Coupling an ingestion orifice model with a gas turbine SAS code for integrated cycle modeling

In a gas turbine, the ingestion phenomenon in the secondary airflow system (SAS) can cause significant damage to the turbine blades and disc components. Three-dimensional conjugate numerical models can capture the complex flow physics of SAS, but their high computational cost limits their use in early-stage design. In contrast, simplified analytical models are efficient; however, they often do not accurately represent the phenomenon of hot gas ingestion. This paper presents a novel algorithm that couples an ingestion orifice model with a SAS element-node code, enabling efficient and reliable prediction of rim seal ingestion. The algorithm utilizes rim seal effectiveness diagrams derived from high-fidelity 3D CFD simulations conducted in ANSYS CFX 19.3, which incorporate realistic engine operating conditions, including wheelspace, rim seal, vane, and blade geometries, as well as cooling paths. This approach offers a more accurate representation of ingestion compared to conventional models. Subsequently, the Maximum Likelihood Estimation (MLE) method is implemented in MATLAB R2014a to extract the minimum dimensionless sealing flow rate, \(\:{\phi\:}_{min}\) , and the ratio of discharge coefficients, \(\:{{\Gamma\:}}_{c}\) from the rim seal effectiveness diagram. Validation against published experimental data confirms the accuracy of the method. As a case study, the upgraded SAS code was executed for the High-Pressure Packing (HPP) path of a Frame 9 gas turbine. Results establish a direct relationship between ingestion and sealing parameters. Neglecting leakage and vane cooling under-predicts the required sealing flow. The method provides a practical tool for SAS analysis to enhance gas turbine performance and durability.