Performance evaluation of supersonic flow for variable geometry radial ejector through CFD models based on DES-turbulence models, GPR machine learning, and MPA optimization

Abstract

This study aims to derive valuable insights for utilizing computational fluid dynamics (CFD) based on reynolds-averaged navier–stokes (RANS) and detached eddy simulation (DES) turbulence models (TMs) to analyze a specific radial ejector configuration known as the variable geometry radial ejector (VGRE). The VGRE features the primary nozzle and ejector duct plates with adjustable disk-like surfaces, allowing for changes in the nozzle and ejector throat areas within a single ejector. Extensive numerical investigations of the VGRE are conducted by systematically validating CFD models with experimental datasets and subsequently using the most appropriate TM to design a new radial ejector. The study reveals that the DES SST k-ω turbulence model achieves the closest agreement with experimental data, with an average entrainment ratio (ω) discrepancy of only 5 %. However, there are challenges in accurately predicting the critical compression ratio (rc*), especially under varying conditions. Based on the CFD results, the original VGRE exhibited ω values ranging from 0.16 to 0.61, rc* values between 1.5 and 3.1, and ejector efficiency (η) values between 7 % and 17 % at expansion ratio (re) values ranging from 89 to 150 for different nozzle throat separations (d) and different duct throat separations (D). Furthermore, this study presents a comprehensive investigation into predicting and optimizing ω, rc*, and η parameters using a multi-output gaussian process regression (GPR) model and a marine predators algorithm (MPA) approach. The multi-output GPR model was constructed to predict the relationships between boundary conditions (primary pressure (Pprimary) and secondary pressure (Psecondary)), geometric parameters (d and D), and the response variables (ω, rc*, and η). The model evaluation employed a 5-fold cross-validation technique to assess predictive performance, demonstrating strong predictive accuracy with low root mean square error (RMSE) and high coefficient of determination (R2) values. The optimization results revealed that the highest achieved values were ω = 0.303, rc* = 2.678, and η = 0.156, corresponding to specific parameter settings (Pprimary = 160 kPa, Psecondary = 1.8 kPa, d = 0.6 mm, and D = 2.3 mm). This study demonstrates the effectiveness of the multi-output GPR model for accurate prediction and the multi-objective MPA optimization approach for identifying optimal input parameters to maximize entrainment ratio, critical compression ratio, and ejector efficiency in fluid dynamics systems.

Keywords

• Radial flow ejectors
• Supersonic flow
• RANS turbulence models
• Detached eddy simulation
• Gaussian process regression
• Machine learning