Coolant Bleed Flow Modeling and Prediction for Aircraft Engines Based on TSO-RF Algorithm
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Coolant bleed flow forecasting is critical for the engineering health of aircraft engines. Using the datasets from Commercial Modular Aero-Propulsion System Simulation (C-MAPSS), this paper aims to establish high-precision coolant bleed flow models by the random forest (RF) algorithm combined with tuna swarm optimization (TSO). 17 sensor variables that are moderately or highly correlated with the low-pressure turbine (LPT) and high-pressure turbine (HPT) are selected as inputs with LPT and HPT as outputs. After being trained and validated on the FD002 and FD004 datasets, the TSO-RF model significantly reduces mean squared error, mean absolute error and root mean squared error, and improves the determination coefficient R² compared to other RF models. It verifies the superiority of the TSO-RF model in predicting engine coolant bleed flow, providing reliable technical support for subsequent evaluation and management in aircraft engine bleed air system.
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