SEARCH · Engineering Papers

Results for “RANS”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Mixed-element USM3D Contributions to the 4th AIAA High-Lift Prediction Workshop

This paper discusses results of the mixed-element USM3D (USM3D-ME) simulations performed for the 4th AIAA High-Lift Prediction Workshop. The workshop was separated into six Technical Focus Groups to investigate the impact of geometry modeling, grid, and computational methods for predicting high lift flows. This work was performed under the Fixed Grid RANS Technical Focus Group. The primary geometry selected for the workshop was the High-Lift Common Research Model. The performed simulations included a flap deflection study and both a grid refinement study and pitch sweep for the nominal flap deflection configuration. The results show that USM3D-ME RANS solutions, generally, tends to underpredict the lift coefficient and to predict a less negative pitching moment relative to the experimental data. The predicted drag coefficient values agree better with experiment for smaller angles of attack but were observed to be larger than experiment for the largest angle of attack simulated. The results of the grid refinement study demonstrated a lack of grid convergence for the provided grid family. The results of the grid refinement study are consistent with the submissions to the Fixed Grid and Mesh Adaptation Technical Focus Groups. Grid convergence for the provided grid family remains elusive for the international community. A 2D Multielement Airfoil configuration was included to enable a turbulence model verification study, which illustrated favorable agreement between USM3D-ME and the solutions provided by other flow solvers.

CFD↗

NASA NTRSDate not supplied

Mixed-Element USM3D Contributions to the 4th AIAA High-Lift Prediction Workshop

CFD↗

NASA NTRSDate not supplied

USM3D-ME Revised SA-neg Implementation and Assessment for Seventh Drag Prediction Workshop Solutions

Simulations performed by an unstructured-grid, cell-centered, finite-volume, mixed-element flow solver USM3D-MEwere presented at the Seventh Drag Prediction Workshop (DPW-7). Reynolds-averaged Navier-Stokes (RANS) solutions for the NASA high-speed common research model wing-body configuration were computed using a new implementation of the negative variant of the Spalart-Allmaras turbulence model (SA-neg) with hierarchical adaptive nonlinear iteration method (HANIM).ThisUSM3D-ME enhancement improved efficiency and robustness of RANS solutions resulting in fast iterative convergence to machine zero residuals on mixed-element unstructured grids provided by the DPW-7 organizing committee. This paper presents theoretical foundations for the new implementation of the SA-neg model and analyzesUSM3D-ME solutions for flow conditions corresponding to the required DPW-7 cases.

Boris Diskin↗

NASA NTRSDate not supplied

Simulations of Mach 0.8 Transonic Truss-Braced Wing Aircraft Aerodynamics at High Angles of Attack

This paper presents numerical simulations of the Mach 0.8 Transonic Truss-Braced Wing (TTBW) aircraft at high angle of attack using CFD solver FUN3D. Three different approaches - steady Reynolds Averaged Navier-Stokes (RANS), Unsteady Reynolds Averaged Navier-Stokes (URANS), and Delayed Detached Eddy Simulation (DDES) are used to simulation the TTBW aircraft at Mach number 0.8 and altitude 40, 000 ft. The pitch break phenomenon is observed at this flight condition. The simulation results show that the pitch break angle of attack occurs at 2.75˚ by DDES, 2.81◦ by URANS, and 2.89˚ by RANS. The DDES unsteady pressure begins to grow at angle of attack of 3.5˚, which might indicate the buffet onset, and rises one order of magnitude larger at angle of attack of 4.0˚. However, the URANS unsteady pressure begins to grow at angle of attack of 4.0˚. Both unsteady simulation results show the pitch break occurs before buffet onset.

TTBW↗

NASA NTRSDate not supplied

Modeling of Cavity Residence Time via Modeled Lagrangian Particle Tracking

One of the critical components in a scramjet is the cavity flame holder. In evaluating the sizing of this component, the residence time of the fuel in the cavity is a common parameter used. However, to obtain the residence time via CFD is an expensive undertaking. One method is to perform an LES simulation of the cavity and use Lagrangian particle tracking to obtain both the mean and probability density function of the time that the particles remain in the cavity. While this method affords a significant amount of information, it is very costly in CPU run time. A simpler method involves performing a time accurate RANS simulation of the cavity, and when converged, overlay a traceable scalar in the cavity region, and then record the decay of this scalar’s flux at a downstream location. The time scale of the decay rate can then be estimated and a residence time inferred. This method is less computationally expensive than the LES approach, but still requires a time-accurate solution while only providing a mean cavity residence time evaluation. In this paper a method of determining the mean cavity residence time as well as the Probability Density Function (PDF) of the residence time is proposed and demonstrated. This method is based on Lagrangian particle tracking modeled by the Generalized Langevin Equation and is implemented as a post-processing step for a steady RANS solution. Computational cost is on the order of minutes and the results are in agreement with values obtained by the LES and scalar decay methods.

Langevin↗

NASA NTRSDate not supplied

Deep Neural Network Based Convergence Classification for Computational Fluid Dynamics

A supervised deep learning approach is coupled with heuristic convergence criteria to construct a classification model for detecting the completion (convergence) of computational fluid dynamics (CFD) simulations. Heuristic convergence criteria alone are not always sufficient and more complex decisions are often left to a human analyst. The proposed approach leverages heuristic convergence criteria as well as two deep neural network (DNN) models, one binary and one multi-class, to improve the efficiency and consistency of convergence classification across a wide range of flight regimes. The DNN models presented are each trained on a subset of ascent aerodynamic CFD simulations for NASA’s Space Launch System and were produced using NASA’s unstructured Navier-Stokes solver FUN3D. Individual solutions are analyzed intermittently and are classified as sufficiently converged, further iterations required, or switch from steady Reynolds Averaged Navier-Stokes (RANS) to unsteady RANS CFD based on the iterative histories of four aerodynamic coefficients. The implemented classification model is shown to produce solutions that closely correlate to solutions produced by a human analyst. This work lays groundwork for expanding the capabilities of DNNs for automating and improving more of the CFD process.

SLS↗

NASA NTRSDate not supplied

Performance Characteristics of a Boundary Layer Ingesting Inlet - Distortion Tolerant Fan Through CFD Code Coupling

A method of constructing a high-fidelity simulation using two NASA CFD codes, Launch Ascent and Vehicle Aerodynamics (LAVA), which is the general-purpose solver, and a turbomachinery specific code NASA-TURBO (TURBO), will be presented. Validation of the code coupling is performed through comparison to experimental data obtained in the 2017 wind tunnel test of NASA’s Boundary Layer Ingesting Inlet - Distortion Tolerant Fan (BLI2DTF). For the simulation, the external flow field is computed by LAVA utilizing a steady-state Reynolds-averaged Navier-Stokes (RANS) methodology. Data from LAVA is exchanged with TURBO, which simulated the propulsor utilizing an unsteady-RANS methodology. A sensitivity analysis is performed using averaged and instantaneous data transfer between the two codes, as well as varying the amount of simulation time TURBO computes between updates. The results of the coupled simulations appear to replicate the BLI2DTF experiment correctly. Static pressure taps leading into and around the nacelle, total pressure ratio and efficiency of the propulsor, and profiles of total temperature, pressure, and Mach number at the rear measurement plane, aft of the propulsor, are in good agreement with experimental data. These results provide confidence that the coupled simulation is behaving in a physically realizable manner.

CFD↗

NASA NTRSDate not supplied

Near-Body Mesh Adaption for Transitional Flows Using OVERFLOW

Accurate modeling of boundary-layer transition is an important aspect of developing greener air transport technologies. In that regard, transition models based on auxiliary transport equations offer a robust approach that is easily integrated into the Reynolds-averaged Navier-Stokes (RANS) solvers. Recent workshops under NATO and AIAA have identified the verification of transport-equations-based transition modeling as a critical aspect of reducing the scatter between the predictions of different CFD codes. Follow-on work has highlighted the need for highly dense grids to achieve an asymptotic convergence of transition related flow metrics. The present work examines the role of automatic near-body mesh adaptation capability in the NASA OVERFLOW CFD solver to enable verification studies in an efficient manner, and for establishing best practices for designing grids for the RANS-based transition models. A sensor function relevant to the Langtry-Menter \gamma-Re_{\theta t}\ transition model has been identified and used for error-based mesh adaptation for canonical configurations comprising a flat plate, and the S809 and NLR-7301 airfoils. The efficacy of the mesh adaptation approach is assessed for flow conditions involving multiple transition scenarios such as natural transition, separation-induced transition, and shock-induced transition. The results from this exploratory study indicate that the meshes adapted using the proposed sensor provide solutions that approach the references solutions obtained with uniformly refined hand-crafted meshes, in terms of the chosen metrics, and yield modest yet significant savings in grid count. We also highlight areas for improvement in the grid adaptation methodology within OVERFLOW.

CFD↗

NASA NTRSDate not supplied

Near-Body Mesh Adaption for Transitional Flows Using OVERFLOW

CFD↗

NASA NTRSDate not supplied

NASA 5.2%-Scale High Lift Common Research Model (CRM-HL) Test in the National Transonic Facility (NTF)

Problem: Predicting CL,max and assessing transition, turbulence models, and Reynolds number effect predictions in CFD requires wind tunnel data for comparison. Objective: Expand the CFD validation database by running multiple models designed using the same reference geometry in various wind tunnels around the world. The wind tunnel data and model geometry will be open source to allow for CFD validation use. Approach: Run the NASA 5.2%-scale High Lift Common Research Model (CRM-HL) semi-span model at the National Transonic Facility (NTF). Test the model at chord Reynolds numbers (Rec) between 1.6 million and 30 million and at varying Q/Es to assess Reynolds number effects and aeroelasticity affects on the model, while collecting force and moment, pressure and wing deformation data. Test the model with ice shapes installed on the leading edges. Results: Ran eight model configurations, including four landing, two landing with ice shapes installed, and two takeoff configurations. Ran in air (120°F) and nitrogen operations (-50°F, -180°F and -250°F) at 7 different chord Reynolds numbers. The data compare well with previous tests of the same model at DLR (The Germany Aerospace Center) and the 14- by 22-Foot Subsonic Tunnel (14x22). Significance: Provided valuable high Reynolds number data on a high lift configuration to the worldwide research community. Provide icing data at flight Reynolds number to the research community.

CRM-HL↗

NASA NTRSDate not supplied

Unsteady CFD Simulations of a Compression Corner Geometry Using Wall-Modeled LES Methods in Loci/CHEM

Several wall-modeled large eddy simulation (WMLES) methods are tested by simulating an unsteady Mach 2.0 compression corner geometry in Mississippi State’s Loci/CHEM solver. This study is conducted to evaluate the usage and requirements of these WMLES methods for applications regarding fluctuating pressure environments on launch vehicles with computational fluid dynamics (CFD). Two hybrid Reynolds-averaged Navier-Stokes (RANS)-large eddy simulation (LES) methods, Dynamic Hybrid RANS-LES (DHRL) and Improved Delayed Detached Eddy Simulation (IDDES), and one wall-stress-model, the Algebraic Wall Model for Wall-Modeled LES (AWMLES), are tested on varying grid and timestep refinement levels. These grid and timestep sizes are chosen to test the minimum requirements for successfully running these WMLES methods. The simulations are evaluated based on turbulent boundary layer properties in the developed boundary layer as well as unsteady quantities relating to fluctuating pressure environments in the region of the compression corner. The DHRL method shows good agreement with the comparison wind tunnel data and shows good grid and timestep convergence. The results from the IDDES and WMLES simulations show good agreement for several quantities with some discrepancies regarding others. The results presented in this paper will be used to inform further studies in predicting unsteady environments on higher-complexity geometries.

Bryson Frank↗

DOE OSTI2020

Evaluating turbulence models at high energy densities

The RESHOCK campaign at Lawrence Livermore National Laboratory has been working on increasing our understanding of the evolution of turbulent, unstable plasma interfaces which are applicable in various HED applications including ICF implosions. A common approach to model such interfaces involves the use of Renolds-Averaged-Navier-Stokes (RANS) models whose parameters have been constrained by theory and experiments. These parameter sets are not unique and the applicability of one set of tuned model parameters to systems with differing shock strengths or material densities has not been well tested. Our new NIF experiments, along with related studies of turbulent shear flows, provide data and model validation for plasma interfaces in the HED regime. Our experiments measure mixing-layer width at an unstable interface in the plasma regime and are specifically designed to challenge mix models of the Reynolds-Averaged-Navier-Stokes (RANS) type where turbulence is assumed to be fully developed, i.e. the flow exhibits a broad spectrum of length scales without memory of the initial condition.4 We utilize precise control of the initial interface conditions (densities on both sides of the interface, an initial rippled perturbation pattern consisting of equally weighted wavelengths between 10 and 20 microns, and initial material compositions) along with a repeatable drive history

36 MATERIALS SCIENCE↗

DOE OSTI2021

Progress on Optimizing Wind Farms and Rotor Designs Using Adjoints

Modern wind plants are increasingly tasked with multiple performance objectives. In addition to designing plants that maximize power output and minimize the levelized cost of energy (LCOE), the design and operation of wind plants is increasingly influenced by challenges regarding grid integration of variable generation renewables. This places a growing emphasis on making wind plants more controllable and predictable. WindSE is a Reynolds-averaged Navier-Stokes (RANS) model designed around analytical gradient and adjoint methods, with the ability to capture terrain-induced effects, as shown in Figure 1. The recent addition of an unsteady solver with an actuator line method (ALM) and ongoing work to enable massively parallel optimizations gives it a unique niche to explore coupled plant-level controls and design problems. This code is an open source python package built on the FEniCS framework that utilizes fast, parallel PETSc solvers to model fluid flow throughout wind-farm scale domains. Two recent studies performed using WindSE demonstrate the capability to optimize under a wide variety of flow conditions and objective functions. In the first, we present an optimization focused on modifying the layout of a wind farm with a fixed number of turbines for maximum total power output [1]. This study highlights the ability to quickly perform simulations using the steady Navier-Stokes solver combined with rotors represented as actuator disks while also stressing the importance of capturing terrain-induced effects. Gradient-based optimization using the RANS equations is viable due to the inclusion of efficiently computed adjoint derivatives. We interpret the physical results of the optimal layout and also discuss the computational cost of scaling to larger problems. In the second study, we present the capabilities of the unsteady Navier-Stokes solver, where rotor-blade profiles represented by actuator lines are optimized to enhance wake steering effects and overall power production [2]. We quantify the wind plant performance gains obtained from this type of simultaneous control co-design optimization as compared to optimizing the blade design and yaw independently. Figure 2 shows the differences between a baseline two-turbine system and an optimized system where we fine-tune the blade chord profile. Results and challenges from each study are quickly summarized and used to motivate the current development efforts within WindSE. Current and future work is focused on enabling higher-resolution studies with more degrees of freedom through parallelization of both the simulation and optimization algorithms. We present benchmarking results to show that WindSE performs well in both weak- and strong-scaling tests and further demonstrate that the optimizer obtains the same convergence rates in both shared- and distributed-memory environments. Using larger wind farms, we can study deep-array effects within an optimization context, allowing the use of objective functions that have been previously unstudied. As an example, we present ongoing work on a blockage metric which characterizes the loss of available kinetic energy due to wake effects from multiple upstream turbines.

adjoint optimization↗

DOE OSTI2024

Measurements of the Neutron Magnetic Form Factor and the Two-Photon Exchange Contribution to the Electron-Neutron Elastic Scattering Cross Section

The Super BigBite Spectrometer (SBS) program in Experimental Hall A of Jefferson Lab is a series of high-precision, large ?2 , electromagnetic form factor experiments. GMn and nTPE were the first two experiments of the SBS program and ran from Oc?tober 2021 through February 2022. Both experiments detect neutron-tagged ?(?, ???) and proton-tagged ?(?, ???) quasi-elastic electrons scattering from a deuterium tar?get. The GMn experiment is a measurement of the magnetic form factor of the neutron, ?? ?, via the ratio method over a ?2 range of 3.5 to 13.5 (GeV/c)2 . The nTPE experiment ran in-parallel with the GMn experiment and shared an overlap?ping kinematic point with GMn. nTPE extends the GMn experiment to assess the differences between Rosenbluth Slop and the single-photon exchange approximation. The nTPE experiment had two kinematic settings corresponding to different electron scatting angles at a fixed ?2 of 4.5 (GeV/c)? . Comparisons of nucleon form factor ratio measurements acquired through polarization transfer and LT separation reveal discrepancies as ?2 increases. Two-photon exchange is strongly favored as the reason for this discrepancy. ?? ? has been extracted at two kinematic settings corresponding to ?2 values of 3.0 and 4.5 (GeV/c)2 . Additionally, I will present an assessment of the differences between Rosenbluth Separation and single-photon exchange measure?ments.

Boyd III, John Alan↗

DOE OSTI2025

CFD Simulations of Lower Plenum Mixing

Review of model development and validation performed in the Advanced Reactor Technologies (ART) program for thermal mixing at the outlet of High Temperature Gas Reactors (HTGRs). Understanding the mixing that occurs in the lower plenum in an HTGR is necessary to facilitate design improvements and to perform reactor safety analysis. Numerical models are one possible approach to gain a better understanding of mixing in the lower plenum. Given the complexity of the geometry and the intense mixing present, it is important to perform validation of numerical models. Three models have been developed during FY2025: a porous media with Pronghorn, a Reynolds Averaged Navier Stokes (RANS) with STAR-CCM+, and a Large Eddy Simulation (LES) with NekRS. The reference facility is a scaled-down version of the lower plenum of the High Temperature Gas-Cooled Reactor - Pebble-bed Module (HTR-PM) demonstration reactor. Preliminary results of the porous media and the RANS shows general good agreement against experimental benchmark data. Future work will leverage high-fidelity results obtained through LES to guide model selection and improvements to the lower-fidelity models, with particular attention to the Pronghorn porous media.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

An adaptive knowledge-based data-driven approach for turbulence modeling using ensemble learning technique under complex flow configuration: 3D PWR sub-channel with DNS data

This work describes a new approach to increase the accuracy of Reynolds-averaged Navier–Stokes (RANS) in modeling turbulence flow leveraging the machine learning technique. Traditionally, different turbulence models for Reynolds stress are developed for different flow patterns based on human knowledge. Each turbulence model has a certain application domain and prediction uncertainty. In recent years, with the rapid improvements of machine learning techniques, researchers start to develop an approach to compensate for the prediction discrepancy of traditional turbulence models with statistical models and data. However, the approach has deficiencies in several aspects. For example, the amount of human knowledge introduced to the statistical model couldn’t be controlled, which makes the statistical model learn from a very naïve stage and limits its application. In this work, a new approach is developed to address those deficiencies. Here, the new approach uses the “ensemble learning” technique to control the amount of human knowledge introduced into the statistical model. Therefore, the new approach could be adaptive to the multiple application domains. In conclusion, according to the results of case study, the new approach shows higher accuracy than both traditional turbulence models and the previous machine learning approach.

42 ENGINEERING↗

DOE OSTI2023

Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers

This dataset contains aerodynamic quantities - including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) - for 1,830 airfoil shapes computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes were designed using the separable shape tensor parameterization that encodes two-dimensional shapes as elements of the Grassmann manifold. This data-driven approach learns two independent spaces of parameter from a collection of sample airfoils. The first captures large-scale, linear perturbations, and the second defines small-scale, higher-order perturbations. For this dataset, we used the G2Aero database of over 19,000 airfoil shapes to learn a parameter space that captured a wide array of shape characteristics. We sampled airfoil designs over both parameter spaces to explore the full range of possible shape variations. The aerodynamic quantities for the generated airfoil were obtained using the HAM2D code, which is a finite-volume Reynolds-averaged Navier-Stokes (RANS) flow solver. We employ a fifth-order WENO scheme for spatial reconstruction with Roe's flux difference scheme for inviscid flux and second-order central differencing for viscous flux. A preconditioned GMRES method is applied for implicit integration. The Spalart-Allmaras 1-eq turbulence model is used for the turbulence closure, and the Medida-Baeder 2-eq transition model is applied to account for the effects of laminar turbulent transition. The airfoil grid is generated with a total of 400 points on the airfoil surface, the initial wall-normal spacing of y+ = 1, and an outer boundary located at 300 chord lengths away from the wall. The CFD simulations are performed at a freestream Mach number of 0.1, for or three different Reynolds' numbers (3M, 6M, and 9M), and for 25 angles of attack from -4 deg. to 20 deg. with 1 degree increments. Across all these various parameters, this dataset includes the results from over 250,000 CFD simulations. The simulations were performed using the Bridges-2 system at the Pittsburgh Supercomputing Center in February 2023 as part of the INTEGRATE project funded by the Advanced Research Projects Agency - Energy, in the U.S. Department of Energy. The data was collected, reformatted, and preprocessed for this OEDI submission in July 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided. The .h5 data file structure can be found in the GitHub Repository resource under explore_airfoil_2k_data.ipynb.

2k↗

DOE OSTI2023

Airfoil Computational Fluid Dynamics - 9k shapes, 2 AoA's

This dataset contains aerodynamic quantities - including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) - for 8,996 airfoil shapes, computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes were designed using the separable shape tensor parameterization that encodes two-dimensional shapes as elements of the Grassmann manifold. This data-driven approach learns two independent spaces of parameter from a collection of sample airfoils. The first captures large-scale, linear perturbations, and the second defines small-scale, higher-order perturbations. For this data, we used the G2Aero database of over 19,000 airfoil shapes to learn a parameter space that captured a wide array of shape characteristics. We fixed the linear deformations to be the mean over the database and sampled new shapes over a four-dimensional parameter space of higher-order perturbation. This sampling approaches allows for isolated analysis of non-linear airfoil shape deformations while holding other aspects (e.g., airfoil thickness) approximately constant. The aerodynamic quantities for the generated airfoil were obtained using the HAM2D code, which is a finite-volume Reynolds-averaged Navier-Stokes (RANS) flow solver. We employ a fifth-order WENO scheme for spatial reconstruction with Roe's flux difference scheme for inviscid flux and second-order central differencing for viscous flux. A preconditioned GMRES method is applied for implicit integration. The Spalart-Allmaras 1-eq turbulence model is used for the turbulence closure, and the Medida-Baeder 2-eq transition model is applied to account for the effects of laminar turbulent transition. The airfoil grid is generated with a total of 400 points on the airfoil surface, the initial wall-normal spacing of y+ = 1, and an outer boundary located at 300 chord lengths away from the wall. The CFD simulations are performed at a freestream Mach number of 0.1, Reynolds number of 9M, and at two angles of attack, 4 deg. and 12 deg. The simulations were performed using the Bridges-2 system at the Pittsburgh Supercomputing Center in February 2023 as part of the INTEGRATE project funded by the Advanced Research Projects Agency - Energy in the U.S. Department of Energy. The data was collected, reformatted, and preprocessed for this OEDI submission in July 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided. The .h5 data file structure can be found in the GitHub Repository resource under explore_airfoil_9k_data.ipynb.

9k↗