Heat Transfer Rate and Fluid Flow Analysis with Design Parameters of Gas Turbine using Beta-clog2-LSTM

A Gas Turbine (GT) is a combustion engine that converts fuel into mechanical energy. None of the conventional models has utilized the stator hub, rotor tip leakage, and inter-stage flow for the optimum design of GT. This study performs an effective design parameter analysis for GT with heat transfer rate and fluid flow detection using Betadecay with cloglog-based Long Short-Term Memory (Beta-clog2-LSTM) and Griewank Siberian Tiger Optimization (G-STO). Initially, the design parameters were taken and the geometry of those parameters was created. Afterward, mesh generation was performed using the Linear Weighted Gradient Smoothing Sliding Mesh Interface (LWGSSMI). Then, the boundaries of the generated mesh were detected. Next, numeric modeling was performed deploying Finite Element Analysis (FEA), followed by flow behavior analysis. The optimal parameters were selected by G-STO. Similarly, the data in a heat transfer rate dataset were preprocessed and the features were extracted. Prediction of heat rate was performed using Beta-clog2-LSTM. Finally, the thermal loss was calculated, and a heat exchanger was utilized to mitigate it. The performance analysis demonstrated the robustness of the proposed method by achieving 0.98 prediction accuracy.