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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.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Modeling and simulation of a turbulent far wake

Work continued on two projects which had been started during previous years. Both projects involve calculations of the subsonic, turbulent far wake of a two-dimensional object at a Reynolds number of 1000 (based on wake momentum thickness). This flow was used as a test case for direct comparison of various turbulence models and a direct numerical simulations (DNS) of this flow were undertaken. In the turbulence model comparison studies, for any particular model tested, a unique self-similar solution was obtained far enough downstream, regardless of inlet conditions. Furthermore, different turbulence models led to different far-wake equilibrium solutions. No turbulence model could correctly predict all features of the turbulent far wake. For example, the spreading rate and turbulent shear stresses were underpredicted by all the standard models (both two-equation and full Reynolds stress models). In cases where a more correct spreading rate was achieved, it was at the expense of the turbulent kinetic energy, which was overpredicted. In general, the Algebraic Dissipation Rate Model of Gatski and Speziale, 1992, when added to any of the standard models, improved the results dramatically. Also, full Reynolds stress closure models did a much better job at predicting the shapes of both the mean and turbulence profiles, but the spreading rate was not significantly improved over that predicted by the simpler two-equation models. There are two main conclusions from these studies: First, in a comparison such as this, it is not enough to compare just one parameter, like the spreading rate. A good prediction for one parameter does not necessarily imply good predictions for all parameters in a flow. Second, since no turbulence model could correctly predict the turbulent far wake, much of the important physics of turbulent free shear flows is apparently lost by the assumptions inherent in today's methods of turbulence modeling; turbulence models must be improved. Direct simulations of this flow were begun last year in order to provide a data base through which some of the deficiencies of the existing turbulence models could be identified. Quantities such as the pressure-strain correlation, turbulent diffusion, and the dissipation rate tensor can be easily calculated from the DNS results, whereas these quantities are nearly impossible to measure experimentally. Improvements to existing turbulence models (and development of new models) require knowledge about flow quantities such as these. During this summer, diagnostics codes were written which will calculate the parameters mentioned above, along with other single-point and multi-point statistics. The DNS calculations are still in progress at the time of this writing. When these calculations are complete, the diagnostics codes will be applied so that the results can aid turbulence modelers. In addition, the results will show whether or not there exists a universal equilibrium turbulent far wake, independent of initial conditions.

Cimbala, John M.↗
NASA NTRS1999

Analyses of Fatigue Crack Growth and Closure Near Threshold Conditions for Large-Crack Behavior

A plasticity-induced crack-closure model was used to study fatigue crack growth and closure in thin 2024-T3 aluminum alloy under constant-R and constant-K(sub max) threshold testing procedures. Two methods of calculating crack-opening stresses were compared. One method was based on a contact-K analyses and the other on crack-opening-displacement (COD) analyses. These methods gave nearly identical results under constant-amplitude loading, but under threshold simulations the contact-K analyses gave lower opening stresses than the contact COD method. Crack-growth predictions tend to support the use of contact-K analyses. Crack-growth simulations showed that remote closure can cause a rapid rise in opening stresses in the near threshold regime for low-constraint and high applied stress levels. Under low applied stress levels and high constraint, a rise in opening stresses was not observed near threshold conditions. But crack-tip-opening displacement (CTOD) were of the order of measured oxide thicknesses in the 2024 alloy under constant-R simulations. In contrast, under constant-K(sub max) testing the CTOD near threshold conditions were an order-of-magnitude larger than measured oxide thicknesses. Residual-plastic deformations under both constant-R and constant-K(sub max) threshold simulations were several times larger than the expected oxide thicknesses. Thus, residual-plastic deformations, in addition to oxide and roughness, play an integral part in threshold development.

Newman, J. C., Jr.↗
DOE OSTI2025

Ethical considerations in infectious disease modelling for public health policy: the case of school closures

Mathematical models of infectious diseases are frequently used as a tool to support public health policy and decisions around the implementation of interventions such as school closures. However, most publications on policy-relevant modelling lack an ethical framework and do not explicitly consider the ethical implications of the work. This creates a risk that the unintended consequences of interventions are overlooked or that models are used to justify decisions that are inconsistent with public health ethics. In this article, we focus on the case study of school closures as a commonly modelled intervention against pandemic influenza, COVID-19 and other infectious disease threats. We briefly review some of the key concepts in public health ethics and describe approaches to modelling the effects of school closures. We then identify a series of ethical considerations involved in modelling school closures. These include accounting for population heterogeneity and inequalities; including a diversity of viewpoints and expertise in model design; considering the distribution of benefits and harms; and model transparency and contextualization. Furthermore, we conclude with some recommendations to ensure that policy-relevant modelling is consistent with some key ethics values.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

Model for premixed turbulent flames

Flame speed and mean degree of reaction profiles are determined for an ensemble mean one-dimensional premixed turbulent flame. Elementary closure models are employed to treat turbulence closure problems arising from the nonlinear convective and chemical reaction rate terms in the time-dependent fuel conservation equation; a constant eddy diffusivity is useed to model correlation terms derived from the convective term. An expression developed for fuel consumption is solved to obtain the turbulent flame speed and the variation of mean fuel mass fraction across the flame. Comparison of the numerical analysis with experimental data indicates the importance of the quenching of the combustion reaction in high-wavenumber eddies.

Gouldin, F. C.↗
NASA NTRS2003

Analytical and Experimental Study of Near-Threshold Interactions Between Crack Closure Mechanisms

The results of an analytical closure model that considers contributions and interactions between plasticity-, roughness-, and oxide-induced crack closure mechanisms are presented and compared with experimental data. The analytical model is shown to provide a good description of the combined influences of crack roughness, oxide debris, and plasticity in the near-threshold regime. Furthermore, analytical results indicate that closure mechanisms interact in a non-linear manner such that the total amount of closure is not the sum of closure contributions for each mechanism.

Newman, John A.↗
NASA NTRS1975

Statistical model for premixed turbulent flames

Statistical concepts and physical arguments are used to develop closure models for the mean energy equation governing one-dimensional, premixed, intense turbulent flames. It is proposed that the turbulent transport terms can be modeled by a turbulent conductivity based on turbulence kinetic energy (k) and energy dissipation rate (epsilon). The models obtained are used to derive correlations for the turbulent flame speed. Results show that there are three different flame regimes: a low-intensity turbulence, wrinkled flame; a high-intensity, fast-chemistry flame; and a high-intensity, finite-rate-chemistry flame.

Gouldin, F. C.↗
DOE OSTI2024

Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Idealized Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data

Abstract We introduce a machine learned surrogate model from high-resolution simulation data to capture the subgrid-scale effects in dry, stratified atmospheric flows. We use deep neural networks (NNs) to model the spatially local state differences between a coarse-resolution simulation and a high-resolution simulation. The setup enables the capture of both dissipative and antidissipative effects in the state differences. The NN model is able to accurately capture the state differences in offline tests outside the training regime. In online tests intended for production use, the NN-coupled coarse simulation has higher accuracy over a significant period of time compared to the coarse-resolution simulation without any correction. We provide evidence of the capability of the NN model to accurately capture high-gradient regions in the flow field. With the accumulation of the errors, the NN-coupled simulation becomes computationally unstable after approximately 90 coarse simulation time steps. Insights gained from these surrogate models further pave the way for formulating stable, complex, physics-based spatially local NN models which are driven by traditional subgrid-scale turbulence closure models. Significance Statement Flows in the atmosphere are highly chaotic and turbulent, comprising flow structures of broad scales. For effective computational modeling of atmospheric flows, the effects of the small- and large-scale structures need to be captured by the simulations. Capturing the small-scale structures requires fine-resolution simulations. Even with the current state-of-the-art supercomputers, it can be prohibitively expensive to simulate these flows when computed for the entire earth over climate time scales. Thus, it is necessary to focus on the larger-scale structures using a coarse-resolution simulation while capturing the effects of the smaller-scale structures using some parameterization (approximation) scheme and incorporating it into the coarse-resolution simulation. We use machine learning to model the effects of the small-scale structures (subgrid-scale effects) in atmospheric flows. Data from a fine-resolution simulation is used to compute the missing subgrid-scale effects in coarse-resolution simulations. We then use machine learning models to approximate these differences between the coarse- and fine-resolution simulations. We see improved accuracy for the coarse-resolution simulations when corrected using these machine learned models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Turbulence kinetic energy equation for dilute suspensions

A multiphase turbulence closure model is presented which employs one transport equation, namely the turbulence kinetic energy equation. The proposed form of this equation is different from the earlier formulations in some aspects. The power spectrum of the carrier fluid is divided into two regions, which interact in different ways and at different rates with the suspended particles as a function of the particle-eddy size ratio and density ratio. The length scale is described algebraically. A mass/time averaging procedure for the momentum and kinetic energy equations is adopted. The resulting turbulence correlations are modeled under less retrictive assumptions comparative to previous work. The closures for the momentum and kinetic energy equations are given. Comparisons of the predictions with experimental results on liquid-solid jet and gas-solid pipe flow show satisfactory agreement.

Abou-Arab, T. W.↗
NASA NTRS2002

Elliptic Relaxation of a Tensor Representation for the Redistribution Terms in a Reynolds Stress Turbulence Model

A formulation to include the effects of wall proximity in a second-moment closure model that utilizes a tensor representation for the redistribution terms in the Reynolds stress equations is presented. The wall-proximity effects are modeled through an elliptic relaxation process of the tensor expansion coefficients that properly accounts for both correlation length and time scales as the wall is approached. Direct numerical simulation data and Reynolds stress solutions using a full differential approach are compared for the case of fully developed channel flow.

Carlson, J. R.↗
DOE OSTI2026

Code for the manuscript "Lagrangian Attention Tensor Networks for Velocity Gradient Statistical Mode

We disclose a python/pytorch implementation of the physics-informed machine learning algorithm described in "Lagrangian Attention Tensor Networks for Velocity Gradient Statistical Modeling", LA-UR-24-30678. Direct numerical simulation (DNS) of ubiquitous turbulence phenomena is computationally infeasible for realistic flows. As a result, reduced modeling for turbulent flows aim to reduce the number of resolved scales while retaining accurate representations of the small-scale physics. The dynamics of the velocity gradient tensor (VGT) is a key ingredient in reduced or subgrid turbulence models. The evolution equation for the VGT involves nonlocal terms, requiring closure modeling. This implementation of the novel methodology of Lagrangian Attention Tensor Networks (LATN), utilizes a structured representation of the history of the VGT to inform a physics-informed machine learning algorithm. This addition of structured memory terms is shown to outperform previous models when trained and evaluated on DNS data.

Livescu, Daniel [LANL]↗
DOE OSTI2013

RANS Simulation VBM of Single Full Scale DOE RM1 MHK Turbine

Attached are the .cas and .dat files along with the required User Defined Functions (UDFs) and look-up table of lift and drag coefficients for Reynolds Averaged Navier-Stokes (RANS) simulation of a single full scale DOE RM1 turbine implemented in ANSYS FLUENT CFD-package. In this case study the flow field around and in the wake of the full scale DOE RM1 turbine is simulated using Blade Element Model (a.k.a Virtual Blade Model [VBM]) by solving RANS equations coupled with k-\omega turbulence closure model. It should be highlighted that in this simulation the actual geometry of the rotor blade is not modeled. The effect of turbine rotating blades are modeled using the Blade Element Theory. This simulation provides an accurate estimate for the performance of device and structure of it's turbulent far wake. Due to the simplifications implemented for modeling the rotating blades in this model, VBM is limited to capture details of the flow field in near wake region of the device.

16 TIDAL AND WAVE POWER↗
DOE OSTI2014

RANS Simulation VBM of Single Lab Scaled DOE RM1 MHK Turbine

Attached are the .cas and .dat files for the Reynolds Averaged Navier-Stokes (RANS) simulation of a single lab-scaled DOE RM1 turbine implemented in ANSYS FLUENT CFD-package. The lab-scaled DOE RM1 is a re-design geometry, based of the full scale DOE RM1 design, producing same power output as the full scale model, while operating at matched Tip Speed Ratio values at reachable laboratory Reynolds number (see attached paper). In this case study the flow field around and in the wake of the lab-scaled DOE RM1 turbine is simulated using Blade Element Model (a.k.a Virtual Blade Model [VBM]) by solving RANS equations coupled with k-\omega turbulence closure model. It should be highlighted that in this simulation the actual geometry of the rotor blade is not modeled. The effect of turbine rotating blades are modeled using the Blade Element Theory. This simulation provides an accurate estimate for the performance of device and structure of it's turbulent far wake. Due to the simplifications implemented for modeling the rotating blades in this model, VBM is limited to capture details of the flow field in near wake region of the device. The required User Defined Functions (UDFs) and look-up table of lift and drag coefficients are included along with the .cas and .dat files.

16 TIDAL AND WAVE POWER↗
DOE OSTI2016

RANS Simulation VBM of Array of Three Coaxial Lab Scaled DOE RM1 MHK Turbine with 5D Spacing

Attached are the .cas and .dat files along with the required User Defined Functions (UDFs) and look-up table of lift and drag coefficients for the Reynolds Averaged Navier-Stokes (RANS) simulation of three coaxially located lab-scaled DOE RM1 turbine implemented in ANSYS FLUENT CFD-package. The lab-scaled DOE RM1 is a re-design geometry, based of the full scale DOE RM1 design, producing same power output as the full scale model, while operating at matched Tip Speed Ratio values at reachable laboratory Reynolds number (see attached paper). In this case study the flow field around and in the wake of the lab-scaled DOE RM1 turbines in a coaxial array is simulated using Blade Element Model (a.k.a Virtual Blade Model [VBM]) by solving RANS equations coupled with k-\omega turbulence closure model. It should be highlighted that in this simulation the actual geometry of the rotor blade is not modeled. The effect of turbine rotating blades are modeled using the Blade Element Theory. This simulation provides an accurate estimate for the performance of each device and structure of their turbulent far wake. The results of these simulations were validated against the developed in-house experimental data. Simulations for other turbine configurations are available upon request.

16 TIDAL AND WAVE POWER↗
DOE OSTI2017

Machine Learning for Turbulence Modeling

This work was conducted as part of a Harry S. Truman Fellowship Laboratory Directed Research and Development project. The goal was to use machine learning methods to provide uncertainty quantification and model improvements for Reynolds Averaged Navier Stokes (RANS) turbulence models. For applications of interest in energy, safety, and security, it is critical to be able to model turbulence accurately. Current RANS models are unreliable for many flows of engineering relevance. Machine learning provides an avenue for developing improved models based on the data generated by high fidelity simulations. In this project, machine learning methods were used to predict when current RANS models would fail. They were also used to develop improved RANS closure models. A key aim was developing a tight feedback loop between scientific domain knowledge and data driven methods. To this end, a methodology for incorporating known invariance constraints into the machine learning models was proposed and evaluated. This work demonstrated that incorporating known constraints into the data driven models provided improved performance and reduced computational cost. This research represents one of the first applications of deep learning to turbulence modeling.

42 ENGINEERING↗
DOE OSTI2021

Integrating CVMix into GOTM (v6.0): a consistent framework for testing, comparing, and applying ocean mixing schemes

Abstract. The General Ocean Turbulence Model (GOTM) is a one-dimensional water column model, including a set of state-of-the-art turbulence closure models, and has widely been used in various applications in the ocean modeling community. Here, we extend GOTM to include a set of newly developed ocean surface vertical mixing parameterizations of Langmuir turbulence via coupling with the Community Vertical Mixing Project (CVMix). A Stokes drift module is also implemented in GOTM to provide the necessary ocean surface waves information to the Langmuir turbulence parameterizations, as well as to facilitate future development and evaluation of new Langmuir turbulence parameterizations. In addition, a streamlined workflow with Python and Jupyter notebooks is also described, enabled by the newly developed and more flexible configuration capability of GOTM. The newly implemented Langmuir turbulence parameterizations are evaluated against theoretical scalings and available observations in four test cases, including an idealized wind-driven entrainment case and three realistic cases at Ocean Station Papa, the northern North Sea, and the central Baltic Sea, and compared with the existing general length scale scheme in GOTM. The results are consistent with previous studies. This development extends the capability of GOTM towards including the effects of ocean surface waves and provides useful toolsets for the ocean modeling community to further study the effects of Langmuir turbulence in a broader scope.

58 GEOSCIENCES↗
NASA NTRS1977

Vortex interactions and decay in aircraft wakes

The dynamic interaction of aircraft wake vortices was investigated using both inviscid and viscous models. For the viscous model, a computer code was developed using a second-order closure model of turbulent transport. The phenomenon of vortex merging which results in the rapid aging of a vortex wake was examined in detail. It was shown that the redistribution of vorticity during merging results from both convective and diffusive mechanisms.

Bilanin, A. J.↗
NASA NTRS2017

Uncertainty Quantification of CFD Data Generated for a Model Scramjet Isolator Flowfield

Computational fluid dynamics is now considered to be an indispensable tool for the design and development of scramjet engine components. Unfortunately, the quantification of uncertainties is rarely addressed with anything other than sensitivity studies, so the degree of confidence associated with the numerical results remains exclusively with the subject matter expert that generated them. This practice must be replaced with a formal uncertainty quantification process for computational fluid dynamics to play an expanded role in the system design, development, and flight certification process. Given the limitations of current hypersonic ground test facilities, this expanded role is believed to be a requirement by some in the hypersonics community if scramjet engines are to be given serious consideration as a viable propulsion system. The present effort describes a simple, relatively low cost, nonintrusive approach to uncertainty quantification that includes the basic ingredients required to handle both aleatoric (random) and epistemic (lack of knowledge) sources of uncertainty. The nonintrusive nature of the approach allows the computational fluid dynamicist to perform the uncertainty quantification with the flow solver treated as a "black box". Moreover, a large fraction of the process can be automated, allowing the uncertainty assessment to be readily adapted into the engineering design and development workflow. In the present work, the approach is applied to a model scramjet isolator problem where the desire is to validate turbulence closure models in the presence of uncertainty. In this context, the relevant uncertainty sources are determined and accounted for to allow the analyst to delineate turbulence model-form errors from other sources of uncertainty associated with the simulation of the facility flow.

Baurle, R. A.↗
NASA NTRS1988

An investigation of turbulence models

The accuracy to which a turbulent boundary layer or wake can be predicted numerically depends on the validity of the turbulence closure model used. The modeling of turbulence physics is one of the most difficult problems in computational fluid dynamics (CFD). In fact, it is one of the pacing factors in the development of CFD. In general, there are three main approaches to the description of trubulence physics. First is turbulence modeling in which the Reynolds averaged Navier-Stokes equations are used and some closure approximation is made for the Reynolds stresses. A second approach to turbulence is large eddy simulation (LES) in which the computational mesh is taken to be fine enough that the large scale structure of the turbulence can be calculated directly. An empirical assumption must be made for the small scale sub-grid turbulence. The third approach is direct simulation. In this technique the Navier-Strokes equations are solved directly on a mesh which if fine enough to resolve the smallest length scale of the turbulence. The Reynolds averaged equations are not used and no closure assumption is required. These last two approaches require extensive computer resources and as such are not engineering tools. The purpose of the work was to investigate the various engineering turbulence models for accuracy and ease of programming. This involved comparison of the models with each other and with experimental data.

Doria, Michael L.↗