  Machine-learning (ML) techniques provide a new and encouraging perspective
for constructing turbulence models for Reynolds-averaged Navier--Stokes (RANS)
simulations. In this study, an iterative ML-RANS computational framework is
proposed that combines an ML algorithm with transport equations of a
conventional turbulence model. This framework maintains a consistent procedure
for obtaining the input features of an ML model in both the training and
predicting stages, ensuring a built-in reproducibility. The effective form of
the closure term is discussed to determine suitable target variables for the ML
algorithm, and the multi-valued problem of existing constitutive theory is
studied to establish a proper regression system for ML algorithms. The
developed ML model is trained under a cross-case strategy with data from
turbulent channel flows at three Reynolds numbers and \textit{a posteriori}
simulations of channel flows show that the framework is able to predict both
the mean flow field and turbulent variables accurately. Interpolation tests for
the channel flow show the proposed framework can reliably predict flow features
that lie between the minimum and maximum Reynolds numbers associated with the
training data. A further test related to the flow over periodic hills also
demonstrates a better result than a traditional turbulence model, indicating a
promising predictive capability of the developed ML model for separated flow
even though the model is only trained with planar channel flow data.
