Optimization of CO Turndown for an Axially Staged Gas Turbine Combustor
Bibliographic record
Abstract
Abstract This paper reports on an optimization study of the CO turndown behavior of an axially staged combustor, in the context of industrial gas turbines (GTs). The aim of this work is to assess the optimally achievable CO turndown behavior limit given system and operating characteristics, without considering flow-induced behaviors such as mixing quality and flame spatial characteristics. To that end, chemical reactor network (CRN) modeling is used to investigate the impact of various system and operating conditions on the exhaust CO emissions of each combustion stage, as well as at the combustor exit. Different combustor residence time combinations are explored to determine their contribution to the exhaust CO emissions. The two-stage combustor modeled in this study consists of a primary (Py) and a secondary (Sy) combustion stage, followed by a discharge nozzle (DN), which distributes the exhaust to the turbines. The Py is modeled using a freely propagating flame (FPF), with the exhaust gas extracted downstream of the flame front at a specific location corresponding to a specified residence time (tr). These exhaust gases are then mixed and combusted with fresh gases in the Sy, modeled by a perfectly stirred reactor (PSR) operating within a set tr. These combined gases then flow into the DN, which is modeled by a plug flow reactor (PFR) that cools the gas to varying combustor exit temperatures within a constrained tr. Together, these form a simplified CRN model of a two-stage, dry-low emissions (DLEs) combustion system. Using this CRN model, the impact of the tr distribution between the Py, Sy, and DN is explored. A parametric study is conducted to determine how inlet pressure (Pin), inlet temperature (Tin), equivalence ratio (ϕ), and Py–Sy fuel split (FS), individually impact indicative CO turndown behavior. Their coupling throughout engine load is then investigated using a model combustor, and its effect on CO turndown is explored. Thus, this aims to deduce the fundamental, chemically driven parameters considered to be most important for identifying the optimal CO turndown of GT combustors. In this work, a parametric study and a model combustor study are presented. The parametric study consists of changing a single parameter at a time, to observe the independent effect of this change and determine its contribution to CO turndown behavior. The model combustor study uses the same CRN, and varies the parameters simultaneously to mimic their change as an engine moves through its steady-state power curve. The latter study thus elucidates the difference in CO turndown behavior when all operating conditions are coupled, as they are in practical engines. The results of this study aim to demonstrate the parameters that are key for optimizing and improving CO turndown.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".