icas 2010 paper 723

INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.

2727thth Congress of the International CouncilCongress of the International Councilof the Aeronautical Sciencesof the Aeronautical Sciences

1919--24 September 201024 September 2010Nice, FranceNice, France

2727thth Congress of the International CouncilCongress of the International Councilof the Aeronautical Sciencesof the Aeronautical Sciences

1919--24 September 201024 September 2010Nice, FranceNice, France

SAFETY WINDOWS: KNOWLEDGE MAPS SAFETY WINDOWS: KNOWLEDGE MAPS FOR ACCIDENT PREDICTION AND PREVENTION FOR ACCIDENT PREDICTION AND PREVENTION IN MULTIFACTOR FLIGHT SITUATIONSIN MULTIFACTOR FLIGHT SITUATIONS

SAFETY WINDOWS: KNOWLEDGE MAPS SAFETY WINDOWS: KNOWLEDGE MAPS FOR ACCIDENT PREDICTION AND PREVENTION FOR ACCIDENT PREDICTION AND PREVENTION IN MULTIFACTOR FLIGHT SITUATIONSIN MULTIFACTOR FLIGHT SITUATIONS

© © 2010, 2010, INTELONICS Ltd.INTELONICS Ltd. 11

IN MULTIFACTOR FLIGHT SITUATIONSIN MULTIFACTOR FLIGHT SITUATIONSIN MULTIFACTOR FLIGHT SITUATIONSIN MULTIFACTOR FLIGHT SITUATIONS

IvanIvan BurdunBurdunChief ScientistChief Scientist

INTELONICS Ltd.INTELONICS Ltd.Novosibirsk, RussiaNovosibirsk, Russia

www.intelonics.comwww.intelonics.com

IvanIvan BurdunBurdunChief ScientistChief Scientist

INTELONICS Ltd.INTELONICS Ltd.Novosibirsk, RussiaNovosibirsk, Russia

www.intelonics.comwww.intelonics.com


Presentation OutlinePresentation OutlinePresentation OutlinePresentation Outline

Multifactor Flight Situation DomainMultifactor Flight Situation Domain

Problem and Solution Approach Problem and Solution Approach

Research MethodologyResearch Methodology

Modeling & Simulation Experiment SetupModeling & Simulation Experiment SetupModeling & Simulation Experiment SetupModeling & Simulation Experiment Setup

Results and DiscussionResults and Discussion

Potential for RealPotential for Real--time Applicationstime Applications

ConclusionsConclusions

Backup SlidesBackup Slides

22




Research Methodology Research Methodology

Modeling & Simulation Experiment SetupModeling & Simulation Experiment Setup


33







Demanding Operational Conditions/ Factors Demanding Operational Conditions/ Factors –– Main GroupsMain GroupsDemanding Operational Conditions/ Factors Demanding Operational Conditions/ Factors –– Main GroupsMain Groups

inin--flightflight icing of lift/ control icing of lift/ control surfacessurfaces

11

heavy rain, heavy rain, tropical showertropical shower

22

nonnon--standard standard atmospheric conditionsatmospheric conditions

ttoo,, p, p, ρρ

FCFC33

55

waterwater--/ice/ice--/snow/snow--covered covered (slippery) runway(slippery) runway

44

66HH

WxgWxg, , WWygyg, , WWzgzg

7, 87, 8

Normally, single operational factors are not critically dangerous. However, any [physically or Normally, single operational factors are not critically dangerous. However, any [physically or logically] logically] meaningful meaningful nn--factor combination factor combination ((ΦΦΦΦΦΦΦΦ ii(1)(1)∧Φ∧Φ∧Φ∧Φ∧Φ∧Φ∧Φ∧Φ ii(2)(2) ∧∧∧∧∧∧∧∧ … … ∧Φ∧Φ∧Φ∧Φ∧Φ∧Φ∧Φ∧Φ ii((jj))∧∧∧∧∧∧∧∧ … … ∧Φ∧Φ∧Φ∧Φ∧Φ∧Φ∧Φ∧Φ ii((nn))) ) can result in a complex can result in a complex ((multifactormultifactor) accident) accident--prone situationprone situation, , ii(j)(j)∈∈11, , 22, … , … 1212, …, , …, nn∈∈2, 3, 4, ….2, 3, 4, ….

terrain/ traffic/other terrain/ traffic/other external threatexternal threat

1212 TT11

PP66

PP11

TT55

……PP33

EE9090t t =120s=120s

EE4545IAS=250IAS=250

EE4646

nnzz < 0 .7 < 0 .7

start...start...

EE11

deviation from standard flight deviation from standard flight scenarioscenario

human pilot error human pilot error or inattentionor inattention

WxgWxg, , WWygyg, , WWzgzg

crosscross--/tail/tail--wind, windwind, wind--shear, ‘microburst’, shear, ‘microburst’, atmospheric/wake turbulenceatmospheric/wake turbulence

??

99

flight control automation flight control automation logic/data error or imperfectionlogic/data error or imperfection

10, 1110, 11

engine malfunction; flight engine malfunction; flight control mechanical failurecontrol mechanical failure

44


catastrophic statecatastrophic state

Multifactor Flight Situation BuildMultifactor Flight Situation Build--up Chain up Chain (Takeoff Example)(Takeoff Example)Multifactor Flight Situation BuildMultifactor Flight Situation Build--up Chain up Chain (Takeoff Example)(Takeoff Example)

safe safe statestate

catastrophic statecatastrophic state

LegendLegend::

ΩΩ((ФФ||SSkk)) –– subsetsubset ofof operationaloperational factorsfactors affectingaffecting scenarioscenario SSkk,, kk == 00,, 11,, ……,, 55:: ΩΩ((SS00)) == ∅∅,, ΩΩ((ФФ|S|S11)) == ФФ44,,ΩΩ((ФФ|S|S22)) == ФФ44,, ФФ1010,, ΩΩ((ФФ|S|S33)) == ФФ44,, ФФ77,, ФФ1010,, ΩΩ((ФФ|S|S44)) == ФФ44,, ФФ66,, ФФ77,, ФФ1010,, ΩΩ((ФФ|S|S55)) == ФФ44,, ФФ66,, ФФ77,, ФФ99,, ФФ1010..

ФФ44,, ФФ66,, ФФ77,, ФФ99,, ФФ1010 –– operational/designoperational/design factorsfactors

(or(or risk/risk/ ’what’what--if’/if’/ flightflight pathpath branchingbranching factors)factors)

–– colorscolors forfor codingcoding flightflight safetysafety levelslevels

SS00,, SS11,, ……,, SS55 –– notionalnotional flightflight situationsituation scenariosscenarios inin thethe orderorder ofof increasingincreasing aircraftaircraft

motionmotion dynamicsdynamics && controlcontrol complexitycomplexity andand operationaloperational riskrisk

–– ‘bud’‘bud’--typetype eventevent forfor ‘implanting’‘implanting’ additionaladditional operationaloperationalfactorsfactors intointo aa [less[less complex]complex] flightflight situationsituation scenarioscenario

55












66


How to analyze/assess, predict and protect aircraft safety How to analyze/assess, predict and protect aircraft safety performance in multifactor performance in multifactor (complex, abnormal, anomalous, (complex, abnormal, anomalous, uncertain) uncertain) flight situations ?flight situations ?

What is the What is the causecause--andand--effect mechanism of irreversible (‘chaineffect mechanism of irreversible (‘chain--reaction’ type) developments of the aircraft flight path under reaction’ type) developments of the aircraft flight path under multifactor conditionsmultifactor conditions: accident precursors, key contributing factors, : accident precursors, key contributing factors, ‘last‘last--changechange--forfor--recovery’ point, good and bad control rules recovery’ point, good and bad control rules

Problem FormulationProblem FormulationProblem FormulationProblem Formulation

‘last‘last--changechange--forfor--recovery’ point, good and bad control rules recovery’ point, good and bad control rules ((“do’s”“do’s” and and “don’ts”)“don’ts”) for human pilot or/and control automatonfor human pilot or/and control automaton,,etc. ?etc. ?

Are there Are there reliable reliable andand affordable techniques available for affordable techniques available for implementation implementation –– during the aircraft’s life cycle (phases: design, test during the aircraft’s life cycle (phases: design, test & certification, training, operation, accident investigation) & certification, training, operation, accident investigation) –– in order in order to help identify and avoid to help identify and avoid (or recovery from) (or recovery from) a potentially a potentially dangerous multifactor situation dangerous multifactor situation ? ?

77

INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.Main PrincipleMain Principle

‘Knowledge is Power’: the ‘Knowledge is Power’: the ‘pilot ‘pilot –– /automaton /automaton –– aircraft aircraft –– operational operational environment’ system model environment’ system model is employed as is employed as ‘knowledge generator’.‘knowledge generator’.

Research Goal Research Goal

Develop and demonstrate a technique for prediction and mapping of Develop and demonstrate a technique for prediction and mapping of aircraft/ project safety performance in multiaircraft/ project safety performance in multi--factor flight situations factor flight situations in in

Solution Approach Solution Approach Solution Approach Solution Approach

aircraft/ project safety performance in multiaircraft/ project safety performance in multi--factor flight situations factor flight situations in in advance, before the vehicle is built/flown.advance, before the vehicle is built/flown.

Techniques Employed Techniques Employed

Applied aerodynamicsApplied aerodynamics, , flight dynamicsflight dynamics,, situational controlsituational control, , multifactor flight multifactor flight domain theory,domain theory, mathematical modeling,mathematical modeling, numeric techniquesnumeric techniques,, computer computer simulation experimentsimulation experiment, , artificial intelligence (AI)artificial intelligence (AI), , graph theory, dynamic data graph theory, dynamic data structures, computer graphicsstructures, computer graphics, , VATES (Virtual Autonomous Test & Evaluation VATES (Virtual Autonomous Test & Evaluation Simulator, v. 5/7) software tool, Simulator, v. 5/7) software tool, PentiumPentium--IV PC, MS Office, MAGE, etc.IV PC, MS Office, MAGE, etc.

Classic Classic techniquestechniques + + Modern techniques Modern techniques = = New New analytical potentialanalytical potential. . Classic Classic techniquestechniques + + Modern techniques Modern techniques = = New New analytical potentialanalytical potential. .

88

INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.Main TasksMain Tasks

1.1. Automated Automated design and fastdesign and fast--time examination of a broad set of realistic time examination of a broad set of realistic scenarios and multifactor operational hypotheses in order to explore scenarios and multifactor operational hypotheses in order to explore potentially unsafe/anomalous flight situations potentially unsafe/anomalous flight situations –– using the system model, using the system model, the vehicle’s ‘parametric definition’, and autonomous flight simulation the vehicle’s ‘parametric definition’, and autonomous flight simulation techniques. techniques.

2.2. Automated Automated ‘mining’ ‘mining’ from flight M&S output datafrom flight M&S output data, ’granulation’ , ’granulation’ (by L. (by L. ZadehZadeh) ) and generalization of new safetyand generalization of new safety--related knowledge related knowledge using a set using a set

Main Tasks. Customer GroupsMain Tasks. Customer GroupsMain Tasks. Customer GroupsMain Tasks. Customer Groups

ZadehZadeh) ) and generalization of new safetyand generalization of new safety--related knowledge related knowledge using a set using a set of anthropomorphic ‘knowledge maps’.of anthropomorphic ‘knowledge maps’.

Target Customer GroupsTarget Customer Groups

Designer, Test Pilot/Engineer, Regulator, Educator/Instructor, Line Pilot, Designer, Test Pilot/Engineer, Regulator, Educator/Instructor, Line Pilot, Investigator, … Investigator, …

In autonomous M&S setup, a research pilot is not needed in the simulation loop. During a In autonomous M&S setup, a research pilot is not needed in the simulation loop. During a simulation experiment, simulation experiment, realistic operational hypotheses realistic operational hypotheses (meaningful combinations of several (meaningful combinations of several operational factors) operational factors) are are automatically automatically generated and added to a baseline scenario for fastgenerated and added to a baseline scenario for fast--time time virtual testing on a 6DOF flight modelvirtual testing on a 6DOF flight model..

In autonomous M&S setup, a research pilot is not needed in the simulation loop. During a In autonomous M&S setup, a research pilot is not needed in the simulation loop. During a simulation experiment, simulation experiment, realistic operational hypotheses realistic operational hypotheses (meaningful combinations of several (meaningful combinations of several operational factors) operational factors) are are automatically automatically generated and added to a baseline scenario for fastgenerated and added to a baseline scenario for fast--time time virtual testing on a 6DOF flight modelvirtual testing on a 6DOF flight model..

99












1010


Operational system Operational system (‘pilot/ automaton (‘pilot/ automaton –– aircraft aircraft ––

operational environment’)operational environment’)

Operational system Operational system (‘pilot/ automaton (‘pilot/ automaton –– aircraft aircraft ––

operational environment’)operational environment’)

Pilot Pilot ((automatonautomaton))


AircraftAircraftAircraftAircraftOperational Operational environmentenvironmentOperational Operational environmentenvironment


AircraftAircraftOperational Operational environmentenvironment

((DDOO∪∪∪∪∪∪∪∪DDT+MT+M))⊂⊂⊂⊂⊂⊂⊂⊂DDVV((DDOO∪∪∪∪∪∪∪∪DDT+MT+M))⊂⊂⊂⊂⊂⊂⊂⊂DDVV TTVV >>>>((TTO O + + TTT+MT+M))TTVV >>>>((TTO O + + TTT+MT+M)) SSVV << SST+MT+MSSVV << SST+MT+M

Flight testFlight test--bed + M&S standbed + M&S standFlight testFlight test--bed + M&S standbed + M&S stand

‘Pilot /Automaton ‘Pilot /Automaton –– Aircraft Aircraft –– Operational Operational Environment’ System Model as Knowledge Environment’ System Model as Knowledge GeneratorGenerator

‘Pilot /Automaton ‘Pilot /Automaton –– Aircraft Aircraft –– Operational Operational Environment’ System Model as Knowledge Environment’ System Model as Knowledge GeneratorGenerator

DDOO –– flight domain flight domain

explored in operationsexplored in operations

DDVV –– flight domain explored in situational flight domain explored in situational

M&S [virtual flight] experiments M&S [virtual flight] experiments

Mathematical Mathematical model of pilot model of pilot ((automatonautomaton))


Mathematical Mathematical model of flight model of flight

dynamicsdynamics


dynamicsdynamics

Math model Math model of operational of operational environmentenvironment




dynamicsdynamics


Virtual flight testVirtual flight test--bed bed (situational system model)(situational system model)

Virtual flight testVirtual flight test--bed bed (situational system model)(situational system model)

DDT+MT+M –– flight domain flight domain

explored in flight tests explored in flight tests and manned simulationsand manned simulations

TTT+M T+M -- test flight and M&S time (flight test and manned M&S experience)test flight and M&S time (flight test and manned M&S experience)TTT+M T+M -- test flight and M&S time (flight test and manned M&S experience)test flight and M&S time (flight test and manned M&S experience)

TTOO -- operational flight timeoperational flight time ((operational flight experience)operational flight experience)TTOO -- operational flight timeoperational flight time ((operational flight experience)operational flight experience)

ТТV V -- virtual test flight time (‘virtual’ test experience)virtual test flight time (‘virtual’ test experience)ТТV V -- virtual test flight time (‘virtual’ test experience)virtual test flight time (‘virtual’ test experience)

SS -- R&D cost (flight safety analysis, prediction and protection)R&D cost (flight safety analysis, prediction and protection)SS -- R&D cost (flight safety analysis, prediction and protection)R&D cost (flight safety analysis, prediction and protection)

Research pilot Research pilot ((automatonautomaton))


Research flight Research flight simulatorsimulator


Simulated Simulated operational operational environmentenvironment





Flight testFlight test--bed + M&S standbed + M&S standFlight testFlight test--bed + M&S standbed + M&S stand

LegendLegend::

Assigned Assigned operational operational constraintsconstraints

1111

INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.MicroMicro--structurestructureMicroMicro--structurestructure MacroMacro--structurestructureMacroMacro--structurestructure

B1B1

SituationaltreeSituationaltree C1C1

B0B0C2C2

B1

Situationaltree C1

B0C2

EventEvent

EE

Event

E

ProcessProcess

ПП

Process

П

Elementary situation


ПjПj

EkEk

EiEi


Пj

Ek

Ei

Flight situation scenario

Flight situation scenario

...

П9

...

П2

П14

...

E2

E4П5

П10

E6

П8

TwoTwo--Level Knowledge Structure of Complex Level Knowledge Structure of Complex Flight Situations Domain Flight Situations Domain TwoTwo--Level Knowledge Structure of Complex Level Knowledge Structure of Complex Flight Situations Domain Flight Situations Domain

Legend: Ei - flight event; Пj – flight

process; Cm – fuzzy constraint; -

system reference state; - system

branching state (‘bud’); - system

target state (‘leaf’); - system source

state (‘root’); B-1 – parent branch; B0 –

main branch or ‘trunk’ (baseline

scenario); Bn – nth-order derivative

branch (scenario with n operational

factors , n = 1, 2, …).

Legend: Ei - flight event; Пj – flight

process; Cm – fuzzy constraint; -

system reference state; - system

branching state (‘bud’); - system

target state (‘leaf’); - system source

state (‘root’); B-1 – parent branch; B0 –

main branch or ‘trunk’ (baseline

scenario); Bn – nth-order derivative

branch (scenario with n operational

factors , n = 1, 2, …).

C4C4C3C3

B2B2

B-1B-1

C4C3

B2

B-1

E1

П3

П4

П1

П6

П12

...

П13

...

E4

E4

П5

П11

E7

...E5

E8

П7

П15

...

MicroMicro-- and macroand macro--structure are structure are two interrelated components two interrelated components of a generalized knowledge model of a generalized knowledge model of a complex flight domain. Each flight path (of a complex flight domain. Each flight path (a branch in the situational treea branch in the situational tree) is modeled according ) is modeled according to an eventto an event--process type scenario process type scenario with a combination of with a combination of nn operational factors, operational factors, nn = 0, 1, 2, …= 0, 1, 2, …..

1212


13

aircraft model

test rig

1 Wind tunnel (*)

6 Flight situation content requirements:

8 Autonomous situational model of the ‘operator (pilot,

automaton) – aircraft – operational environment’ system behavior (VATES)

dt= f (x,u,w,t)

dx

9 Customer (aerodynamicist, 10

Flightsituation scenario

А Aircraft model ‘parametricdefinition’

B7 Library of flight situation

scenarios for virtual testing and certification

2 Experimental data measurement

and processing system

3 Output test data files

(‘3 forces and 3 moments’)

4 Computational aerodynamics (‘virtual wind

tunnel’), aircraft parametric definition database formation tools

5 Aircraft flight model input

database (aerodynamics, thrust,inertias, geometry, etc.)

M&S Based Flight Safety Virtual Testing CycleM&S Based Flight Safety Virtual Testing CycleM&S Based Flight Safety Virtual Testing CycleM&S Based Flight Safety Virtual Testing Cycle

12 Virtual‘test-bed’

6 Flight situation content requirements:

АП/FAR/JAR, compliance testing methods, or flight test programs, orPilot’s Manual, or flight test/ accident records

9 Customer (aerodynamicist,

dynamicist, pilot, …)

11 Operational (‘what …, if …?’)

hypothesis for virtual testing –situational tree ‘genotype’

10Computer

15 Maps of aircraft’s safety performance in complex

situations (system-level knowledge of multifactor effects of operational conditions on flight safety)

13‘Flight’

&size [n_columns] [n_rows]

&name time [var01] [var02]

&unit s [unit01] [unit02]

&format (f6.2, 20f10.4)

[time] 499.9999 236.1820 3.8520

[time] 499.9782 236.2703 3.8821

[time] 499.8870 236.3342 3.9107

...

14 Systems model’s output database (‘flights’,

hypotheses, statistics, etc.)

Legend: - direction of information flow processing; 1, …, 15 - flight safety T&C process components; (*) – courtesy of Dr.

N.Sokhi; - feedback link; A and B – system model’s two main input data sets. 1313


Fuzzy ConstraintFuzzy Constraint µµCC((VVFL.D.FL.D.)) CC: ‘permitted flaps: ‘permitted flaps--down flying IASdown flying IAS’’

green green (‘norm’), (‘norm’), ξξGG

yellow/ amber yellow/ amber (‘attention’), (‘attention’), ξξYY

blackblack (‘catastrophe’), (‘catastrophe’), ξξBB

grey/whitegrey/white ((‘uncertainty’‘uncertainty’)), , ξξWW

redred (‘danger’), (‘danger’), ξξRR

Safety PaletteSafety Palette Color is natural and, perhaps, Color is natural and, perhaps, the most effective and economic the most effective and economic medium medium for communicating safetyfor communicating safety--related information related information to / to / from an from an operator (a pilot or automaton).operator (a pilot or automaton).

Safety Palette. Fuzzy Constraint Safety Palette. Fuzzy Constraint Safety Palette. Fuzzy Constraint Safety Palette. Fuzzy Constraint

Operational Operational constraints under multiconstraints under multi--factor flight conditions factor flight conditions are not known precisely. are not known precisely. They are They are inherently ‘fuzzy’inherently ‘fuzzy’. . The The notions notions of fuzzy constraint (by L.A. of fuzzy constraint (by L.A. ZadehZadeh) and ) and safety safety palette are employed palette are employed for approximate measurement of the compatibility of for approximate measurement of the compatibility of current current ((i.e. measured at time instants i.e. measured at time instants tt))system system state state with operational constraints for key with operational constraints for key (monitored) (monitored) system variables.system variables.

LegendLegend:: cc, , d d –– characteristic characteristic

points of the carrier of fuzzy points of the carrier of fuzzy

setset--constraint constraint CC, , µµCC((xx)) –– L.A. L.A.

Zadeh’sZadeh’s fuzzy set membership fuzzy set membership

functionfunction

Fuzzy ConstraintFuzzy Constraint

‘red’‘red’‘green’‘green’ ‘black’‘black’‘yellow’‘yellow’

11CC: ‘permitted flaps: ‘permitted flaps--down flying IASdown flying IAS’’

ddсс

00410410390390 VVFL.DFL.D.. [km/h][km/h]

……

470470

……

……

1414


- green (‘norm’), ξ

Legend: ΣΣΣΣk – partial safety spectrum for variable xk, k = 1, …, p; p – total number of monitored constraints/ variables, p = 20. ΣΣΣΣ – integral safety spectrum; t – flight time; ξi – color from safety palette, i ∈ B (black), R (red), Y(yellow), G (green),…; < – ‘colder than’ operation for comparing two safety colors; max – operation of selecting the ‘hottest’ color at time instant t; || -operation of safety colors concatenation in ΣΣΣΣ; [t*; t*] – examined flight time interval; ∆ – spectrum construction time step.

Mo

nit

ore

d v

ari

ab

les/

co

nst

rain

ts

ΣΣΣΣ1ΣΣΣΣ2

...

...

ΣΣΣΣk

Pa

rtia

l flig

ht

safe

ty s

pe

ctr

a

IAS (δδδδF = 0, airborne)

IAS (δδδδF > 0, airborne)

SideslipLoad_factor

East_rate (groundroll)East (groundroll)

North (groundroll)Bank (airborne)

Bank (groundroll)Pitch (airborne)

Pitch (groundroll)Vert_rate (airborne)

AoA (δδδδF= 0)

AoA (δδδδF> 0)

Wheels (airborne)Wheels (groundroll)

Elevator (airborne)

Partial Safety Spectra. Integral Safety Partial Safety Spectra. Integral Safety Spectrum Spectrum Partial Safety Spectra. Integral Safety Partial Safety Spectra. Integral Safety Spectrum Spectrum

For each simulated situation, its safety level is measured for selected key variables xk at recorded time instants. As a result, a family of Partial Safety Spectra ΣΣΣΣk, k = 1, …, p, and an Integral Safety Spectrum ΣΣΣΣ can be calculated for this situation. The integral safety spectrum is a color-coded time-history of all violations and restorations of the monitored fuzzy constrains during the situation.

(∀t) (t∈[t*;t*]) (∃ξ(xk(t)) (ξ(xk(t))∈ ξW, ξG, ξY, ξR, ξB, … ∧ (ξW < ξG < ξY < ξR < ξB))

(ξ(t) = max ξ(xk(t)), k = 1, …, p) ⇒ (ξ(t)∈ΣΣΣΣ ∧ ΣΣΣΣ = ξ(t*) || ξ(t*+∆) || ξ(t*+2∆) || … || ξ(t*))

Integral Safety Spectrum Calculation Algorithm:

- yellow (‘attention’), ξY

- black (‘catastrophe’), ξB

- gray/white (‘uncertainty’), ξW

- red (‘danger’), ξR

- green (‘norm’), ξG

Mo

nit

ore

d v

ari

ab

les/

co

nst

rain

ts

ΣΣΣΣ20

Pa

rtia

l flig

ht

safe

ty s

pe

ctr

a

ΣΣΣΣ

Elevator (airborne)Elevator (groundroll)

AileronRudder

Integral spectrum

time, s

1515


Flight Safety Classification CategoriesFlight Safety Classification CategoriesFlight Safety Classification CategoriesFlight Safety Classification Categories

In order to measure safety performance for a flight situation in overall, a special ‘safetyruler’ consisting of five classification categories I, …, V is employed. Why five? – becauseexperts cannot reliably recognize and use more than 5-10 gradations of a complex, difficult-to-formalize system-level property (e.g.: Cooper-Harper scale). ‘Light green’, RGB (192; 255;0), and ‘orange’, RGB (255; 192; 0), are interim colors used to denote Categories II-a and III.

1616


‘Virtual flight test

T130: “Takeoff and initial

climb, ‘very strong’ wind-

shear, variations/ errors

of commanded flight

path (ΘG) and bank (γG)

angles”

Situational Tree of Flight Situational Tree of Flight Situational Tree of Flight Situational Tree of Flight

Legend: ΘG∈2о, …, 20о – commanded flight path errors, γG∈-45о, …, +45о – commanded bank angle errors,

T130|Г(Ф1×Ф2×Ф3)=F2682, …, F2811 – situational tree, Г(Ф1×Ф2×Ф3) – tree’s genotype (operational hypothesis), Фk –

operational factor, Ф1≡ΘG, Ф2≡γG, Ф3≡(Wxg,Wzg=f(t)) – ‘very strong’ wind-shear; N(T130)=130 – number of branches in

T130, ∆t(Bi)=60s – branch ‘length’, i=1, 2, …, 130; - safety palette

‘Virtual flight test

experience’ accumulated

in tree T, hrs:

A composition of a baseline situation scenario and an operational multifactor combination inM&S experiment results in a situational tree. The tree’s branches (flight paths) stand for ‘what-if’derivative (non-standard) situations. All branches are color-coded using ‘integral safety spectra’.

1717


3

Safety Chances

Distribution Pie Chart

AA

BBCC

Example of mapping a situational tree Example of mapping a situational tree SS11⋅⋅ГГ1111: Takeoff.: Takeoff. Errors of SelectingErrors of SelectingCommandedCommanded Flight pathFlight path and Bank Angles in Climband Bank Angles in Climb

Safety Window

Safety Window. Safety Chances DistributionSafety Window. Safety Chances Distribution

100130Σnj, Σχj | S1⋅ΓΓΓΓ11

00V

4355IV

11III

2229II-b

68II-a

2837I

χj, %njξjCategory Let us Let us map safety map safety levels levels (categories) obtained for all situations (categories) obtained for all situations from a from a tree tree onto onto a twoa two--factor planefactor plane. This results in a . This results in a Flight Safety Flight Safety Window Window (FSW). In (FSW). In FSW above, cell FSW above, cell CC is located at ‘column is located at ‘column AA -- row row BB’ crossing. This cell ’ crossing. This cell depicts depicts safety status of safety status of oneone flight pathflight path--branchbranch from the treefrom the tree.. This is This is a nona non--standard standard situation with values of 14situation with values of 14oo andand 3030oo of factors of factors ΦΦΦΦΦΦΦΦ77 andand ΦΦΦΦΦΦΦΦ1111 in in SS11. . The The cell is cell is colored using the colored using the safety category safety category color color ‘‘orangeorange’. Note that t’. Note that the he FSW hFSW has a as a dangerous ‘corner’ dangerous ‘corner’ (upper(upper--left)left). . Rapid transition (Rapid transition (33) from safe (‘salad ) from safe (‘salad green’) to dangerous (‘red’) zone is possible green’) to dangerous (‘red’) zone is possible ((Cat. Cat. IIII--a a →→ IVIV)), bypassing , bypassing interim zones (interim zones (IIII--bb, , IIIIII)). . Flight control Flight control at such ‘corners’ at such ‘corners’ obviously requires obviously requires enhanced enhanced pilot attention.pilot attention.

3

1818


Flight Safety ‘Topology’O

pe

ratio

na

l/d

esi

gn

fa

cto

rФ

1

12

5

33

4

2

V

IV

III

II-b

II-a

I

63

Op

era

tio

na

l/

Operational/ design factor Ф2

1 4

6 3

2

1 ‘Abyss’ (catastrophe)

2 ‘Hill’ (danger)

3 ‘Slope’ (reversible state

transitions)

4 ‘Valley’ (standard safety, norm)

5 ‘Lake’ (maximum safety, optimum)

6 ‘Precipice’ (abrupt, irreversible

state transitions, or ‘chain reaction’)

Flig

ht

Sa

fety

C

ate

go

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s

1, 2,…, 6 - main object types of flight safety ‘topology’:

Transitions 6 must be known and prevented!

Transitions 3 must be known and controlled!

1919












2020


21Baseline Flight Scenarios Baseline Flight Scenarios

Baseline scenario Si is a plan of some ‘central’/reference (any standard or non-standard)flight situation, which variations (derivative cases) are virtually tested in autonomous M&Sexperiments. The goal is to evaluate combined effects of selected operational/design factorson flight safety in these scenarios. The sources of data for baseline scenarios are: airworthinessrequirements, flight test data/programs, ACs, Pilot’s Manuals, real flight data records, flightaccidents/ incidents statistics.

2121


Joint Graph of Baseline ScenariosJoint Graph of Baseline Scenarios

E44: engineout speed44

F1: left-hand engine failure …

S3S5

S1 T2: maintain commanded bank γG & heading ΨG angles

E88: altitude 200 m 88

T5: maintain commanded bank γG

& sideslip βG angles

T2: maintain commanded bank γG and heading

E190: situation end

190

E6: altitude 10.7 m

6 P3: wheels - up…

E7: altitude 120 m

7 P4: flaps - up…

W1: crosswind 10 m/s (left-to-right)

S2

E1: situation start

1

P1: set engines #1,2 leversto takeoff rating

…

T1: maintain path in groundroll along runway’s centerline

E55: in airborne

55

22

Scenario consists of events and processes. It can be depicted as a directed graph. The scenario defines logic and content of a flight situation. It is also clear to the pilot. Scenarios S1, …, S5 are structurally close. They can be easily modified.

S4

W1: crosswind -10 m/s (right-to-left)

bank γG and heading ΨG angles

E5: pitch 8о5

T3: maintain commanded flight path angle ΘG1 (initial phase of climb)

E3: VR achieved

3P2: elevator –up for rotation

…

E12: flaps retracted

12

T4: maintain commanded flight path angle θG2 (2

nd

phase of climb)

P5: maintain given indicator airspeed

W2: ‘strong’ wind shear

E44: engine out speed44 - event

F1: left-hand engine failure- process

Legend:

2222


Operational Factors Selected for TestingOperational Factors Selected for Testing

Operational /design factors are modified or new events and/or processes in a flight scenario,which can improve (or worsen) the aircraft safety performance. There are three groups ofoperational factors: ‘operator’, ‘aircraft’ and ‘external environment’. The sources of informationon operational factors are airworthiness requirements, FMEA, statistics on flight operations, andaccidents/incidents data.

2323


Design Field of Operational Hypotheses

Г1

Г2

Г10

Г3

Г5Г6

Г7

Г4

θθθθG2Ф8

Commanded flight path angle during 2nd phase of climb

Wyg

Ф5

Crosswind velocity

HFLФ6

Flaps-up start altitude

µµµµФ4

Wheels - runway surface adhesion factor

VR

Ф2

Rotationairspeed

CGxФ1

Longitudinal C.G.

∆δ∆δ∆δ∆δe

Ф3

Elevator deflection for rotation

Г8

Г9

Г11

Г12

Г13

θθθθG1 Ф7

Commanded flight path angle during initial phase of climb

kW Ф9 Intensity of wind-shear

γγγγG

Ф11

Commanded bank angle

ζζζζLHEФ13

Left-hand engine

failure at VEF

VEF

Ф12‘Engine out’

indicator airspeedkP

Ф10Engines power rating at takeoff

Wyg Ф5Cross wind velocity

- operational factor independent

dependent- link between

factors in Г

Г13 - operational hypothesis

Legend: Many operational factors from this list are not critically dangerous alone. Much more important to learn in advance effects of unfavorable combinations of these factors on flight safety.

2424


Plan and Statistics of M&S Experiments

Legend: i – code of baseline scenario Si, i=1, …, 5; k – code of operational hypothesis Гk, k=1, …, 13; N(Ф) – number

of operational factors in Гk; n – size of ‘flight’ series Ωk(F), Ωk(F)=Fi1, …, Fj, …, Fin

, n=in-i1+1, j – ‘flight’ code; ∆t –

planned duration of ‘flight’ Fj, Fj∈Ωk(F); ℑ|Si⋅Гk – ‘virtual flight test experience’ accumulated in tree Si⋅Гk; notation of

coordinate axes corresponds to ISO 1151.

Composition of baseline scenario Si and operational hypothesis Гk results in a family of derivative (‘neighboring’) situations – a ‘situational tree’ Si⋅Гk. Construction of a ‘forest’ of such trees - based on FMEA, flight test/operation/ incidents/accidents data - and studying their safety ‘topology’ in autonomous M&S experiments is the goal of virtual flight T&C.

2525












2626


Composition Composition SS11⋅⋅ГГ11

Safety Chances Distribution

0, 0%

Category ξj χj, %

I 100

II-a 0

II-b 0

III 0

Integral Safety Spectra mP mF VR# ∆δe

Flight situation code

Tested operational factors

Normal TakeoffNormal Takeoff. . Variations Variations of of C.G. C.G. and and VVR R Speed Speed ((with with

Correction Correction of of Elevator Elevator Deflection Deflection in in Rotation)Rotation)

66, 100%

Legend: in nj, χj% nj – number of ‘flights’ belonging to

Cat. ξj, χj% - percentage of ‘flights’ of Cat. ξj, j=I, …, V.

III 0

IV 0

V 0

‘Flights’ in total - 66 100

All situations from Composition S1⋅Г1 are safe, i.e. they belong to Category I cluster. Note how location of events E3 and E7 on integral safety spectra is changed due to situation (operational factors).

time, s 2727

INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.INTELONICS LTD.S1:Normal takeoff, steering commanded flight path andbank angles during initial climb

Flight Safety Flight Safety WindowWindow

⇒ In FSW below, cell 1 located at ‘column 2 - row 3’crossing is a color code of flight safety Category ofone situation from Composition S1⋅Г1. This situation isobtained by combining values 4 and 5 ofoperational factors 6 and 7 in scenario S1.

2

6

Composition Composition SS11⋅⋅ГГ11Normal TakeoffNormal Takeoff. . Variations Variations of of C.G. C.G. and and VVR R Speed Speed ((with with Correction Correction of of

Elevator Deflection Elevator Deflection in in Rotation)Rotation)

This Flight Safety Window constructed for Composition S1⋅Г1 situations has ‘trivial topology’ –one continuous green ‘valley’. That is, for a given aircraft/project all examined combinations oflongitudinal C.G. location and VR speed variations are acceptable safety-wise (NB: providedthat all other conditions of scenario S1 are fulfilled).

1

4

5

7

3

2828


Composition S2⋅Г2 Normal Takeoff. Variations of Crosswind Velocity and ‘Wheels -

Runway Surface’ Adhesion Factorµ k⋅Wy

g

#Integral Safety Spectra

k=10-1

21; 33% 22; 35%

Safety Chances Distribution

Variants with strong crosswind of |15|…|20| m/s exhibit danger during groundroll up to event E3 (VR) - ref. next slide for FSW. These variants constitute 45% of all tested flight situations from composition S2⋅Г2. Remaining situations (55%) are safe - they belong to Categories I and II. Note how the location of events E3 and E7 in IFSS is changed due to the effect of (µ, Wyg) combinations.

2; 3%6; 10%12; 19%

2929


Flight Safety Window

S2: Normal takeoff under cross-wind and varying conditions of runway surface, steering commanded flight path and bank angles during initial climb

Composition S2⋅Г2Normal Takeoff. Variations of

Crosswind Velocity and ‘Wheels –Runway Surface’ Adhesion Factor

11

Shown above is Flight Safety Window constructed for situational tree S2⋅Г2. It contains onecentral green ‘valley’, two side red ‘hills’ and two connecting ‘slopes’: (1) a steep ‘slope’ – fordry and semi-wet runway, and (2) not steep ‘slope’ - for wet and water-covered runway. As theabsolute value of cross-wind velocity increases, transitions from safe to dangerous states occur(1) sharply and (2) gradually, respectively. The shape and position of ‘crosswind velocity –adhesion factor’ constraints can be seen as well.

22

3030


0, 0%

0, 0%

5, 14%

5, 14%

0, 0%

θθG1G1## θθG2G2 HHFLFLIntegral Safety SpectraIntegral Safety SpectraSafety Chances Safety Chances DistributionDistribution

SS11⋅⋅ГГ33 Normal Takeoff. Forward C.G. Location. Normal Takeoff. Forward C.G. Location. Variations/Errors of Variations/Errors of Selection Selection of of Commanded Commanded

Flight Path Angles (Initial Flight Path Angles (Initial and and 22ndnd Phases Phases of of Climb) Climb) and and FlapsFlaps--up Start Altitudeup Start Altitude

25, 72%

1414%% ofof variantsvariants fromfrom situationalsituational treetree SS11⋅⋅ГГ33,,whichwhich havehave commandedcommanded flightflight pathpath angleangle(during(during initialinitial phasephase ofof climb)climb) moremore thanthan1212oo,, exhibitexhibit dangerdanger.. NoteNote alsoalso how,how, forforexample,example, eventevent EE77:: ‘altitude‘altitude 120120 m’m’changeschanges itsits locationlocation inin IFSSIFSS duedue toto θθGG11..

3131



SS11⋅⋅ГГ33 Normal Takeoff. Forward C.G. Location. Normal Takeoff. Forward C.G. Location. VariationsVariations/ Errors of / Errors of Selection of Commanded Selection of Commanded

Flight Path Angles (Initial Flight Path Angles (Initial and and 22ndnd Phases Phases of of Climb) Climb) and and FlapsFlaps--up Start Altitudeup Start Altitude

S1: Normal takeoff, steering commanded flight path and bank angles during initialclimb

For composition S1⋅Г3, sharp transitions (1) from safe situations to unsafe ones are observed atcommanded flight path angles θG1/θG2>12/10o for all values of HFL. Owing to high thrust-to-weightratio, errors in selection of flaps-up start altitude do not worsen the aircraft’s flight safetyperformance, provided (NB) that other conditions of scenario S1 are preserved.

3232


7, 17% 18, 43%

8, 19%

SS33⋅⋅ГГ55 Continued TakeoffContinued Takeoff. . LeftLeft--hand Engine Out At hand Engine Out At VVEFEF=150 =150 km/hkm/h. . Variations/ Errors of Selection of Commanded Variations/ Errors of Selection of Commanded

Flight Path Angles During Initial Flight Path Angles During Initial and and 22ndnd PhasesPhases

θθG1G1## θθG2G2Integral Safety SpectraIntegral Safety Spectra Safety Chances Safety Chances DistributionDistribution

0, 0%

8, 19%

1, 2%

IfIf leftleft--handhand engineengine failsfails duringduring groundground--rollroll (at(atVVEFEF==150150 km/hkm/h)) takeofftakeoff safetysafety cannotcannot bebesecuredsecured atat commandedcommanded flightflight pathpath angleangleθθGG11≥≥55oo (during(during initialinitial phasephase ofof climb)climb).. ForForexaminedexamined domaindomain ofof operationaloperational factors,factors, shareshareofof safesafe situationssituations isis 3636%%..

3333


SS33⋅⋅ГГ55 Continued TakeoffContinued Takeoff. . LeftLeft--hand Engine Out hand Engine Out at at VVEFEF=150 =150 km/hkm/h. . Variations/ Errors of Selection of Commanded Variations/ Errors of Selection of Commanded

Flight Path Angles During Initial Flight Path Angles During Initial and and 22ndnd PhasesPhases

S3: Continued takeoff (left-hand engine out at given VEF), steering commandedflight path and bank angles during initial climb

Flight Safety WindowFlight Safety Window

Left-hand engine failure during ground-roll decreases the limit of flight path angle admissiblein initial climb to 2o…4o compared to θG1=10o …12o in composition S1⋅Г3. ‘Precipice’ typetransitions (1) are observed at θG2=0o. ‘Abyss’ type states are likely to occur at flight path anglesθG1>4o (initial climb) for any θG2 (2nd phase of climb).

3434


SS44⋅⋅ГГ66 Normal TakeoffNormal Takeoff. . Variations Variations of Windof Wind--shear shear Intensity and Intensity and Errors of Selection of FlapsErrors of Selection of Flaps--up Start up Start

AltitudeAltitude

S4: Normal takeoff under windshear conditions, steering commanded flight path andbank angles during initial climb


In scenario S4 we have θG1/θG2=8o/8o. If ‘strong’ or worse windshear is expected (kW≥1) takeoffis prohibited. In order to evaluate possibility of safe outcomes at kW<1 it is expedient to expandFlight Safety Window downward. If windshear intensity increases from ‘very strong’ (kW>1.4) to‘hurricane’ (kW=2), ‘precipice’ type transitions (1) are most likely to occur at flaps-up start altitudeHFL∈[60; 70] м. If aircraft unintentionally enters a zone of ‘very strong’ windshear (kW=1.2 …1.6)flaps must be retracted as late as possible to stay within ‘orange’ zone (2).

2

3535


SS44⋅⋅ГГ77 Normal Takeoff. Forward C.G. LocationNormal Takeoff. Forward C.G. Location. . Variations Variations of Windof Wind--shear shear Intensity Intensity and Commanded and Commanded Flight Path Flight Path

Angles (During Initial Angles (During Initial and and 22ndnd Phases)Phases)

SS44:: NormalNormal takeofftakeoff underunder windshearwindshear conditionsconditions, , steering steering commandedcommanded flightflight pathpath andandbankbank anglesangles duringduring initialinitial climbclimb


FForor compositioncomposition SS44⋅⋅ГГ77 mainmain objectsobjects ofof safetysafety ‘topology’‘topology’ areare:: smallsmall greengreen ‘valley’‘valley’ (at(at leftleft lowerlowercorner),corner), orangeorange ‘slope’,‘slope’, extensiveextensive redred ‘hill’‘hill’ adjacentadjacent toto blackblack ‘abyss’‘abyss’ (at(at rightright upperupper corner)corner).. AtAttakeofftakeoff underunder ‘strong’‘strong’ andand ‘very‘very strong’strong’ windshearwindshear conditionsconditions ((11<<kkWW≤≤11..66)):: maximummaximum safetysafety isisachievedachieved atat θθGG11//θθGG22==55oo//33oo;; itit isis prohibitedprohibited toto climbclimb atat θθGG11//θθGG22>>77oo//55oo;; irreversibleirreversible transitionstransitions arearelikelylikely atat θθGG11≥≥1212oo..

3636


S5⋅Г10 Continued Takeoff. Left-hand Engine Out at VEF. Variations of Left-hand Engine Out Speed and

Cross-wind Velocity

Г10 = Ф13×Ф12×Ф4 ≡ ζLHE×VEF×Wyg

S5: Continued takeoff (left-hand engine out at VEF), under cross-wind conditions, steering commanded flight path and bank angles during initial climb


This Flight Safety Window has central green ‘valley’ and two side red ‘hills’. Adjacent to left‘hill’ is a potentially catastrophic ‘abyss’ located at lower left corner. It is created at small andmedium values of VEF and is linked to ‘valley’ by ‘precipice’ type transitions. Small ‘abyss’ is alsorevealed at crosswind velocity of ~18 m/s and VEF∈[175; 190] km/h.

3737


SS11⋅⋅ГГ1111 Normal Takeoff. Variations/ Errors Normal Takeoff. Variations/ Errors in in Selection Selection of of Commanded Flight Commanded Flight Path and Bank Path and Bank Angles Angles

(During Initial Phase (During Initial Phase of of Climb)Climb)

Г11 =Ф7×Ф11 ≡ θG1×γG

SS11:: NormalNormal takeofftakeoff, , steeringsteering commandedcommanded flightflight pathpath andand bankbank anglesanglesduringduring initialinitial climbclimb


ThisThis FlightFlight SafetySafety WindowWindow hashas aa potentiallypotentially dangerousdangerous ‘corner’‘corner’ correspondingcorresponding toto ((θθGG11,, γγGG)) ≅≅((1212oo……1414oo,, --3030oo……--3737..55oo)).. SharpSharp transitiontransition ((11)) ofof statesstates fromfrom safesafe (‘green’)(‘green’) toto dangerousdangerous (‘red’)(‘red’) zonezoneisis possiblepossible ((CatCat.. II→→IV)IV),, bypassingbypassing interiminterim zoneszones (Cat(Cat.. II,II, IIIIII)).. FlightFlight atat suchsuch ‘corners’‘corners’ requiresrequiresenhancedenhanced attentionattention andand accurateaccurate pilotingpiloting fromfrom pilotpilot..

3838


7, 5%

10, 8%

26, 20%

20, 15%

SS44⋅⋅ГГ1313 Normal Takeoff. ‘Very’ Strong Normal Takeoff. ‘Very’ Strong WindWind--shearshear. . Variations /Errors Variations /Errors of of Selection Selection of of Commanded Flight Commanded Flight

Path and Path and Bank Angles in ClimbBank Angles in Climb

θθG1G1## γγGGIntegral Safety SpectraIntegral Safety Spectra θθG1G1## γγGGIntegral Safety SpectraIntegral Safety Spectra

Safety Safety Chances DistributionChances Distribution

57, 44%

10, 8%

‘Very strong’ wind‘Very strong’ wind--shear may worsen flight shear may worsen flight safety ‘topology’ of safety ‘topology’ of takeoff catastrophically at takeoff catastrophically at small values of small values of commanded flight path commanded flight path angle angle θθG1G1≤≤44oo..

3939



SS44⋅⋅ГГ1313 Normal Takeoff. ‘Very’ Strong Normal Takeoff. ‘Very’ Strong WindWind--shearshear. . Variations /Errors Variations /Errors of of Selection Selection of of Commanded Commanded

Flight Flight Path and Path and Bank Angles in ClimbBank Angles in Climb

ГГ13 13 = Ф= Ф99××ФФ77××ФФ11 11 ≡≡ kkWW×θ×θG1G1×γ×γG G ((kkWW=1.5)=1.5)

SS44:: NormalNormal takeofftakeoff underunder windshearwindshear conditionsconditions, , steeringsteering commandedcommanded flightflight pathpath andandbankbank anglesangles duringduring initialinitial climbclimb

Flight safety ‘topology’ obtained for ‘very strong’ Flight safety ‘topology’ obtained for ‘very strong’ windwind--shear shear conditions at small conditions at small θθGG11 and any and any γγGG contains a stable catastrophic ‘abyss’ contains a stable catastrophic ‘abyss’ ((black strip in the bottom) and ‘‘precipice’ type black strip in the bottom) and ‘‘precipice’ type transitions (1). That is, an attempt of initial climb at small values of commanded flight path angle transitions (1). That is, an attempt of initial climb at small values of commanded flight path angle (2(2oo…4…4oo) ) inevitably leads the vehicle to a fatal outcome.inevitably leads the vehicle to a fatal outcome.

4040












4141


Dynamic Safety Window Sequence

Normal Takeoff. Variations Normal Takeoff. Variations of Windof Wind--shear Intensity, shear Intensity, Errors/ Variations Errors/ Variations of of Selection Selection of of Commanded Commanded Flight Path Flight Path and and Bank Angles Bank Angles in Initial in Initial Climb Climb –– ‘forest’ of ‘forest’ of situational treessituational trees

Optimal modes -maximum safety

t = t1

(‘strong’

wind-shear)

t = t0

(‘benign

weather’)

42

The developed safety ‘topology’ maps, including Flight Safety Window, Safety Chances Pie Chart and other formats, can be potentially useful for flight operations.

The goal is to monitor operational constraints and dynamically adapt piloting tactics under multifactor conditions in real time, provided that there exist onboard technical means to measure operational factors in real time.

wind-shear)

t = t2

(‘very strong’

wind-shear)

4242


Safety Window Safety Window (commanded (commanded

‘flight path angle ‘flight path angle –– bank angle’ )bank angle’ )

Safety Chances Safety Chances DistributionDistribution

Potential Contribution to Integrated Potential Contribution to Integrated Intelligent Flight Deck Initiatives (1)Intelligent Flight Deck Initiatives (1)Potential Contribution to Integrated Potential Contribution to Integrated Intelligent Flight Deck Initiatives (1)Intelligent Flight Deck Initiatives (1)

windwind--shear impact shear impact realreal--time analysistime analysis

‘‘strongstrong’’ ‘‘very strongvery strong’’‘‘benign weatherbenign weather’’

DistributionDistribution

WindWind--shear shear forecast forecast

-- Optimum (safetyOptimum (safety--wise) wise) piloting modespiloting modes

The concept of dynamic safety window can be potentially useful The concept of dynamic safety window can be potentially useful to help pilot/automaton to help pilot/automaton predict aircraft safety performance in various ‘whatpredict aircraft safety performance in various ‘what--if’ scenarios and find optimum control if’ scenarios and find optimum control tactics under demanding conditions tactics under demanding conditions (in this specific takeoff and initial climb case (in this specific takeoff and initial climb case –– commanded commanded ‘flight path angle ‘flight path angle –– bank angle’ pairs).bank angle’ pairs).

4343


14

15

16

17

18

19

H

J

L

I

K

S↑ S↓

8

9

10

11

12

13

E

F

G

Dynamics Safety Window Tree. Safety Chances Distribution TimeDynamics Safety Window Tree. Safety Chances Distribution Time--historyhistory

Potential Contribution to Integrated Potential Contribution to Integrated Intelligent Flight Deck Initiatives (2)Intelligent Flight Deck Initiatives (2)Potential Contribution to Integrated Potential Contribution to Integrated Intelligent Flight Deck Initiatives (2)Intelligent Flight Deck Initiatives (2)

Dynamics Safety Dynamics Safety Window Tree and Window Tree and Safety Chances Safety Chances Distribution TimeDistribution Time--history maps history maps

i i020406080100-1

0

1

2

3

4

5

6

7

A

B

C

D

H

S0

χj, %

0 20 40 60 80 100-1

0

1

2

3

4

5

6

7

8

A

B

C

D

E

S0

χj, %4444

history maps history maps are expedient to study as analytical tools for supporting automatic or manual recovery decision-making in emergency situationsunder uncertainty.








Potential for RealPotential for Real--RealReal--time Applicationstime Applications




4545



The The developed twodeveloped two--level ‘pilot level ‘pilot / / automaton automaton -- aircraft aircraft ––operational operational environment’ environment’ system model system model : :

is powerful, affordable and is powerful, affordable and easyeasy--toto--use systemuse system--level level safety safety mapping, analysis and prediction toolmapping, analysis and prediction tool

focuses on complex (focuses on complex (multifactormultifactor, , uncertain, uncertain, anomalous) anomalous) flight situation domainsflight situation domains

enables systematic enables systematic aircraft safety research beginning from aircraft safety research beginning from early design early design phases phases

incorporates advanced safety incorporates advanced safety ‘‘knowledgeknowledge--mapping’ mapping’ techniques techniques incorporates advanced safety incorporates advanced safety ‘‘knowledgeknowledge--mapping’ mapping’ techniques techniques including ones for potential realincluding ones for potential real--time applications time applications

provides provides 101022--101033 times increase times increase in M&S based structured in M&S based structured (‘granulated’) information on flight safety in advance(‘granulated’) information on flight safety in advance

helps enhance aircraft helps enhance aircraft flight safety flight safety performance performance aprioriapriori, i.e. , i.e. notnot necessarily based necessarily based on accident statistics on accident statistics

complements flight testing & manned simulations,complements flight testing & manned simulations,especially when studying multiespecially when studying multi--factor cases factor cases

does require, however, a does require, however, a complete ‘complete ‘parametric definition’ parametric definition’ of of the the vehicle/project for the flight domain of interestvehicle/project for the flight domain of interest. .

4646


Thank you. QuestionsThank you. Questions, please …, please …Thank you. QuestionsThank you. Questions, please …, please …

4747











4848


A400MA400M Prototype Prototype Military Transport (FLA FMilitary Transport (FLA F--93A) 93A) Project (Project (CranfieldCranfield University, UK)University, UK)

Advanced Advanced HypersonicHypersonic ManeuveringManeuvering Aerospace Aerospace Plane Plane Project Project ******

Advanced Advanced Notional 4++ Generation HighlyNotional 4++ Generation Highly--Maneuverable Fighter (TVC) Maneuverable Fighter (TVC) Project Project ******

Airbus A300Airbus A300--600600 LongLong--Range AirlinerRange AirlinerAmphibious Amphibious WingWing--InIn--Ground GA Plane Ground GA Plane ProjectProjectAntonovAntonov--2828 CommuterCommuter AirplaneAirplaneBerievBeriev--103103 Amphibious GA Airplane Amphibious GA Airplane ******BoeingBoeing--737737--300 Medium300 Medium--Range Airliner (GIT)Range Airliner (GIT)

IlyushinIlyushin--8686 MediumMedium--Range AirlinerRange AirlinerIlyushinIlyushin--9696--300 Long300 Long--Range AirlinerRange AirlinerKamovKamov--3232 MultiMulti--Purpose HelicopterPurpose HelicopterMilMil--2626 HeavyHeavy--Lift HelicopterLift HelicopterMilMil--88 Medium MultiMedium Multi--Purpose HelicopterPurpose HelicopterSukhoiSukhoi--4949 Primary Pilot Training Airplane Primary Pilot Training Airplane ******SukhoiSukhoi--8080GP MultiGP Multi--Purpose Commuter Purpose Commuter

Airplane Airplane ******Supersonic Business Jet (Supersonic Business Jet (SSBJSSBJ) Project (GIT)) Project (GIT)TupolevTupolev--134134AA//B ShortB Short--Range AirlinerRange AirlinerTupolevTupolev--136136 Regional CargoRegional Cargo--Transport Transport

System Model Application Experience System Model Application Experience (Simulated Aircraft Types/Projects: 1978(Simulated Aircraft Types/Projects: 1978--2010)2010)System Model Application Experience System Model Application Experience (Simulated Aircraft Types/Projects: 1978(Simulated Aircraft Types/Projects: 1978--2010)2010)

4949

BoeingBoeing--737737--300 Medium300 Medium--Range Airliner (GIT)Range Airliner (GIT)BuranBuran Hypersonic Aerospace VehicleHypersonic Aerospace VehicleCessna Cessna Citation XCitation X Business Jet (UTA) Business Jet (UTA) *** *** *** *** ConcordConcord Supersonic Passenger AirplaneSupersonic Passenger AirplaneHighHigh--Speed Civil Transport (Speed Civil Transport (HSCTHSCT) Project (GIT)) Project (GIT)Hybrid (Aerostatic + Aerodynamic) MultiHybrid (Aerostatic + Aerodynamic) Multi--Purpose Purpose

Transport Aircraft Project (Transport Aircraft Project (GTLAGTLA) ) *** *** *** *** IlyushinIlyushin--114114 Regional Transport/Cargo AirplaneRegional Transport/Cargo AirplaneIlyushinIlyushin--6262M LongM Long--Range AirlinerRange Airliner

TupolevTupolev--136136 Regional CargoRegional Cargo--Transport Transport Project (cryogenic LNGProject (cryogenic LNG--fuel) fuel) ******

TupolevTupolev--154, 154, --154M 154M MediumMedium--Range AirlinerRange AirlinerTupolevTupolev--204204 LongLong--Range AirlinerRange AirlinerTupolevTupolev--334334--100 Short100 Short--/Medium Range /Medium Range

Airliner Airliner ******UAV and UUV UAV and UUV Projects Projects *** *** *** *** XVXV--1515 Bell Helicopter Textron TiltBell Helicopter Textron Tilt--Rotor (GIT)Rotor (GIT)YakovlevYakovlev--4242 MediumMedium--Range AirlinerRange Airliner

LegendLegend: : 31 aircraft and projects 31 aircraft and projects in total, including: in total, including: HypersonicHypersonic ((22), ), SupersonicSupersonic ((44), ), SubsonicSubsonic

((2525, including , including 2121 fixedfixed--wing and wing and 44 rotaryrotary--wing vehicles). GIT wing vehicles). GIT –– Georgia Institute of Technology (USA). Georgia Institute of Technology (USA).

UTA UTA –– University of Texas at Arlington (USA).University of Texas at Arlington (USA). ****** –– VATES v.7 based macroVATES v.7 based macro--structural M&S (other structural M&S (other –– VATES VATES

v.5 based microv.5 based micro--structural M&S). TVC structural M&S). TVC –– thrust vectoring control.thrust vectoring control. *** *** –– ongoing M&S research. ongoing M&S research.


Overview INTELONICS Ltd. Overview INTELONICS Ltd. –– Dr. Ivan Dr. Ivan BurdunBurdun

EDUCATION

1997 Special Non-Degree Research Course Georgia Institute of Technology (GIT), USA

1993-1996 Doctorate Degree Research Course CranfieldUniversity, UK (thesis writing up not finished)

1982 PhD Award RCAEI, USSR

1977-1980 Doctorate Degree Research Course Riga Civil Aviation Engineering Institute (RCAEI), USSR

1971-1977 MSc Course in Aviation Mechanical Engineering Riga Civil Aviation Engineering Institute, USSR

CONTRACTS/COLLABORATION

Boeing Company, USACentral Aero-Hydrodynamic Institute (TsAGI), RussiaCentral R&D Inst. of Aerospace Systems Ltd. (TsNIIARKS Ltd.),

RussiaChinese Aeronautical Establishment, P.R. ChinaCity of Moscow Government, Department of Science &

Technology Policy (DNPP), Russia Cranfield University, UKFlight Safety Service of MoD Aviation, RussiaGeorgia Institute of Technology (GIT), USAIlyushin Design Bureaux, USSRInstitute, USSR

PROFESSIONAL BACKGROUND

Since 2007 Chief Scientist & Director INTELONICS Ltd., Russia

Since 2006 Associate Prof. Novosibirsk State Technical University, Russia

2000-2007 Chief Research Officer Siberian Aeronautical Research Institute, Russia

1997-2000 Research Engineer II GIT, USA

1983-1993 Senior Research Officer Riga Branch of Civil Aviation State Research Institute, USSR/Latvia

1980-1983 Lecturer Assistant Riga Civil Aviation Engineering Institute, USSR

1977-1983 Graduate Research Assistant Riga Civil Aviation Engineering Institute, USSR

Ilyushin Design Bureaux, USSRKiev Civil Aviation Engineering Institute (KII GA), USSRMinistry of Civil Aviation (MGA), USSRMoD Flight Test Center, RussiaMolniya Science & Production Holding (NPO Molniya), USSRMoscow Civil Aviation Engineering Institute (MII GA), USSRNASA Langley Research Center, Aeronautics Systems Analysis

Branch (ASAB), USARiga Civil Aviation Engineering Institute (RCAEI), USSRRiga Technical University Aviation Institute (RTU), Latvia Siberian Aeronautical Research Institute (SibNIA), RussiaSiberian State University of Telecommunication and Informatics

(SibGUTI), RussiaState Research Inst. of Civil Aviation (GosNIIGA), USSR/RussiaSukhoi Design Bureaux, RussiaThe University of Oklahoma, USATupolev Design Bureaux, RussiaUlyanovsk Civil Aviation Training School (UVAUGA), RussiaUniversity of Texas at Arlington (UTA), USA

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Acknowledgements Acknowledgements

The author wishes to thank:

Prof. David Allerton (University of Sheffield, UK), Dr. Jean-Pierre Cachelet (Airbus, France), Dr. Bernd Chudoba (University of Texas at Arlington, USA), Dr. Dimitri Mavris (Georgia Institute of Technology, USA), and Dr. Andrew Moroz (Lommeta JSC, Russia) Dr. Andrew Moroz (Lommeta JSC, Russia)

- for their multi-aspect support of this research, valuable advice and cooperation.

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Selected ReferencesSelected References

1. Burdun I.Y. UAV ‘Built-in’ Safety Protection: A Knowledge-Centered Approach. Proc. of AUVSIUnmanned Systems Europe 2007 Conference & Exhibition, 8-9 May 2007, Köln, Germany, 49 pp, 2007.

2. Бурдун Е.И. Прогнозирование безопасности полёта самолета гражданской авиации в сложных условиях. Автореферат диссертации на соискание ученой степени доктора инженерных наук, РТУ, Рига, 36 с, 2008 [in Russian].

3. Burdun I.Y. A Technique for Aircraft «Built-In» Safety Protection in Complex (Multifactor) Conditions Based on Situational Modeling and Simulation. Proc. Of VIII International Conference Conditions Based on Situational Modeling and Simulation. Proc. Of VIII International Conference ‘System Identification and Control Problems’, V.A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, January 26-30, 2009, Moscow, Russia, 44 pp, 2009. [in Russian].

4. Burdun I.Y. The Intelligent Situational Awareness And Forecasting Environment (The S.A.F.E. Concept): A Case Study. Proc. of 1998 SAE Advances in Flight Safety Conference and Exhibition, April 6-8, 1998, Daytona Beach, FL, USA, Paper 981223, pp 131-144, 1998.

5. Программно-моделирующий комплекс (ПМК) для исследований безопасности поведения системы «оператор (лётчик, автомат) – летательный аппарат (ЛА) – эксплуатационная среда» в сложных (многофакторных) полётных ситуациях (ПМК VATES). Свидетельство об официальной регистрации программы для ЭВМ 2007613256, выданное Федеральной службой по интеллектуальной собственности, патентам и товарным знакам РФ. Правообладатель: ООО «ИНТЕЛОНИКА». Автор: Бурдун И.Е. Зарегистрировано в Реестре программ для ЭВМ 02.08.2007, Москва, 1 с, 2007 [VATES Patent, in Russian].

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icas 2010 paper 723

Technology

flight dynamics

aircraft flight path

uncertain flight situations

multifactor conditions

multifactor flight situations

flight ms output data

multifactor complex

multifactor flightaerodynamics