Browse Learn C1892

Aircraft Virtual Flight Testing and Certification in Off-nominal Multifactorial Situations C1892

The behavior of a 'pilot-automaton-aircraft-operating environment' system (the System) in off-nominal situations with multiple risks can be unpredictably dangerous. Most multifactorial flight scenarios (corner cases) are considered as theoretically improbable. Such anomalies do nonetheless occur in operations and can lead to inconceivable accidents - 'black swan' events. This intensive educational course introduces the audience to the technology of aircraft virtual flight testing and certification (VFTС) for safety, considering the cross-coupling effects of various combinations of heterogeneous risk factors characteristic to the four components of the System.

The seminar begins by formalizing the task of predicting aircraft flight safety in multifactorial situations during the lifecycle. An introduction is given to the technology that allows for easing the 'curse of dimensionality' of this task, increasing the volume and accuracy of the System-level safety knowledge, and cutting budget and time of classic flight test and certification. The technology includes a high-fidelity mathematical model of the System dynamics, a software for autonomous fast-time computer experimentation with the model, automatic mining and mapping of safety knowledge for parallel analysis of large trees of off-nominal multifactorial 'what-if' situations. The workshop concludes with examples of the technology applications to support various phases of the lifecycle for several aircraft types and design projects. It is demonstrated how VFTC can complement classical techniques of flight research for safety. Advantages, benefits, limitations and pitfalls of the VFTC technology are summarized for next generation aircraft.

Who Should Attend

This course has been tailored for engineers and others involved in aircraft aerodynamics, flight controls, powerplant, undercarriage, safety avionics, flight simulators, pilot-aircraft interface, and pilot cognitive aids. Individuals who work with aircraft flight performance evaluation, flight testing and certification/assessment, flight accident/incident analysis and prevention and flight safety management may also benefit from this course.


Basic knowledge of aircraft flight physics and control, testing and certification, as well as a familiarity with the system approach is required. It is recommended that the participant has an undergraduate or graduate degree in Aeronautics or equivalent industry experience.


  • Research task formulation
    • Off-nominal multifactorial flight situation: definition and examples
    • 'Chain reaction' accident example - logical buildup mechanism
    • Limitations of classic flight research techniques
    • Problem formulation. Solution approach
    • Key principle - ‘de-materialization’ of extreme (corner) test cases
    • Virtual autonomous fast-time proactive exploration of ‘alternative futures’
    • Classic vs. augmented FTC cycle. Overall goals
  • Off-nominal (complex) multifactorial flight domain
    • The 'curse of dimensionality': mitigation principle and its implementation
    • Two-level knowledge model of complex flight domain
    • Flight event. Flight process. Elementary situation. Representation and examples
    • Key types of flight processes for VFTC. Examples
    • Flight situation scenario. Formalization. Representation forms. Examples
    • Risk factor: definition, main groups - pilot, automation, aircraft, external conditions
    • Multifactorial risk hypothesis: design field, formula, logic consistency, examples
  • Situational (tactical) tree of a complex flight domain
    • Experience-driven branching organization of long-term memory in humans
    • Exploration of multifactorial risk space: requirements formulated by test pilots
    • The principle of branching in pilot's situational knowledge tree - example
    • Definition. Purpose. Visualization. Principles of construction and growth control
    • Total virtual flight test time. Other metrics: competence, specialization, etc.
    • Human pilot’s situational knowledge tree: selected phenotypes and main defects
    • Fractal growth as an ideal model of operator's tactical expertise development
  • Flight safety measurement, assessment and mapping
    • Safety color coding (safety palette). Fuzzy constraints. Examples
    • Partial safety spectra. Integral safety spectrum. Calculation algorithm. Examples.
    • Flight safety index. Fuzzy constrains violation statistics. Examples
    • Carpet of integral safety spectra. Examples
    • Flight situation safety classification categories
    • Safety window. Safety distribution pie chart. Examples
    • Safety topology of multifactorial risk space
  • System dynamics model - overview
    • Definition. Model components. Assumptions. Grandfather's roots.
    • Flight physics
    • Human pilot model, automation model
    • Operating environment model
    • Other modules: data processing, knowledge mining and mapping, safety analysis
    • Distinguishing features and limitations
  • Software implementation
    • Development history. VFTC technology evolution
    • Algorithms and data structures - overview
    • Program structure. Functionality
    • Input and output data files - overview
    • Technical specifications. Flight simulation performance
  • Past applications overview
    • Development and applications - geography, aircraft types and design projects
    • Risk factors. Multifactorial hypotheses - examples
    • Research problems solved - examples
    • Application statistics
  • Validation examples
    • Aircraft classes. Risk factors. Source data. Library of validation cases
    • Takeoff cases - normal and continued takeoff
    • Landing approach and landing cases - normal and continued landing
    • Go-around and level flight cases
    • Validity assessment of the System model and its implementation software
  • Virtual autonomous fast-time flight test cycle for early exploration of complex domains
    • General layout
    • Key processing agents and data flows
    • Standardized implementation algorithm
  • Requirements to an aircraft's 'parametric definition'
    • Components. Data sources and ownership. Generalized representation
    • 'Stitching' input characteristics for various aerodynamic configurations
    • Quality metrics ('richness', etc.). 'Parametric definition' examples
    • Automated generation of a 'parametric definition'. Examples
    • Data utilization in the System model - open and black-box formats
  • Design of baseline flight scenarios - examples
    • Information sources: airworthiness regulations (ARs), flight test guides (FTGs), SOPs, AFMs, training syllabuses (TSs), accident databases (ADs)
    • Ground-roll, take-off and initial climb
    • Landing approach, landing and ground-roll. Go-around
    • Climb. En-route and level flight. Descent
    • Special maneuvers
  • Formalization of risk factors
    • Taxonomy of risk factors (aircraft design, flight test and operations)
    • Human pilot errors
    • Mechanical subsystems failures
    • Automatic control logic and data flaws
    • Demanding weather conditions: wind, icing, rain, runway, atmosphere, etc.
    • Variations: aircraft mass, aerodynamic configuration, baseline flight scenario
  • Planning multifactorial risk hypotheses
    • Notional aircraft types/design projects - examples (TBD): short-range/ commuter airplane, turbo-prop, regional jet, middle-range jet, long-range jet, tilt-rotor craft
    • Implementation algorithm
    • Examples - takeoff, climb, level flight, descent, landing, etc.
  • Planning and running fast-time flight simulation experiments
    • Implementation process
    • Controlling scenario errors and situational tree growth
    • Examples
    • Simulation performance
  • Knowledge mining and mapping
    • Purpose
    • Library of output data formats and knowledge maps
    • Construction of selected knowledge maps - algorithms
    • Examples
  • Single situation analysis (benign and multifactorial cases)
    • Notional aircraft types/design projects - examples (TBD)
    • Risk factors and multifactorial risk hypotheses
    • Baseline scenarios
    • Ground-roll, take-off (normal, continued, aborted) and initial climb cases
    • Landing approach (normal, continued, go-around), landing and ground-roll cases
    • Level flight cases. Special cases
  • Multiple situations analysis (multifactorial scenarios)
    • Notional aircraft types/design projects (TBD)
    • Risk factors and multifactorial risk hypotheses
    • FMEA matrix in VFTC process - example
    • Baseline scenarios
    • Cases: take-off, landing, level flight, special maneuvers, etc.
  • Future developments and prospective applications
    • Advanced ground and onboard applications - concepts
    • Situational trees: screening and mapping of complex operational domains
    • Integral safety spectra: parallel analysis of complex operational domains
    • Safety windows: ‘bird’s eye view’ prediction and protection of flight safety
    • Dynamic safety windows: identification of fatal and recovery control tactics for the prevention of ‘11.09.2001’ and ‘24.03.2015’ class accidents (notional cases)
  • User benefits. Challenges. Pitfalls and limitations
    • VFTC technology - distinguishing features
    • User benefits for main user categories
    • Research and organizational challenges
    • Pitfalls and limitations
  • Concluding remarks
    • Learning assessment
    • Course summary
    • Conclusions

Dr. Ivan BURDUN has over 30 years of cross-cultural research and academic experience at the School of Aerospace Engineering at the Georgia Institute of Technology, the College of Aeronautics at the Cranfield University, the Department of Aerodynamics and Flight Dynamics at the Riga Civil Aviation Engineering Institute, the Aircraft Aerodynamics and Flight Dynamics Research Division of the Siberian Aeronautical Research Institute, and other institutions. His competences include high-fidelity mathematical modeling, autonomous fast-time flight simulation, artificial intelligence, knowledge mining and representation for predicting the 'pilot/ automaton - aircraft - operating environment' system dynamics and safety performance in multifactorial (complex) and unknown situations. These techniques have been applied to 30 aircraft types and design projects: fixed- and rotary-wing, tilt-rotor; sub-, super- and hypersonic. Dr. BURDUN's current research is focused on intelligent technologies for flight safety prediction and protection, identification of irreversible anomalies in the system behavior, and pilot-AI cognitive interface for manned and unmanned vehicles and robotic swarms.

Hotel & Travel Information

Fees: $1745.00
SAE Members: $1396.00 - $1571.00

2.0 CEUs
You must complete all course contact hours and successfully pass the learning assessment to obtain CEUs.

To register, click the Register button above or contact SAE Customer Service 1-877-606-7323 (724-776-4970 outside the U.S. and Canada) or at

Duration: 3 Days
May 13-15, 2019 (8:30 a.m. - 4:30 p.m.) - Toulouse, France