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Record W3135381672 · doi:10.14339/sto-tr-avt-296-nu

Rotorcraft Flight Simulation Model Fidelity Improvement and Assessment

2021· article· en· W3135381672 on OpenAlexaboutno aff
Mark B. Tischler, Mark White, Michael Jones, Pavle Šćepanović, Susanne Seher-Weiß, Vincent Myrand-Lapierre, Michel Nadeau-Beaulieu, Sylvain Richard, Andrea Ragazzi, Stefano D’Agosto, Chengjian He, R. K. Lehman, David G. Miller, Xin Hong, Arthur Gubbels, Kenneth Hui, Armin Taghizad, Eric Tobias, Steffen Greiser, Marilena Pavel, Olaf Stroosma, İlkay Yavrucuk, Gareth D. Padfield, Neil Cameron, J. V. R. Prasad, Feyyaz Güner, Ondrej Juhasz, Josef Horn, Jonathan Soong, Samuel Nadell, Olivia Lee

Bibliographic record

Venueelib (German Aerospace Center) · 2021
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsnot available
Fundersnot available
KeywordsFidelityFlight simulatorCertificationTrainerComputer scienceIdentification (biology)EngineeringSimulationFlight testAeronauticsSystems engineering

Abstract

fetched live from OpenAlex

Rotorcraft flight dynamics simulation models require high levels of fidelity to be suitable as prime items in support of life cycle practices, particularly vehicle and control design and development, and system and trainer certification. On the civil side, both the FAA (US) and EASA (Europe) have documented criteria (metrics and practices) for assessing model and simulator fidelity as compared to flight-test data, although these have not been updated for several decades. On the military side, the related practices in NATO nations are not harmonised and often only developed for specific applications. Methods to update the models for improved fidelity are mostly ad-hoc and lack a rational and methodical approach. Modern rotorcraft system identification (SID) and inverse simulation methods have been developed in recent years that provide new approaches well suited to pilot-in-the-loop fidelity assessment and systematic techniques for updating simulation models to achieve the needed level of fidelity. To coordinate efforts and improve the knowledge in this area, STO Applied Vehicle Technology Panel Research Task Group (STO AVT-296 RTG) was constituted to evaluate update methods used by member nations to find best practices and suitability for different applications including advanced rotorcraft configurations.
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\nThis report presents the findings of the AVT-296 RTG. An overview of previous rotorcraft simulation fidelity Working Groups is presented, followed by a review of the metrics that have been used in previous studies to quantify the fidelity of a flight model or the overall perceptual fidelity of a simulator. The theoretical foundations of the seven different update methods and a description of the eight flight databases (Bell 412, UH-60, Iris+, EC135, CH-47, AW139, AW109, and X2, provided by the National Research Council of Canada, US Army, Airbus Helicopters, Boeing, Leonardo Helicopter Division, and Sikorsky) used by the RTG is presented. Both time- and frequency-domain fidelity assessment methods are considered, including those in current use by simulator qualification authorities and those used in the research community. Case studies are used to show the application, utility, and limitations of the update and assessment methods to the flight-test data.
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\nThe work of the RTG has shown that time- and frequency-domain SID based metrics are suitable for use for assessing the model fidelity across a wide range of rotorcraft configurations. Gain and time delay update methods work well for well-developed flight dynamics models and can be used for flight control system design, but do not provide physical insights into the sources of errors in a model. Deriving stability and control derivatives from flight-test data and nonlinear simulation models using SID provides insight into the missing dynamics of the simulation model, which can subsequently be updated using additional forces and moments to significantly improve the fidelity of the model and can be used to update models for flight simulation training application methods. Reduced order model and physics-based correction methods provide large benefits when extrapolating to other flight conditions but does require detailed flight-test data. SID can quickly provide accurate point models, if detailed flight-test data is available, which can be "stitched" together to produce models suitable for real-time piloted simulation and control design applications. However, the dependency on flight-test data means that this method is not suitable for early aircraft development activities.
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\nThis documentation of rotorcraft simulation fidelity assessment and model update strategies will benefit NATO nations by allowing for common, agreed-upon best practices and recommendations, ensuring each country's flight dynamics and simulation models are of the highest calibre possible. The collaboration between industry, academia, and government laboratories has been key to the success of this RTG; this cooperation model should be adopted in future research activities. As industries strive to achieve greater efficiency and safety in their products, the fidelity of simulation should match commercial aspirations to ensure that the "right first time" ethos is fully embedded into industrial best practices. Militaries will be able to use the methods and metrics presented to set criteria that will underpin the use of modelling and simulation in certification to accelerate development and acquisition and reduce the cost of new aircraft systems, e.g. advanced high-speed rotorcraft and legacy system upgrades. The criteria may also set standards for training devices used to support the expansion of synthetic environments for training to offset the high costs of flight hours. This RTG has identified that current flight training simulator standards could be updated to use the flight model and perceptual fidelity metrics presented in this report to ensure that models are not "over-tuned" and a more rigorous method of subjective simulator assessment is adopted.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.273
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2021
Admission routes1
Has abstractyes

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