P.-M. Basset, ONERA/ DCSDJSO Aerial Robotics, 2 Oct. 2014

UAVs preliminary sizing: past and present studies at Onera

ROTARY WINGS UAVs

2

Outline

1. Context: Multiple Applications/ Multi. concepts

2. Examples of previous studies

3. CREATION Workshop

4. Exple of present RW-UAV presizing

5. Conclusion

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1- CONTEXT : Position of the problematics1- CONTEXT : Position of the problematics

Civil and Military Missions :Transports of persons or loads

Observation, SaR, Combat, …

Multiple applications Multiple concepts

?

What is the most suited concept for a kind of missions ?

Mini-drones in France

~400 entreprises,90% RW-UAVs

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1- CONTEXT : typology of RW concepts1- CONTEXT : typology of RW concepts

FireScout

Bell Eagle Eye

InfotronIT180-5

BombardierGuardian

Tail-SitterSkyTote

K-MaxBURRO

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2- Example2- Example

Concepts de

systèmeConcepts

Spécification

systèmeRequirements

ÉvaluationEvaluation

Conception

généraleDESIGNAnalyse du

besoin opérationnelAPPLICATIONS

H.A.L.E.

M.A.L.E.

R.W.U.A.V.

CAPECON projectApplications survey …., 7 vehicle designsCAPECON projectApplications survey …., 7 vehicle designs

2001~2005

6

Modeling of the specificities of each conceptneeded for their Presizing and Evaluation

Modeling of the specificities of each conceptneeded for their Presizing and Evaluation

CAPECON Coaxial UAV ADOPIC Coaxial UAV

7

Required powers at ISA/SL (M=499kg)

0

50

100

150

200

250

300

0 50 100 150 200 250 300

Forward speed (km/h)

Pow

er (

kW)

Pn helico (kW)

Pn tilt-rotor (kW)

Pn tandem (kW)

Pn coaxial (kW)

P.-M. Basset, J. Deslous :“Performances Comparisons of Different Rotary Wing UAV Configurations”,31st European Rotorcraft Forum, Florence, Italy, 13 - 15 September 2005.

Comparisons of Different Rotary Wing UAV Configurations

Comparisons of Different Rotary Wing UAV Configurations

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PEA ExDro : Expertise DVI and alternate VTOL Concep tsPEA ExDro : Expertise DVI and alternate VTOL Concep ts

Expertise Arial Vehicles DVI (Drone Vtol Interarmées)

Alternate VTOL Concepts Objectif

Contribute to the study of RW-UAVs as alternate solutions wrt industry proposals in DVI

• Helico Orka-VSR700 / VertiVision (EADS+Guimbal)

• Helico Unmanned Little Bird (Thalès+Boeing)

• Tilt-Rotor Eagle-Eye(Sagem+Bell)

2- Example of previous study2- Example of previous study 2008-2009

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PEA ExDro: alternate VTOL concepts1) Review of RC concepts and preliminary selection

2) Pre-sizing of 4 concepts

• Helicopter with variable rotor speed (e.g. Humingbird A160),

• Coaxial contra rotating rotors,

• « Tilt-Rotor + Tilt-Wing» (e.g. ERICA)

• Compound helico: wings + vectoring thrust

3) Evaluation and comparisons of their flight performances

2- Example of previous study2- Example of previous study

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Payload or Useful load

Gross weight

Take-off Power

Choice of engine

Empty weight

Engine & Fuel weight

Lifting Rotor Diameter

Disc loading

CLm~0.6

Number of blades

Mean chord

M tip~0.6

Rotation speed

…

First rough presizing methodFirst rough presizing method

1111

3- CREATION : 3 milestones3- CREATION : 3 milestones

Milestone 1 – 2011setting up modules & workflows

case of an existing helicopter•Evaluation capability

Milestone 2 – 2012setting up models & methods

case of a new helicopter•Presizing capability

Milestone 3 – 2013-2014generalizing to alternate conceptsapply it for an innovative concept

•INNOVATION capability

… ?Dauphin 365N

Evaluations:Flight perfo & pollutions

Presizing:Rotors, moteur, fuselage,empennage, dérive, …

Innovations:

5 Departments (DCSD, DAAP, DCPS, DSNA, DADS)

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7 fundamental modules3- CREATION : Tool Organization3- CREATION : Tool Organization

Multidisciplinary Modules :

Goals Modules

Weight & Structures

Architecture Geometry

Mission & Specification

PowerGeneration

EnvironmentalImpacts

Aerodynamics

FlightPerformances

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3-Dimensional view3- CREATION : Tool Organization3- CREATION : Tool Organization

Multidisciplinary & Multi-modeling levels calculation chain

Level 3: NFM

Level 2: AFM

Level 1: BP

Level 0: Statistics and reduced models

NFM: Numerical Flight MechanicsAFM: Analytical Flight MechanicsBP: Balance of Power

1st « guess »

1st « presizing loop »

More refinedoptim

More refinedoptim

Concept H90 - NASA

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Milestone 2: presizing of a helicopter « ab initio »

Case of study: Heavy transport helico 90 pax(inspired from a NASA study which provides a reference)

Milestone 2: helicopter presizingMilestone 2: helicopter presizing

Mission and Specifications:•90 passengers•Range ≥ 1000 km•Cruse speed ≥ 280 km.h-1

•Cruse altitude= 12 000 ft

•Mission profile:

Alti

tude

[m]

Distance [km]0 926 d_fin

01524

1544

d_palier

0

1

2

3

4

5

12000 ftRange ~1000 km

Vcruse ≥ 150 kt = 278 km/h

3658m

Case 1: heavy civil transport

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Problem set-up ~ definitionMilestone 2: helicopter presizingMilestone 2: helicopter presizing

Choice of objectives:

W fuel Minimizing the fuel consumption

Wempty Minimizing the empty weight (~Maximizing Wuseful)

Facou Minimizing the noise (on the landing approach phase)

Constraints:

• Rmr є [10; 20] m Level 0: 13.7 m• cmr є [0.5; 1.5] m Level 0: 0.805 m• bmr є [6; 8] SU Level 0 + bdd : 8 max• Umr є [200; 230] m/s Level 0: 216 m/s

• Rmr/cmr є [10; 20] SU NDARC: R/c < 18 • Wmto < 50 000 kg Own spec

Design parameters: Main Rotor

R c Ub

Blade average chord: c

U = R Ω

MR diameter: 2.R

Rotation speed: Nr ou Ω

Number of blades: b

Method 1: Hybrid « Genetic Algo + Determinist Algo »

Method 1: Hybrid « Genetic Algo + Determinist Algo »

A) Multi objectives optimization with a Genetic Algorithm: Global exploration of the design space giving the Pareto Front

B) Selection of a best compromisesolution by a Determinist Algorithm :From the Pareto Front: Min and Max of each objective => normalization of each objective

Global Norm = Distance / Utopian PointUP= (Wfuelmin, Wemptymin, Facoumin)

Global optimum = optimal solution minimizing the distance / UP

(Euclidean norm)

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Computational time too long for GA => Approximation of the chain of models by a ResponseSurface model

1 – Definition of a Design of Experiment:

Representative points in the design space

1 plan for R, c, U

Elimination points / constraints

Duplication for each blade number

b : 6, 7, 8 => 3*197 = 591 points

4 – Exploration

Method 1:Method 1:

2 – Calculationwith the complete

chain of models for each point of the

DoE

DoE

Presizing

Evaluation

R, c, b, U

Wempty

Facou

Wfuel

3 – Kriging tech. -> RSM(1 RSM for each blade number)

Optimizer

RSMWempty

Facou

Wfuel

R, c, b, U

Step A: RSM Pareto frontGA

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Normalization of the objectives and Norm = Distance / the Utopian Point

From the Pareto front: > Min and max of each objective > Norm=distance/ Utopian Point UP (Wfuelmin, Wemptymin, Facoumin) Level diagrams Final Optim / Determinist Algo

Minimal Euclidian Norm on the Pareto Front:

Design parameters Values of objectives

R 16,439 m Wempty 27 223 kg

c 0,893 m Facou 69,59 dBA

U 200,0 m/s Wfuel 9 745 kg

b 8 Wmto 45 935 kg

Wempty (kg) Wfuel (kg) Facou (dBA)

b (S

U)

b (S

U)

Euc

lidia

n N

orm

R (m) c (m) U (m)

Euc

lidia

n N

orm

Method 1:Method 1:Step B: Pareto Front Utopian Point Selec t 1 best compromise

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1. Finding the Utopian Point: separated optimizations of each of the 3 objectives by a Determinist Algo

• min(b,R,C,U) Wempty• min(b,R,C,U) Wfuel• min(b,R,C,U) Facou Utopian Point (Wemptymin,Wfuelmin, Facoumin)

2. Finding the global optimal design values (b*,R*,C*,U*) : minimization of the Euclidian norm to be as close as possible to the UP

(b*,R*,C*,U*)= argmin(b,R,C,U) ||Wemptymin-MOEWempty||2 + ||Wfuelmin-MOEWfuel||

2 +||Facoumin-MOEFacou||2

Method 2: Multi objectives optimization by a Determinist Algo in 2 steps

Method 2: Multi objectives optimization by a Determinist Algo in 2 steps

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4500m 3000m 3000m

10h 30 min

1h

15 min 5 min

BACK

GO

Detection phase /RADARIdentification Phase /

EO/IR

Milestone 2: helicopter UAV presizingMilestone 2: helicopter UAV presizingCase 2: RW-UAV for the Marine

RADARSAR

EO/IR

Mission profilePayload and eqpt: 182 kgEndurance: 12hSurface: 220x220 (km2)

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Min WfuelMin Wfuel

Variables : Vcruise, UAV sizing ,

rotor speed.

Variables : Vcruise, UAV sizing ,

rotor speed.

Output: Design 1 with Wfuel min

Output: Design 1 with Wfuel min

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Min WemptyMin Wempty

Variables : Vcruise, UAV sizing ,

rotor speed.

Variables : Vcruise, UAV sizing ,

rotor speed.

Output: Design 2 with Wempty min

Output: Design 2 with Wempty min

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Min Preq/VcrMin Preq/Vcr

Variables : Vcruise, UAV sizing ,

rotor speed.

Variables : Vcruise, UAV sizing ,

rotor speed.

Output: Design 3 with Vbr

Output: Design 3 with Vbr

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Multi-Objectives Optimization

(Mfuel - Mfuelmin1)² + (Mempty - Memptymin2)² + (Preqtot_div_v - Preqtot_div_vmin3)²

Multi-Objectives Optimization

(Mfuel - Mfuelmin1)² + (Mempty - Memptymin2)² + (Preqtot_div_v - Preqtot_div_vmin3)²

Variables : Vcruise, UAV sizing , rotor speed.Variables : Vcruise, UAV sizing , rotor speed.

Output : final designOutput : final design

Method 2: Multi objectives optimization by a Determinist Algo

Method 2: Multi objectives optimization by a Determinist Algo

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2.39m

2.97m

6.4m

3.48m

8.34m

6.02m

Parameters Design

VBR 57 m/s

VBE 42,5 m/s

Empty Weight 517,5 kg

Fuel Weight 530 kg

MTOW 1047 kg

4.18 m

Resulting Design Resulting Design

Conclusion and Future workConclusion and Future work

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Previous studies have paved the way toward the definition and construction of a general analysis tool for rotorcraft concepts CREATION

On-going work for dealing with other RC configurati ons Either predefined: Tilt-Rotor, Compounds, … Or not: CREATION tool generates new configurations

Among remaining difficulties: Weight models : highly dependent on the technologies Uncertainties : internal (models fidelity) and external (operational parameters, …) Modeling the aero interactions and installation effects

ARF RIO: Rotorcraft Innovation Orientation Multidepartment action for building the capability to evaluate in a more global way the

environmental impact of RC (acoustics, air pollution …)