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Technological Institute of Aeronautics
Prof. Bento Silva de Mattos
Nov. 19-21, 2013 - São José dos Campos - São Paulo - Brazil
Research Activities at Aircraft Design Department
ENERGY EFFICIENT AIRCRAFT
CONFIGURATIONS, TECHNOLOGIES AND
CONCEPTS OF OPERATION
Areas of Interest Department of Aircraft Design
• Multi-disciplinary design and optimization
• Airframe-engine integration
• Futuristic air vehicles
• Flight Physics
• Ecranoplanes
• Fuselage design
• Interior layout
• Wingtip devices design
• History of the aeronautical technology
• Aeroacoustics
• UAV
• Micro UAVs
• Advanced technology
Website
http://www.aer.ita.br/~bmattos
MTOW Estimation
Design Diagram
High-Performance Computing
• Multi-disciplinary Design and Optimization
• Computational Fluid Dynamics
Department of Aircraft Design
Professional Experience @ EMBRAER
Fuselage Design CBA-123 (1988)
Department of Aircraft Design
Recontouring of the aft fuselage to
improve airflow on the propellers
(DFVLR Panel Code)
Professional Experience @ EMBRAER
E-Jets Forward Fuselage Design
Department of Aircraft Design
Mach contours in the longitudinal symmetry plane obtained by a CFD
simulation with Fluent. By properly shaping the fuselage a smooth subcritical
airflow can be achieved (Mach = 0.80, α = 0o).
Professional Experience @ EMBRAER
Winglet Design
Department of Aircraft Design
E-170/175
EMB-145 SA
Legacy 600/650
ERJ 145XR
High-Performance Computing
CFD Analysis
Study on feasibility of double-curvature windshield
Mach number contours
Navier-Stokes Simulation
Fuselage Design
Forward Fuselage Optimization for a 70-seater Airliner
Department of Aircraft Design
High-Performance Computing
Ice detector installation
Department of Aircraft Design
High-Performance Computing
Ice detector installation
Department of Aircraft Design
High-Performance Computing
Fuselage Aerodynamic Analysis
Department of Aircraft Design
Mach = 0.76
Impingement analysis
00.76 1.5M
Red regions recorded sound levels above 80 dB
Simulations performed with the Fluent code
Broadband Noise of Typical Ice probe @ Cruise
00.45 1.5M
Simulations performed with the Fluent code
CFD Simulations for A340 ice probe installation
Static Pressure Contours
0.13 3oM
CFD Simulations for A340 landing gear
0.13 3oM
CFD Simulations for A340 landing gear
Lower-deck galley for Airliners
A340-300 Boeing 767-200 Boeing 777-200
Economy class typical 238 175 280
Extra seats in the economy class 28 27 34
Capacity variation +11.8 % +15.4 % +12.1 %
A340-300 Boeing 767-200 Boeing 777-200
DOC per seat mile variation -4.17 % -5.42 % -3.25 %
Capacity variation +11.8 % +15.4 % +12.1 %
Framework for Conceptual Design of Airliners
Framework Structure for Conceptual Design of Airliners
BAERO(Weight Calculation and fuselage sizing)
Flight Mechanics
Field PerformanceDOC
Noise signature(PANPA)
De-coding En-codingInitial population
Selection
New populationCrossoverCrossover MutationMutation
Fitness evaluation
StopStop
Ok!
AIDMIN
AEROCAL
GERETIC
Optimization Framework
GERETIC: optimization engine
Some design constraints: Second segment climb
Engine thrust must overcome drag at beginning of cruise
Landing and takeoff field length for a given set of airport locations
Noise levels recorded at ICAO spots
Flight quality
Fuel storage
Range
AEROCAL: airplane automated design parameter calculator
BAERO: Compilation of airplane conceptual design methods for: Geometric definition (tailplanes are designed according to static stability and controllability criteria)
Engine deck
Landing gear sizing
Aerodynamic calculations (Roskam Class II)
Stability (longitudinal flight quality and Dutch –roll check)
Weight and CG estimation
Performance (field performance, operational envelope, route analysis, payload-range diagram)
Operating costs
Clmax estimation with BLWF (critical section method)
PANPA Engine noise (NASA)
Airframe noise (ESDU)
ADMIN
Optimization Framework
Cabin Sizing
Ref: Prof. Dieter Scholz, Hoschule für Angewandte Wissenschaften Hamburg
Aeronautical Airplane
Aeronautical Airplane Cabin Sizing
Aeronautical Airplane Structural Sizing (Megson’s Method)
Aeronautical Airplane Structural Sizing (Megson’s Method)
Airplane Actual Mass [kg] Calculated Mass [kg] Error [%]
F28 3230 3120 3.53
737-200 5000 5500 -9.09
DC-9-30 5261 5050 4.18
A319-100 8732 9360 -6.71
A320-200 8766 9530 -8.02
727-200 8956 9300 -3.70
A321-200 10000 10500 -4.76
Some Aeronautical Airplane Outputs (BAERO Interactive Version)
Optimization Framework
Engine module Validation
Thrust @ TO (lbf)
SFC TO airflow
TO (lb/s)
Thrust @
Cruise (lbf)
SFC @ Cruise
Weight (lbf)
Engine CPR Actual Data
Manufacturer Bypass Engine Module
Diameter (m) Error (%)
CF6-45A 21 46500 - 1393 11250 0,63 8768
GE 4,64 46221 - 1371 11223 0,60 9412
2,2 -0.6% - -1.6% -0.2% -5.1% 7.3%
PW2237 17 36600 - 1210 6500 0,58 7185
Pratt Whitney 5,8 35180 - 1145 7400 0,59 6488
2,01 -3.9% - -5.4% 13.8% 0.8% -9.7%
CFM56-5A1 17 25000 0,33 852 5000 0,60 4995
GE 6 25535 0,33 856 5351 0,60 4605
1,73 2.1% 1.1% 0.5% 7.0% 0,3% -7.8%
Tay 620 8 13850 - 410 - 0.69 3135
Rolls Royce 3,04 14035 - 355 4575 0.65 2673
1,118 1.3% - -13.4% - -5.7% -14.7%
BR710A1-10 15 14750 0,39 435 - - 3520
BMW / RR 4,2 14792 0,40 428 - - 2688
1,23 0.3% 3.2% -1.6% - - -23.6%
Optimization Framework
Engine module Validation
Thrust @ TO (lbf)
SFC TO airflow
TO (lb/s)
Thrust @
Cruise (lbf)
SFC @ Cruise
Weight (lbf)
Engine CPR Actual Data
Manufacturer Bypass Engine Module
Diameter (m) Error (%)
CF6-45A 21 46500 - 1393 11250 0,63 8768
GE 4,64 46221 - 1371 11223 0,60 9412
2,2 -0.6% - -1.6% -0.2% -5.1% 7.3%
PW2237 17 36600 - 1210 6500 0,58 7185
Pratt Whitney 5,8 35180 - 1145 7400 0,59 6488
2,01 -3.9% - -5.4% 13.8% 0.8% -9.7%
CFM56-5A1 17 25000 0,33 852 5000 0,60 4995
GE 6 25535 0,33 856 5351 0,60 4605
1,73 2.1% 1.1% 0.5% 7.0% 0,3% -7.8%
Tay 620 8 13850 - 410 - 0.69 3135
Rolls Royce 3,04 14035 - 355 4575 0.65 2673
1,118 1.3% - -13.4% - -5.7% -14.7%
BR710A1-10 15 14750 0,39 435 - - 3520
BMW / RR 4,2 14792 0,40 428 - - 2688
1,23 0.3% 3.2% -1.6% - - -23.6%
Optimization Framework
ITAIL is a Stabilizer Calculator That Was Incorporated into BAERO
Optimization Framework
ITAIL’s Validation
Airplane
HT area (m2) VT area (m2)
Calculated Actual Deviation (%) Calculated Actual Deviation (%)
Boeing 757-200
(RB211-535E4) 52.06 50.35 +3.40 35.61 34.27 +3.91
Fokker 100
(R&R Tay 620) 21.60 21.72 -0.55 12.28 12.30 -0.16
Boeing 747-100
(JT9D-7A) 139.50 136.60 +2.12 77.99 77.10 +1.15
Canadair CRJ-100ER
(GE CF34-A) 9.67 9.44 +2.44 9.73 9.18 +5.99
Boeing 737-100
(JT8D-7) 30.68 31.31 -2.01 19.32 19.70 -1.93
Optimization Framework
ITAIL Incorporation into the Airplane Calculator Application
Optimization Framework
BAERO Validation
Optimization Framework
BAERO Validation
Airplane Calculator
High-BPR Engine Noise Sources
Source: Wesley K Lord, Pratt&Whitney
Fuselage
Trailing-edge flaps
Main Sources of Airplane Generated Noise
Wing
Landing gear, doors and wheelbays
Leading-edge slats and flaps
Tailplanes Engines, nacelles and intakes
Reference Spots - ICAO
Airplane Noise Modeling for Airplane Optimization
• Noise estimation methods used for design optimization are:
– Airframe noise: ESDU Data Sheets;
– Engine: NASA interim reports;
– Atmospheric attenuation: FAA
• Methods were used in NASA’s ANOPP (Aircraft NOise Prediction Program), developed in the late 1970s from both experimental and theoretical data;
• All the methods provide means to calculate SPLs for each of the 24 standard frequencies (50 Hz to 10 kHz);
• Airframe and Engine components’ SPLs are combined into Airplane SPLs;
• Airplane SPLs are converted into EPNdBs;
• Methods were coded and combined into a noise calculation unit called PANPA (Parametric Airliner Noise Prediction Architecture).
PANPA Workflow
Calculate approach flight-path
Calculate aiframe noise Calculate engine noise
Calculate atmospheric atenuation
Convert SPL to Noy
Convert Noy to PNL
Convert PNL to PNLT
Convert PNLT to EPNdB
Approach Noise in EPNdB
Calculate takeoff flight-path
Calculate aiframe noise Calculate engine noise
Calculate atmospheric atenuation
Convert SPL to Noy
Convert Noy to PNL
Convert PNL to PNLT
Convert PNLT to EPNdB
Takeoff Noise in EPNdB Sideline Noise in EPNdB
PANPA MDO Noise Estimation
Methodology
Turbofan Engine Noise Estimation
Department of Aircraft Design
Overall Noise Estimation
Department of Aircraft Design
Airplane Fly-over Sideline Approach
Certified Estimated Error Certified Calculado Error Certified Estimated Error EPNdB EPNdB EPNdB EPNdB EPNdB EPNdB EPNdB EPNdB EPNdB
B757-200 89.7 84.7 -5.0 94.2 93.7 -0.5 98.1 96.3 -1.8 A320-200 83.9 82.3 -1.6 91.4 91.8 0.4 94.3 96.6 2.3 DC-9-50 97.8 99.8 2.0 102.2 109.2 7.0 101.9 91.4 -10.5
B767-300ER 89.9 85.4 -4.5 97.6 93.3 -4.3 97.7 89.9 -7.8 A330-300 94.3 88.5 -5.8 98.3 96.1 -2.2 98.0 90.1 -7.9
Integrated Design
Projeto do sistema de transporte focado na aeronave ou na malha aérea:
• Projeto da aeronave em cima de uma missão típica.
• Projeto da malha com aeronaves pré-existentes.
Metodologia
• Christine Taylor (MIT) PhD Thesis.
• Three different approaches were carrieD out:
– Case 1: airline network only
– Case 2: airplane optimzation only
– Case 3: integrated design (ariline network+airplane)
Caso 1
• Otimização da malha (alocação de aeronaves e cargas)
• 3 tipos de aeronaves pré-existentes
Implementação
Perturba
𝑥
𝑛𝑖𝑘𝐴
𝑛𝑖𝑘𝐵
𝑛𝑖𝑘𝐶
𝑥𝑖𝑗𝑘
Determina
custo por rota
𝐷𝑂𝐶𝑖𝑘𝐴
𝑓(𝑥)
Calcula
Mínimo?
Não
Sim
CPLEX
𝑤𝐴 𝑣𝐴
Dados
𝑟𝐴
𝐴 ∙ 𝑥 ≤ 𝑏 𝐴𝑒𝑞 ∙ 𝑥 = 𝑏𝑒𝑞
𝑙𝑏 ≤ 𝑥 ≤ 𝑢𝑏
Restrições:
Four major issues:
Transportation System Modeling
Objective
Operation cost minimization
Operational constraints
• Route operation
• Cargo/passenger capacity
Airplane
• Performance
• Constraints
• DOC
Airline network
• Allocation of airplanes / cargo
• Demand constraints
• Noise regulations
• Avaailble airport runway
Caso 2
• Otimização da aeronave
• Malha Hub-Spoke
Implementação
Perturba
𝑥
𝑟 𝑤
𝑣
Mínimo?
Não
Sim
RS ou AG
𝑙𝑏 ≤ 𝑥 ≤ 𝑢𝑏 Restrições:
𝑓(𝑥)
Calcula
Determina
custo por rota
𝐷𝑂𝐶𝑖𝑘
Calcula
𝑛𝑖𝑘
Caso 3
• Otimização integrada
Implementação
Não
Sim
Perturba
𝑥1
𝑟 𝑤
𝑣
Determina custo por rota
𝐷𝑂𝐶𝑖𝑘
Mínimo?
Não
Sim
RS ou AG
𝑙𝑏 ≤ 𝑥 1 ≤ 𝑢𝑏 Restrições:
Perturba
𝑥2
𝑛𝑖𝑘 𝑥𝑖𝑗𝑘 𝑓(𝑥2)
Calcula
Mínimo?
CPLEX
𝐴 ∙ 𝑥2 ≤ 𝑏 𝐴𝑒𝑞 ∙ 𝑥2 = 𝑏𝑒𝑞
𝑙𝑏 ≤ 𝑥2 ≤ 𝑢𝑏
Restrições:
Dados
• Four networks:
– First Seven USA cities (alphabetical order)
– Seven USA cities considering cargo transportation rank
– Seven Brazilian cities considering cargo transportation rank
– Five Brazilian Southwest cities ranked in cargo transportation
Dados
• Exemplo 1 – Primeiras 7 cidades dos EUA
Distancia (nm) Demanda (lb)
ABQ ATL BOS CLT ORD CVG CLE
ABQ 0 1.222 1.933 1.426 1.160 1.209 1.393
ATL 1.222 0 934 208 622 400 619
BOS 1.933 934 0 731 882 755 563
CLT 1.426 208 731 0 682 423 448
ORD 1.160 622 882 682 0 260 309
CVG 1.209 400 755 423 260 0 219
CLE 1.393 619 563 448 309 219 0
ABQ ATL BOS CLT ORD CVG CLE
ABQ 0 2.356 2.051 673 4.572 214 747
ATL 2.356 0 14.045 4.610 31.313 1.465 5.112
BOS 2.051 14.045 0 4.014 27.261 1.276 4.451
CLT 673 4.610 4.014 0 8.948 419 1.461
ORD 4.572 31.313 27.261 8.948 0 2.844 9.923
CVG 214 1.465 1.276 419 2.844 0 464
CLE 747 5.112 4.451 1.461 2.844 464 0
Dados
• Exemplo 2 – 7 maiores cidades dos EUA
Distancia (nm) Demanda (lb)
ATL BOS ORD DFW LAX JFK SFO
ATL 0 934 622 688 1.921 756 2.179
BOS 934 0 882 1.538 2.629 183 2.729
ORD 622 882 0 806 1.767 713 1.866
DFW 688 1.538 806 0 1.257 1.360 1.518
LAX 1.921 2.629 1.767 1.257 0 2.454 330
JFK 756 183 713 1.360 2.454 0 2.560
SFO 2.179 2.729 1.866 1.518 330 2.560 0
ATL BOS ORD DFW LAX JFK SFO
ATL 0 14.045 31.313 19.984 34.506 57.949 37.318
BOS 14.045 0 27.261 17.398 30.041 50.451 32.489
ORD 31.313 27.261 0 38.788 66.975 112.479 72.434
DFW 19.984 17.398 38.788 0 42.743 71.784 46.227
LAX 34.506 30.041 66.975 42.743 0 123.948 79.820
JFK 57.949 50.451 112.479 71.784 123.948 0 134.050
SFO 37.318 32.489 72.434 46.227 79.820 134.050 0
Dados
• Exemplo 3 – 7 maiores cidade do Brasil
Distancia (nm) Demanda (lb)
SAO MAO REC SSA FOR BSB POA
SAO 0 1.455 1.133 783 1.266 461 467
MAO 1.455 0 1.530 1.418 1.289 1.051 1.694
REC 1.133 1.530 0 350 339 893 1.598
SSA 783 1.418 350 0 548 585 1.248
FOR 1.266 1.289 339 548 0 913 1.728
BSB 461 1.051 893 585 913 0 866
POA 467 1.694 1.598 1.248 1.728 866 0
SAO MAO REC SSA FOR BSB POA
SAO 0 308.265 81.729 80.121 72.325 83.878 42.697
MAO 308.265 0 7.598 13.010 18.557 34.056 663
REC 81.729 7.598 0 12.024 32.524 13.452 4.043
SSA 80.121 13.010 12.024 0 13.046 11.711 1.037
FOR 72.325 18.557 32.524 13.046 0 12.126 1.599
BSB 83.878 34.056 13.452 11.711 12.126 0 6.814
POA 42.697 663 4.043 1.037 1.599 6.814 0
Dados
• Exemplo 4 – 5 maiores cidades do sudeste do Brasil
Distancia (nm) Demanda (lb)
SAO CNF RIO VIX VCP
SAO 0 267 182 394 45
CNF 267 0 195 213 269
RIO 182 195 0 225 215
VIX 394 213 225 0 417
VCP 45 269 215 417 0
SAO CNF RIO VIX VCP
SAO 0 25.702 40.875 11.691 1.431
CNF 25.702 0 4.598 1.336 3.681
RIO 40.875 4.598 0 8.989 4.247
VIX 11.691 1.336 8.989 0 4.869
VCP 1.431 3.681 4.247 4.869 0
Resultados – Caso 1
• Aeronaves dos exemplos 1 e 2
• Aeronaves dos exemplos 3 e 4
Parâmetro Aeronave A Aeronave B Aeronave C
Fairchild Expediter B757-200F MD11F
Carga paga (lb) 5.000 72.210 202.100
Alcance (nm) 1.063 3.000 3.950
Velocidade (kt) 252 465 526
Parâmetro
Aeronave A Aeronave B Aeronave C
Cessna Caravan 208A
(Cargomaster) B727-200F DC10-30F
Carga paga (lb) 3.000 58.000 152.964
Alcance (nm) 1.115 2.140 3.100
Velocidade (kt) 184 515 490
Resultados – Caso 1
Exemplo 1 Exemplo 2
Taylor $ 107.888,00 $ 517.030,00
Calculado $ 107.869,54 $ 516.967,72
Diferença -0,02% -0,01%
Same network as those found by Taylor
Airplane A Airplane B Airplane C
Related airplane Fairchild Expediter B757-200F MD11F
Payload (lb) 5,000 72,210 202,100
Range (nm) 1,063 3,000 3,950
Speed (kt) 252 465 526
Fixed cost (US$/day)
1,481 10,616 26,129
Variable cost (US$/h)
758 3,116 7,194
Resultados – Caso 1
Exemplo 1
• Custo total: $ 143.046,84
Resultados – Caso 1
Exemplo 2
• Custo total: $ 854.686,76
Resultados – Caso 1
Exemplo 3
• Custo total: $ 439.676,69
Resultados – Caso 1
Exemplo 4
• Custo total: $ 45.635,00
Resultados – Caso 1
Exemplo 4
• Custo total: $ 45.635,00
Resultados – Caso 2
Exemplo 1
Parâmetro AG RS
Carga paga 𝑤 (lbs) 28.374,18 28.287,00
Alcance 𝑟 (nm) 1.259,20 1.222,00
Velocidade 𝑣 (kts) 549,48 550,00
Custo mínimo ($) 82.123,11 81.501,64
1− 𝐶𝑎𝑠𝑜2 𝐶𝑎𝑠𝑜1 42,59% 43,02%
Resultados – Caso 2
Exemplo 2
Parâmetro AG RS
Carga paga 𝑤 (lbs) 201.341,55 201.171,50
Alcance 𝑟 (nm) 1.866,00 1.866,00
Velocidade 𝑣 (kts) 549,35 550,00
Custo mínimo ($) 767.945,41 766.558,35
1− 𝐶𝑎𝑠𝑜2 𝐶𝑎𝑠𝑜1 10,15% 10,31%
Resultados – Caso 2
Exemplo 3
Parâmetro AG RS
Carga paga 𝑤 (lbs) 76.565,06 76.461,48
Alcance 𝑟 (nm) 1.517,65 1.455,00
Velocidade 𝑣 (kts) 548,80 550,00
Custo mínimo ($) 412.294,26 408.068,74
1− 𝐶𝑎𝑠𝑜2 𝐶𝑎𝑠𝑜1 6,23% 7,19%
Resultados – Caso 2
Exemplo 4
Parâmetro AG RS
Carga paga 𝑤 (lbs) 35.499,97 35.317,00
Alcance 𝑟 (nm) 1.000,00 1.000,00
Velocidade 𝑣 (kts) 549,30 550,00
Custo mínimo ($) 32.079,39 31.925,12
1− 𝐶𝑎𝑠𝑜2 𝐶𝑎𝑠𝑜1 29,70% 30,04%
Resultados – Caso 3
Exemplo 1
Parâmetro AG RS
Carga paga 𝑤 (lbs) 18.308,10 9.713,50
Alcance 𝑟 (nm) 1.161,98 1.222,08
Velocidade 𝑣 (kts) 545,95 550,00
Custo mínimo ($) 66.714,75 67.728,30
1− 𝐶𝑎𝑠𝑜3 𝐶𝑎𝑠𝑜2 18,76% 16,90%
1− 𝐶𝑎𝑠𝑜3 𝐶𝑎𝑠𝑜1 53,36% 52,65%
Resultados – Caso 3
Exemplo 2
Parâmetro AG RS
Carga paga 𝑤 (lbs) 126.169,66 131.972,55
Alcance 𝑟 (nm) 2.011,72 1.921,00
Velocidade 𝑣 (kts) 527,52 550,00
Custo mínimo ($) 698.333,29 676.808,26
1− 𝐶𝑎𝑠𝑜3 𝐶𝑎𝑠𝑜2 9,06% 11,71%
1− 𝐶𝑎𝑠𝑜3 𝐶𝑎𝑠𝑜1 18,29% 20,81%
Resultados – Caso 3
Exemplo 3
Parâmetro AG RS
Carga paga 𝑤 (lbs) 56.975,45 65.808,14
Alcance 𝑟 (nm) 1.522,18 1.455,00
Velocidade 𝑣 (kts) 547,97 550,00
Custo mínimo ($) 387.308,86 373.780,11
1− 𝐶𝑎𝑠𝑜3 𝐶𝑎𝑠𝑜2 6,06% 8,40%
1− 𝐶𝑎𝑠𝑜3 𝐶𝑎𝑠𝑜1 11,91% 14,99%
Resultados – Caso 3
Exemplo 4
Parâmetro AG RS
Carga paga 𝑤 (lbs) 14.759,54 14.227,83
Alcance 𝑟 (nm) 1.000,14 1.000,00
Velocidade 𝑣 (kts) 544,97 550,00
Custo mínimo ($) 29.290,71 28.354,80
1− 𝐶𝑎𝑠𝑜3 𝐶𝑎𝑠𝑜2 8,69% 11,18%
1− 𝐶𝑎𝑠𝑜3 𝐶𝑎𝑠𝑜1 35,82% 37,87%
Efficiency metrics
• Capabilidade de carga utilizada:
• Capabilidade propulsiva utilizada:
𝑖𝑐𝑎𝑝 /𝑐𝑎𝑟𝑔𝑎 = 𝑥𝑖𝑗𝑘
𝑁
𝑘=1
𝑁
𝑗=1
𝑁
𝑖=1
2 𝑛𝑖𝑘𝑤
𝑁
𝑘=1
𝑁
𝑖=1
𝑖𝑐𝑎𝑝 /𝑝𝑟𝑜𝑝 = 𝑛𝑖𝑘𝐷𝑖𝑘
𝑁
𝑘=1
𝑁
𝑖=1
𝑛𝑖𝑘 𝑟
𝑁
𝑘=1
𝑁
𝑖=1
Comparativo
Network No. 1 Network No. 2
Network No. 3 Network No. 4
𝒊𝒄𝒂𝒑/𝒄𝒂𝒓𝒈𝒂 𝒊𝒄𝒂𝒑/𝒑𝒓𝒐𝒑
Caso 1 47% 32%
Caso 2 - AG 50% 55%
Caso 2 - RS 50% 56%
Caso 3 - AG 64% 53%
Caso 3 - RS 73% 54%
𝒊𝒄𝒂𝒑/𝒄𝒂𝒓𝒈𝒂 𝒊𝒄𝒂𝒑/𝒑𝒓𝒐𝒑
Caso 1 73% 30%
Caso 2 - AG 52% 61%
Caso 2 - RS 52% 61%
Caso 3 - AG 65% 55%
Caso 3 - RS 62% 57%
𝒊𝒄𝒂𝒑/𝒄𝒂𝒓𝒈𝒂 𝒊𝒄𝒂𝒑/𝒑𝒓𝒐𝒑
Caso 1 79% 40%
Caso 2 - AG 74% 68%
Caso 2 - RS 70% 73%
Caso 3 - AG 79% 62%
Caso 3 - RS 81% 68%
𝒊𝒄𝒂𝒑/𝒄𝒂𝒓𝒈𝒂 𝒊𝒄𝒂𝒑/𝒑𝒓𝒐𝒑
Caso 1 57% 10%
Caso 2 - AG 61% 21%
Caso 2 - RS 61% 21%
Caso 3 - AG 81% 23%
Caso 3 - RS 84% 23%
Integrated Design Remarks
• Redução no custo total alcançado, com mínimo de 6%.
• Limitações devido à simplificação dos modelos.
• Limitações computacionais para modelos mais complexos.
(Some) Design Parameters • Range with 230 pax: 3500 nm • Passenger capacity (single economy class, 32 pol pitch): 230 • Wing washout angle: -3o • Seat width: 0.46 m • Wing break station positioning: placed at 35% semispan • Aisle width: 0.48 m • HT sweepback angle = wing sweepback angle + 5o • Landing flap deflection: 45o • Takeoff flap deflection: 8o • VT aspect ratio: 1.6 • VT quarter chord sweepback angle: 35o
• VT taper ratio: 0.50 • Service ceiling: 41,000 ft • Wing dihedral angle: 2.5o • MMO: 0.82 • Turbine inlet temperature: 1450 K • Fuel for 100 nm alternate destination + 45 min holding • JetA1 gallon price: US$ 3.28
Optimization Framework
Design Variables
• Wing aspect ratio • Wing taper ratio • Wing reference area • Wing quarter-chord sweepback angle • Maximum relative wing thickness at root • Maximum relative wing thickness at tip • Seating abreast • Number of corridors • Number of engines • Engine location • Tail configuration • Wing location • HT taper ratio • VT reference area • HT volume coefficient
• Fan diameter
• Engine by-pass ratio
• Engine overall pressure ratio
• Fan pressure ratio
Optimization Framework
Intregrated Design (Network+Airliner)
Optimization Framework
Brazilian Small Network
Simulations carried-out with MATLAB® and IBM® CPLEX
VCP
GRU
CNF VIX
GIG
1
2
1
1
1
1
1
Total: 8 airplanes
Intregrated Design (Network+Airliner)
Optimization Framework
Brazilian Small Network
Simulations carried-out with MATLAB® and IBM® CPLEX
MTOW
(kg)
Seating capacity (single class)
Seating abreast
Range with max.
payload
(nm)
MMO Wing area
(m2) ARW
Wing sweepback
angle (degrees)
Optimal airplane
61,500 191 6 1,185+ 0.78 129.6 10.47 29.90
Boeing 737-800
CFM56-7B24
(without winglets)
70,534α 184 6 700* 0.82 124.6 9.45 25.0
Fan
diameter (m)
BPR OPR FPR ARHT
HT sweepback
angle
(degrees)
Central fuselage diameter
(m)
Optimal Airplane
1.7 6.3 30 1.73 4.37 29.9 Circular
3.85
Boeing 737-800
CFM56-7B24 (without winglets)
1.55 5.3 32.8 unknown 6.16 30.0
Elliptical
3.76 wide
4.01 tall α Boeing 737-800 basic version. Boeing also offers a 79 t version presenting a 2200-nm range with maximum payload. The latter delivers a range
of 1,100 nm with 72.9 t TOW.
+ Long-range cruise at 41,000 ft.
* 31-35-39,000 ft step LRC
Drag and Moment Coefficient Estimation with Artificial Neural Network
Review
• R. Wallach 2006 - Master thesis @Technological Institute
of Aeronautical (ITA), Brazil
• Mailema C. 2008 - Master thesis@ technological
Institute of Aeronautics (ITA), Brazil
• Manas Khurana – AIAA Paper on airfoil Shape
Optimization Hadi Winarto RMIT University, Australia
2007
• TsAGI: ANN with full-potential code (Berntein, 2007)
100.000 lower Processing time
Error magnitude:2,3%
Review
Neural Network to Aerodynamic Coefficient Estimation
Department of Aircraft Design
wk1
wk2
wk3
wkm
x2
x3
xm
x1
Input
signals
∑
Synaptic
weights
Bias
bk
φ(.) vk
Activation
function Output
yk
)( kk vy
kk
m
k
kkmk
bw
x
where
xwv
0
0
0
1
4
5
3
4
2
321 xaxaxaxaxayt
6
6
5
5
4
4
3
3
2
21 xbxbxbxbxbxbyc
ctu yyy
Polinomial for the thickness line representation
ctl yyy
Upper-side s (yu) and lower-side (yl) coordinates
Department of Aircraft Design
Neural Network to Aerodynamic Coefficient Estimation
Polinomial for the camber line representation
Angle of Attack
Cd
Department of Aircraft Design
Neural Network to Aerodynamic Coefficient Estimation
Mach number
Cd
Department of Aircraft Design
Neural Network to Aerodynamic Coefficient Estimation
Mach number
Cd
Department of Aircraft Design
Neural Network to Aerodynamic Coefficient Estimation
Cl
Mach number
Department of Aircraft Design
Neural Network to Aerodynamic Coefficient Estimation
Mach number
Cl
Department of Aircraft Design
Neural Network to Aerodynamic Coefficient Estimation
Mach number
Cl
Department of Aircraft Design
Neural Network to Aerodynamic Coefficient Estimation
Aerodynamic Code
Boundary Layer Wing-Fuselage (BLWF 28)
BLWF28 Analysis of a Wing-body Configuration
Cp distribution on a wing-body configuration calculated with the FPWB full-potential code. Mach number of 0.78 and CL= 0.47.
Aerodynamic Code
BLWF 56
External inviscid flow: solution of the conservative full potential equation; “Chimera” technique for complex configurations.
Viscous region: finite-difference inverse method for calculation of 3-d compressible laminar and turbulent boundary layer; 2-d integral method for viscous wake calculations.
Viscous-inviscid coupling: quasi-simultaneous viscous-inviscid coupling scheme. (Rapid obtaining completely self-consistent solution: 6–8 viscous-inviscid iterations in case of moderate separation zones.)
Aerodynamic Code
BLWF 56 Further Validation
Source: Zhang e Hepperle, 2007.
CL=0.5
Re=3·106
Aerodynamic Code
Neural Networks
• ANN (Artifical Neuron Network) for aircraft drag prediction:
CDwf NN
Wing geometry
Root, kink and tip airfoils
Altitude, Mach and CL
40 Variables
Database for NN training:
•Analysed with BLWF 28.
•100.000 airplanes.
CDwf CL
Mach
Neurons in the 1st hidden layer
Ne
uro
ns in
th
e 2
nd
hid
de
n la
ye
r
20 40 60 80 10020
30
40
50
60
70
80
90
100
6
6.5
7
7.5
8
8.5
9
9.5
10
x 10-7
Neural Networks
• Testing different architectures.
Neural Networks
• ANN (Artifical Neural Network) for airplane drag prediction:
Altitude 31000 ft, α=1o.
Method CPU Time (s)
BLWF 43
NN 0,01
40-40-60-1 network
0.2 0.3 0.4 0.5 0.6 0.7 0.80.01
0.015
0.02
0.025
0.03
0.035
Mach Number
CD
BLWF28, = 10o
ANN, = 10o
BLWF28, = 25o
ANN, = 25o
0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.750.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
Mach number
CD
1/4
=15o BLWF
1/4
=15o NN
1/4
=30o BLWF
1/4
=30o NN
0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.750.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
Mach number
CD
1/4
=15o BLWF
1/4
=15o NN
1/4
=30o BLWF
1/4
=30o NN
Framework for Conceptual Design of Solar-powered UAV
Solar-powered High-altitude UAV
Wing airfoil polar curve Tailplanes airfoil polar curve
Solar irradiation (Rsun)
Solar-powered High-altitude UAV
modeFrontier Workflow
Available power
Solar-powered High-altitude UAV
Single-objective Simulation
Selected configurations
Flight Simulator Lab
Integration of PaceLab Suite
Integration of PaceLab Suite
PaceLab Suite
PaceLab Suite
Pace Cabin 7
Pace Cabin 7
Integration of PaceLab Suite
História da
• Embraer
• Petrobras (Brazilian Oil Company)
• University of Berlin
•SUPAERO, Toulouse, France
• Potiers, France
•Sikorsky
• Lufthansa
Cooperation ans Exchange Programs