H Ho ow w l lo on ng g d do o p pi il lo ot ts s l lo oo ok k f fo or rw wa ar rd d? ? i i P Pr ro os sp pe ec ct ti iv ve e V Vi is su ua al l G Gu ui id da an nc ce e i in n T Te er rr ra ai in n-H Hu ug gg gi in ng g F Fl li ig gh ht t
At the heart of a flight simulator resides the mathematical representation of aircraft behaviour in response to control inputs, atmospheric disturbances and system inputs including failures and malfunctions. While this mathematical model can never be wholly accurate, its fidelity, in comparison with real world behaviour, underpins the usefulness of the flight simulator. The present paper examines the state of the art achieved in validating mathematical models for helicopter simulators, addressing the strengths and weaknesses of the present European standard for the qualification of helicopter flight simulators, JAR FSTD-H (previously JAR-STD-1H/2H/3H). Essential questions are examined, such as: What is the required model fidelity to guarantee a simulation is sufficiently representative to be fit for purpose? Are the tolerances set in the current standards fine enough that they lead to only minor changes in handling qualities? What is an acceptable tuning process for the simulation? What is the effect of modelling fidelity on the overall pilot control strategy? What is the relationship between the settings of the simulator cueing
Between 2018 and 2020 the NATO Science and Technology Organization (STO) engaged a Research Task Group (RTG) on rotorcraft flight simulation model fidelity. The primary goal of this RTG was to apply and compare flight simulation model enhancement methods and fidelity assessment criteria based on flight-test case studies. Under the AVT panel 296 (Applied Vehicle Technology), 20 partners from NATO nations' industries, governmental research establishments and universities tested 7 different model updating or "renovation" methods through comparison of simulation models vs flight data. Comparisons between update methods have been investigated to find best practices and suitability for different applications including advanced rotorcraft configurations. Most of the existing methods make extensive use of System Identification (SID) to generate improved-fidelity state-space models. Thereafter, these models are used as a reference for nonlinear model improvements. Among the case-studies carried-out, ONERA, US-Army and THALES Training and Simulation run a collaborative work to investigate the potential use of flight data from simulator’s Qualification Test Guides (QTG) in System Identification. The objective behind this work was to assess whether the state-space models resulting from this identification could be used and contribute to enhance the physics-based model. The renovation technique based on deltas of forces and moments was applied to select the most relevant derivatives of the identified model. The analysis of the derivatives helped identify some improvement axes of the physics-based model. This paper will present the work carried out in the scope of this collaboration.
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