Department of Aircraft Design - ITA

Technological Institute of Aeronautics 

Aircraft Design Department 

São José dos Campos - São Paulo - Brazil 

2014-V1m


Content 

• Staff and facilities 

• Aerodynamics 

• Aeroacoustics 

• Airplane design framework 

• Integrated optimization (airplane+ airline network) 

• ANN application for aerodynamic coefficient 

• High-altitude Solar-powered UAV 

• New galley system concept


Staff and Facilities


Staff


Staff 

Industrial Experience – Aircraft Design 

Coke-shaped 

rear fuselage 

Wing-fuselage fairing 

Wing planform


Staff 

Professional experience with winglet design 

E-170/175 

EMB-145 SA 

ERJ 145XR 

Legacy 600/650


Staff 

Industrial Experience – Aircraft Design


Staff 

Industrial Experience – Flight Simulator


Facilities 

Virtual Aircraft 

• Multi-disciplinary Design and Optimization 

• Computational Fluid Dynamics


Facilities 

Flight and Systems Simulation 

60 MHZ 

Simulation 

Software 

(X-Plane) 

Projection 


Software IG 

Simulation 


IOS


Facilities 


1 

2 

3 

Spherical screen 

3 

Projection 

software


Facilities 


Beechcraft King Air


Facilities 

Flight and Systems Simulation – Helicopter Flight Simulator 

Thanks to Sikorsky Innovations support


Undergraduate Course – Airplanes designed by the students 

Class 2013 BWB 220-pax airliner 

Class 2012 220-pax airliner Eagle 

Class 2013 joined-wings 220-pax airliner 

Class 2011 220-pax airliner Copacabana


Aerodynamics


Fuselage design 

M ∞ = 0.60 α = 0 o



Mach number contours 

M ∞ = 0.85 

M ∞ = 0.85



Forward Fuselage Optimization for a 70-seater Airliner


System Integration 

High-Performance Computing 

Fuselage Aerodynamic Analysis 

Mach contours for the three ice-detector configurations calculated with CFD++ (M∞ = 0.76, α = 1.5 o )



Aerodynamic and Impingement Analysis 

Freestream Mach number = 0.76 

Ice droplet colliding with probe 

Local Mach number ≈ 2


Winglet Design 

Aerodynamics analysis


Flow control 

Flow control Investigation 

Research on flow control by 

experimental investigation of 

aerodynamic profile with waveshaped 

leading and trailing edge 

at low Reynolds number


Aeroacoustics


Aeroacoustics 

Sound pressure levels above 80 dB 

M 

 

0.775 1.21 

0 

Ill-designed wing-fuselage fairing 

Simulations performed with the Fluent code 

Ice probe



Static Pressure Contours 

M 

 

0.45 1.5 

0 

Simulations performed with the Fluent code



M 

 

0.13 3 o



Broadband noise 

Nose landing gear



Broadband noise 

Main landing gear


Aeroacoustics 

Ramps and fairings to reduce LDG noise 

Powerflow


Aeroacoustics 

Fairing over wheel axis 

Noise reduction : 0.7 dB


Aeroacoustics 

Ramp 

Noise reduction : 1 dB


Airplane design 

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).



Engine noise model:



Airframe noise model:



Overall Noise Estimation Validation 

Fly-over Sideline Approach 

Airplane 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


Airplane Machine



• May involve optimization of a given 

airline network 

• Flight quality considered as 

constraint 

Optimization 

algorithms 

DOE 

Airplane 

Machine 

Additional 

disciplines 

(ex. 

Aeroelasti 

city) 

Optimal 

airplanes 

• Contains already some optimizations inside in order to calculate 

airplane characteristics 

• Able to handle from subsonic to supersonic airplanes 

• May make use of metamodels for aerodynamic coefficients calculation 

or any other discipline 

• Must output noise signature and emission profile



• MATLAB® framework 

Airplane Machine 

• Transport aircraft only 

• Performance calculation 

• Configuration parameters (seating abreast; wing location, engine location, number of aisles, etc…) 

• Engine and airplane can be designed simultaneously 

• Noise signature and emission profile 

• Wing airfoils design integration into current framework 

• Consideration of wingtip devices 

• Enable assessment of new technology 

• Tail surfaces designed by static and dynamic stability criteria (no tail volume coefficient employed) 

• Aeroelasticity of flexible wings (under development)



Airplane Modeler - Fuselage 

- Variety of cross section (double deck, cargo compartment able to accommodate 

any sort of containers) 

- Windshield with single or double curvature 

- Assement of outside view area for the pilot 

- Variety of tail cone shapes



Pace Cabin 7



Pace Cabin 7



Wing structural layout



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 

Load calculation with BLWF



Airplane Modeler


Airplane design



BLWF Validation - Regional twinjet wing station 

Mach number = 0.775, Reynolds = 3.1·10 6 , AoA = 1.21 o



Aircraft Stabilizer Design – MATLAB Tool 

Features 

• Roskam’s methodology for stability derivatives calculation 

• Static stability employed for design 

• VMCA and Dutch roll post analysis 

• Conventional or “T”-tail configurations 

• Extremely fast calculation




VMCA



Aircraft Stabilizer Design – MATLAB Tool




Airplane 

HT area (m 2 ) VT area (m 2 ) 

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 


(JT9D-7A) 

129.28 136.60 -5.35 77.99 77.10 +1.15 

Canadair CRJ-100ER 

(GE CF34-A) 

9.67 9.44 +2.44 9.73 9.18 +5.99 


(JT8D-7) 

19.46 20.81 -6.50 29.77 28.99 +2.70



Engine module Validation 

Thrust @ 

TO (lbf) 

SFC TO 

airflow 

TO 

(lb/s) 

Thrust 

@ 

Cruise 

(lbf) 

Engine CPR Actual Data 

Manufacturer Bypass Engine Module 

Diameter (m) Error (%) 

SFC @ 

Cruise 

Weight 

(lbf) 

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) 

Engine CPR Actual Data 

Manufacturer Bypass Engine Module 

Diameter (m) Error (%) 

SFC @ 

Cruise 

Weight 

(lbf) 

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%



Airplane Machine



APU 

Fuel System Architecture 

CHECK/RELIEF VALVE 

PRESSURE SWITCH 

SHUTOFF VALVE 

ELECTRICAL PUMP 

EJECTOR PUMP 

REFUELING ADAPTER 

BOOST PRESSURE 

REFUELING LINE 

EJECTOR PUMP OUTPUT



Integration of PaceLab Suite



Airplane Machine Validation 


Fokker 100 Standard 


Bombardier CRJ-200LR



Airplane 

Fokker 100 Standard with 

R&R Tay 620 engines 

Airplane Machine Validation 

MTOW 

(kg) 

OEW 

(kg) 

MTOW (AA) 

(kg) 

OEW (AA) 

(kg) 

43,090 24,593 43,097 24,957 

Bombardier CRJ-200LR 24,154 13,835 24,011 14,238 

Boeing 737-300 with 

CFM56-3B1 engines 

56,473 31,480 55,491 31,121 

Boeing 757-200 115,650 62,100 115,501 59,544 

Airplane 

Fokker 100 Standard with 

R&R Tay 620 

MTOW Error 

(%) 

OEW Error 

(%) 

0 1.5 

Bombardier CRJ-200LR 0.6 2.3 

Boeing 737-300 with 

CFM5-3B1 engines 

1.7 1.1 

Boeing 757-200 0.12 4.1


Airliner Optimization with Noise and 

Emissions Considerations



Long-range transcontinental jet (LRJ): 

– Requirements: 

• 250 passengers, single-aisle, single class, six abreast; 

• Range: 6,482km (3,500nm); 

• Minimum cruise altitude: 11,278m (37,000ft); 

• Cruise Mach number: M0.80; 

• TOFL @ MTOW ≤ 2,500m; 

• LFL @ MLW ≤ 2,500m; 

• Maximum Payload: 24,000kg; 

• Maximum Usable Fuel: 34,000kg.



LRJA – DOC-optimized design: 

Parameter 

Value 

Wing area (m 2 ) 180.0 

Wing sweep 

29.71 o 

Wing aspect ratio 11.29 

Wing taper ratio 0.202 

Wing crank position 27.5 

Horizontal tail aspect ratio 6.000 

Flap deflection, takeoff 6.4 

Engine by-pass ratio 6.49 

Engine fan diameter (m) 1.856 

Engine overall pressure ratio 35.0



LRJB – Noise-optimized design: 

Parameter 

Value 

Wing area (m 2 ) 180.0 

Wing sweep 

26.70 o 











LRJC – NO X -optimized design: 

Parameter 

Final value 

Wing area (m 2 ) 182.7 

Wing sweep 

34.79 o 











LRJ – all: 

Parameter Unit LRJA 

(DOC) 

LRJB 

(Noise) 

LRJC 

(Emission) 

MTOW kg 102,868 101,916 104,339 

Fuel for 3,500nm kg 26,085 25,850 26,507 

Rated takeoff thrust kN 140.3 163.0 144.6 

DOC US$/km 9.73 10.17 10.69 

Fly-over noise EPNdB 81.0 75.0 82.5 

Sideline noise EPNdB 87.6 88.5 88.4 

Approach noise EPNdB 89.6 88.3 90.1 

LTO NOX emissions kg 1.528 1.497 0.818



Cumulative Noise Reduction [EPNdB] 

Relative LTO NOx 

LRJ – two-objective optimization: 

0.5 

0 

-0.5 

-1 

LRJ Two-objetive Optimization - Pareto front 

100 gen 

150 gen 

200 gen 

250 gen 

300 gen 

1.05 

1 

0.95 


100 gen 

150 gen 

200 gen 

250 gen 

300 gen 

-1.5 

0.9 

-2 

0.85 

-2.5 

-3 

-3.5 

-4 

-4.5 

-5 

-5.5 

-6 

-6.5 

-7 

0.96 0.98 1 1.02 1.04 1.06 

Relative DOC 

0.8 

0.75 

0.7 

0.65 

0.6 

0.55 

0.5 

0.45 

0.99 1 1.01 1.02 1.03 1.04 1.05 1.06 1.07 

Relative DOC



• LRJ – three-objective optimization:



Mid-range Regional Jet (MRJ): 

Requirements: 

100 passengers, single-aisle, single class, four abreast; 

Range: 3,704km (2,000nm); 

Minimum cruise altitude: 11,887m (39,000ft); 

Cruise Mach number: M0.80; 

TOFL @ MTOW ≤ 1,800m; 

LFL @ MLW ≤ 1,800m; 

Maximum Payload: 13,500kg; 

Maximum usable fuel: 13,000kg.



Mid-range Regional Jet (MRJ) 

Critical design constraints: 

Parameter Unit Min value Max value 

Wing area m² 80.0 100.0 

Wing sweep deg 18.0 35.0 

Wing aspect ratio - 6.0 12.0 

Wing taper ratio - 0.20 0.30 

Wing crank position % 20.0 40.0 

Horizontal tail aspect ratio - 3.0 6.0 

Flap deflection, takeoff deg 5.0 30.0 

Engine by-pass ratio - 3.5 5.5 

Engine fan diameter m 1.200 1.500 

Engine overall pressure ratio - 20.0 30.0



MRJA – DOC-optimized design: 

Parameter 

Final value 

Wing area (m 2 ) 80.0 

Wing sweep 

28.53 o 











MRJB – Noise-optimized design: 

Parameter 

Final value 

Wing area (m 2 ) 82.9 

Wing sweep 

21.34 o 











MRJC – NO X -optimized design: 

Parameter 

Final value 

Wing area (m 2 ) 93.92 

Wing sweep 

31.31 o 











MRJ – all: 

Parameter Unit LRJA 

(DOC) 

LRJB 

(Noise) 

LRJC 

(Emission) 

MTOW kg 38,195 38,377 42,288 

Fuel for 2,000nm kg 9,657 9,702 10,716 

Rated takeoff thrust kN/eng 67.9 98.2 67.2 

DOC US$/km 4.89 5.30 5.60 

Fly-over noise EPNdB 72.0 68.2 76.6 

Sideline noise EPNdB 84.4 84.9 84.7 

Approach noise EPNdB 85.6 84.0 85.7 

LTO NOX emissions kg 0.495 0.697 0.286



Cumulative Noise Reduction [EPNdB] 


MRJ – two-objective optimization: 

1 

0.5 

0 

MRJ Two-objetive Optimization - Pareto front 

100 gen 

150 gen 

200 gen 

250 gen 

300 gen 

1.05 

1 

MRJ Two-objetive Optimization - Pareto front 

100 gen 

150 gen 

200 gen 

250 gen 

300 gen 

-0.5 

0.95 

-1 

0.9 

-1.5 

-2 

0.85 

-2.5 

0.8 

-3 

0.75 

-3.5 

0.7 

-4 

0.65 

-4.5 

0.6 

-5 

-5.5 

0.96 0.98 1 1.02 1.04 1.06 1.08 1.1 1.12 

Relative DOC 

0.55 

0.5 

0.99 1 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.1 1.11 1.12 1.13 1.14 1.15 1.16 

Relative DOC



• MRJ – three-objective 

Test cases and 

optimization: 

results


Network-airplane Integrated Design



Usual approach: 

Integrated Design 

• Airplane design for a given typical mission 

or 

• Network optimization for existing airplanes



Methodology 

• Based on Christine Taylor (MIT) PhD Thesis. 

• Three different design cases were carried out: 

– Case I: airline network only 

– Case I: airplane optimization only 

– Case III: integrated design (airline network + airplane)


Airplane integrated design 

Transportation System Modeling 

Objective 

Operating cost minimization 

Airplane 

• Performance 

• Constraints 

• Direct Operating Cost (DOC) 

Airline network 

• Allocation of airplanes / cargo 

• Demand constraints 

• Noise regulations 

• Availble airport runway 

Operational constraints 

• Route operation 

• Cargo/passenger capacity


Comparative study with rotorcraft vehicles 

IBM CPLEX 

The CPLEX solver from IBM ILOG is a high performance solver for Linear 

Programming (LP), Mixed Integer Programming (MIP) and Quadratic Programming 

(QP/QCP/MIQP/MIQCP) problems.



Implementation of Case I 

• Only network optimization (Cargo airplane allocation) 

• Three existing airplanes are available 

Restrições: 

A ∙ x ≤ b 

A eq ∙ x = b eq 

lb ≤ x ≤ ub 

Perturba 

x 

Não 

Mínimo 

CPLEX 

Sim 

n ik 

A 

n ik 

B 

n ik 

C 

Calcula 

f(x) 

x ijk 

Dados 

r A 

w A 

v A 

Determina 

custo por rota 

DOC ik 

A



Case II 

• Consider airplane optimization only 

• Hub-Spoke network 

Restrições: 

lb ≤ x ≤ ub 

Perturba 

x 

r 

w 

v 

Determina 


DOC ik 

Calcula 

n ik 

RS = Simulated annealing 

AG = Genetic algorithm 

Não 

Mínimo 

Calcula 

f(x) 

RS ou AG 

Sim



Case III - Integrated optimization (airline network and airplane) 

Restrições: 

lb ≤ x 1 ≤ ub 

Perturba 

x 1 

Não 

Mínimo 

RS ou AG 

Sim 

RS = Simulated annealing 

AG = Genetic algorithm 

r 

w 

v 

Determina 


DOC ik 

Restrições: 

A ∙ x 2 ≤ b 

A eq ∙ x 2 = b eq 

lb ≤ x 2 ≤ ub 

Perturba 

x 2 

Não 

Mínimo 

n ik 

Calcula 

x ijk f(x 2 ) 

CPLEX 

Sim



Four networks (Examples): 

– First Seven USA cities (alphabetical order) 

– Seven USA cities considering cargo transportation rank 

– Seven Brazilian cities considering a rank in cargo transportation 

– Five Brazilian Southwest cities ranked in cargo transportation



• Example 1 – First seven cities in USA 

Distance (nm) 

Demand (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



• Example 2 – Seven major USA cities 

Distance (nm) 

Demand (lb) 

ATL BOS ORD DFW LAX JFK SFO 

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 

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 2.179 2.729 1.866 1.518 330 2.560 0 

SFO 37.318 32.489 72.434 46.227 79.820 134.050 0



Example 3 – Seven major Brazilian cities 

Distance (nm) 

Demand (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



Example 4 – 5 major Southeast Brazilian cities 

Distance (nm) 

Demand (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



Results – Case I (network optimization) 

• Airplanes for Examples 1 and 2 

Parameter 

Airplane A Airplane B Airplane C 

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 

• Airplanes for Examples 3 and 4 

Parameter 


Cessna Caravan 

208A 

(Cargomaster) 

B727-200F 

DC10-30F 

Payload (lb) 3.000 58.000 152.964 

Range (nm) 1.115 2.140 3.100 

Speed (kt) 184 515 490



Results – Case I 

Same network as those found by Taylor 


Related airplane Fairchild Expediter B757-200F MD11F 

Example 1 Example 2 

C. Taylor $ 107.888,00 $ 517.030,00 

Present work $ 107.869,54 $ 516.967,72 

Difference -0,02% -0,01% 

Payload (lb) 5,000 72,210 202,100 

Range (nm) 1,063 3,000 3,950 

Speed (kts) 252 465 526 

Fixed cost 

(US$/day) 

Variable cost 

(US$/h) 

1,481 10,616 26,129 

758 3,116 7,194



Example 1 

• Total cost: $ 143,046.84 

Results – Case I



Example 2 

• Total cost: $ 854,686.76 

Results – Case I



Example 3 

• Total cost: $ 439,676.69 

Results – Case I



Example I 

• Total cost: $ 45,635.00 

Results – Case I



Example 4 

• Total cost: $ 45,635.00 

Results – Case I



Results – Case II 

Example 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%



Results – Caso II 

Example 2 

Parameter 

Genetic 

algorithm 

Simulated 

annealing 

Payload (lb) 201,341.55 201,171.50 

Range r (nm) 1,866 1,866 

Speed v (kts) 549 550 

Minimum cost ($) 767,945.4 766,558.4 

1 – Case II/Case I 10.15 % 10.31 %




Example 3 

Parameter 

Genetic 

algorithm 

Simulated 

annealing 

Payload (lb) 76,565 76,462 

Range r (nm) 1,517.65 1,455.00 

Speed v (kts) 548.8 550 

Minimum cost ($) 412,294,3 408,068,7 

1 – Case II/Case I 6.23 % 7.19 %




Example 4 

Parameter 

Genetic 

algorithm 

Simulated 

annealing 

Payload (lb) 35,500 35,317 

Range r (nm) 1,000 1,000 

Speed v (kts) 549.3 550 

Minimum cost ($) 32,079.39 31,925.12 

1 – Case II/Case I 29.70 % 30.04 %



Results – Case III 

Exemplo 1 



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%



Results – Caso III 

Example 2 



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%



Example 3 


Parameter 

Genetic 

algorithm 

Simulated 

annealing 

Payload (lb) 56,975 65,808 

Range r (nm) 1,522 1,455 


Minimum cost ($) 387,308.86 373,780.11 

1 – Case III/Case II 6.06 % 8.40 % 

1- Case III/Case I 11.91 % 14.99 %




Example 4 

Parameter 

Genetic 

Algorithm 

Simulated 

annealing 

Payload (lb) 14,769 14,228 

Range r (nm) 1,000 1,000 


Minimum cost ($) 29,290.71 28,354.80 

1 – Case III/Case II 8.69 % 11.18 % 

1 – Case III/Case I 35.82 % 37.87 %



Efficiency metrics 

• Utilization of available cargo capacity: 

N 

N 

N 

N 

N 

i cap /carga = 

x ijk 

2 n ik w 

i=1 

j =1 k=1 

i=1 

k=1 

• Utilization of available range: 

N 

N 

N 

N 

i cap /prop = 

n ik D ik 

n ik r 

i=1 

k=1 

i=1 

k=1



Network No. 1 Network No. 2 

i cap/carga 

i cap/prop 

i cap/carga 

i cap/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% 

Network No. 3 Network No. 4 

i cap/carga 

i cap/prop 

i cap/carga 

i cap/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% 

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.



Design Parameters 

• Wing washout angle: -3 o 

• Seat width: 0.46 m 

• Wing break station positioning: placed at 35% semispan 

• Aisle width: 0.48 m 

• HT sweepback angle = wing sweepback angle + 5 o 

• Landing flap deflection: 45 o 

• Takeoff flap deflection: 8 o 

• VT aspect ratio: 1.6 

• VT quarter chord sweepback angle: 35 o 

• VT taper ratio: 0.50 

• Service ceiling: 41,000 ft 

• Wing dihedral angle: 2.5 o 

• 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



Integrated Design (Network + Airliner) 

Brazilian (very) Small Network 

1 

CNF 

1 

VIX 

1 

1 

1 

2 

GIG 

VCP 

1 

GRU 

Total: 8 airplanes 

Simulations carried-out with MATLAB ® and IBM ® CPLEX



Network + Airplane 

Optimal 

airplane 


CFM56-7B24 

MTOW 

(kg) 

Seating 

capacity 

(single 

class) 

Seating 

abreast 

Range with 

max. 

payload 

(nm) 

MMO 

Wing area 

(m 2 ) 

AR W 

Wing 

sweepback 

angle 

(degrees) 

61,500 191 6 1,185 + 0.78 129.6 10.47 29.90 

70,534 α 184 6 700 * 0.82 124.6 9.45 25.0 

(without winglets) 

Optimal 

Airplane 


CFM56-7B24 

Fan 

diameter 

(m) 

BPR OPR FPR AR HT 

HT 

sweepback 

angle 

(degrees) 

1.7 6.3 30 1.73 4.37 29.9 

1.55 5.3 32.8 unknown 6.16 30.0 

Brazilian Small Network 

Central 

fuselage 

diameter 

(m) 

Circular 

3.85 

Elliptical 

3.76 wide 

4.01 tall 

(without winglets) 

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 

Simulations carried-out with MATLAB ® and IBM ® CPLEX



Cytoscape 

Cytoscape is an open source software platform for visualizing complex 

networks and integrating these with any type of attribute data. A lot of Apps 

are available for various kinds of problem domains, including bioinformatics, 

social network analysis, and semantic web.



Comparative Table 

Topic BA 609 S-76 S-92 

Pressurized yes no no 

Flight into known icing 

conditions 

yes no no 

speed good fair fair 

range fair insatisfactory fair 

Entry into service 2014 ++ 1982 2004 

Fuel consumption fair unsatisfactory unsatisfactory 

Service ceiling fair Too low Too low 

Maintenance Vey expensive expensive expensive 

Acquisition price Very expensive (US$ 29 mi) expensive Very expensive (US$ 26 mi)



Vehicle data 

Aircraft 

Variable cost 

(US$/h) 

Fixed Cost 

Max pax 

seating 

capacity 

Cruise speed 

(km/h) 

Max. Range 

(km) 

MTOW 

(kg) 

Rotor 

diameter 

(m) 

Pressurized 

BA609 2670 * 4005 * 9/12 509 1296 7620 2 x 7.92 yes 

S-92 2670 4005 * 19 280 999 12200 17.17 no 

S-76C+ 1675 2513 * 13 287 761 5307 13.41 no 

* Estimated by authors



Network Optimization 

• Demand matrix 

• Distance matrix 

• No scheduling 

• Aircraft starts from City A, flies to City B, and come back to City A 

• Cargo or passenger is allowed to stay in a intermediate city before being 

transported to destination 

• Code written in MATLAB ® 

• IBM CPLEX ® was used as optimization engine (the application is called 

inside MATLAB ® )



Variable cost components 

• Fuel 

• Fuel additives 

• Lubricants 

• Maintenance – Labor 

• Maintenance – Parts 

• Engine restoration cost 

• Major periodic maintenance 

• Propeller overhaul 

• APU maintenance overhaul 

• Landing and parking fees 

• Crew expenses 

• Small supplies and catering 

Source: http://www.conklindd.com/Page.aspxcid=1115



1 

2 

390 km 

Network – Distances 

4 

190 km 

3 

55 km 

5 

6 7



Results – Number of planes 

Case 

Distance 

matrix 

Demand 

Total Cost 

(US$) 

S-92 S-76 BA609 

A 1 1 55,942.85 2 10 0 

B 1 2 481,675.00 2 96 0 

C 2 2 1066528.00 20 73 0


Artificial Neural Network to Model 

Aerodynamic Coefficients


ANN application for aerodynamics 


Design of efficient aircraft is highly complex and 

requires a lot of computational power 

1.05 

1 

0.95 


100 gen 

150 gen 

200 gen 

250 gen 

300 gen 

0.9 

0.85 

0.8 

0.75 

0.7 

0.65 

0.6 

0.55 

0.5 

0.45 

0.99 1 1.01 1.02 1.03 1.04 1.05 1.06 1.07 

Relative DOC



Computational cost of each AA module: 

Used Roskam 

Class II method 

for drag estimation. 

We wanted to 

increase fidelity 

for future analysis ... 

... without 

jeopardizing the 

computational cost!



Procedure 

1. Generate the database. 

2. Implement the ANN training routines. 

3. Choose the best architecture. 

4. Evaluate the ANNs performance. 

5. Test the ANNs on an optimization problem.



Database creation: 

– 40 design variables. 

– Latin hypercube sampling. 

– Used BLWF code to calculate targets. 

– 110,000 test cases. 

40 INPUTS 

Wing geometry 

Root, kink and tip airfoils 

Altitude, Mach and 

•Wing area 

•Aspect ratio 

•Taper ratio 

•Leading edge sweep 

•Kink position 

•Inner panel dihedral 

•Outer panel dihedral



Database creation: 

– 40 design variables. 

– Latin hypercube sampling. 

– BLWF code was employed to calculate targets. 

– 110,000 test cases. 

40 INPUTS 

Wing geometry 

Root, kink and tip airfoils 

Altitude, Mach and 

•Airfoil incidence 

•Leading edge radius 

•Maximum t/c 

•Thickness line angle @ TE 

•Max. t/c position 

•Camber line angle @ LE 

•Camber line angle @ TE 

•Maximum y/c 

•y/c @ max. t/c position 

•Max. y/c position



Evaluate the ANN performance 

• Drag divergence and wing sweep: 

– Altitude: 10,500m and AoA: 1 deg 

3-ANN drag prediction



Test case with an optimization problem involving airfoils 

Design variables: 

• Airfoil incidence 

• Leading edge radius 

• Maximum t/c 

• Thickness line angle @ TE 

• Max. t/c position 

• Camber line angle @ LE 

• Camber line angle @ TE 

• Maximum y/c 

• y/c @ max. t/c position 

• Max. y/c position 

Objective function: 

• Minimize C D 

Constraints: 

• C L = 0.4 

• Database boundaries 

An adjoint first-order method was employed



Test case with an optimization problem involving airfoils



y/c 

y/c 

y/c 

Test case with an optimization problem involving airfoils 

0.1 

0.05 

Root airfoil 

GD Starting point 

GD Optimal 

0 

-0.05 

0.1 

0.05 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

x/c 

Kink airfoil 


GD Optimal 

0 

-0.05 

0.1 

0.05 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

x/c 

Tip airfoil 


GD Optimal 

0 

-0.05 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

x/c


Framework Design for High-altitude 

Solar-powered UAV

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



Selected configurations


New Galley Concept for Long-haul Airliners



New Lower-deck galley system – Motivation 

• Improve airplane economics 

• Reduce pax cabin crew workload 

• Improved space utilization 

http://www.jatm.com.br/ojs/index.php/jatm/article/view/145



New Lower-deck galley system 

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 %

Thank You!

Department of Aircraft Design - ITA

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