A new journal article was recently published in the Journal of Computational Physics:

This paper presents a data-driven computational model for simulating unsteady turbulent flows, where sparse measurement data is available. The model uses the retrospective cost adaptation (RCA) algorithm to automatically adjust the closure coefficients of the Reynolds-averaged Navier-Stokes (RANS) k - ω turbulence equations to improve agreement between the simulated flow and the measurements. The RCA-RANS k - ωmodel is verified for steady flow using a pipe-flow test case and for unsteady flow using a surface-mounted-cube test case. Measurements used for adaptation of the verification cases are obtained from baseline simulations with known closure coefficients. These verification test cases demonstrate that the RCA-RANS k - ω model can successfully adapt the closure coefficients to improve agreement between the simulated flow field and a set of sparse flow-field measurements. Furthermore, the RCA-RANS k - ω model improves agreement between the simulated flow and the baseline flow at locations at which measurements do not exist. The RCA-RANS k - ω model is also validated with experimental data from 2 test cases: steady pipe flow, and unsteady flow past a square cylinder. In both test cases, the adaptation improves agreement with experimental data in comparison to the results from a non-adaptive RANS k - ω model that uses the standard values of the k - ω closure coefficients. For the steady pipe flow, adaptation is driven by mean stream-wise velocity measurements at 24 locations along the pipe radius. The RCA-RANS k - ω   model reduces the average velocity error at these locations by over 35%. For the unsteady flow over a square cylinder, adaptation is driven by time-varying surface pressure measurements at 2 locations on the square cylinder. The RCA-RANS k - ω   model reduces the average surface-pressure error at these locations by 88.8%.

[1] Li, Z., Bailey, S. C. C., Hoagg, J. B., and Martin, A., “A retrospective cost adaptive Reynolds-averaged Navier-Stokes k-w model for data-driven unsteady turbulent simulation,” Journal of Computational Physics, 2018.
DOI:10.1016/j.jcp.2017.11.037