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Record W4411439296 · doi:10.1139/dsa-2025-0003

Mermoz<sup>©</sup> simulator: use of a database to estimate the photovoltaic input on a UAV. <i>Application to a transatlantic voyage</i>

2025· article· en· W4411439296 on OpenAlexvenueno aff
Vincent Mahout, Jean‐Marc Moschetta, Nikola Gavrilović

Bibliographic record

VenueDrone Systems and Applications · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsModular designPhotovoltaic systemSimulationComputer sciencePropulsionAutomotive engineeringAerospace engineeringFlight simulatorEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper introduces the Mermoz simulator, developed to predict the photovoltaic energy contribution of solar cells mounted on a low-altitude unmanned aerial vehicle (UAV) over long-distance flights. This MATLAB-based tool implements a modular model of the UAV and its environment from an energy-focused perspective. Leveraging 20 years of data from the NASA Prediction of Worldwide Energy Resource Weather Database, the simulator provides an estimate of the confidence interval for solar and wind energy contributions expressed in Wh, or, to facilitate comparisons, in earned kilometers. Its modular design enables easy testing and comparison of various configurations and flight strategies (e.g., optimal departure times, autonomy gains, or cruise speeds). To facilitate understanding, the structure and operation of the simulator are illustrated using the Mermoz drone as a case study. The Mermoz UAV features a 4 m wingspan and is powered by an electric propulsion system. It combines a fuel cell with a liquid hydrogen tank, enabling fully electric, greenhouse gas-free flight. Simulation results indicate that wing-mounted solar cells can provide up to 5% additional electrical energy during a transatlantic flight.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.275
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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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