Error and uncertainty quantification of a Stochastic Noise Generation and Radiation model
D. Alonso (TU Delft - Aerospace Engineering)
A.H. van Zuijlen – Mentor (TU Delft - Aerodynamics)
D. Casalino – Graduation committee member (TU Delft - Wind Energy)
S.J. Hulshoff – Graduation committee member (TU Delft - Aerodynamics)
Rahim Rezaeiha – Mentor (ASML)
Nicholas Waterson – Mentor (ASML)
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Abstract
Accurately predicting flow-induced vibrations (FIV) in high-precision lithography equipment is challenging, as standard Reynolds-Averaged Navier-Stokes (RANS) simulations fail to capture the driving turbulent fluctuations.
This work evaluates the accuracy and uncertainty of a Stochastic Noise Generation and Radiation (SNGR) pipeline that reconstructs time-resolved, divergence-free velocity fluctuation fields from RANS statistics to improve in FIV and aero-acoustic assessments. Therefore, helping to ensure nanometer-scale manufacturing precision. The MATLAB based implementation ingests CGNS/HDF5 solver output, assembles modal Fourier fields from a prescribed energy spectrum, enforces incompressibility, applies anisotropic tensor mapping to match Reynolds stresses, and offers both a time-marching (single time-loop) and an ensemble snapshot mode. Validation is performed against canonical
Direct Numerical Simulation (DNS) reference data for turbulent channel flow (Lee & Moser, up to Reτ ≈ 5200)
and a benchmark backward-facing step, and comparisons are made with RANS (StarCCM+) results. A modular MATLAB pipeline automates diagnostics and produces slice-wise statistics (RMSE, absolute/relative errors), spatial heatmaps, PSDs, and GIF visualizations. Results show that SNGR successfully reproduces spectral content and the
spatial distribution of turbulence, yielding instantaneous fields that correlate with RANS TKE maps. However, the analysis reveals that discrepancies - particularly concentrated in near-wall amplitudes, outer-layer overshoots and the smallest resolved scales - are primarily inherited from biases in the initial RANS model. The report quantifies how these RANS biases, domain and boundary conditions choices and numeric resolution propagate into SNGR
reconstructions, and it recommends practical diagnostics and sensitivity checks for the robust industrial application of the SNGR method.