
),
under the constraints
tan−1 ϕ = λ1 + a′
1− a ,
a
1− a = 1sin2 ϕ
(µCL cosϕ+ µCD sinϕ) ,
a′
1− a = 1λ sin2 ϕ
(µCL sinϕ− µCD cosϕ) .
and with
µCL = σ(r)CL(γλ − ϕ)4 µCD = σ(r)CD(γλ − ϕ)
4 .
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Outline
1 The Glauert’s modelLocal/Macro decompositionReformulationCorrection of the modelExistence of solutions
2 Classical solverUsual algorithmConvergence issues
3 OptimizationFunctionalUsual algorithmWith correction ?
4 Conclusion
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OptimizationUsual algorithm
If the correction on a is not included, the following procedure is used :
"Wind Energy Explained", Manwell et al. 2nd Ed.
J(µCL , µCD ) = a′(1− a)(
1− CD(ϕ− γλ)CL(ϕ− γλ) tan−1 ϕ
)⇓ CD ≈ 0
J(µCL) = a′(1− a).
After this step α? = ϕ− γλ is fixed !
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OptimizationUsual algorithm
Consider then :J(µCL) = a′(1− a).
Taking into account that µCL(ϕ) = µG(ϕ) := sinϕ tan(θλ − ϕ), onecan rewrite J only in term of ϕ.
J(ϕ) = 12λ
sin2 ϕ
sin θλsin(2(ϕ− θλ))
whose optimum is obtained for :
ϕ? = 23θλ
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OptimizationUsual algorithm
End of the design procedure :
Recall that α? = ϕ− γλ and µCL = σ(r)CL(ϕ− γλ)4 , then
γ?λ = α? + ϕ?
c? = 8πrBCL(α?)µG(ϕ?)
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OptimizationUsual algorithm
Summary :
min J(µCL , µCD , ϕ) s.t. Eq(µCL , µCD , ϕ) = 0
⇓ (1)
min J(µCL , ϕ) s.t. Eq(µCL , ϕ) = 0
⇓ (2)
min J(f(ϕ), ϕ)
⇓
Explicit solution !
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Outline
1 The Glauert’s modelLocal/Macro decompositionReformulationCorrection of the modelExistence of solutions
2 Classical solverUsual algorithmConvergence issues
3 OptimizationFunctionalUsual algorithmWith correction ?
4 Conclusion
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OptimizationWith correction ?
Using the expansion :
a = 1−
√ψ(1− ac)µCD (0) ϕ3/2,
we obtain
J(µCL , µCD ) ≈ ψ(1− ac) tan θλλ
(1− µCD (0))ϕ2.
TheoremThere exists a0
c < 1 such that for ac ≤ a0c, the optimal solution does
not activate the thrust correction.
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Outline
1 The Glauert’s modelLocal/Macro decompositionReformulationCorrection of the modelExistence of solutions
2 Classical solverUsual algorithmConvergence issues
3 OptimizationFunctionalUsual algorithmWith correction ?
4 Conclusion
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Conclusion
The BEM is a 0D × 2D coupled model.Condition on γλ and CL to guarantee existence of solution ofinterestPossible multiple solutionsOptimization : existence, definition of a research interval
Possible extension to 1D × 2D ?
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Pub
Une conférence Maths/Énergies Marines :https://emrsim2019.sciencesconf.org/
4-7 Juillet
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Pub
Une conférence Maths/Énergies Marines :https://emrsim2019.sciencesconf.org/
4-7 Juillet
⇒ À Roscoff ! !