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Results for “computational fluid dynamics simulation”

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 1,549 records · Page 86

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NASA NTRSDate not supplied

Computational Investigation of the Effect of Chemistry on Mars Retropropulsion Environments using a Massively Parallel GPU Approach

In this work, we investigate the effects of chemistry on a human-scale Mars lander concept using scale-resolving computational fluid dynamics (CFD) with finite-rate chemistry and a graphics processing unit (GPU)-enabled implementation of the NASA FUN3D flow solver, enabling run-times of a few days for the simulations presented. Simulations are carried out on Summit at Oak Ridge Leadership Computing Facility using thousands of GPUs. Retropropulsion ground tests require significant compromises on physical scale, instrumentation, configuration, and environments. Ground tests of retropropulsion configurations thus far have neglected effects of chemistry due to physical constraints of wind tunnel models and facilities; most experiments use inert simulant gases at low temperatures. As such, a strong reliance on high-fidelity computational analyses such as those presented in this work is required to expand the knowledge of retropropulsion aerodynamics. An overview of the GPU approach will be presented. Results are compared to a previous scaled perfect gas (air) campaign.

retropropulsion↗

DOE OSTI2024

Assessment of Machine Learning Wall Modeling Approaches for Large Eddy Simulation of Gas Turbine Film Cooling Flows: An a Priori Study

Here, in this work, a priori analysis of machine learning (ML) strategies is carried out with the goal of data-driven wall modeling for large eddy simulation (LES) of gas turbine film cooling flows. High-fidelity flow datasets are extracted from wall-resolved LES (WRLES) of flow over a flat plate interacting with the coolant flow supplied by a single row of 7-7-7 shaped cooling holes inclined at 30 degrees with the flat plate at different blowing ratios (BR). The WRLES are performed using the high-order Nek5000 spectral element computational fluid dynamics (CFD) solver. Light gradient boosting machine (LightGBM) is employed as the ML algorithm for the data-driven wall model. Parametric tests are conducted to systematically assess the influence of a wide range of input flow features (velocity components, velocity gradients, pressure gradients, and fluid properties) on the accuracy of ML wall model with respect to prediction of wall shear stress. In addition, the use of spatial stencil and time delay is also explored within the ML wall modeling framework. It is shown that features associated with gradients of the streamwise and spanwise velocity components have a major impact on the prediction fidelity of wall model, while the effect of gradients of wall-normal velocity component is found to be negligible. Moreover, adding flow feature information from an x-y-z spatial stencil significantly improves the ML model accuracy and generalizability compared to just using local flow features from the matching location. Overall, highest prediction accuracy is achieved when both spatial stencil and time delay features are incorporated within the data-driven wall modeling paradigm.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2015

Higher-Order Methods for Compressible Turbulent Flows Using Entropy Variables

Turbulent flows have a large range of spatial and temporal scales which need to be resolved in order to obtain accurate predictions. Higher-order methods can provide greater efficiency for simulations requiring high spatial and temporal resolution, allowing for solutions with fewer degrees of freedom and lower computational cost than traditional second-order computational fluid dynamics (CFD) methods.1 Higher-order methods have been widely used for turbulent flows. However, the reduced numerical stabilization present in higher-order schemes implies that special care needs to be taken in the development of numerical methods to suppress nonlinear instabilities.2–6 In this work we present the development of a higher-order space-time discontinuous Galerkin method with a focus on the aspects of our numerical scheme required for ensuring nonlinear stability for turbulent simulations at high Reynolds numbers.

Diosady, Laslo T.↗

NASA NTRS1991

CFD code validation for nozzle flowfields

Renewed interest in hypersonic airbreathing flight has resulted in heavy reliance on computational fluid dynamics for all aspects of aerodynamic and scramjet component modeling. Credible numerical performance predictions require calibration to unit type flowfields. This paper presents results of one such calibration to nonreacting simulated scramjet nozzle flowfields. The experimental test program was performed on a static thrust stand. Computed results are in excellent agreement with measured wall pressure, pitot pressure profiles, and thrust. Agreement with thrust is shown to be within the reported accuracy of the experimental measurements.

Jentink, Thomas N.↗

NASA NTRS2012

Comprehensive Approach to Verification and Validation of CFD Simulations Applied to Backward Facing Step-Application of CFD Uncertainty Analysis

There are inherent uncertainties and errors associated with using Computational Fluid Dynamics (CFD) to predict the flow field and there is no standard method for evaluating uncertainty in the CFD community. This paper describes an approach to -validate the . uncertainty in using CFD. The method will use the state of the art uncertainty analysis applying different turbulence niodels and draw conclusions on which models provide the least uncertainty and which models most accurately predict the flow of a backward facing step.

Groves, Curtis E.↗

DOE OSTI2021

Impact of Numerical Hydrodynamics in Turbulent Mixing Transition Simulations

Underresolved simulations are unavoidable in high Reynolds (Re) and Mach (Ma) number turbulent flow applications at scale. Implicit large-Eddy simulation (ILES) often becomes the effective strategy to capture the dominating effects of convectively driven flow instabilities. We evaluate the impact of three distinct numerical strategies in simulations of transition and turbulence decay with ILES: the Harten–Lax–van Leer (HLL) Riemann solver applying Strang splitting and a Lagrange-plus-Remap formalism to solve the directional sweep—denoted split; the Harten–Lax–Van Leer-Contact (HLLC) Riemann solver using a directionally unsplit strategy and parabolic reconstruction—denoted unsplit; and the HLLC Riemann solver using unsplit and a low-Ma correction (LMC)—denoted unsplit*. Three case studies are considered: (1) a shock tube problem prototyping shock-driven turbulent mixing, (2) the Taylor–Green Vortex (TGV) prototyping transition to turbulence, and, (3) an homogeneous isotropic turbulence (HIT) case, focusing on the impact of discretization on transition and decay from fixed well-characterized initial conditions. Significantly more accurate predictions are provided by the unsplit schemes, in particular, when augmented with the LMC. For given resolution, only the unsplit schemes predict the turbulent mixing transition after reshock observed in the shock tube experiments. Relevant comparisons of ILES based on Euler and Navier–Stokes equations addressing potential occurrence of low-Re regimes in the applications are presented. Unsplit schemes are instrumental in allowing to capture the spatial development of the TGV flow and its validation at prescribed Re with significantly less resolution. HIT analysis confirms higher simulated turbulence Re and increased small-scale content associated with the unsplit discretizations.

74 ATOMIC AND MOLECULAR PHYSICS↗

NASA NTRS2014

Wind-US Results for the AIAA 2nd Propulsion Aerodynamics Workshop

This presentation contains Wind-US results presented at the 2nd Propulsion Aerodynamics Workshop. The workshop was organized by the American Institute of Aeronautics and Astronautics, Air Breathing Propulsion Systems Integration Technical Committee with the purpose of assessing the accuracy of computational fluid dynamics for air breathing propulsion applications. Attendees included representatives from government, industry, academia, and commercial software companies. Participants were encouraged to explore and discuss all aspects of the simulation process including the effects of mesh type and refinement, solver numerical schemes, and turbulence modeling. The first set of challenge cases involved computing the thrust and discharge coefficients for a 25deg conical nozzle for a range of nozzle pressure ratios between 1.4 and 7.0. Participants were also asked to simulate two cases in which the 25deg conical nozzle was bifurcated by a solid plate, resulting in vortex shedding (NPR=1.6) and shifted plume shock (NPR=4.0). A second set of nozzle cases involved computing the discharge and thrust coefficients for a convergent dual stream nozzle for a range of subsonic nozzle pressure ratios. The workshop committee also compared the plume mixing of these cases across various codes and models. The final test case was a serpentine inlet diffuser with an outlet to inlet area ratio of 1.52 and an offset of 1.34 times the inlet diameter. Boundary layer profiles, wall static pressure, and total pressure at downstream rake locations were examined.

Wind-US↗

DOE OSTI2020

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. Here, a Machine Learning-Grid Gradient Ascent (ML-GGA) approach was developed to optimize the performance of internal combustion engines. ML offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine Learning-Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variable limit extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools, and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a research octane number (RON) of 80. The ML-GGA approach yielded >2% improvements in the merit function, compared with the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared with traditional approaches.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

ENGINE COMBUSTION SYSTEM OPTIMIZATION USING CFD AND MACHINE LEARNING: A METHODOLOGICAL APPROACH

Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. In this work, a Machine Learning-Grid Gradient Algorithm (ML-GGA) approach was developed to optimize the performance of internal combustion engines. Machine learning (ML) offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine learning Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variables limits extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a Research Octane Number (RON) of 80. The ML-GGA approach yielded > 2% improvements in the merit function, compared to the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared to traditional approaches.

engine optimization↗

NASA NTRS1989

Hierarchial parallel computer architecture defined by computational multidisciplinary mechanics

The goal is to develop an architecture for parallel processors enabling optimal handling of multi-disciplinary computation of fluid-solid simulations employing finite element and difference schemes. The goals, philosphical and modeling directions, static and dynamic poly trees, example problems, interpolative reduction, the impact on solvers are shown in viewgraph form.

Padovan, Joe↗

NASA NTRS1991

Enhancing aeropropulsion research with high-speed interactive computing

NASA-Lewis has committed to a long range goal of creating a numerical test cell for aeropropulsion research and development. Efforts are underway to develop a first generation Numerical Propulsion System Simulation (NPSS). The NPSS will provide a unique capability to numerically simulate advanced propulsion systems from nose to tail. Two essential ingredients to the NPSS are: (1) experimentally validated Computational Fluid Dynamics (CFD) codes; and (2) high performing computing systems (hardware and software) that will permit those codes to be used efficiently. To this end, NASA-Lewis is using high speed, interactive computing as a means for achieving Integrated CFD and Experiments (ICE). The development is described of a prototype ICE system for multistage compressor flow physics research.

Szuch, John R.↗

NASA NTRS1991

Enhancing aeropropulsion research with high-speed interactive computing

Szuch, John R.↗

NASA NTRS1994

Computed Flow Through An Artificial Heart And Valve

NASA technical memorandum discusses computations of flow of blood through artificial heart and through tilting-disk artificial heart valve. Represents further progress in research described in "Numerical Simulation of Flow Through an Artificial Heart" (ARC-12478). One purpose of research to exploit advanced techniques of computational fluid dynamics and capabilities of supercomputers to gain understanding of complicated internal flows of viscous, essentially incompressible fluids like blood. Another to use understanding to design better artificial hearts and valves.

Rogers, Stuart E.↗

NASA NTRS1994

Sonic and Supersonic Jet Plumes

Study of sonic and supersonic jet plumes are relevant to understanding such phenomenon as jet-noise, plume signatures, and rocket base-heating and radiation. Jet plumes are simple to simulate and yet, have complex flow structures such as Mach disks, triple points, shear-layers, barrel shocks, shock- shear- layer interaction, etc. Experimental and computational simulation of sonic and supersonic jet plumes have been performed for under- and over-expanded, axisymmetric plume conditions. The computational simulation compare very well with the experimental observations of schlieren pictures. Experimental data such as temperature measurements with hot-wire probes are yet to be measured and will be compared with computed values. Extensive analysis of the computational simulations presents a clear picture of how the complex flow structure develops and the conditions under which self-similar flow structures evolve. From the computations, the plume structure can be further classified into many sub-groups. In the proposed paper, detail results from the experimental and computational simulations for single, axisymmetric, under- and over-expanded, sonic and supersonic plumes will be compared and the fluid dynamic aspects of flow structures will be discussed.

Venkatapathy, E.↗

NASA NTRS1994

Experimental and Computational Study of Sonic and Supersonic Jet Plumes

Study of sonic and supersonic jet plumes are relevant to understanding such phenomenon as jet-noise, plume signatures, and rocket base-heating and radiation. Jet plumes are simple to simulate and yet, have complex flow structures such as Mach disks, triple points, shear-layers, barrel shocks, shock-shear-layer interaction, etc. Experimental and computational simulation of sonic and supersonic jet plumes have been performed for under- and over-expanded, axisymmetric plume conditions. The computational simulation compare very well with the experimental observations of schlieren pictures. Experimental data such as temperature measurements with hot-wire probes are yet to be measured and will be compared with computed values. Extensive analysis of the computational simulations presents a clear picture of how the complex flow structure develops and the conditions under which self-similar flow structures evolve. From the computations, the plume structure can be further classified into many sub-groups. In the proposed paper, detail results from the experimental and computational simulations for single, axisymmetric, under- and over-expanded, sonic and supersonic plumes will be compared and the fluid dynamic aspects of flow structures will be discussed.

Venkatapathy, E.↗

NASA NTRS2009

Physical Principle for Generation of Randomness

A physical principle (more precisely, a principle that incorporates mathematical models used in physics) has been conceived as the basis of a method of generating randomness in Monte Carlo simulations. The principle eliminates the need for conventional random-number generators. The Monte Carlo simulation method is among the most powerful computational methods for solving high-dimensional problems in physics, chemistry, economics, and information processing. The Monte Carlo simulation method is especially effective for solving problems in which computational complexity increases exponentially with dimensionality. The main advantage of the Monte Carlo simulation method over other methods is that the demand on computational resources becomes independent of dimensionality. As augmented by the present principle, the Monte Carlo simulation method becomes an even more powerful computational method that is especially useful for solving problems associated with dynamics of fluids, planning, scheduling, and combinatorial optimization. The present principle is based on coupling of dynamical equations with the corresponding Liouville equation. The randomness is generated by non-Lipschitz instability of dynamics triggered and controlled by feedback from the Liouville equation. (In non-Lipschitz dynamics, the derivatives of solutions of the dynamical equations are not required to be bounded.)

Zak, Michail↗

NASA NTRS2014

Designing Liquid Rocket Engine Injectors for Performance, Stability, and Cost

NASA is developing the Space Launch System (SLS) for crewed exploration missions beyond low Earth orbit. Marshall Space Flight Center (MSFC) is designing rocket engines for the SLS Advanced Booster (AB) concepts being developed to replace the Shuttle-derived solid rocket boosters. One AB concept uses large, Rocket-Propellant (RP)-fueled engines that pose significant design challenges. The injectors for these engines require high performance and stable operation while still meeting aggressive cost reduction goals for access to space. Historically, combustion stability problems have been a critical issue for such injector designs. Traditional, empirical injector design tools and methodologies, however, lack the ability to reliably predict complex injector dynamics that often lead to combustion stability. Reliance on these tools alone would likely result in an unaffordable test-fail-fix cycle for injector development. Recently at MSFC, a massively parallel computational fluid dynamics (CFD) program was successfully applied in the SLS AB injector design process. High-fidelity reacting flow simulations were conducted for both single-element and seven-element representations of the full-scale injector. Data from the CFD simulations was then used to significantly augment and improve the empirical design tools, resulting in a high-performance, stable injector design.

Westra, Douglas G.↗

NASA NTRS2019

Building Booster Separation Aerodynamic Databases for Artemis II

NASA’s Artemis II mission will mark the return of humans to near-lunar space for the first time since Apollo. Shortly after launch on the Space Launch System (SLS), a critical phase of ascent occurs when 16 small rockets fire to push the boosters away from the core. Minimizing the risk of failure during separation requires the construction of multiple 13-dimensional databases, including perturbations in position, flight conditions, and engine thrust. The SLS Computational Fluid Dynamics team used NASA’s FUN3D flow solver on the Pleiades and Electra supercomputers to run 5,780 simulations at nominal conditions and over 8,000 simulations with a core stage engine failure to generate the databases needed to verify the booster separation system for Artemis II.

Meeroff, Jamie G.↗