Optimizing Fuel Management for Halifax Class Frigates: Leveraging Sensor Data for Enhanced Efficiency
Notice bibliographique
Résumé
This article explores how operational data from sensors integrated with Royal Canadian Navy (RCN) Halifax Class Frigates onboard Integrated Platform Management System (IPMS) can be leveraged to minimize fuel consumption. The idea is to use IPMS data logged by the L3HARRIS Equipment Health Monitoring (EHM) software tool. This, along with data collected from temporary fuel flowmeters installed on several Halifax Class Frigates, aids in the development of fuel consumption models for onboard power generating and propulsion equipment. The study aims to identify the most fuel-efficient driving mode among the options available for specific operational conditions, leveraging the CODOG propulsion system and twin shaft arrangement of the Halifax Class Frigates. Building upon this, the developed fuel consumption models are employed to implement various fuel optimization methods on a specific ship platform. This entails the adaptation and integration of these methods into dashboards for enhanced accessibility, with the fuel consumption models providing essential input data. The process of fuel optimization methods development consists of several steps. First, the targeted power generating and propulsion equipment operational data and fuel flowmeters data were selected. Secondly, data from these sources was consolidated and preprocessed and data analysis was conducted. Then, equipment fuel consumption baselines were generated and validated using available historical data. After that, fuel consumption prediction models for each type of equipment were built. Finally, a Fuel Management Application prototype was developed to facilitate user access to current operational data, fuel consumption-related information as well as fuel consumption optimization tools. In the course of the study, several fuel optimization techniques were examined, revealing valuable information about their applicability in specific cases, taking into account factors such as data availability and reliability. The development process of equipment fuel consumption models showcased how sensors designed for operational support could enhance fuel consumption optimization efforts. Enhanced value could be realized with the installation of high-quality fuel flowmeters during ship construction or the prolonged use of temporary fuel flowmeters to capture data across the ship's speed and load ranges. L3HARRIS IPMS emerged as a valuable information source supporting fuel optimization initiatives. Validation of the Fuel Management Application's performance in the field and its value to end-users is pending; however, the progress achieved thus far shows promising potential.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».