<p>The
main accomplishments of this research are </p>
<p>(1) developed
a high fidelity computational methodology based on large eddy simulation to
capture lean blowout (LBO) behaviors of different fuels; </p>
<p>(2)
developed fundamental insights into the combustion processes leading to the
flame blowout and fuel composition effects on the lean blowout limits; </p>
<p>(3) developed
artificial intelligence-based models for early detection of the onset of the lean
blowout in a realistic complex combustor. </p>
<p>The
methodologies are demonstrated by performing the lean blowout (LBO)
calculations and statistical analysis for a conventional (A-2) and an alternative
bio-jet fuel (C-1).</p>
<p>High-performance computing methodology is developed based on
the large eddy simulation (LES) turbulence models, detailed chemistry and
flamelet based combustion models. This methodology is employed for predicting
the combustion characteristics of the conventional fuels and bio-derived
alternative jet fuels in a realistic gas turbine engine. The uniqueness of this
methodology is the inclusion of as-it-is combustor hardware details such as
complex hybrid-airblast fuel injector, thousands of tiny effusion holes,
primary and secondary dilution holes on the liners, and the use of highly
automated on the fly meshing with adaptive mesh refinement. The flow split and
mesh sensitivity study are performed under non-reacting conditions. The
reacting LES simulations are performed with two combustion models (finite rate
chemistry and flamelet generated manifold models) and four different chemical
kinetic mechanisms. The reacting spray characteristics and flame shape are
compared with the experiment at the near lean blowout stable condition for both
the combustion models. The LES simulations are performed by a gradual reduction
in the fuel flow rate in a stepwise manner until a lean blowout is reached. The
computational methodology has predicted the fuel sensitivity to lean blowout
accurately with correct trends between the conventional and alternative bio-jet
fuels. The flamelet generated manifold (FGM) model showed 60% reduction in the
computational time compared to the finite rate chemistry model. </p>
<p>The statistical analyses of the results from the high
fidelity LES simulations are performed to gain fundamental insights into the
LBO process and identify the key markers to predict the incipient LBO condition
in swirl-stabilized spray combustion. The bio-jet fuel (C-1) exhibits
significantly larger CH<sub>2</sub>O concentrations in the fuel-rich regions
compared to the conventional petroleum fuel (A-2) at the same equivalence ratio.
It is observed from the analysis that the concentration of formaldehyde
increases
significantly in the primary zone indicating partial oxidation as we approach
the LBO limit. The analysis also showed that the temperature of the
recirculating hot gases is also an important parameter for maintaining a stable
flame. If this temperature falls below a certain threshold value for a given
fuel, the evaporation rates and heat release rated decreases significantly and
consequently leading to the global extinction phenomena called lean blowout.
The present study established the minimum recirculating gas temperature needed to
maintain a stable flame for the A-2 and C-1 fuels. </p>
The artificial intelligence
(AI) models are developed based on high fidelity LES data for early
identification of the incipient LBO condition in a realistic gas turbine
combustor under engine relevant conditions. The first approach is based on the
sensor-based monitoring at the optimal probe locations within a realistic gas
turbine engine combustor for quantities of interest using the Support Vector
Machine (SVM). Optimal sensor locations are found to be in the flame root
region and were effective in detecting the onset of LBO ~20ms ahead of the
event. The second approach is based on
the spatiotemporal features in the primary zone of the combustor. A
convolutional autoencoder is trained for feature extraction from the mass
fraction of the OH (
data for all time-steps resulting
in significant dimensionality reduction. The extracted features along with the
ground truth labels are used to train the support vector machine (SVM) model
for binary classification. The LBO indicator is defined as the output of the
SVM model, 1 for unstable and 0 for stable. The LBO indicator stabilized to the
value of 1 approximately 30 ms before complete blowout.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/11320805 |
| Date | 06 December 2019 |
| Creators | Veeraraghava Raju Hasti (8083571) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/HIGH-PERFORMANCE_COMPUTING_MODEL_FOR_A_BIO-FUEL_COMBUSTION_PREDICTION_WITH_ARTIFICIAL_INTELLIGENCE/11320805 |
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