Figure 1.Critical attributes for gas turbine combustors.
Customers are demanding improved performance at lower cost from their aero-engines and land-based gas turbines.
As a result, engineers designing turbine combustors must meet the high standards of various critical-to-quality (CTQ) performance factors (Figure 1).
There are several elements involving fuel delivery systems and combustors that affect these CTQ factors:
- Spray.
- Pressure drop.
- Flow-split ratios.
- Fuel preparation.
- Ignition.
- Heat transfer.
Each element has multiple variables, so engineers that optimize these systems rely on simulation to deliver high-accuracy results quickly.
How ANSYS’ Simulation Solutions Help Engineers Design their Combustors
Understanding combustion requires a detailed knowledge of the physical, chemical properties and reactions of fuels (kinetics, reaction mechanisms, thermodynamic properties).
ANSYS’ Model Fuel Library (MFL) includes validated models for over 60 master fuel components that can simulate the combustion of most common fuels and additives. ANSYS Chemkin-Pro uses these fuel models to simulate reaction kinetics, emissions and combustion stability.
The Chemkin-Pro Reaction Workbench reduces the reaction mechanism size to model combustion using less computational power. The performance of these reduced mechanisms can be compared with their parent mechanisms to assess accuracy (Figure 2) .
Figure 2. Performance of a reduced mechanism developed using ANSYS Chemkin-Pro Reaction Workbench (blue) vs the parent mechanism (orange).
Flow properties are also important in combustion. ANSYS Fluent performs computational fluid dynamics (CFD) simulations to model the following:
- Sprays.
- Combustion/combustion dynamics.
- Ignition.
- Pressure drop.
- Flow-split calculations.
- Emissions.
For an example involving sprays, Figure 3 shows the primary breakup of a jet in a cross-flow configuration, which is often used to atomize fuel in gas turbine combustors.
Figure 3. Animation of jet in cross-flow configuration
Figure 4 shows the simulated intact core of this jet using a high-fidelity transition model between the volume of fluid (VOF) and discrete phase model (DPM).
Figure 4. Predicted intact liquid core from a VOF-DPM spray simulation
The results of this simulation compare well with experimental results (Sallam et al. Breakup of Turbulent and Non-Turbulent Liquid Jets in Gaseous Crossflows. AIAA Aerospace Sciences Meeting and Exhibit, 2006, Jan. 9–12, pp. 1–13.).
Fluent can also model the ignition sequence (Figure 5).
Figure 5. Ignition sequence in a SP9 KIAI multiburner.
The ignition sequence for the burners is 2-3-4-1-5. This sequence was captured using a flamelet-generated manifold (FGM) model in a large eddy simulation (LES) framework using Fluent.
Figure 6 demonstrates good agreement between experimental and simulated data for successive ignition time for injectors (Barré et al. Flame propagation in aeronautical swirled multiburners: Experimental and numerical investigation. Combusion and Flame, Elsevier, 2014, 161 (9), pp.2387–2405.).
Figure 6. Comparison of successive ignition time for injectors in a SP9 KIAI multiburner
Figure 7. Predicted peak-to-peak pressure variation for a LDI combustor.
Fluent can also simulate self-excited combustion dynamics. Figure 7 and 8 show the predicted peak-to-peak pressure variation and power spectrum density variation for a lean direct injection (LDI) combustor. In this case, a flamelet-based turbulent combustion model was used in an LES framework.
Figure 8. Power spectrum density variation for a LDI combustor.
These simulated results correlate well with experimental data (Gejji et al. A Parametric Study of Combustion Dynamics in a Single-Element Lean Direct Injection Gas Turbine Combustor: Part II: Experimental Investigation. 52nd Aerospace Sciences Meeting, AIAA SciTech Forum, 2014, Jan).
Another area of concern for engineers is exposure of the combustor’s components to variable high temperatures — a leading cause of thermo-mechanical fatigue (TMF). Predicting the failure location and the number of cycles to failure can be done by simulating the system’s response to thermal and mechanical loads using ANSYS solutions. ANSYS’ nonlinear material models help capture material behaviors seen in heat resistant alloys like nickel-based alloys.
To learn more about ANSYS’ combustion solutions, register for the webinar Using Detailed Chemistry in ANSYS Chemkin-Pro for More Accurate CFD Combustion and Reacting Flow Simulations Aug. 21, 2018, at 11 a.m. (EDT).
The post Simulation Helps Engineers Optimize Combustors Based on Critical-to-Quality Performance Factors appeared first on ANSYS.