VATES Tool : Purpose, Output and Application Goal


The purpose of the VATES software is to screen in advance the system ‘alternative futures’ under complex conditions – by generating hundreds of thousands of virtual (yet realistic) multifactor flight situations – both safe and unsafe.

The output of VATES based virtual flight experiments is a safety knowledge base of multifactor flight domains. Theoretically, its volume can be as much as 103 … 105 times larger than the volume of a knowledge base of all aviation accidents in history.

The tool application goal is to identify and assess the effects of combinations of risk factors on the system dynamics, including pilot errors, onboard equipment malfunctions, and demanding weather, before the vehicle is built or flown.



VATES v. 7 Software Prototype – Main Components


VATES v.7 Input-Output Data System


Input Data Requirements

  1. A ‘parametric definition’ (*) of the vehicle or project/ prototype to be virtually tested : aerodynamics, aerostatics, moments of inertia, thrust, geometry, automatic control logic, undercarriage reaction, etc.
  2. General description of the content of the flight phases to be studied in M&S (modes, scenarios, phases, manoeuvres), other flight content requirements.
  3. A subset of operational factors and operational factor complexes (‘what-if’ hypotheses) to be examined in M&S experiments.
  4. General formulation of the research project task (customer category, problem class, research goal and objectives).

Legend: (*) to protect the customer’s IP, it can be supplied as a pre-compiled ‘black box’ or as a dummy ‘parametric definition’ or as a ‘parametric definition’ for a prototype or as a ‘parametric definition’ of an old generation vehicle.

Automated/Automatic Processes of Flight Safety Research

Bases on VATES v.7 Software



Parametric tune-up of the system model on to a specific vehicle.


Planning of multifactor flight scenarios and operational hypotheses for M&S.


Planning of autonomous flight simulation experiments with the system model.


Running and control of autonomous fast-time flight simulation experiments.


Formation and management of a flight M&S output database.


Generation (’mining’) of system-level safety knowledge from M&S output data.


Mapping and generalization (‘granulation’) of the system safety knowledge.


Library of Output Knowledge Maps (Single Situation Analysis)



Library of Output Knowledge Maps (Multiple Situations Analysis)




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