energy efficient aircraft configurations, … efficient aircraft... · convert noy to pnl convert...

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)

Recontouring of the aft fuselage to

improve airflow on the propellers

(DFVLR Panel Code)

E-Jets Forward Fuselage 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).

Winglet Design

E-170/175

EMB-145 SA

Legacy 600/650

ERJ 145XR

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

Ice detector installation

Fuselage Aerodynamic Analysis

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

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

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)

Cabin Sizing

Ref: Prof. Dieter Scholz, Hoschule für Angewandte Wissenschaften Hamburg

Aeronautical Airplane

Aeronautical Airplane Cabin Sizing

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)

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%

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%

ITAIL is a Stabilizer Calculator That Was Incorporated into BAERO

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

ITAIL Incorporation into the Airplane Calculator Application

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

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

Overall Noise Estimation

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

𝑛𝑖𝑘𝐴

𝑛𝑖𝑘𝐵

𝑛𝑖𝑘𝐶

𝑥𝑖𝑗𝑘

Determina

custo por rota

𝐷𝑂𝐶𝑖𝑘𝐴

𝑓(𝑥)

Calcula

Mínimo?

𝑤𝐴 𝑣𝐴

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

𝑟 𝑤

Mínimo?

RS ou AG

𝑙𝑏 ≤ 𝑥 ≤ 𝑢𝑏 Restrições:

𝑓(𝑥)

Calcula

Determina

custo por rota

𝐷𝑂𝐶𝑖𝑘

Calcula

𝑛𝑖𝑘

Caso 3

• Otimização integrada

Implementação

Perturba

𝑟 𝑤

Determina custo por rota

𝐷𝑂𝐶𝑖𝑘

Mínimo?

RS ou AG

𝑙𝑏 ≤ 𝑥 1 ≤ 𝑢𝑏 Restrições:

Perturba

𝑛𝑖𝑘 𝑥𝑖𝑗𝑘 𝑓(𝑥2)

Calcula

Mínimo?

𝐴 ∙ 𝑥2 ≤ 𝑏 𝐴𝑒𝑞 ∙ 𝑥2 = 𝑏𝑒𝑞

𝑙𝑏 ≤ 𝑥2 ≤ 𝑢𝑏

Restrições:

• 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

• 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

• Exemplo 2 – 7 maiores cidades dos EUA

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

• Exemplo 3 – 7 maiores cidade do Brasil

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

• Exemplo 4 – 5 maiores cidades do sudeste do Brasil

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

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

Exemplo 1

• Custo total: $ 143.046,84

Exemplo 2

• Custo total: $ 854.686,76

Exemplo 3

• Custo total: $ 439.676,69

Exemplo 4

• Custo total: $ 45.635,00

Exemplo 4

• Custo total: $ 45.635,00

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%

Exemplo 2

Parâmetro AG RS

Carga paga 𝑤 (lbs) 201.341,55 201.171,50

Alcance 𝑟 (nm) 1.866,00 1.866,00

Custo mínimo ($) 767.945,41 766.558,35

1− 𝐶𝑎𝑠𝑜2 𝐶𝑎𝑠𝑜1 10,15% 10,31%

Exemplo 3

Parâmetro AG RS

Carga paga 𝑤 (lbs) 76.565,06 76.461,48

Alcance 𝑟 (nm) 1.517,65 1.455,00

Custo mínimo ($) 412.294,26 408.068,74

1− 𝐶𝑎𝑠𝑜2 𝐶𝑎𝑠𝑜1 6,23% 7,19%

Exemplo 4

Parâmetro AG RS

Carga paga 𝑤 (lbs) 35.499,97 35.317,00

Alcance 𝑟 (nm) 1.000,00 1.000,00

Custo mínimo ($) 32.079,39 31.925,12

1− 𝐶𝑎𝑠𝑜2 𝐶𝑎𝑠𝑜1 29,70% 30,04%

Exemplo 1

Parâmetro AG RS

Carga paga 𝑤 (lbs) 18.308,10 9.713,50

Alcance 𝑟 (nm) 1.161,98 1.222,08

Custo mínimo ($) 66.714,75 67.728,30

1− 𝐶𝑎𝑠𝑜3 𝐶𝑎𝑠𝑜2 18,76% 16,90%

1− 𝐶𝑎𝑠𝑜3 𝐶𝑎𝑠𝑜1 53,36% 52,65%

Exemplo 2

Parâmetro AG RS

Carga paga 𝑤 (lbs) 126.169,66 131.972,55

Alcance 𝑟 (nm) 2.011,72 1.921,00

Custo mínimo ($) 698.333,29 676.808,26

1− 𝐶𝑎𝑠𝑜3 𝐶𝑎𝑠𝑜2 9,06% 11,71%

1− 𝐶𝑎𝑠𝑜3 𝐶𝑎𝑠𝑜1 18,29% 20,81%

Exemplo 3

Parâmetro AG RS

Carga paga 𝑤 (lbs) 56.975,45 65.808,14

Alcance 𝑟 (nm) 1.522,18 1.455,00

Custo mínimo ($) 387.308,86 373.780,11

1− 𝐶𝑎𝑠𝑜3 𝐶𝑎𝑠𝑜2 6,06% 8,40%

1− 𝐶𝑎𝑠𝑜3 𝐶𝑎𝑠𝑜1 11,91% 14,99%

Exemplo 4

Parâmetro AG RS

Carga paga 𝑤 (lbs) 14.759,54 14.227,83

Alcance 𝑟 (nm) 1.000,14 1.000,00

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

𝑘=1

𝑗=1

𝑖=1

2 𝑛𝑖𝑘𝑤

𝑘=1

𝑖=1

𝑖𝑐𝑎𝑝 /𝑝𝑟𝑜𝑝 = 𝑛𝑖𝑘𝐷𝑖𝑘

𝑘=1

𝑖=1

𝑛𝑖𝑘 𝑟

𝑘=1

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%

Caso 1 73% 30%

Caso 2 - AG 52% 61%

Caso 2 - RS 52% 61%

Caso 3 - AG 65% 55%

Caso 3 - RS 62% 57%

Caso 1 79% 40%

Caso 2 - AG 74% 68%

Caso 2 - RS 70% 73%

Caso 3 - AG 79% 62%

Caso 3 - RS 81% 68%

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

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

Intregrated Design (Network+Airliner)

Brazilian Small Network

Simulations carried-out with MATLAB® and IBM® CPLEX

CNF VIX

Total: 8 airplanes

Intregrated Design (Network+Airliner)

Brazilian Small Network

Simulations carried-out with MATLAB® and IBM® CPLEX

Seating capacity (single class)

Seating abreast

Range with max.

payload

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

diameter (m)

BPR OPR FPR ARHT

HT sweepback

(degrees)

Central fuselage diameter

Optimal Airplane

1.7 6.3 30 1.73 4.37 29.9 Circular

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

• TsAGI: ANN with full-potential code (Berntein, 2007)

100.000 lower Processing time

Error magnitude:2,3%

Review

Neural Network to Aerodynamic Coefficient Estimation

signals

Synaptic

weights

φ(.) vk

Activation

function Output

)( kk vy

321 xaxaxaxaxayt

21 xbxbxbxbxbxbyc

ctu yyy

Polinomial for the thickness line representation

ctl yyy

Upper-side s (yu) and lower-side (yl) coordinates

Polinomial for the camber line representation

Angle of Attack

Mach number

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

Neurons in the 1st hidden layer

20 40 60 80 10020

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

Mach Number

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

Mach number

=15o BLWF

=15o NN

=30o BLWF

=30o NN

0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.750.005

Mach number

=15o BLWF

=15o NN

=30o BLWF

=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)

modeFrontier Workflow

Available power

Single-objective Simulation

Selected configurations

Flight Simulator Lab

Integration of PaceLab Suite

PaceLab Suite

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

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