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Record W7008869289

Design of a Vertical Axis Rotating Machine for the Development of a Digital Twin

2023· other· fr· W7008869289 on OpenAlexaboutno aff

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2023
Typeother
Languagefr
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationTurbineVertical axisMachine designHydro power
DOInot available

Abstract

fetched live from OpenAlex

RÉSUMÉ: Cette étude aborde le besoin croissant de production d'énergie fiable et durable, en mettant l'accent sur l'énergie hydroélectrique. Alors que la population mondiale et les besoins en énergie augmentent de manière exponentielle, l'impact environnemental des combustibles fossiles traditionnels devient de plus en plus problématique et irrémédiable. L'énergie hydroélectrique, notamment dans des régions comme le Québec, au Canada, où elle représente 94$\%$ de la production d'électricité, offre une alternative plus propre. Cependant, le développement de nouveaux projets hydroélectriques est confronté à des défis liés aux préoccupations environnementales, à l'opposition du public et aux coûts initiaux élevés, tandis que la conservation des unités déjà existantes fait face à une demande croissante et à des opérations irrégulières de plus en plus importantes. Ce projet vise à contribuer au développement d'un jumeau numérique (DT) d'une turbine hydroélectrique, en commençant par la conception, la construction et l'instrumentation d'une machine rotative à axe vertical (VARM). Cette machine sert de source de données empiriques pour les chercheurs travaillant sur les premières versions de modèles de DT de turbine hydroélectrique. La VARM fournit des données d'entraînement et de validation pour le développement de tels modèles en simulant des modes de défaillance contrôlés en laboratoire. La VARM est conçue pour être modulaire et flexible, capable de fonctionner avec différents matériaux, arbres, rotors et paliers pour simuler de nombreux cas d'essais différents. Sa conception est d'abord basée sur le modèle de machine tournante de Jeffcott qui offre un équilibre entre simplicité et précision. Elle aussi est accompagnée d'un modèle par éléments finis (FEM) offrant une source de données de référence régie par des paramètres connus servant à l'entraînement précoce du DT et la comparaison avec des données empiriques enregistrées sur la VARM. La conception et la construction complétées de la VARM sont ensuite accompagnées d'une série de tests préliminaires sous différentes configurations et cas de chargements. Des paramètres clés du système tels que la fréquence naturelle et le taux d'amortissement sont estimés par le biais de diverses techniques pour plusieurs cas. Le comportement de la VARM est également comparé aux résultats de simulation du modèle d'éléments finis permettant d'identifier un certain type de défaillance grâce à sa simplicité. De nombreuses améliorations pourraient encore être apportées à la VARM afin d'améliorer ses performances et ses capacités d'acquisition et de traitement de données, tout en offrant déjà une large gamme de mesures. ABSTRACT: This study addresses the increasing need for reliable and sustainable energy production, with a focus on hydroelectric power. As the world's population and energy needs grow exponentially, the environmental impact of traditional fossil fuels becomes increasingly problematic and irremediable. Hydroelectric power, particularly in regions like Quebec, Canada, where it accounts for 94$\%$ of electricity generation, offers a cleaner alternative. However, the development of new hydroelectric projects is confronted with challenges due to environmental concerns, public opposition, and high upfront capital costs while the conservation of already existing units faces an evermore increasing demand and irregular operations. This project aims to contribute to the development of a digital twin (DT) of a hydroelectric turbine, starting with the design, construction, and instrumentation of a Vertical-Axis Rotating Machine (VARM). This machine serves as an empirical data source for researchers working on early versions of hydroelectric turbine DT models. The VARM provides training and validation data for the development of such models by simulating lab-induced failure modes similar to those found in actual hydroelectric turbines and rotating machinery. The VARM is designed to be modular and flexible, capable of operating under different conditions, shafts, rotors, and bearings to simulate numerous test cases. Its design is initially based on the Jeffcott rotating model, which provides a balance between simplicity and accuracy. It is then accompanied by a finite-element model (FEM) offering an accurate and versatile source of reference data governed by known parameters for early training of the DT and comparison with the empirical data recorded on the VARM. The successful design and construction of the VARM, which will contribute to future researchers in need of experimental data of a vertical-axis rotating machine, is then accompanied by a series of preliminary tests under different configurations and loading schemes. Key parameters such as natural frequency and damping ratio are estimated through a variety of techniques for multiple cases. The behavior of the VARM is also compared to the simulation results from the finite-element model, which, in its simplicity allows the identification of a certain type of failure. Many improvements could still be applied to the VARM to improve its performance and data acquisition and treatment capabilities while still already offering a wide array of measurements.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.024
GPT teacher head0.254
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2023
Admission routes1
Has abstractyes

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Same venuePolyPublie (École Polytechnique de Montréal)French-language works237,207