Laminar Flame Speed Measurements and Modeling of Pure Alkanes and Alkane Blends at Elevated Pressures
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Notice bibliographique
Résumé
Alkanes such as methane, ethane, and propane make up a large portion of most natural gas fuels. Natural gas is the primary fuel used in industrial gas turbines for power generation. Because of this, a fundamental understanding of the physical characteristics such as the laminar flame speed is necessary. Most importantly, this information is needed at elevated pressures to have the most relevance to the gas turbine industry for engine design. This study includes experiments performed at elevated pressures, up to 10 atm initial pressure, and investigates the fuels in a pure form as well as in binary blends. Flame speed modeling was done using an improved version of the kinetics model that the authors have been developing over the past few years. Modeling was performed for a wide range of conditions, including elevated pressures. Experimental conditions include pure methane, pure ethane, 80/20 mixtures of methane/ethane, and 60/40 mixtures of methane/ethane at initial pressures of 1 atm, 5 atm, and 10 atm. Also included in this study are pure propane and 80/20 methane/propane mixtures at 1 atm and 5 atm. The laminar flame speed and Markstein length measurements were obtained from a high-pressure flame speed facility using a constant-volume vessel. The facility includes optical access, a high-speed camera, a schlieren optical setup, a mixing manifold, and an isolated control room. The experiments were performed at room temperature, and the resulting images were analyzed using linear regression. The experimental and modeling results are presented and compared with previously published data. The data herein agree well with the published data. In addition, a hybrid correlation was created to perform a rigorous uncertainty analysis. This correlation gives the total uncertainty of the experiment with respect to the true value rather than reporting the standard deviation of a repeated experiment. Included in the data set are high-pressure results at conditions where in many cases for the single-component fuels few data existed and for the binary blends no data existed prior to this study. Overall, the agreement between the model and data is excellent.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle