Evaluation of PANS Methodology With Nonlinear Eddy Viscosity Closure: Flow Past a Heated Sphere

The partially averaged Navier–Stokes (PANS) approach has emerged as a viable scale-resolving bridging method over the last decade. Conventional PANS method, based on the linear eddy viscosity closure, overcomes the scale-resolving inadequacies of Reynolds-averaging but still suffers from limitations arising from linear constitutive modeling of turbulent stresses. Linear PANS has been evaluated in a variety of complex flow fields, including the benchmark case of flow around a sphere. In this work, the authors assess the potential of nonlinear eddy viscosity closure and further extend the evaluation of nonlinear closure in predicting thermal characteristics (besides hydrodynamics) of flow past a sphere. The presented evaluation has been performed on the basis of various surface-related and wake-related quantities. Our results are compared against available experimental and direct numerical simulation (DNS)/large eddy simulation studies. Our study shows that for the same value of the filter-control parameters, nonlinear PANS performs significantly better than linear PANS.

A Functional Approach to Vertical Turbulent Transport of Scalars in the Atmospheric Surface Layer

Eddy covariance has been the de facto method of analyzing scalar turbulent transport data. To refine the information available from these data, we derive a simplified version of the turbulent scalar-transport equation for the surface layer, which employs a more explicit form of signal decomposition and dispenses with Reynolds averaging in favour of an averaging operator based on the relevant scalar-flux driving variables. The resulting method, termed functional covariance, provides five areas of improvement in flux estimation: (i) Better representation of surface fluxes through closer correspondence of turbulent exchange with variations in the driving variables. (ii) An approximate 25% reduction in flux uncertainty resulting from improved independence of turbulent-flux samples. (iii) Improved data retention through less onerous quality control (stationarity) testing. (iv) Improved estimation of low-frequency flux contributions through reduced uncertainty and avoidance of driving-variable nonstationarity. (v) Potential elimination of flux-storage estimation when state driving-variables are used to define the functional-covariance flux averaging. We describe the important considerations required for application of functional covariance, apply both functional- and eddy-covariance methods to an example dataset, compare the resulting eddy- and functional-covariance estimates, and demonstrate the aforementioned benefits of functional covariance.

STRUCT: A Second-Generation URANS Approach for Effective Design of Advanced Systems

This work presents the recently developed STRUCT hybrid turbulence model and assesses its potential to address the poor grid consistency and limited engineering applicability typical of hybrid models. Renouncing the ability to consistently bridge RANS, LES and DNS based on the computational grid size, we aim at addressing the engineering design needs with a different mindset. We opt to leverage the robustness and computational efficiency of URANS in all nearly homogeneous flow regions while extending it to locally resolve complex flow structures, where the concept of Reynolds averaging is poorly applicable. The proposed approach is best characterized as a second generation URANS closure, which triggers controlled resolution of turbulence inside selected flow regions. The resolution is controlled by a single-point parameter representing the turbulent timescale separation, which quantitatively identifies topological flow structures of interest. The STRUCT approach demonstrates LES-like capabilities on much coarser grids, and consistently increases the accuracy of the predictions from the baseline URANS at increasing grid finesse. The encouraging results show the potential to support effective design application through resolution of complex flow structures while controlling the computational cost. The ultimate objective is to continue improving the robustness and computational efficiency while further assessing the accuracy and range of applicability.