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About this paper
Muhammad Irfan Zafar, Saleh Abuhanieh, Şahin Yiğit
The authors develop a learned initialization for steady Reynolds-averaged Navier–Stokes (RANS) solvers, predicting mean-flow and turbulence fields directly on the target mesh for the conventional solver to correct. On 95 eligible held-out CRM cases, an N=24 model achieved a 2.34× geometric-mean iteration speed-up versus 1.34× for nearest-condition restarts; this compares iterations, not elapsed time.
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