Notice bibliographique
Résumé
Abstract Hydrogen is an essential feedstock for a variety of chemical and industrial processes. Refineries with a global share of over 30% are amongst the largest consumers. Traditional methods of generating hydrogen involve the reformation of fossil-fuel sources with the help of steam. These methods release CO2 as a side product and are thus carbon-intensive. A zero-carbon approach of producing hydrogen can be achieved via electrolysis of water powered by surplus renewable energy sources, which in return helps to balance the intermittency of solar photovoltaic (PV) or wind. Hydrogen is also often a by-product of industrial processes. These synthetic waste gases have typically been flared in the past. Burning these gases in gas turbines instead can significantly boost the economic case and reduce carbon emissions compared to flaringbecause of the utilization of the waste energy. Gas turbines are typically designed for natural gas operation, and accommodating high levels of hydrogen poses significant challenges due to its different physical properties. First, hydrogen is the lightest molecule with a lower volumetric energy content and higher diffusivity. This has an impact on the fuel delivery system, as sealings and piping materials need to be upgraded. Second, local mixing between fuel and air may not be perfect as hydrogen flames tend to stabilize further upstream, where mixing quality is lower and are more compact. As hydrogen has a higher flame temperature, local hotspots can lead to higher NOx emissions. This, in return, may require performance adaptations to meet the local emissions standards. Perhaps the most challenging aspect of hydrogen use in turbines is its significantly higher reactivity. Hydrogen has a substantially higher flame speed (up to 10 times) and lower ignition delay time than natural gas, which increases the risk of flashback and explosions. To overcome all these challenges and guarantee safe operation with high hydrogen fuels, focused development is required, particularly with regards to the combustor. Following an iterative rapid prototyping approach, the design optimization is typically achieved via high-fidelity ComputerAided Engineering(CAE) simulations coupled with validation through high-pressure testing at engine conditions. Here, the use of Additive Manufacturing (AM) for generating prototypes has been a critical success factor in recent years. In addition to reducing the overall lead time by up to 70%, AM offers the opportunity to generate and manufacture more efficient aero designs for e.g., cooling and fuel routing schemes. This paper focuses on the use of hydrogen in gas turbines and discusses the required development steps. General challenges of accommodating hydrogen in gas turbines and the implications on design modification and operation will be examined in detail. Examples of achieving up to 100% hydrogen operation on a 25MW scale gas turbine from recent testing programs will be subsequently presented. Use cases of gas turbines operating with high hydrogen fuels in industrial processes will alsobe discussed.
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 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».