27th Congress of the International Council                 of the Aeronautical Sciences                     19-24 Septembe...
Presentation Outline                                             INTELONICS LTD.    Multifactor Flight Situation Domain   ...
Presentation Outline                                             INTELONICS LTD.    Multifactor Flight Situation Domain   ...
Demanding Operational Conditions/ Factors– Main Groups                                                                    ...
Multifactor Flight Situation Build-up Chain                             Build-(Takeoff Example)                           ...
Presentation Outline                                             INTELONICS LTD.    Multifactor Flight Situation Domain   ...
Problem Formulation                                                                   INTELONICS LTD. How to analyze/asses...
Solution Approach Main Principle                                                           INTELONICS LTD. ‘Knowledge is P...
Main Tasks. Customer Groups Main Tasks                                                                             INTELON...
Presentation Outline                                             INTELONICS LTD.    Multifactor Flight Situation Domain   ...
‘Pilot /Automaton – Aircraft – OperationalEnvironment’ System Model as KnowledgeGenerator                              Ope...
Two-Two-Level Knowledge Structure of ComplexFlight Situations Domain                                                      ...
M&S Based Flight Safety Virtual Testing Cycle                                                                             ...
Safety Palette. Fuzzy Constraint                                                                                          ...
Partial Safety Spectra. Integral SafetySpectrum                                                                           ...
Flight Safety Classification Categories                                                                                   ...
Situational Tree of Flight                                                                                                ...
Safety Window. Safety Chances DistributionExample of mapping a situational tree S1⋅Г11: Takeoff. Errors of Selecting      ...
Flight Safety ‘Topology’Operational/ design factor Ф1                                                  3                  ...
Presentation Outline                                             INTELONICS LTD.    Multifactor Flight Situation Domain   ...
Baseline Flight Scenarios                                                                      21                         ...
Joint Graph of Baseline Scenarios                                                                                         ...
Operational Factors Selected for Testing                                                                                  ...
Design Field of Operational Hypotheses                Elevator deflection for                                             ...
Plan and Statistics of M&S Experiments                                                                                    ...
Presentation Outline                                             INTELONICS LTD.    Multifactor Flight Situation Domain   ...
Composition S1⋅Г1                                       Normal Takeoff. Variations of                                     ...
Composition S1⋅Г1                          Normal Takeoff. Variations of C.G.                                             ...
Composition S2⋅Г2                            Normal Takeoff. Variations of                                         Crosswi...
Composition S2⋅Г2                               Normal Takeoff. Variations of                                           Cr...
S1⋅Г3                     Normal Takeoff. Forward C.G. Location.                    Variations/Errors of Selection of Comm...
S1⋅Г3                      Normal Takeoff. Forward C.G. Location.                    Variations/                    Variat...
S3⋅Г5 Continued Takeoff. Left-hand Engine Out At VEF=150                Takeoff. Left-         km/h.         km/h. Variati...
S3⋅Г5      Continued Takeoff. Left-hand Engine Out at VEF=150                       Takeoff. Left-           km/h.        ...
S4⋅Г6                        Normal Takeoff. Variations of Wind-shear                                     Takeoff.        ...
S4⋅Г7          Normal Takeoff. Forward C.G. Location. Variations                                             Location.    ...
S5⋅Г10                 Continued Takeoff. Left-hand Engine Out at VEF.                        Variations of Left-hand Engi...
S1⋅Г11               Normal Takeoff. Variations/ Errors in Selection of                         Commanded Flight Path and ...
S4⋅Г13                 Normal Takeoff. ‘Very’ Strong Wind-shear.                                                     Wind-...
S4⋅Г13                    Normal Takeoff. ‘Very’ Strong Wind-shear.                                                       ...
Presentation Outline                                             INTELONICS LTD.    Multifactor Flight Situation Domain   ...
Dynamic Safety Window Sequence                                                               INTELONICS LTD.    t = t0  (‘...
Potential Contribution to IntegratedIntelligent Flight Deck Initiatives (1)                                               ...
Potential Contribution to IntegratedIntelligent Flight Deck Initiatives (2)                                               ...
Presentation Outline                                                INTELONICS LTD.    Multifactor Flight Situation Domain...
ConclusionsThe developed two-level ‘pilot / automaton - aircraft –               two-                                     ...
Thank you. Questions, please …           Questions,                                 INTELONICS LTD.                       ...
Presentation Outline                                             INTELONICS LTD.    Multifactor Flight Situation Domain   ...
System Model Application Experience(Simulated Aircraft Types/Projects: 1978-2010)                                    1978-...
Overview INTELONICS Ltd. – Dr. Ivan Burdun                                                                                ...
Acknowledgements                                                                           INTELONICS LTD.  The author wis...
Selected References                                                                                              INTELONIC...
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Icas 2010 paper 723

  1. 1. 27th Congress of the International Council of the Aeronautical Sciences 19-24 September 2010 19- Nice, France INTELONICS LTD.SAFETY WINDOWS: KNOWLEDGE MAPSFOR ACCIDENT PREDICTION AND PREVENTIONIN MULTIFACTOR FLIGHT SITUATIONS Ivan Burdun Chief Scientist INTELONICS Ltd. Novosibirsk, Russia www.intelonics.com © 2010, INTELONICS Ltd. 1
  2. 2. Presentation Outline INTELONICS LTD. Multifactor Flight Situation Domain Problem and Solution Approach Research Methodology Modeling & Simulation Experiment Setup Results and Discussion Potential for Real-time Applications Real- Conclusions Backup Slides 2
  3. 3. Presentation Outline INTELONICS LTD. Multifactor Flight Situation Domain Problem and Solution Approach Research Methodology Modeling & Simulation Experiment Setup Results and Discussion Potential for Real-time Applications Real- Conclusions Backup Slides 3
  4. 4. Demanding Operational Conditions/ Factors– Main Groups INTELONICS LTD. 1 2 3 FC 4 to, p, ρ in- in-flight icing of lift/ control heavy rain, non- non-standard water-/ice-/snow- water-/ice-/snow-covered surfaces tropical shower atmospheric conditions (slippery) runway H 5 6 7, 8 Wxg, yg, Wxg, Wyg, Wzg terrain/ traffic/other human pilot error cross-/tail- cross-/tail-wind, wind-shear, ‘microburst’, wind- external threat or inattention atmospheric/wake turbulence T1 9 10, 11 12 E1 t =120s E90 T5 start... P1 IAS=250 E45 E46 ? P6 nz < 0 .7 P3 … flight control automation engine malfunction; flight deviation from standard flight logic/data error or imperfection control mechanical failure scenario Normally, single operational factors are not critically dangerous. However, any [physically orlogically] meaningful n-factor combination (Φ i(1)∧Φ i(2) ∧ … ∧Φ i(j)∧ … ∧Φ i(n)) can result in a complex(multifactor) accident-prone situation, i(j)∈{1, 2, … 12, …}, n∈{2, 3, 4, …}. multifactor) accident- situation, (j)∈ 12, 4
  5. 5. Multifactor Flight Situation Build-up Chain Build-(Takeoff Example) INTELONICS LTD. catastrophic state Legend: Legend: Ф4, Ф6, Ф7, Ф9, Ф10 – operational/design factors (or risk/ ’what-if’/ flight path branching factors) ’what- – colors for coding flight safety levels – ‘bud’-type event for ‘implanting’ additional operational ‘bud’- factors into a [less complex] flight situation scenario safe state S0, S1, …, S5 – notional flight situation scenarios in the order of increasing aircraft motion dynamics & control complexity and operational riskΩ(Ф|Sk) – subset of operational factors affecting scenario Sk, k = 0, 1, …, 5: Ω(S0) = ∅, Ω(Ф|S1) = {Ф4},Ω(Ф|S2) = {Ф4, Ф10}, Ω(Ф|S3) = {Ф4, Ф7, Ф10}, Ω(Ф|S4) = {Ф4, Ф6, Ф7, Ф10}, Ω(Ф|S5) = {Ф4, Ф6, Ф7, Ф9, Ф10}. 5
  6. 6. Presentation Outline INTELONICS LTD. Multifactor Flight Situation Domain Problem and Solution Approach Research Methodology Modeling & Simulation Experiment Setup Results and Discussion Potential for Real-time Applications Real- Conclusions Backup Slides 6
  7. 7. Problem Formulation INTELONICS LTD. How to analyze/assess, predict and protect aircraft safety performance in multifactor (complex, abnormal, anomalous, uncertain) flight situations ? What is the cause-and-effect mechanism of irreversible (‘chain- cause-and- (‘chain- reaction’ type) developments of the aircraft flight path under multifactor conditions: accident precursors, key contributing factors, conditions: ‘last-change-for- ‘last-change-for-recovery’ point, good and bad control rules (“do’s” and “don’ts”) for human pilot or/and control automaton, automaton, etc. ? Are there reliable and affordable techniques available for implementation – during the aircraft’s life cycle (phases: design, test & certification, training, operation, accident investigation) – in order to help identify and avoid (or recovery from) a potentially dangerous multifactor situation ? 7
  8. 8. Solution Approach Main Principle INTELONICS LTD. ‘Knowledge is Power’: the ‘pilot – /automaton – aircraft – operational environment’ system model is employed as ‘knowledge generator’. Research Goal Develop and demonstrate a technique for prediction and mapping of aircraft/ project safety performance in multi-factor flight situations in multi- advance, before the vehicle is built/flown. Techniques Employed Applied aerodynamics, flight dynamics, situational control, multifactor flight aerodynamics, dynamics, control, domain theory, mathematical modeling, numeric techniques, computer techniques, simulation experiment, artificial intelligence (AI), graph theory, dynamic data experiment, (AI), structures, computer graphics, VATES (Virtual Autonomous Test & Evaluation graphics, Simulator, v. 5/7) software tool, Pentium-IV PC, MS Office, MAGE, etc. Pentium- Classic techniques + Modern techniques = New analytical potential. potential. 8
  9. 9. Main Tasks. Customer Groups Main Tasks INTELONICS LTD. 1. Automated design and fast-time examination of a broad set of realistic fast- scenarios and multifactor operational hypotheses in order to explore potentially unsafe/anomalous flight situations – using the system model, the vehicle’s ‘parametric definition’, and autonomous flight simulation techniques. 2. Automated ‘mining’ from flight M&S output data, ’granulation’ (by L. data, Zadeh) Zadeh) and generalization of new safety-related knowledge using a set safety- of anthropomorphic ‘knowledge maps’. Target Customer Groups { Designer, Test Pilot/Engineer, Regulator, Educator/Instructor, Line Pilot, Investigator, … } In autonomous M&S setup, a research pilot is not needed in the simulation loop. During a simulation experiment, realistic operational hypotheses (meaningful combinations of several operational factors) are automatically generated and added to a baseline scenario for fast-time fast- virtual testing on a 6DOF flight model. model. 9
  10. 10. Presentation Outline INTELONICS LTD. Multifactor Flight Situation Domain Problem and Solution Approach Research Methodology Modeling & Simulation Experiment Setup Potential for Real-time Applications Real- Results and Discussion Conclusions Backup Slides 10
  11. 11. ‘Pilot /Automaton – Aircraft – OperationalEnvironment’ System Model as KnowledgeGenerator Operational system INTELONICS LTD. (DO∪DT+M)⊂DV TV >>(TO + TT+M) SV < ST+M >>( (‘pilot/ automaton – aircraft – operational environment’)DV – flight domain explored in situational DO – flight domain PilotM&S [virtual flight] experiments explored in operations (automaton) automaton) Operational Aircraft environment Flight test-bed + M&S stand test- Research pilot (automaton) automaton) Simulated Research flight operational simulator environmentDT+M – flight domain Assignedexplored in flight tests operational Virtual flight test-bed test- constraints (situational system model)and manned simulations MathematicalLegend:Legend: model of pilotTT+M - test flight and M&S time (flight test and manned M&S experience) (automaton) automaton)TO - operational flight time (operational flight experience) Mathematical Math model model of flight of operationalТV - virtual test flight time (‘virtual’ test experience) dynamics environmentS - R&D cost (flight safety analysis, prediction and protection) 11
  12. 12. Two-Two-Level Knowledge Structure of ComplexFlight Situations Domain INTELONICS LTD. Micro- Micro-structure Macro- Macro-structure Elementary Flight situation Situational situation scenario tree C1 Event ... ... E Ek ... П14 П2 E6 Пj П9 C2 B0 П B1 Ei E2 Process П8 П10 E4 П5 П1 П3 B2Legend: Ei - flight event; Пj – flight E8 П4 ...process; Cm – fuzzy constraint; - E1system reference state; - system E5 ...branching state (‘bud’); - system П7 П11target state (‘leaf’); - system source П15 П6state (‘root’); B-1 – parent branch; B0 – E7main branch or ‘trunk’ (baseline E4scenario); Bn – nth-order derivative П13 C4 П12 C3branch (scenario with n operational ...factors , n = 1, 2, …). ... B-1 Micro- Micro- and macro-structure are two interrelated components of a generalized knowledge model macro- of a complex flight domain. Each flight path (a branch in the situational tree) is modeled according (a tree) to an event-process type scenario with a combination of n operational factors, n = 0, 1, 2, …. event- …. 12
  13. 13. M&S Based Flight Safety Virtual Testing Cycle 131 Wind tunnel (*) 2 Experimental data measurement 3 Output test data files 4 Computational aerodynamics (‘virtual wind and processing system (‘3 forces and 3 moments’) tunnel’), aircraft parametric definition database INTELONICS LTD. formation tools test rig aircraft model 8 Autonomous situational model of the ‘operator (pilot, automaton) – aircraft – operational environment’ system behavior (VATES)7 Library of flight situation 5 Aircraft flight model inputscenarios for virtual testing and А Flight Aircraft model B database (aerodynamics, thrust, dxcertification situation = f (x,u,w,t) ‘parametric inertias, geometry, etc.) scenario dt definition’6 Flight situation content requirements: 9 Customer (aerodynamicist, 10 dynamicist, pilot, …) ComputerАП/FAR/JAR, compliance testingmethods, or flight test programs, or 13Pilot’s Manual, or flight test/ accident ‘Flight’records15 Maps of aircraft’s safety performance in complexsituations (system-level knowledge of multifactoreffects of operational conditions on flight safety) 12 14 Systems model’s output database (‘flights’, Virtual hypotheses, statistics, etc.) ‘test-bed’ &size [n_columns] [n_rows] &name time [var01] [var02] &unit s [unit01] [unit02] 11 Operational (‘what …, if …?’) &format (f6.2, 20f10.4) [time] 499.9999 236.1820 3.8520 hypothesis for virtual testing – [time] 499.9782 236.2703 3.8821 [time] 499.8870 236.3342 3.9107 situational tree ‘genotype’ ... 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. 13
  14. 14. Safety Palette. Fuzzy Constraint INTELONICS LTD.Safety Palette green (‘norm’), ξG yellow/ amber (‘attention’), ξY Color is natural and, perhaps, the most effective and economic red (‘danger’), ξR medium for communicating safety- safety- black (‘catastrophe’), ξB related information to / from an operator (a pilot or automaton). grey/white (‘uncertainty’), ξW ‘uncertainty’)Fuzzy Constraint µC(VFL.D.) C: ‘permitted flaps-down flying IAS’ flaps- IAS’ 1 с dLegend:Legend: c, d – characteristic …points of the carrier of fuzzy 0 … … 390 410 470 VFL.D.. [km/h] FL.Dset-constraint C, µC(x) – L.A.set-Zadeh’s fuzzy set membership …function ‘green’ ‘yellow’ ‘red’ ‘black’ Operational constraints under multi-factor flight conditions are not known precisely. They are multi- inherently ‘fuzzy’. The notions of fuzzy constraint (by L.A. Zadeh) and safety palette are employed ‘fuzzy’. Zadeh) for approximate measurement of the compatibility of current (i.e. measured at time instants t) system state with operational constraints for key (monitored) system variables. 14
  15. 15. Partial Safety Spectra. Integral SafetySpectrum INTELONICS LTD. δ IAS (δF = 0, airborne) Σ1 Legend: Σ k – partial safety spectrum δ IAS (δF > 0, airborne) Sideslip Σ2 for variable xk, k = 1, …, p; p – total Monitored variables/ constraints number of monitored constraints/ Partial flight safety spectra Load_factor variables, p = 20. Σ – integral safety East_rate (groundroll) East (groundroll) ... spectrum; t – flight time; ξi – color from North (groundroll) safety palette, i ∈ {B (black), R (red), Y Bank (airborne) (yellow), G (green),…}; < – ‘colder than’ Bank (groundroll) operation for comparing two safety Pitch (airborne) Pitch (groundroll) Σk colors; max – operation of selecting the ‘hottest’ color at time instant t; || - Vert_rate (airborne) operation of safety colors δ AoA (δF = 0) concatenation in Σ ; [t*; t*] – examined δ AoA (δF > 0) flight time interval; ∆ – spectrum Wheels (airborne) ... construction time step. Wheels (groundroll) Elevator (airborne) - green (‘norm’), ξG Elevator (groundroll) - yellow (‘attention’), Aileron Rudder Σ20 ξY (‘danger’), ξR - red Integral spectrum Σ - black (‘catastrophe’), ξB - gray/white (‘uncertainty’), ξW time, s Integral Safety Spectrum Calculation Algorithm: (∀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)∈Σ ∧ Σ = ξ(t*) || ξ(t*+∆) || ξ(t*+2∆) || … || ξ(t*)) Σ 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 Σk, k = 1, …, p, and an Integral Safety Spectrum Σ 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. 15
  16. 16. Flight Safety Classification Categories INTELONICS LTD. In order to measure safety performance for a flight situation in overall, a special ‘safety ruler’ consisting of five classification categories I, …, V is employed. Why five? – because experts 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. 16
  17. 17. Situational Tree of Flight INTELONICS LTD. T130: “Takeoff and initial climb, ‘very strong’ wind- shear, variations/ errors of commanded flight path (ΘG) and bank (γG) angles” ‘Virtual flight test experience’ accumulated in tree T, hrs: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 inT130, ∆t(Bi)=60s – branch ‘length’, i=1, 2, …, 130; - safety palette A composition of a baseline situation scenario and an operational multifactor combination in M&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’. 17
  18. 18. Safety Window. Safety Chances DistributionExample of mapping a situational tree S1⋅Г11: Takeoff. Errors of Selecting INTELONICS LTD.Commanded Flight path and Bank Angles in Climb Safety Window A Safety Chances Distribution Pie Chart C B 3 Let us map safety levels (categories) obtained for all situations from a Category ξj nj χj , % tree onto a two-factor plane. This results in a Flight Safety Window (FSW). In two- plane. I 37 28 FSW above, cell C is located at ‘column A - row B’ crossing. This cell depicts safety status of one flight path-branch from the tree. This is a non-standard path- tree. non- II-a 8 6 situation with values of 14 o and 30o of factors Φ and Φ II-b 29 22 7 11 in S1. The cell is colored using the safety category color ‘orange’. Note that the FSW has a orange’. the has III 1 1 dangerous ‘corner’ (upper-left). Rapid transition (3) from safe (‘salad (upper-left). (3 IV 55 43 green’) to dangerous (‘red’) zone is possible (Cat. II-a → IV), bypassing IV) interim zones (II-b, III). Flight control at such ‘corners’ obviously requires (II- III) V 0 0 enhanced pilot attention. Σnj, Σχj | S1⋅Γ11 Γ 130 100 18
  19. 19. Flight Safety ‘Topology’Operational/ design factor Ф1 3 6 2 INTELONICS LTD. 5 I 2 1 II-a II-b 3 III 3 IV V 4 Transitions 6 must be Transitions 3 must be Flight Safety Categories known and prevented! known and controlled! 6 3 1 4 2 Operational/ design factor Ф2 1 ‘Abyss’ (catastrophe) 4 ‘Valley’ (standard safety, norm) 1, 2,…, 6 - main 2 ‘Hill’ (danger) 5 ‘Lake’ (maximum safety, optimum) object types of flight 3 ‘Slope’ (reversible state 6 ‘Precipice’ (abrupt, irreversible safety ‘topology’: transitions) state transitions, or ‘chain reaction’) 19
  20. 20. Presentation Outline INTELONICS LTD. Multifactor Flight Situation Domain Problem and Solution Approach Research Methodology Modeling & Simulation Experiment Setup Results and Discussion Potential for Real-time Applications Real- Conclusions Backup Slides 20
  21. 21. Baseline Flight Scenarios 21 INTELONICS LTD. 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&S experiments. The goal is to evaluate combined effects of selected operational/design factors on flight safety in these scenarios. The sources of data for baseline scenarios are: airworthiness requirements, flight test data/programs, ACs, Pilot’s Manuals, real flight data records, flight accidents/ incidents statistics. 21
  22. 22. Joint Graph of Baseline Scenarios W1: crosswind 10 m/s (left-to-right) F1: left-hand engine S3 S5 INTELONICS LTD. E44: engine failure … 44 out speed S2 E88: altitude 200 m 88 6 P3: wheels - up S1 E6: altitude 10.7 m … T2: maintain T5: maintain commanded bank γG & commanded bank γG P1: set engines #1,2 levers … 7 P4: flaps - up … heading ΨG angles & sideslip β G angles to takeoff rating E7: altitude 120 m T1: maintain path in 1 groundroll along runway’s 55 190 centerline E1: situation start E55: in airborne E190: situation end T2: maintain commanded bank γG and heading 3 P2: elevator – ΨG angles up for rotation T4: maintain E3: VR achieved … P5: maintain given commanded flight T3: maintain commanded flight indicator airspeed path angle θG2 (2nd path angle ΘG1 (initial phase of phase of climb) 5 climb) E5: pitch 8о E12: flaps retracted 12 S4Legend: W2: ‘strong’ wind W1: crosswind -10 m/s (right-to-left) shear44 Scenario consists of events and processes. It can be depicted as a E44: engine out speed- event directed graph. The scenario defines logic and content of a flight F1: left-hand engine failure situation. It is also clear to the pilot. Scenarios S1, …, S5 are structurally - process close. They can be easily modified. 22 22
  23. 23. Operational Factors Selected for Testing INTELONICS LTD. 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 of operational factors: ‘operator’, ‘aircraft’ and ‘external environment’. The sources of information on operational factors are airworthiness requirements, FMEA, statistics on flight operations, and accidents/incidents data. 23
  24. 24. Design Field of Operational Hypotheses Elevator deflection for Wheels - runway surface rotation Ф3 Ф4 adhesion factor ∆δe µ Crosswind INTELONICS LTD. Rotation Ф5 velocity Г2 airspeed Ф2 Wyg VR Г1 Г10 Г4 Flaps-up start Ф6 HFL altitude Г3Longitudinal C.G. Ф1 xCG Г5 Г6 Commanded flight Г7 θG2 Ф8 path angle during 2nd phase of climb Left-hand engine Ф13 ζLHE Г8 Г13failure at VEF Г9 θG1 Ф7 Commanded flight Г11 path angle during VEF Г12 initial phase of climb ‘Engine out’ Ф12 indicator airspeed kW Ф9 Intensity of wind- kP γG shear Engines power Ф10 Ф11 Legend: rating at takeoff Many operational factors from this list Commanded bank are not critically dangerous alone. Much Г13 - operational hypothesis angle more important to learn in advance independent - link between effects of unfavorable combinations of Cross wind - operational factor Wyg Ф5 velocity dependent these factors on flight safety. factors in Г 24
  25. 25. Plan and Statistics of M&S Experiments INTELONICS LTD. 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. 25
  26. 26. Presentation Outline INTELONICS LTD. Multifactor Flight Situation Domain Problem and Solution Approach Research Methodology Modeling & Simulation Experiment Setup Results and Discussion Potential for Real-time Applications Real- Conclusions Backup Slides 26
  27. 27. Composition S1⋅Г1 Normal Takeoff. Variations of Takeoff. Flight situation code C.G. and VR Speed (with Correction of ElevatorIntegral Safety Spectra # mP mF VR ∆δe Deflection in Rotation) INTELONICS LTD. Tested operational factors Safety Chances Distribution 0, 0% Category ξj χj , % I 100 II-a 0 II-b 0 III 0 IV 0 V 0 66, 100% ‘Flights’ in total - 66 100 Legend: in nj, χj% nj – number of ‘flights’ belonging to Cat. ξj, χj% - percentage of ‘flights’ of Cat. ξj, j=I, …, V. 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 27
  28. 28. Composition S1⋅Г1 Normal Takeoff. Variations of C.G. Takeoff. and VR Speed (with Correction of Elevator Deflection in Rotation)S1:Normal takeoff, steering INTELONICS LTD. commanded flight path and ⇒ In FSW below, cell 1 located at ‘column 2 - row 3’ bank angles during initial climb crossing is a color code of flight safety Category of one situation from Composition S1⋅Г1. This situation is obtained by combining values 4 and 5 of operational factors 6 and 7 in scenario S1. Flight Safety Window 2 6 47 3 5 1 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 of longitudinal C.G. location and VR speed variations are acceptable safety-wise (NB: provided that all other conditions of scenario S1 are fulfilled). 28
  29. 29. Composition S2⋅Г2 Normal Takeoff. Variations of Crosswind Velocity and ‘Wheels - Integral Safety Spectra # µ k⋅Wy g Runway Surface’ Adhesion Factor INTELONICS LTD. k=10-1 Safety Chances Distribution 21; 33% 22; 35% 12; 19% 6; 10% 2; 3% 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. 29
  30. 30. Composition S2⋅Г2 Normal Takeoff. Variations of Crosswind Velocity and ‘Wheels – Runway Surface’ Adhesion Factor INTELONICS LTD. S2: Normal takeoff under cross-wind and varying conditions of runway surface, steering commanded flight path and bank angles during initial climb Flight Safety Window 1 1 2 2 Shown above is Flight Safety Window constructed for situational tree S2⋅Г2. It contains one central green ‘valley’, two side red ‘hills’ and two connecting ‘slopes’: (1) a steep ‘slope’ – for dry and semi-wet runway, and (2) not steep ‘slope’ - for wet and water-covered runway. As the absolute 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. 30
  31. 31. S1⋅Г3 Normal Takeoff. Forward C.G. Location. Variations/Errors of Selection of Commanded Flight Path Angles (Initial and 2nd Phases of INTELONICS LTD. Climb) and Flaps-up Start Altitude Flaps- Integral Safety Spectra # θG1 θG2 HFL Safety Chances Distribution 0, 0% 5, 14% 0, 0% 0, 0% 5, 14% 25, 72% 14% of variants from situational tree S1⋅Г3, 14% which have commanded flight path angle (during initial phase of climb) more than 12o, exhibit danger. Note also how, for danger. example, event E7: ‘altitude 120 m’ changes its location in IFSS due to θG1. 31
  32. 32. S1⋅Г3 Normal Takeoff. Forward C.G. Location. Variations/ Variations/ Errors of Selection of Commanded Flight Path Angles (Initial and 2nd Phases of Climb) INTELONICS LTD. and Flaps-up Start Altitude Flaps- S1: Normal takeoff, steering commanded flight path and bank angles during initial climb Flight Safety Window For composition S1⋅Г3, sharp transitions (1) from safe situations to unsafe ones are observed at commanded flight path angles θG1/θG2>12/10o for all values of HFL. Owing to high thrust-to-weight ratio, errors in selection of flaps-up start altitude do not worsen the aircraft’s flight safety performance, provided (NB) that other conditions of scenario S1 are preserved. 32
  33. 33. S3⋅Г5 Continued Takeoff. Left-hand Engine Out At VEF=150 Takeoff. Left- km/h. km/h. Variations/ Errors of Selection of Commanded Flight Path Angles During Initial and 2nd Phases INTELONICS LTD. Integral Safety Spectra # θG1 θG2 Safety Chances Distribution 7, 17% 18, 43% 8, 19% 0, 0% 1, 2% 8, 19% If left-hand engine fails during ground-roll (at left- ground- VEF=150 km/h) takeoff safety cannot be km/h) secured at commanded flight path angle θG1≥5o (during initial phase of climb). For climb). examined domain of operational factors, share of safe situations is 36%. 36% 33
  34. 34. S3⋅Г5 Continued Takeoff. Left-hand Engine Out at VEF=150 Takeoff. Left- km/h. km/h. Variations/ Errors of Selection of Commanded Flight Path Angles During Initial and 2nd Phases INTELONICS LTD. S3: Continued takeoff (left-hand engine out at given VEF), steering commanded flight path and bank angles during initial climb Flight 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). 34
  35. 35. S4⋅Г6 Normal Takeoff. Variations of Wind-shear Takeoff. Wind- Intensity and Errors of Selection of Flaps-up Start Flaps- Altitude INTELONICS LTD. S4: Normal takeoff under windshear conditions, steering commanded flight path and bank angles during initial climb Flight Safety Window 2 In scenario S4 we have θG1/θG2=8o/8o. If ‘strong’ or worse windshear is expected (kW≥1) takeoff is prohibited. In order to evaluate possibility of safe outcomes at kW<1 it is expedient to expand Flight 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 altitude HFL∈[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). 35
  36. 36. S4⋅Г7 Normal Takeoff. Forward C.G. Location. Variations Location. of Wind-shear Intensity and Commanded Flight Path Wind- Angles (During Initial and 2nd Phases) INTELONICS LTD. S4: Normal takeoff under windshear conditions, steering commanded flight path and conditions, bank angles during initial climb Flight Safety Window For composition S4⋅Г7 main objects of safety ‘topology’ are: small green ‘valley’ (at left lower are: corner), orange ‘slope’, extensive red ‘hill’ adjacent to black ‘abyss’ (at right upper corner). At corner). takeoff under ‘strong’ and ‘very strong’ windshear conditions (1<kW≤1.6): maximum safety is achieved at θG1/θG2=5o/3o; it is prohibited to climb at θG1/θG2>7o/5o; irreversible transitions are likely at θG1≥12o. 36
  37. 37. S5⋅Г10 Continued Takeoff. Left-hand Engine Out at VEF. Variations of Left-hand Engine Out Speed and Cross-wind Velocity INTELONICS LTD.S5: Continued takeoff (left-hand engine out at VEF), under cross-wind conditions, steeringcommanded flight path and bank angles during initial climbГ10 = Ф13×Ф12×Ф4 ≡ ζLHE×VEF×Wyg Flight Safety Window 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 and medium values of VEF and is linked to ‘valley’ by ‘precipice’ type transitions. Small ‘abyss’ is also revealed at crosswind velocity of ~18 m/s and VEF∈[175; 190] km/h. 37
  38. 38. S1⋅Г11 Normal Takeoff. Variations/ Errors in Selection of Commanded Flight Path and Bank Angles (During Initial Phase of Climb) INTELONICS LTD.S1: Normal takeoff, steering commanded flight path and bank angles takeoff, during initial climb Г11 = Ф7×Ф11 ≡ θG1×γG Flight Safety Window This Flight Safety Window has a potentially dangerous ‘corner’ corresponding to (θG1, γG) ≅ (12o…14o, -30o…-37.5o). Sharp transition (1) of states from safe (‘green’) to dangerous (‘red’) zone 37. is possible (Cat. I→IV), bypassing interim zones (Cat. II, III). Flight at such ‘corners’ requires Cat. IV), (Cat. III) enhanced attention and accurate piloting from pilot. pilot. 38
  39. 39. S4⋅Г13 Normal Takeoff. ‘Very’ Strong Wind-shear. Wind-shear. Variations /Errors of Selection of Commanded Flight Path and Bank Angles in Climb INTELONICS LTD.Integral Safety Spectra # θG1 γG Integral Safety Spectra # θG1 γG Safety Chances Distribution 7, 5% 26, 20% 20, 15% 10, 8% 10, 8% 57, 44% ‘Very strong’ wind- wind- shear may worsen flight safety ‘topology’ of takeoff catastrophically at small values of commanded flight path angle θG1≤4o. 39
  40. 40. S4⋅Г13 Normal Takeoff. ‘Very’ Strong Wind-shear. Wind-shear. Variations /Errors of Selection of Commanded Flight Path and Bank Angles in Climb INTELONICS LTD. S4: Normal takeoff under windshear conditions, steering commanded flight path and conditions, bank angles during initial climb Г13 = Ф9×Ф7×Ф11 ≡ kW×θG1×γG (kW=1.5) Flight Safety Window Flight safety ‘topology’ obtained for ‘very strong’ wind-shear conditions at small θG1 and any wind- γG contains a stable catastrophic ‘abyss’ (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 (2o…4o) inevitably leads the vehicle to a fatal outcome. 40
  41. 41. Presentation Outline INTELONICS LTD. Multifactor Flight Situation Domain Problem and Solution Approach Research Methodology Modeling & Simulation Experiment Setup Results and Discussion Potential for Real-time Applications Real- Conclusions Backup Slides 41
  42. 42. Dynamic Safety Window Sequence INTELONICS LTD. t = t0 (‘benign Normal Takeoff. Variations weather’) of Wind-shear Intensity, Wind- Errors/ Variations of Optimal modes - maximum safety Selection of Commanded Flight Path and Bank Angles in Initial Climb – ‘forest’ of t = t1 situational trees (‘strong’ wind-shear) The developed safety ‘topology’ maps, including Flight Safety Window, Safety Chances Pie Chart and other formats, can be potentially useful for flight operations. t = t2 (‘very strong’ The goal is to monitor operational wind-shear) 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. 42
  43. 43. Potential Contribution to IntegratedIntelligent Flight Deck Initiatives (1) INTELONICS LTD. Safety Window (commanded‘flight path angle – bank angle’ ) Safety Chances Distribution Wind- Wind-shear ‘benign weather’ weather’ ‘strong’ strong’ ‘very strong’ strong’ forecast - Optimum (safety-wise) (safety- wind- wind-shear impact piloting modes real- real-time analysis The concept of dynamic safety window can be potentially useful to help pilot/automaton predict aircraft safety performance in various ‘what-if’ scenarios and find optimum control ‘what- tactics under demanding conditions (in this specific takeoff and initial climb case – commanded ‘flight path angle – bank angle’ pairs). 43
  44. 44. Potential Contribution to IntegratedIntelligent Flight Deck Initiatives (2) INTELONICS LTD.Dynamics Safety Window Tree. Safety Chances Distribution Time-history Time- 19 13 L 18 12 G K 17 11 J 16 S↑ S↓ 10 Dynamics Safety F Window Tree and 15 9 I Safety Chances 14 8 Distribution Time- Time- H E history maps 7 7 D D are expedient to 6 6 study as analytical C C tools for supporting 5 5 automatic or 4 4 manual recovery S0 S0 decision-making in 3 3 B B emergency 2 2 situations 1 1 under uncertainty. A 0 0 A -1 -1 100 80 60 40 20 0 0 20 40 60 80 100 i i χj, % χj, % 44
  45. 45. Presentation Outline INTELONICS LTD. Multifactor Flight Situation Domain Problem and Solution Approach Research Methodology Modeling & Simulation Experiment Setup Potential for Real-Real-time Applications Real-Real- Results and Discussion Conclusions Backup Slides 45
  46. 46. ConclusionsThe developed two-level ‘pilot / automaton - aircraft – two- INTELONICS LTD.operational environment’ system model : is powerful, affordable and easy-to-use system-level easy-to- system- safety mapping, analysis and prediction tool focuses on complex (multifactor, uncertain, (multifactor, anomalous) flight situation domains enables systematic aircraft safety research beginning from early design phases incorporates advanced safety ‘knowledge-mapping’ techniques knowledge- including ones for potential real-time applications real- provides 102-103 times increase in M&S based structured (‘granulated’) information on flight safety in advance helps enhance aircraft flight safety performance apriori, apriori, i.e. not necessarily based on accident statistics complements flight testing & manned simulations, especially when studying multi-factor cases multi- does require, however, a complete ‘parametric definition’ of ‘parametric the vehicle/project for the flight domain of interest. interest. 46
  47. 47. Thank you. Questions, please … Questions, INTELONICS LTD. 47
  48. 48. Presentation Outline INTELONICS LTD. Multifactor Flight Situation Domain Problem and Solution Approach Research Methodology Modeling & Simulation Experiment Setup Results and Discussion Conclusions Backup Slides 48
  49. 49. System Model Application Experience(Simulated Aircraft Types/Projects: 1978-2010) 1978- INTELONICS LTD. A400M Prototype Military Transport (FLA F-93A) F- Ilyushin- Medium- Ilyushin-86 Medium-Range Airliner Project (Cranfield University, UK) (Cranfield Ilyushin-96- Ilyushin-96-300 Long-Range Airliner Long- Advanced Hypersonic Maneuvering Aerospace Kamov- Multi- Kamov-32 Multi-Purpose Helicopter Plane Project *** Mil-26 Heavy-Lift Helicopter Mil- Heavy- Advanced Notional 4++ Generation Highly-Highly- Mil-8 Medium Multi-Purpose Helicopter Mil- Multi- Maneuverable Fighter (TVC) Project *** Sukhoi- Sukhoi-49 Primary Pilot Training Airplane *** Airbus A300-600 Long-Range Airliner A300- Long- Sukhoi-80GP Multi- Sukhoi-80GP Multi-Purpose Commuter Amphibious Wing-In-Ground GA Plane Project Wing-In- Airplane *** Antonov- Antonov-28 Commuter Airplane Supersonic Business Jet (SSBJ) Project (GIT) (SSBJ) Beriev- Beriev-103 Amphibious GA Airplane *** Tupolev-134A Short- Tupolev-134A/B Short-Range Airliner Boeing-737- Boeing-737-300 Medium-Range Airliner (GIT) Medium- Tupolev- Tupolev-136 Regional Cargo-Transport Cargo- Buran Hypersonic Aerospace Vehicle Project (cryogenic LNG-fuel) *** LNG- Cessna Citation X Business Jet (UTA) *** *** Tupolev- Tupolev-154, -154M Medium-Range Airliner Medium- Concord Supersonic Passenger Airplane Tupolev- Tupolev-204 Long-Range Airliner Long- High- High-Speed Civil Transport (HSCT) Project (GIT) (HSCT) Tupolev-334- Tupolev-334-100 Short-/Medium Range Short- Hybrid (Aerostatic + Aerodynamic) Multi-Purpose Multi- Airliner *** Transport Aircraft Project (GTLA) *** *** (GTLA) UAV and UUV Projects *** *** Ilyushin- Ilyushin-114 Regional Transport/Cargo Airplane XV-15 Bell Helicopter Textron Tilt-Rotor (GIT) XV- Tilt- Ilyushin-62M Long- Ilyushin-62M Long-Range Airliner Yakovlev- Medium- Yakovlev-42 Medium-Range AirlinerLegend:Legend: 31 aircraft and projects in total, including: Hypersonic (2), Supersonic (4), Subsonic(25, including 21 fixed-wing and 4 rotary-wing vehicles). GIT – Georgia Institute of Technology (USA). 25, fixed- rotary-UTA – University of Texas at Arlington (USA). *** – VATES v.7 based macro-structural M&S (other – VATES macro-v.5 based micro-structural M&S). TVC – thrust vectoring control. *** – ongoing M&S research. micro- 49
  50. 50. Overview INTELONICS Ltd. – Dr. Ivan Burdun INTELONICS LTD. EDUCATION CONTRACTS/COLLABORATION 1997 Special Non-Degree Research Course Boeing Company, USA Georgia Institute of Technology (GIT), USA Central Aero-Hydrodynamic Institute (TsAGI), Russia 1993-1996 Doctorate Degree Research Course Cranfield Central R&D Inst. of Aerospace Systems Ltd. (TsNIIARKS Ltd.), University, UK (thesis writing up not finished) Russia 1982 PhD Award RCAEI, USSR Chinese Aeronautical Establishment, P.R. China City of Moscow Government, Department of Science & 1977-1980 Doctorate Degree Research Course Riga Civil Technology Policy (DNPP), Russia Aviation Engineering Institute (RCAEI), USSR Cranfield University, UK 1971-1977 MSc Course in Aviation Mechanical Flight Safety Service of MoD Aviation, Russia Engineering Riga Civil Aviation Engineering Georgia Institute of Technology (GIT), USA Institute, USSR Ilyushin Design Bureaux, USSR Kiev Civil Aviation Engineering Institute (KII GA), USSR PROFESSIONAL BACKGROUND Ministry of Civil Aviation (MGA), USSR MoD Flight Test Center, Russia Molniya Science & Production Holding (NPO Molniya), USSR Since 2007 Chief Scientist & Director INTELONICS Ltd., Moscow Civil Aviation Engineering Institute (MII GA), USSR Russia NASA Langley Research Center, Aeronautics Systems Analysis Since 2006 Associate Prof. Novosibirsk State Technical Branch (ASAB), USA University, Russia Riga Civil Aviation Engineering Institute (RCAEI), USSR 2000-2007 Chief Research Officer Siberian Aeronautical Riga Technical University Aviation Institute (RTU), Latvia Research Institute, Russia Siberian Aeronautical Research Institute (SibNIA), Russia 1997-2000 Research Engineer II GIT, USA Siberian State University of Telecommunication and Informatics 1983-1993 Senior Research Officer Riga Branch of Civil (SibGUTI), Russia Aviation State Research Institute, USSR/Latvia State Research Inst. of Civil Aviation (GosNIIGA), USSR/Russia Sukhoi Design Bureaux, Russia 1980-1983 Lecturer Assistant Riga Civil Aviation The University of Oklahoma, USA Engineering Institute, USSR Tupolev Design Bureaux, Russia 1977-1983 Graduate Research Assistant Riga Civil Ulyanovsk Civil Aviation Training School (UVAUGA), Russia Aviation Engineering Institute, USSR University of Texas at Arlington (UTA), USA 50
  51. 51. Acknowledgements INTELONICS LTD. 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) - for their multi-aspect support of this research, valuable advice and cooperation. 51
  52. 52. Selected References INTELONICS LTD. 1. Burdun I.Y. UAV ‘Built-in’ Safety Protection: A Knowledge-Centered Approach. Proc. of AUVSI Unmanned 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 ‘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]. 52
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