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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Investigation of Nonlinear Difference-Frequency Wave Excitation on a Semisubmersible Offshore-Wind Platform with Bichromatic-Wave CFD Simulations: Preprint

The natural surge and pitch frequencies of semisubmersible offshore wind platforms are typically designed to be below the wave frequencies to avoid direct excitation. However, surge or pitch resonance can be excited by the nonlinear low-frequency loads generated by irregular incident waves. Second-order potential-flow models with added Morison drag have been found to underpredict this low-frequency excitation and response. As part of the OC6 project, Computational Fluid Dynamics (CFD) simulations were performed to enable a better understanding of the low-frequency loads and the limitations of lower-fidelity models. The focus of the current paper is to set up a computationally cost-effective CFD simulation of a fixed semisubmersible platform to investigate nonlinear difference-frequency loads and establish the corresponding uncertainty in the results. Due to the high computing cost, CFD simulations of irregular waves can be challenging. Instead, simulations were performed with bichromatic waves having a shorter repeat period. A preliminary comparison with Quadratic Transfer Functions from second-order potential-flow theory shows that CFD models consistently predict higher nonlinear wave loads at the difference frequency, likely due to flow separation and viscous drag not accounted for in potential-flow theory.

2nd order↗
NASA NTRSDate not supplied

Computational Modeling of Two Mars Powered Descent Vehicle Concepts Tested in the Langley Unitary Plan Wind Tunnel

Future human Mars missions will require powered descent starting at supersonic conditions, something which has never been done before at Mars. Computational powered descent flowfield simulations have been completed at full-scale Mars conditions, but the available ground test data are not suitable for calibrating computational uncertainties for aerodynamic interference on proposed Mars descent vehicles. Testing was conducted in the NASA Langley Unitary Plan Wind Tunnel in order to investigate the aerodynamic interference of sub-scale versions of two Mars powered descent vehicle concepts at supersonic Mach numbers (2.4 and 3.5): a model based on a blunt hypersonic inflatable aerodynamic decelerator (HIAD) and the second representing a more slender rigid vehicle with body flaps (CobraMRV). This paper covers computational flowfield predictions completed at wind tunnel conditions and comparisons to the test data. On the blowing HIAD models, the time-averaged pressure inboard of the nozzles was generally well-predicted, especially if the nozzles are canted outward, when the nozzles are located further from the nose. At intermediate CobraMRV thrust coefficients, CFD pressures are more accurately predicted than they are for the HIAD models, largely due to the nozzle locations and pointing directions. Overall, the CFD pressure coefficients were predicted within 0.2 of the steady pressure measurements for all blowing models, with smaller discrepancies at higher HIAD thrust, and larger discrepancies at lower HIAD CobraMRV thrust. All HIAD models were predicted to have a gradually decreasing axial force coefficient as the total thrust increases, in agreement with available pressure sensitive paint data. On models with canted nozzles or with nozzles further from the nose, the force coefficient was slightly higher for a given thrust. On the CobraMRV model, the CFD also shows consistent results between solvers and follows trends revealed in the data; the aerodynamic force coefficient remains near the non-blowing value at a tunnel Mach number of 2.4 regardless of thrust, and increases above that level at a Mach number of 3.5, consistent with the discrete pressure data. CFD analysis at tunnel and flight conditions will continue as flight system designs concepts mature.

Supersonic Retropropulsion↗
NASA NTRSDate not supplied

Computational Modeling of Two Mars Powered Descent Vehicle Concepts Tested in the Langley Unitary Plan Wind Tunnel

Future human Mars missions will require powered descent starting at supersonic conditions, something which has never been done before at Mars. Computational powered descent flowfield simulations have been completed at full-scale Mars conditions, but the available ground test data are not suitable for calibrating computational uncertainties for aerodynamic interference on proposed Mars descent vehicles. Testing was conducted in the NASA Langley Unitary Plan Wind Tunnel in order to investigate the aerodynamic interference of sub-scale versions of two Mars powered descent vehicle concepts at supersonic Mach numbers (2.4 and 3.5): a model based on a blunt hypersonic inflatable aerodynamic decelerator (HIAD) and the second representing a more slender rigid vehicle with body flaps (CobraMRV). This paper covers computational flowfield predictions completed at wind tunnel conditions and comparisons to the test data. On the blowing HIAD models, the time-averaged pressure inboard of the nozzles was generally well-predicted, especially if the nozzles are canted outward, when the nozzles are located further from the nose. At intermediate CobraMRV thrust coefficients, CFD pressures are more accurately predicted than they are for the HIAD models, largely due to the nozzle locations and pointing directions. Overall, the CFD pressure coefficients were predicted within 0.2 of the steady pressure measurements for all blowing models, with smaller discrepancies at higher HIAD thrust, and larger discrepancies at lower HIAD CobraMRV thrust. All HIAD models were predicted to have a gradually decreasing axial force coefficient as the total thrust increases, in agreement with available pressure sensitive paint data. On models with canted nozzles or with nozzles further from the nose, the force coefficient was slightly higher for a given thrust. On the CobraMRV model, the CFD also shows consistent results between solvers and follows trends revealed in the data; the aerodynamic force coefficient remains near the non-blowing value at a tunnel Mach number of 2.4 regardless of thrust, and increases above that level at a Mach number of 3.5, consistent with the discrete pressure data. CFD analysis at tunnel and flight conditions will continue as flight system designs concepts mature.

Supersonic Retropropulsion↗
DOE OSTI2021

Preparing an Incompressible-Flow Fluid Dynamics Code for Exascale-Class Wind Energy Simulations

The U.S. Department of Energy has identified exascale-class wind farm simulation as critical to wind energy scientific discovery. A primary objective of the ExaWind project is to build high-performance, predictive computational fluid dynamics (CFD) tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next-generation, exascale-class supercomputers. Here, we report on our efforts in preparing the ExaWind unstructured mesh solver, Nalu-Wind, for exascale-class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is insufficient. To achieve high performance, one needs novel algorithms that are application aware, memory efficient, and optimized for the latest-generation GPU devices. The result of our efforts are unstructured-mesh simulations of wind turbines that can effectively leverage thousands of GPUs. In particular, we demonstrate a first-of-its-kind, incompressible-flow simulation using Algebraic Multigrid solvers that strong scales to more than 4000 GPUs on the Summit supercomputer.

algebraic multigrid↗
NASA NTRS2016

NASA ERA Integrated CFD for Wind Tunnel Testing of Hybrid Wing-Body Configuration

The NASA Environmentally Responsible Aviation (ERA) Project explored enabling technologies to reduce impact of aviation on the environment. One project research challenge area was the study of advanced airframe and engine integration concepts to reduce community noise and fuel burn. To address this challenge, complex wind tunnel experiments at both the NASA Langley Research Center's (LaRC) 14'x22' and the Ames Research Center's 40'x80' low-speed wind tunnel facilities were conducted on a BOEING Hybrid Wing Body (HWB) configuration. These wind tunnel tests entailed various entries to evaluate the propulsion-airframe interference effects, including aerodynamic performance and aeroacoustics. In order to assist these tests in producing high quality data with minimal hardware interference, extensive Computational Fluid Dynamic (CFD) simulations were performed for everything from sting design and placement for both the wing body and powered ejector nacelle systems to the placement of aeroacoustic arrays to minimize its impact on vehicle aerodynamics. This paper presents a high-level summary of the CFD simulations that NASA performed in support of the model integration hardware design as well as the development of some CFD simulation guidelines based on post-test aerodynamic data. In addition, the paper includes details on how multiple CFD codes (OVERFLOW, STAR-CCM+, USM3D, and FUN3D) were efficiently used to provide timely insight into the wind tunnel experimental setup and execution.

Computational Fluid Dynamics↗
NASA NTRSDate not supplied

Multihierarchy Gaussian Process Models for Probabilistic Aerodynamic Databases using Uncertain Nominal and Off-Nominal Configuration Data

Probabilistic aerodynamic databases are a crucial component of the development lifecycle for aerospace vehicles. A key challenge when building aerodynamic databases is that most data used to construct them represent various simplifications of the real flight vehicle. For example, wind tunnel models often simplify the vehicle geometry and surface roughness characteristics, while CFD computations often make simplifications to the physics being modeled, such as fully laminar or turbulent calculations. Multifidelity data fusion models rely on a user being able to define a hierarchy of fidelity levels anchored to some "truth" data. This approach is unsatisfactory when no data can be considered to accurately reflect real flight conditions. In this work, we provide an alternative approach by presenting a consistent mathematical framework for building probabilistic aerodynamic databases in the form of a conditional probability distribution described by an ensemble of multifidelity Gaussian Processes. Instead of relying on a single hierarchy of data fidelity levels, the presented framework identifies a "nominal" configuration and potential corrections to the nominal which represent specific physical phenomena not represented in the nominal data. The nominal and correction functions themselves are constructed as multifidelity Gaussian Processes and linearly combined to form an ensemble model which fuses the uncertainties associated nominal and correction models. Results obtained using the proposed framework on a simplified Orion Crew Module wind tunnel dataset demonstrate the predictive capability of the multihierarchy framework. We further demonstrate the benefits of such a probabilistic aerodynamic database approach through function sampling and computing the conditional distributions of derived quantities, such as the trim angle of attack and aerodynamic coefficients at trim.

Gaussian Processes↗
NASA NTRS2001

Wind Code Application to External Forebody Flowfields with Comparisons to Experimental Results

The WIND Code, a general purpose Navier-Stokes solver, has been utilized to obtain supersonic external flowfield Computational Fluid Dynamics (CFD) solutions over an axisymmetric, parabolic forebody with comparisons made to wind tunnel experimental results. Various cases have been investigated at supersonic freestream conditions ranging from Mach 2.0 to 3.5, at 0 deg and 3 deg angles-of-attack, and with either a sharp-nose or blunt-nose forebody configuration. Both a turbulent (Baldwin-Lomax algebraic turbulence model) and a laminar model have been implemented in the CFD. Obtaining the solutions involved utilizing either the parabolized- or full-Navier-Stokes analyses supplied in WIND. Comparisons have been made with static pressure measurements, with boundary-layer rake and flowfield rake pitot pressure measurements, and with temperature sensitive paint experimental results. Using WIND's parabolized Navier-Stokes capability, grid sequencing, and the Baldwin-Lomax algebraic turbulence model allowed for significant reductions in computational time while still providing good agreement with experiment. Given that CFD and experiment compare well, WIND is found to be a good computational platform for solving this type of forebody problem, and the grids developed in conjunction with it will be used in the future to investigate varying freestream conditions not tested experimentally.

Frate, F. C.↗
DOE OSTI2024

Enhanced Models for the Simulation of Two-Phase Boiling Flow and Critical Heat Flux with the STAR-CCM+ and NEK-2P CFD Codes: Executive Summary (Final Report)

The goal of the project was to develop, verify and validate an advanced two-phase boiling flow modeling capability that enhances the existing two-phase flow modeling of the STAR-CCM+ Computational Fluid Dynamics (CFD) code. The project focused on the simulation of two phase flow and heat transfer phenomena that occur in Boiling Water Reactor (BWR) and Pressurized Water Reactor (PWR) fuel bundles under various operating conditions. The extended STAR-CCM+ two-phase flow models implemented during the project follow the approach similar to the Extended Boiling Framework [1-6] previously developed at Argonne but include more fundamental physical models of boiling phenomena. The development and validation of the extended two-phase models is supported by the U.S. DOE GAIN program and performed in collaboration with Framatome U.S. Inc.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

On the Modeling of Thermal Radiation at the Top Surface of a Vacuum Arc Remelting Ingot

Two models have been implemented for calculating the thermal radiation emitted at the ingot top in the VAR process, namely, a crude model that considers only radiative heat transfer between the free surface and electrode tip and a more detailed model that describes all radiative exchanges between the ingot, electrode, and crucible wall using a radiosity method. From the results of the second model, it is found that the radiative heat flux at the ingot top may depend heavily on the arc gap length and the electrode radius, but remains almost unaffected by variations of the electrode height. Both radiation models have been integrated into a CFD numerical code that simulates the growth and solidification of a VAR ingot. The simulation of a Ti-6-4 alloy melt shows that use of the detailed radiation model leads to some significant modification of the simulation results compared with the simple model. This is especially true during the hot-topping phase, where the top radiation plays an increasingly important role compared with the arc energy input. Thus, while the crude model has the advantage of its simplicity, use of the detailed model should be preferred.

36 MATERIALS SCIENCE↗
DOE OSTI2021

AN AUTOMATED MACHINE LEARNING-GENETIC ALGORITHM FRAMEWORK WITH ACTIVE LEARNING FOR DESIGN OPTIMIZATION

The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

Owoyele, Opeoluwa↗
NASA NTRS2015

Update on Advection-Diffusion Purge Flow Model

Gaseous purge is commonly used in sensitive spacecraft optical or electronic instruments to prevent infiltration of contaminants and/or water vapor. Typically, purge is sized using simplistic zero-dimensional models that do not take into account instrument geometry, surface effects, and the dependence of diffusive flux on the concentration gradient. For this reason, an axisymmetric computational fluid dynamics (CFD) simulation was recently developed to model contaminant infiltration and removal by purge. The solver uses a combined Navier-Stokes and Advection-Diffusion approach. In this talk, we report on updates in the model, namely inclusion of a particulate transport model.

purging↗
NASA NTRS2013

CFD Script for Rapid TPS Damage Assessment

This grid generation script creates unstructured CFD grids for rapid thermal protection system (TPS) damage aeroheating assessments. The existing manual solution is cumbersome, open to errors, and slow. The invention takes a large-scale geometry grid and its large-scale CFD solution, and creates a unstructured patch grid that models the TPS damage. The flow field boundary condition for the patch grid is then interpolated from the large-scale CFD solution. It speeds up the generation of CFD grids and solutions in the modeling of TPS damages and their aeroheating assessment. This process was successfully utilized during STS-134.

McCloud, Peter↗
DOE OSTI2021

Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization

In recent years, the use of machine learning-based surrogate models for computational fluid dynamics (CFD) simulations has emerged as a promising technique for reducing the computational cost associated with engine design optimization. However, such methods still suffer from drawbacks. One main disadvantage is that the default machine learning (ML) hyperparameters are often severely suboptimal for a given problem. This has often been addressed by manually trying out different hyperparameter settings, but this solution is ineffective in case of a high-dimensional hyperparameter space. Besides this problem, the amount of data needed for training is also not known a priori. In response to these issues that need to be addressed, the present work describes and validates an automated active learning approach, AutoML-GA, for surrogate-based optimization of internal combustion engines. In this approach, a Bayesian optimization technique is used to find the best machine learning hyperparameters based on an initial dataset obtained from a small number of CFD simulations. Subsequently, a genetic algorithm is employed to locate the design optimum on the ML surrogate surface. In the vicinity of the design optimum, the solution is refined by repeatedly running CFD simulations at the projected optima and adding the newly obtained data to the training dataset. It is demonstrated that AutoML-GA leads to a better optimum with a lower number of CFD simulations, compared to the use of default hyperparameters. The proposed framework offers the advantage of being a more hands-off approach that can be readily utilized by researchers and engineers in industry who do not have extensive machine learning expertise.

Owoyele, Opeoluwa↗
DOE OSTI2023

Aerosol Particle Deposition on a Spent Nuclear Fuel Assembly Spacer Grid

The flow and particle deposition patterns on surfaces in an idealized spacer grid for a 17x17 pressurized water reactor (PWR) assembly in a spent fuel canister are modeled using computational fluid dynamics (CFD) with laminar flow. The effects of gravitational settling, non-Stokesian flow, and particle slip are first rigorously analyzed. From the analysis, non-Stokesian effects and slip may be neglected for the particle sizes and conditions expected in a canister. For particles that do not settle out, a swirling flow pattern at the corners of a spacer grid channel directs particles to the leeward side of the flow vanes where much of the deposition occurs. Particle deposition increases with increasing particle diameter. Deposition also increases with decreasing flow velocity as this provides more time for particles to settle and deposit on the leeward side of the flow vanes. The fraction of particles that are transmitted through a spacer grid is determined as a function of inlet gas velocity and particle diameter by running the CFD calculation for each set of conditions and for each particle diameter. Curve fits of the transmission curve as a function of particle diameter for a specified spacer grid and flow velocity are applied to a lognormal particle mass density function for the inlet particles. The resulting mass density function and aerosol mass fraction that passes through the spacer grid can be determined analytically without resorting to numerical iteration. A sample calculation of the analytical solution is demonstrated for a lognormal particle mass density function.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Learning Optimal Aerodynamic Designs

This project created a framework for efficient, accurate, and scalable deep neural network representations of design optimization problem solutions. The inputs to these DNN representations are the vector of design requirement parameters, the outputs are the optimal design variables, and the goal is to learn the map from inputs to outputs (i.e., inverse design). The team addressed the problem of the optimal shape design of aerodynamic lifting surfaces—in particular aircraft wings—using a Reynolds-Average Navier Stokes model to govern the CFD-based aerodynamic shape optimization. The inverse design map for such problems is very complex and high-dimensional, involving inputs and outputs on the order of 1000s. To approximate this inverse design map, the team developed algorithms to construct parsimonious DNN architectures, which automatically identify low-dimensional manifolds in which design requirements affect optimal shape parameters, and trained these architectures with multifidelity optimization methods. The resulting methodology accurately and automatically designs optimal aerodynamic lifting surfaces with very high accuracy (99%) at interactive speeds, of the order of milliseconds, resulting in factors of one million or more speedup relative to CFD-based design optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

An Adaptive Timestepping Algorithm for Particle Time Integration in Coupled CFD-DEM Simulations

Coupled Computational Fluid Dynamics(CFD) - Discrete Element Method (DEM) models provide an accurate description of multiphase physical systems where a granular phase exists in an underlying continuous medium. The time integration of the granular phase in these simulations are typically handled using an explicit time integration scheme with a global time step that is much smaller than fluid time scales, in order to resolve inter-particle collisions. An adapative time integration technique that avoids this global constraint for the granular phase is presented in this work. The algorithm involves the use of a local time stepping approach to resolve collisional time scales for only a subset of particles that are in close proximity to potential collision partners, thereby resulting in substantial reduction of computational cost. This approach is observed to be 2-3X faster than traditional explicit methods for problems that involve both dense and dilute regions, while maintaining the same level of accuracy.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Quantitative characterization of a nonreacting, supersonic combustor flowfield using unified, laser-induced iodine fluorescence

A calibrated, nonintrusive optical technique, laser-induced iodine fluorescence (LIIF) was used to quantify the steady, compressible flowfield of a nonreacting, supersonic combustor. The combustor was configured with single and staged, transverse-air injection into a supersonic-air freestream behind a rearward-facing step. Pressure, temperature, two-velocity components, and injectant mole fraction were measured with high spatial resolution in the three-dimensional flowfields. These experimental results provide a benchmark set of data for validation of computational fluid dynamic (CFD) codes being developed to model supersonic combustor flowfields.

Fletcher, D. G.↗
NASA NTRS1993

CFD analyses of combustor and nozzle flowfields

The objectives of the research are to improve design capabilities for low thrust rocket engines through understanding of the detailed mixing and combustion processes. A Computational Fluid Dynamic (CFD) technique is employed to model the flowfields within the combustor, nozzle, and near plume field. The computational modeling of the rocket engine flowfields requires the application of the complete Navier-Stokes equations, coupled with species diffusion equations. Of particular interest is a small gaseous hydrogen-oxygen thruster which is considered as a coordinated part of an ongoing experimental program at NASA LeRC. The numerical procedure is performed on both time-marching and time-accurate algorithms, using an LU approximate factorization in time, flux split upwinding differencing in space. The integrity of fuel film cooling along the wall, its effectiveness in the mixing with the core flow including unsteady large scale effects, the resultant impact on performance and the assessment of the near plume flow expansion to finite pressure altitude chamber are addressed.

Tsuei, Hsin-Hua↗