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Enhancing RANS fidelity in scramjets: A machine learning calibration of turbulence model parameters for coupled turbulent combusion
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DOI：10.1016/j.dt.2026.07.030.png)
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Abstract
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• A bi-level HSO-BNN framework globally calibrates SST model for scramjet combustion. • The method doubles convergence speed vs. conventional BNN, robust for tiny samples. • Calibrated model halves combustor wall pressure error ∼50%, clarifying baseline SST bias. • Framework reliably quantifies uncertainty, aiding refined scramjet design.
Keywords:
scramjet
turbulence-combustion interaction
Bayesian neural network
model parameter calibration
uncertainty quantification
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