Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".