Energies, Vol. 18, Pages 4877: Assessment of a Cost-Effective Multi-Fidelity Conjugate Heat Transfer Approach for Metal Temperature Prediction of DLN Gas Turbine Combustor Liners

Energies doi: 10.3390/en18184877

Authors:
Gianmarco Lemmi
Stefano Gori
Giovanni Riccio
Antonio Andreini

Over the last decades, Computational Fluid Dynamics (CFD) has become a fundamental tool for the design of gas turbine combustors, partly making up for the costs and duration issues related to the experimental tests involving high-pressure reactive processes. Nevertheless, high-fidelity simulations of reactive flows remain computationally expensive, particularly for conjugate heat transfer (CHT) analyses aimed at predicting liner metal temperatures and characterising wall heat losses. This work investigates the robustness of a cost-effective numerical setup for CHT simulations, focusing on the prediction of cold-side thermal loads in industrial combustor liners under realistic operating conditions. The proposed approach is tested using both Reynolds-Averaged Navier–Stokes (RANS) and unsteady Stress-Blended Eddy Simulation (SBES) turbulence models for the combustor flame tube, coupled via a time desynchronisation strategy with transient heat conduction in the solid domain. Cold-side heat transfer is modelled using a 1D correlation-based tool, runtime coupled with the CHT simulation to account for cooling-induced thermal loads without explicitly resolving complex cooling passages. The methodology is applied to a single periodic sector of the NovaLTTM16 annular combustor, developed by Baker Hughes and operating under high-pressure conditions with natural gas. Validation against experimental data demonstrates the methodology’s ability to predict liner metal temperatures accurately, account for modifications in cooling geometries, and support design-phase evaluations efficiently. Overall, the proposed approach offers a robust trade-off between computational cost and predictive accuracy, making it suitable for practical engineering applications.

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