“Multibody Analysis of Rotorcraft-Pilot...

2nd IMSD, Stuttgart – Friday June 1, 2012 2nd IMSD, Stuttgart – Friday June 1, 2012

“Multibody Analysis of Rotorcraft-Pilot

Coupling”

V. Muscarello, P. Masarati, G. Quaranta

Dipartimento di Ingegneria Aerospaziale

2nd IMSD, Stuttgart – Friday June 1, 2012


2 OUTLINE

Multibody Analysis of Rotorcraft-Pilot Coupling

1. INTRODUCTION

2. NUMERICAL APPROACHES

3. MODEL DESCRIPTION

4. PILOT BIOMECHANICS

5. RESULTS

6. CONCLUSIONS


3 OUTLINE


1. INTRODUCTION




5. RESULTS

6. CONCLUSIONS


4 INTRODUCTION


Rotorcraft-Pilot Coupling Phenomena: PIO

Pilot Induced Oscillation;

Rotorcraft-Pilot Coupling Phenomena: PAO

Pilot Assisted Oscillation;

Intentional application of erroneous controls;

Consequence of insufficient or misleading cues;

Frequency range: below 1 [Hz];

Unintentional application of controls;

Caused by vibrations in the cockpit at frequencies above the bandwidth of

the pilot himself;

Frequency range: between 2 8 [Hz];


5 INTRODUCTION


Rotorcraft Comprehensive Modeling:

Detailed aeromechanics of the rotors;

Structural dynamics of the airframe;

Flight mechanics of the vehicle;

Actuators and sensors dynamics;

Flight Control Systems;

The voluntary/involuntary behavior of the pilot;

Pilot

Rotorcraft

“The analysis of this class of problems

requires very versatile and, at the

same time, very efficient tools”

Drive Train dynamics;


6 INTRODUCTION


Typical rotorcraft frequencies:

“Rotorcraft models need to cover all aspects of aeromechanics”


7 OUTLINE


1. INTRODUCTION




5. RESULTS

6. CONCLUSIONS


8 NUMERICAL APPROACHES


Multibody Analysis: MBDyn

Free general purpose multibody software - http://www.mbdyn.org

Formulation: DAE

Newton-Euler equations of motion of rigid bodies;

Connections with elastic/viscoelastic internal forces

or kinematic constraints;

Possibilities to introduce deformable components:

Lumped linear/angular springs;

Non-Linear FE Beams and Shells;

Simple Aerodynamics using BE/MT approach.

Analysis:

Time marching analysis using A/L-stable linear multistep integration schemes;

Eigenvalues and eigenvectors obtained using system identification based on POD.


9 NUMERICAL APPROACHES

Multibody Analysis of Rotorcraft Pilot Coupling

Linearized Analysis: MASST

Numerical code, for aeroservoelastic analysis of Rotary Wing Aircraft

Key elements:

State Space approach;

Connector of Reduced Order Models (ROM);

Availability for rotary wing technologies;

Easy integration with theory of control;

Developed in MatLab®, easily usable through a GUI;

Previous work about MASST:

P. Masarati, V. Muscarello, G. Quaranta. “Linearized aeroservoelastic analysis of rotary-wing aircraft”. In

36th European Rotorcraft Forum. Paris, France, September 7-9 2010;

P. Masarati, V. Muscarello, G. Quaranta. “Robust aeroservoelastic stability of helicopters: application to the

air/ground resonance”. In 67th AHS Annual Forum, Virginia Beach, VA, May 3-5 2011;

P. Masarati, V. Muscarello, G. Quaranta, A. Locatelli, D. Mangone, L. Riviello, L. Viganò. “An integrated

environment for helicopter aeroservoelastic analysis: the ground resonance case”. In 37th European Rotorcraft

Forum. Ticino Park, Italy, September 13-15 2011.


10

Modern Aeroservoelastic State Space Tools:

Sub – Structures / Dynamic Models Original

Domain

Example source

software

1. Airframe Structural Dynamics Time NASTRAN or GVT

2. Airframe Unsteady Aerodynamics Frequency NASTRAN (DLM)

3. Rotors Aeroelasticity Time CAMRAD JA

4. Drive Train Time MatLab / Simulink

5. Servo-Actuators Frequency MatLab / Simulink

6. Sensors and Filters Frequency MatLab / Simulink

7. Controllers (FCS) Time MatLab / Simulink

8. Pilot Biomechanics Time MatLab / Simulink

For each element type,

an arbitrary number of

blocks can be added

to the main model!

Items 1 4: Basic Aeroelastic Analysis;

Items 1 5: Aeroservoelastic Analysis;

Items 1 8: Closed Loop Aeroservoelastic Analysis;

NUMERICAL APPROACHES

Multibody Analysis of Rotorcraft Pilot Coupling


11 OUTLINE


1. INTRODUCTION




5. RESULTS

6. CONCLUSIONS


12 MODEL DESCRIPTION


IAR-330 PUMA

General characteristics:

Parameter Value Units

Gross Weight 7400 [Kg]

Max speed 140 [Kts]

MR Radius 7.49 [m]

MR Solidity 0.0913 [-]

MR Lock Number 8.70 [-]

MR Speed 270 [rpm]

MR Flap frequency 1.03 [/rev]

MR Lag frequency 0.26 [/rev]

Aeroelastic data references:

W.G. Bousman, C. Young, F. Toulmay, N. E. Gilbert, R. C. Strawn, J. V. Miller, T. H. Maier, M. Costes and P.

Beaumier. “A comparison of lifting-line and CFD methods with flight test data from a research Puma helicopter.”

TM 110421, NASA, October 1996.




Airframe structural model

Generic Aerodynamic forces for

Fuselage aerodynamics;

Horizontal/Vertical tail aerodynamics.

Rigid Body element;

Main rotor aeroelastic model

Blade structure: 5 three-node non-linear beams;

Blade aero: Blade Element / Momentum Theory.

Exact kinematics of pitching mechanism;

Tail rotor model

Lateral follower force Y;

Servo-actuators

MR swashplate actuators;

TF Servo – valve;

Sensors

Accelerations on pilot seat;

Positions on pilot seat;

8 Normal modes (Modal Joint);

DPTRIM YYYY

MBDyn: IAR-330 dynamic model set-up




MASST: IAR-330 dynamic model set-up

Airframe structural model

6 Rigid body modes;

Stability derivatives;

Reduced order model;

Main rotor aeroelastic model

DOFs in multiblade coordinates;

3 bending & 2 torsion modes;

LTI models about trim condition;

6 hub modes for CMS connection;

Displacement set (MR hub, TR hub, pilot seats);

Tail rotor model

Lateral force Y;

Loop ;

Scheduled gain with V∞;

Servo-actuators

3 Main rotor, 1 Tail rotor ;

TF Servo - valve;

TF Dynamic compliance;

Sensors

Accelerations on pilot seat;

Positions on pilot seat;

8 Normal modes;

Pv PYvYY //




Model Validation: Isolated Main Rotor – Southwell diagram

Small discrepancies only on

the second lead-lag mode; MBDyn versus MASST

Blade collective frequencies in vacuum;

Frequencies at RPM 100 [%]

correlate very well also with

those reported by Bousman et

al.

The linear Lead/Lag damper

has been included in the

analyses, with a nominal value

of Cξ = 7000 [N*m*s/rad].


16

MBDyn versus MASST

Blade mode shapes in vacuum;

RPM = 100% - COLL = 0 [deg];

1st Lead-Lag 1st Flap

Good correlation of the mode

shapes, considering also the

pitch – bending couplings;

MODEL DESCRIPTION


Model Validation: Mode Shapes (1/3)


17

2nd Flap 2st Lead-Lag




MODEL DESCRIPTION



MBDyn versus MASST


RPM = 100% - COLL = 0 [deg];


18

3rd Flap + Torsion 1st Torsion




MODEL DESCRIPTION



MBDyn versus MASST


RPM = 100% - COLL = 0 [deg];


19

CT/ σ - Thrust Coefficient

Discrepancies at high

collective pitch values, related to

large non-linear displacements.

MODEL DESCRIPTION


Model Validation: Rotor performances in hover

MBDyn versus MASST

Performances in Axial Flow;

Collective pitch ranging from -2° to 15°;

CP/ σ - Power Coefficient


20 OUTLINE


1. INTRODUCTION




5. RESULTS

6. CONCLUSIONS


21 PILOT BIOMECHANICS


Active Pilot (AP) Model:

Intentional/Voluntary action to control the vehicle;

Passive Pilot (PP) Model:

Origin: vibrations produced by the helicopter and filtered by the pilot body;

Pilot as “regulator” to maintain the steady condition;

Follow a desired pattern: “pursuit task” (predict the vehicle behavior);

Consequence: the pilot’s arm vibrates, introducing involuntary commands

into the control system;

Model: “Crossover” model proposed by McRuer & Jex.

Model: Mayo TFs pilot models introduced to investigate “Collective Bounce”

phenomena.




Pilot Loop Closure for Collective Control:

Rotorcraft model

)(sHAP

)(sHPP

θ0 θ0AP

θ0PP

+

+

+

-

zref

zseat

zseat

zseat ..

seatPPseatrefAP

PPAP zsHzzsH )()()(000

Pilot Task: maintain the vertical position




Active Pilot (AP) Model:

1) Crossover model:

2) Task: Maintain vertical position

3) Thrust: Blade Element Theory

4) Task TF:

ejcAPtask e

jjHjH

)()(

gMTzM

R

z

R

INT b

446

2

2

2

4

6)(

R

INMss

R

IN

sH

b

b

task

ACTIVE PILOT TF

Crossover ωc = 1.00 [rad/sec]

Time Delay τe = 0.38 [sec]

Cut-Off Frequency ωl = 3.10 [rad/sec]

LP

jctaskAP He

jHH e

1

Task + Integrator/Crossover;

Delay: Second order Padè;

2nd order Butterworth LP filter;




1) Mayo Ectomorphic TF:

2) Passive Pilot TF:

3) High Pass filter:

3.45270.13

3.45219.5)(

2

ss

s

z

zsH

seat

collMayo

PASSIVE PILOT TF

Gear Ratio Gc = π/10 [rad/%]

Collective bar length L = 0.35 [m]

Collective bar angle θL = (35/180) π [rad]

Cut-Off Frequency ωh = 3.10 [rad/sec]

Passive Pilot (PP) Model:

1

1)(

2

0

seat

coll

L

c

seat

PPz

z

sL

G

zsH

1

1)(

2

0

seat

coll

hL

c

seat

PPz

z

sL

G

zsH


25 OUTLINE


1. INTRODUCTION




5. RESULTS

6. CONCLUSIONS


26 RESULTS


MBDyn: Trim with Active Pilot ad Tail Rotor follower force

Trim performances in Hover and FF

Total pin joint at the CG;

Plunge motion and yaw rotations permitted;

Hover: Velocities – Modal Node Collective (AP) – TR Force (PD Controller)

The Active Pilot and the Tail

Rotor controller are able to

stabilize the motion, maintaining

the trim condition.


27 RESULTS


Stability of the Active Pilot Model

Gain on AP model:

Gain factor on the crossover frequency;

Increasing GAP, the pilot becomes more

aggressive, destabilizing the motion;

The Passive Pilot is not

considered in this step.

Analyses have been performed

at different AP gains.

Rotorcraft model

)(sHAP

)(sHPP

θ0 θ0

AP

+ +

-

zref

zseat

zseat APG


28 RESULTS


Stability of the Active Pilot Model

Gain on AP model: Hover

Aggressive AP is not able to reach the trim;

Increasing GAP, corrections on the collective

lever are introduce with high amplitude;

Frequency/Damping AP mode w.r.t. GAP Real/Imag part of AP mode w.r.t. GAP

LIMIT GAIN IN HOVER

MBDyn GAP = 2.00

MASST GAP = 2.15


29 RESULTS


Stability of the Passive Pilot Model

Gain on PP model:

GPP is interpreted as a scaling of gearing ratio;

GPP can be also associated to amplifications of

the seat acceleration caused by deformations.

Small changes in the PP gearing

ratio, related to GPP, doesn’t affect

the active pilot.

A real active pilot would adapt

the gain changes of gearing ratio.

Rotorcraft model

)(sHAP

)(sHPP

θ0 θ0

AP

+ +

-

zref

zseat

zseat 1

PPG

zseat ..

θ0PP

+


30 RESULTS



Gain on PP model: Hover & FF at μ = 0.2

Perfect agreement between MBDyn/MASST;

Lower gains are required in forward flight;

Hover: Frequency/Damping w.r.t. GPP FF: Frequency/Damping w.r.t. GPP

Hover FF μ = 0.2

MBDyn GPP = 1.40 GPP = 1.30

MASST GPP = 1.40 GPP = 1.35


31


RESULTS


1 Pilot passive model; 2 Collective lever ; 3 Main rotor actuators (1,2,3);

4 Main rotor collective pitch; 5 Main rotor collective flap; 6 Airframe plunge motion;

Pilot pole eigenvector: RPC mechanism / Vertical Bounce


32 RESULTS


Active Pilot performing maneuver:

ADS-33: Vertical maneuver

Good candidate to trigger collective bounce;

Anti wind-up on AP input: Limit integral action

of crossover model.

Saturation boundaries have

been set to +/- 5 meters;

Rotorcraft model

)(sHAP

)(sHPP

θ0 θ0

AP

+ +

-

zref

zseat

zseat

zseat ..

θ0PP

+


33 RESULTS


Vertical displacements Collective control

Active Pilot performing maneuver:

ADS-33: Vertical maneuver

Active pilot able to follow the trajectory;

Pilot-vehicle response is very slow.

The Active Pilot model has been

designed to maintain the position

(position task). When performing

vertical maneuver the pilot model

is characterized by a velocity

task.


34 RESULTS


Remarks: Active Pilot

The crossover model is able to stabilize the helicopter motion maintaining the

trim conditions (position task);

Pilot aggressiveness is analyzed considering a fictitious gain, GAP, in the

feedback loop, increasing the crossover frequency;

The “aggressive” active pilot is not able to reach the trim condition. The

corrections on the collective lever are introduced with high amplitude and the

strong thrust variations move the helicopter away from the reference

condition;

When performing vertical maneuver the crossover model is able to follow the

requested trajectory, even if the pilot-vehicle response is slow. In these

cases, the pilot is characterized by a velocity task, in order to reach the

requested trajectory as soon as possible;

Future research will be focused on the design of a most sophisticated active

pilot model, characterized by a task switching/scheduled approach.


35 RESULTS


Remarks: Active Pilot

The passive pilot TF is introduced to analyze the helicopter proneness at the

collective/vertical bounce phenomena, considering different gains, GPP, in the

control loop;

Remarks: Passive Pilot

The gain GPP can be interpreted as a scaling of the gearing ratio between the

collective lever displacement and the main rotor collective pitch, or it can be

associated to amplifications of the seat acceleration caused by airframe

deformation;

The instability mechanism, in hover and forward flight, is predicted by MBDyn

and MASST. Both analyses show similar results. Thus the collective bounce

phenomena is essentially linear;

It should be noted that rotorcraft dynamics are characterized by coupled

symmetric and antisymmetric motions, whose coupling is magnified at high

flight speed. The coupling with cyclic controls and the possibility to analyze

unsteady maneuvers could represent an interesting extension of the present

research.


36 OUTLINE


1. INTRODUCTION




5. RESULTS

6. CONCLUSIONS


37 CONCLUSIONS


Conclusions:

State of the art in the aeroservoelastic analysis of rotorcraft pilot coupling

phenomena;

Two approaches used:

1 Multibody: to investigate the detailed aeromechanics of the vehicle;

2 Linear, using comprehensive rotorcraft aeromechanics solvers;

The voluntary and involuntary interaction of the pilot with the vehicle has been

investigated with respect the collective control loop;

Both models are able to predict the instabilities generated by the voluntary

and involuntary control inputs;

Transient responses have been performed considering the vertical maneuver

prescribed by ADS-33;

Future developments: active pilot model with task switching/scheduled

approach and coupling with cyclic controls.


38 CONCLUSIONS


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