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Author:
Jameil Kolliyil
Engineer, Technical Marketing
From refineries to planes, gas turbines are vital to several industries. In addition to providing thrust to keep planes in the air, gas turbines account for almost a quarter of the world’s electricity production.1 Given their prominence in the industry, reducing emissions from gas turbines is crucial. Hydrogen has emerged as one of the more attractive alternative fuels for gas turbines and is backed by several nations to replace or supplement conventional fuels. Hydrogen offers numerous advantages: it is a carbon-free energy carrier, has a higher calorific value, and can be blended with existing fuels without major changes to the combustor.
While the use of hydrogen fuel is desirable, there are a number of design, storage, and operational challenges that come with it. One major challenge in designing new gas turbines or retrofitting old ones is the prevention of a phenomenon called flashback in the combustor. During flashback, the flame propagates upstream at speeds higher than the incoming gas flow. Sustained upstream propagation can cause substantial thermal damage to the combustor hardware. Hydrogen has faster kinetics and a higher flamespeed than conventional fuels, making it more prone to flashback. To mitigate the phenomenon, various studies are being performed to find the limits of safe operation for hydrogen fuel. At Convergent Science, we used CONVERGE to perform one such study to analyze flashback in a swirling combustor.2 We compared our simulation results with experimental work performed at The University of Texas at Austin by D. Ebi.3
Figure 1 shows the geometry of the swirling combustor that was investigated in our study. Premixed fuel and air enter through the bottom, pass the swirler, and ignite in the combustion chamber. To accurately predict flashback, we employed the dynamic structure large eddy simulation (LES) model and a detailed chemistry mechanism4 fully coupled with the flow solver. Because the flame travels upstream during flashback, the mesh in the premixing section and the combustion chamber must be refined enough to capture the flame front. However, such an approach will result in unrealistically long simulation times. To obtain accurate results in a reasonable timeframe, we used CONVERGE’s Adaptive Mesh Refinement (AMR) technology to add mesh resolution along the flame front while maintaining a coarser mesh in other parts of the computational domain.
In Figure 2, we have shown a visual comparison between experimental3 and simulation results for a CH4 + air (equivalence ratio Φ = 0.8) fuel mixture. You can see there is a good resemblance in the flame structure and temporal location. We also analyzed the flashback limit for a CH4 + H2 + air (Φ = 0.4) fuel mixture. For this particular fuel mixture, the experimental value for the onset of flashback is 75% H2 by volume.3 Based on our simulations, we predicted a value of 77% of H2 by volume.
The present study demonstrates an engineering solution for accurately predicting flashback and analyzing flame propagation using CONVERGE. For more details about this research, take a look at our paper here! With a long history of simulating complex geometries and combustion, CONVERGE is the go-to tool for all your gas turbine flow simulations. Check out our gas turbine webpage for more information on how CONVERGE can help you design the gas turbines of the future!
[1] “bp Statistical Review of World Energy, 2022 | 71st Edition”, bp, 2022. https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2022-full-report.pdf
[2] Kumar, G., and Attal, N., “Accurate Predictions of Flashback in a Swirling Combustor with Detailed Chemistry and Adaptive Mesh Refinement,” AIAA SciTech Forum, San Diego, CA, United States, Jan 3–7, 2022. DOI: 10.2514/6.2022-1722
[3] Ebi, D.F., “Boundary Layer Flashback of Swirl Flames,” Ph.D. thesis, The University of Texas at Austin, Austin, TX, United States, 2016. https://repositories.lib.utexas.edu/handle/2152/38721
[4] G.P. Smith, Y. Tao, and H. Wang, Foundational Fuel Chemistry Model Version 1.0 (FFCM-1), https://web.stanford.edu/group/haiwanglab/FFCM1/pages/download.html, 2016.
