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Optimization of the Propulsion System for a
“Silent Aircraft”
David Benveniste
Vai-Man LeiAlexis Manneville
Presentation Outline
• Project presentation • Simulation model • Gradient-based optimization
• Heuristic search technique • Multi-objective optimization
• Conclusion & Future work
The “Silent Aircraft” project
• Objective – Reduce aircraft noise below background noise in a well populated area
• Motivation – Improve quality of life near airports – Reduce costs associated with noise (airport taxes, soundproofing …) – Enable growth of air transportation
• Approach – Design an aircraft with noise as a prime objective applying
revolutionary concepts and using advanced technologies for noise reduction
This project• Focus on propulsion system design (principal noise source at takeoff)
• Multidisciplinary system design problem – Acoustics – Engine design and performance analysis – Takeoff flight dynamics (trajectory) – Cost, Range, Weight
• Revolutionary concepts – Ultra-High Bypass ratio – Multiple fans engines – “Distributed exhaust” (many small engines)
• Objectives – Assess these concepts using MSDO – Understand tradeoffs between noise and performance
Simulation model• Design Variables
– Bypass ratio (BPR) : D
– Fan pressure ratio (FPR) : Sf
– Total Thrust : Ftot [N] – Number of engines : Neng
– Number of fans per engine : Nfan
• Potential objectives / Constraints– Takeoff Noise level : LTO [EPNdB] – Relative Range variation : ' rR [%] – Relative Cost variation : ' rC [%] – Relative Weight variation : ' rW [%]
Simulation model
D Sf Ftot NengNfan
Cycle Analysis
Off-design Performance
Takeoff Flight Dynamics
Range
Cost
Noise
Weight LTO ' rR ' rC ' rW
Optimizer
NASA ANOPP
Simulation model
• Benchmarking – Need details of the aircraft configuration
• Aerodynamic coefficients • Engine cycle settings • Noise Certification conditions
� Assume relative variations are well predicted – Baseline configuration: BWB
• ref: Configuration Control Document-2 (01/26/1996)
F
D� �����Sf �����
totN ��N
eng
fan ��
� 801 kN
LTO �104.8 EPNdB ' rR �� ' rC �� ' rW ��
Gradient Based Method
g
•iSIGHT SQP algorithm
Minimize LTO(x) s.t.
1 (x) = – ' R – 0.5 ˺ 0r
g2 (x) = ' W – 0.2 ˺ 0r
g��(x) = ' C – 0.2 ˺ 0r
•Design Variables bounds:•15 < D�< 50 •1.1 < Sf < 2 •600 kN < Ftot < 1,000 kN
•Constant Integer Design Variables •N ��eng•Nfan ��
BPR FPR Total Thrust (N)
TO Noise (EPNdB)
relative 'Range
relative 'Cost
relative 'Weight
BWB baseline 19.5 1.37 800967 104.8 ~0% ~0% ~0%
“Optimized” design 37.1 1.2 998760 100.3 +2% +13% +20%
Sensitivity Analysis
wJ x *
* calculated with forward differencing with 'x* = 0.002x** wx J
-0.348 D ( -0.050
Sf 1.045
Normalized sensitivity Design varibles F (Total thrust) (optimized) Bypass ratio)
(Fan pressure ratio)
Low influence of bypass ratio is unexpected
Scaling
FPR~O(1)
BPR~O(10)
Total Thrust~O(106)
16% reduction in no.Rough scale of 10-6 for of iterationTotal Thrust
Scaling with the Hessian matrix
§¨
~ 5 F ·§¨ ¨
· 10�F 9% further reduction¸ ¨ ~ 7
¸ ¹¸D10�©¹
¸D© in no. of iteration
Heuristic Search Technique
GA findings
•By far more efficient than gradient based optimization
•Very expensive: simulations limited to a few thousands runs (several hours)
•Very likely to give a global optimum (hit most of the constraints)
GA resultsOptimal solution
Subpopulation size Number of islands Number of generations Mutation rate (%) Total Thrust
Fan pressure ratio Number of engines Fans per engines ' rR
' rC ' rW Total Noise (dB) Number of simulations
15 3 100 5 1,023,189 N 59.94 1.128 1 8 -15.9 +19.9 +14.4 66.505 4500 5:33
Bypass Ratio
Computation time (h:mn)
120
100
80
60
40
20
0 total noise (dB)
BWB baseline 1% MR, 100 runs 1.05% MR, 600 runs 2% MR, 1200 runs 5% MR, 4500 runs
Active constraint
Objective
Multi objective optimization
• Second objective: range, competing with noise from the GA results.
• Pareto front: to get the Pareto front we used the GA with different weighs and scaling factors
• Too computationally expensive to run a real full factorial experiment.
Pareto Front
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
60 70 80 90 100 110
Noise (EPNdB)
Rel
ativ
e ra
nge
varia
tion
(%)
1-l=11-l=0.99991-l=0.9991-l=0.991-l=0.81-l=0.51-l=0.21-l=0.01best
From all our runs we extracted the non dominated (feasible) solutions to obtain our best possible estimate of the Pareto front (in black)
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
60 70 80 90 100 110
Noise (EPNdB)R
elat
ive
rang
e va
riatio
n (%
) best
pareto
4000randomruns
Conclusion
•Gradient based method trapped easily at local minimum
•GA gives a credible global optimum
•Objectives of minimizing noise and maximizing range are opposingwith noise dominating
•Ultra-High Bypass ratio and Multiple fans engines can achieve significant noise reduction (~35dB)
Future work
•Data to validate modules
•Improve weight and cost modules
•Multi-objective optimization with cost, more realistic constraints
•Include other noise sources (airframe,…)
•Study effect of other design variables (Tt4, …)