The elemental equation governing heat transfer in aerodynamic flows is the internal energy equation. For a boundary layer flow, a double integration of the Reynolds-averaged form of this equation provides an expression of the wall heat flux in terms of the integrated effects, over the boundary layer, of various physical processes: turbulent dissipation, mean dissipation, turbulent heat flux, etc. Recently available direct numerical simulation data for a Mach 11 cold-wall turbulent boundary layer allows a comparison of the exact contributions of these terms in the energy equation to the wall heat flux with their counterparts modeled in the Reynolds-averaged Navier-Stokes (RANS) framework. Various approximations involved in RANS, both closure models as well as approximations involved in adapting incompressible RANS models to a compressible form, are assessed through examination of the internal energy balance. There are a number of potentially problematic assumptions and terms identified through this analysis. Here, the effect of compressibility corrections of the dilatational dissipation type is explored, as is the role of the modeled turbulent dissipation, in the context of wall heat flux predictions. The results indicate several potential avenues for RANS model improvement for hypersonic cold-wall boundary-layer flows.
Machine-learned models, specifically neural networks, are increasingly used as “closures” or “constitutive models” in engineering simulators to represent fine-scale physical phenomena that are too computationally expensive to resolve explicitly. However, these neural net models of unresolved physical phenomena tend to fail unpredictably and are therefore not used in mission-critical simulations. In this report, we describe new methods to authenticate them, i.e., to determine the (physical) information content of their training datasets, qualify the scenarios where they may be used and to verify that the neural net, as trained, adhere to physics theory. We demonstrate these methods with neural net closure of turbulent phenomena used in Reynolds Averaged Navier-Stokes equations. We show the types of turbulent physics extant in our training datasets, and, using a test flow of an impinging jet, identify the exact locations where the neural network would be extrapolating i.e., where it would be used outside the feature-space where it was trained. Using Generalized Linear Mixed Models, we also generate explanations of the neural net (à la Local Interpretable Model agnostic Explanations) at prototypes placed in the training data and compare them with approximate analytical models from turbulence theory. Finally, we verify our findings by reproducing them using two different methods.
The development of a next generation high-fidelity modeling code for wind plant applications is one of the central focus areas of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative. The code is based on a highly scalable framework, currently called Nalu-Wind. One key aspect of the model development is a coordinated formal validation program undertaken specifically to establish the predictive capability of Nalu-Wind for wind plant applications. The purpose of this document is to define the verification and validation (V&V) plan for the A2e high-fidelity modeling capability. It summarizes the V&V framework, identifies code capability users and use cases, describes model validation needs, and presents a timeline to meet those needs.
A blind CFD validation challenge is being organized for the unsteady transonic shock motion induced by the Sandia Axisymmetric Transonic Hump, which echoes the Bachalo-Johnson configuration. The wind tunnel and model geometry will be released at the start of the validation challenge along with flow boundary conditions. Primary data concerning the unsteady separation region will be released at the conclusion of the challenge after computational entrants have been submitted. This paper details the organization of the challenge, its schedule, and the metrics of comparison by which the models will be assessed.
An experimental characterization of the flow environment for the Sandia Axisymmetric Transonic Hump is presented. This is an axisymmetric model with a circular hump tested at a transonic Mach number, similar to the classic Bachalo-Johnson configuration. The flow is turbulent approaching the hump and becomes locally supersonic at the apex. This leads to a shock-wave/boundary-layer interaction, an unsteady separation bubble, and flow reattachment downstream. The characterization focuses on the quantities required to set proper boundary conditions for computational efforts described in the companion paper, including: 1) stagnation and test section pressure and temperature; 2) turbulence intensity; and 3) tunnel wall boundary layer profiles. Model characterization upstream of the hump includes: 1) surface shear stress; and 2) boundary layer profiles. Note: Numerical values characterizing the experiment have been redacted from this version of the paper. Model geometry and boundary conditions will be withheld until the official start of the Validation Challenge, at which time a revised version of this paper will become available. Data surrounding the hump are considered final results and will be withheld until completion of the Validation Challenge.
An implicit, low-dissipation, low-Mach, variable density control volume finite element formulation is used to explore foundational understanding of numerical accuracy for large-eddy simulation applications on hybrid meshes. Detailed simulation comparisons are made between low-order hexahedral, tetrahedral, pyramid, and wedge/prism topologies against a third-order, unstructured hexahedral topology. Using smooth analytical and manufactured low-Mach solutions, design-order convergence is established for the hexahedral, tetrahedral, pyramid, and wedge element topologies using a new open boundary condition based on energy-stable methodologies previously deployed within a finite-difference context. A wide range of simulations demonstrate that low-order hexahedral- and wedge-based element topologies behave nearly identically in both computed numerical errors and overall simulation timings. Moreover, low-order tetrahedral and pyramid element topologies also display nearly the same numerical characteristics. Although the superiority of the hexahedral-based topology is clearly demonstrated for trivial laminar, principally-aligned flows, e.g., a 1x2x10 channel flow with specified pressure drop, this advantage is reduced for non-aligned, turbulent flows including the Taylor–Green Vortex, turbulent plane channel flow (Reτ395), and buoyant flow past a heated cylinder. With the order of accuracy demonstrated for both homogenous and hybrid meshes, it is shown that solution verification for the selected complex flows can be established for all topology types. Although the number of elements in a mesh of like spacing comprised of tetrahedral, wedge, or pyramid elements increases as compared to the hexahedral counterpart, for wall-resolved large-eddy simulation, the increased assembly and residual evaluation computational time for non-hexahedral is offset by more efficient linear solver times. Finally, most simulation results indicate that modest polynomial promotion provides a significant increase in solution accuracy.
A new wind tunnel experiment is underway to provide a comprehensive CFD validation dataset of an unsteady, transonic flow. The experiment is based on the work of Bachalo and Johnson; an axisymmetric model with a spherical hump is tested at a transonic Mach number. The flow is turbulent approaching the hump and becomes locally supersonic at the apex. This leads to a shock-wave/boundary-layer interaction, an unsteady separation bubble, and flow reattachment downstream. A suite of diagnostics characterizes the flow: oil-flow surface visualization for shock and reattachment locations, particle image velocimetry for mean flow and turbulence properties, fast pressure-sensitive paint for model pressure distributions and unsteadiness, high-speed Schlieren for shock position and motion, and oil-film interferometry for surface shear stress. This will provide a new level of detail for validation studies; therefore, a blind comparison, or ‘CFD Challenge’ is proposed to the community. Participants are to be provided the geometry, incoming boundary layer, and boundary conditions, and are free to simulate with their method of choice and submit their results. A blind comparison will be made to the new experimental data, with the goal of evaluating the state of various CFD methods for use in unsteady, transonic flows.
Near-wall turbulence models in Large-Eddy Simulation (LES) typically approximate near-wall behavior using a solution to the mean flow equations. This approach inevitably leads to errors when the modeled flow does not satisfy the assumptions surrounding the use of a mean flow approximation for an unsteady boundary condition. Herein, modern machine learning (ML) techniques are utilized to implement a coordinate frame invariant model of the wall shear stress that is derived specifically for complex flows for which mean near-wall models are known to fail. The model operates on a set of scalar and vector invariants based on data taken from the first LES grid point off the wall. Neural networks were trained and validated on spatially filtered direct numerical simulation (DNS) data. The trained networks were then tested on data to which they were never previously exposed and comparisons of the accuracy of the networks’ predictions of wall-shear stress were made to both a standard mean wall model approach and to the true stress values taken from the DNS data. The ML approach showed considerable improvement in both the accuracy of individual shear stress predictions as well as produced a more accurate distribution of wall shear stress values than did the standard mean wall model. This result held both in regions where the standard mean approach typically performs satisfactorily as well as in regions where it is known to fail, and also in cases where the networks were trained and tested on data taken from the same flow type/region as well as when trained and tested on data from different respective flow topologies.
Deep - water offshore sites are an untapped opportunity to bring large - scale offshore wind energy to coastal population centers. The primary challenge has been the projected high costs for floating offshore wind systems. T his work presents a comprehensive investigat ion of a new opportunity for deep - water offshore wind using large - scale vertical axis wind turbines. Owing to inherent features of this technology , t here is a potential transformational opportunity to address the major cost drivers for floating w ind using vertical axis wind turbines . T he focus of this report is to evaluate the technical potential for this new technology. The approach to evaluating this potential wa s to perform system design studies focused on improving the understanding of technical performance parameters while l ooking for cost reduction opportunities. VAWT design codes we re developed in order to perform these design studies. To gain a better understanding of the desi gn space for floating VAWT systems , a comprehensive design study of multiple rotor configuration options was carried out . Floating platforms and moorings were then sized and evaluated for each of the candidate rotor configurations . Preliminary LCOE estimates and LCOE ranges were produced based on the design stu dy results for each of the major turbine and system components . The major outcomes of this study are a comprehensive technology assessment of VAWT performance and preliminary LCOE estimates that demonstrate that floating VAWTs may have favorable performanc e and costs in comparison to conventional HAWTs in the deep - water offshore environment where floating systems are required , indicating that this new technology warrants further study .