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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Internal Flow Analysis of Large L/D Solid Rocket Motors

Traditionally, Solid Rocket Motor (SRM) internal ballistic performance has been analyzed and predicted with either zero-dimensional (volume filling) codes or one-dimensional ballistics codes. One dimensional simulation of SRM performance is only necessary for ignition modeling, or for motors that have large length to port diameter ratios which exhibit an axial "pressure drop" during the early burn times. This type of prediction works quite well for many types of motors, however, when motor aspect ratios get large, and port to throat ratios get closer to one, two dimensional effects can become significant. The initial propellant grain configuration for the Space Shuttle Reusable Solid Rocket Motor (RSRM) was analyzed with 2-D, steady, axi-symmetric computational fluid dynamics (CFD). The results of the CFD analysis show that the steady-state performance prediction at the initial burn geometry, in general, agrees well with 1-D transient prediction results at an early time, however, significant features of the 2-D flow are captured with the CFD results that would otherwise go unnoticed. Capturing these subtle differences gives a greater confidence to modeling accuracy, and additional insight with which to model secondary internal flow effects like erosive burning. Detailed analysis of the 2-D flowfield has led to the discovery of its hidden 1-D isentropic behavior, and provided the means for a thorough and simplified understanding of internal solid rocket motor flow. Performance parameters such as nozzle stagnation pressure, static pressure drop, characteristic velocity, thrust and specific impulse are discussed in detail and compared for different modeling and prediction methods. The predicted performance using both the 1-D codes and the CFD results are compared with measured data obtained from static tests of the RSRM. The differences and limitations of predictions using ID and 2-D flow fields are discussed and some suggestions for the design of large L/D motors and more critically, motors with port to throat ratios near one, are covered.

Laubacher, Brian A.↗
DOE OSTI2022

Deep learning-based quasi-continuum theory for structure of confined fluids

Predicting the structural properties of water and simple fluids confined in nanometer scale pores and channels is essential in, for example, energy storage and biomolecular systems. Classical continuum theories fail to accurately capture the interfacial structure of fluids. In this work, we develop a deep learning-based quasi-continuum theory (DL-QT) to predict the concentration and potential profiles of a Lennard-Jones (LJ) fluid and water confined in a nanochannel. The deep learning model is built based on a convolutional encoder–decoder network (CED) and is applied for high-dimensional surrogate modeling to relate the fluid properties to the fluid–fluid potential. The CED model is then combined with the interatomic potential-based continuum theory to determine the concentration profiles of a confined LJ fluid and confined water. Further, we show that the DL-QT model exhibits robust predictive performance for a confined LJ fluid under various thermodynamic states and for water confined in a nanochannel of different widths. The DL-QT model seamlessly connects molecular physics at the nanoscale with continuum theory by using a deep learning model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

Solid rocket booster performance evaluation model. Volume 1: Engineering description

The space shuttle solid rocket booster performance evaluation model (SRB-II) is made up of analytical and functional simulation techniques linked together so that a single pass through the model will predict the performance of the propulsion elements of a space shuttle solid rocket booster. The available options allow the user to predict static test performance, predict nominal and off nominal flight performance, and reconstruct actual flight and static test performance. Options selected by the user are dependent on the data available. These can include data derived from theoretical analysis, small scale motor test data, large motor test data and motor configuration data. The user has several options for output format that include print, cards, tape and plots. Output includes all major performance parameters (Isp, thrust, flowrate, mass accounting and operating pressures) as a function of time as well as calculated single point performance data. The engineering description of SRB-II discusses the engineering and programming fundamentals used, the function of each module, and the limitations of each module.

Source record↗
NASA NTRS1984

Structural analysis of the Space Shuttle Orbiter

The structural analysis of the Space Shuttle orbiter was planned with two concepts in mind: use derivatives or subsets of the same basic finite element model whenever feasible, and substantiate the model's predictive capability by performing ground tests. The analysis cycle (model modal loads stress (MMLS)) starts with the finite element model conception and ends with the detailed stress analysis and margins of safety. The structural analysis of the orbiter encompasses a variety of static and dynamic problems. The salient features of these problems and their solutions are examined.

Henry R. Grooms↗
DOE OSTI2020

The Power Curve Working Group's assessment of wind turbine power performance prediction methods

Wind turbine power production deviates from the reference power curve in real-world atmospheric conditions. Correctly predicting turbine power performance requires models to be validated for a wide range of wind turbines using inflow in different locations. The Share-3 exercise is the most recent intelligence-sharing exercise of the Power Curve Working Group, which aims to advance the modeling of turbine performance. The goal of the exercise is to search for modeling methods that reduce error and uncertainty in power prediction when wind shear and turbulence digress from design conditions. Herein, we analyze data from 55 wind turbine power performance tests from nine contributing organizations with statistical tests to quantify the skills of the prediction-correction methods. We assess the accuracy and precision of four proposed trial methods against the baseline method, which uses the conventional definition of a power curve with wind speed and air density at hub height. The trial methods reduce power-production prediction errors compared to the baseline method at high wind speeds, which contribute heavily to power production; however, the trial methods fail to significantly reduce prediction uncertainty in most meteorological conditions. For the meteorological conditions when a wind turbine produces less than the power its reference power curve suggests, using power deviation matrices leads to more accurate power prediction. We also determine that for more than half of the submissions, the data set has a large influence on the effectiveness of a trial method. Overall, this work affirms the value of data-sharing efforts in advancing power curve modeling and establishes the groundwork for future collaborations.

17 WIND ENERGY↗
DOE OSTI2020

Generation IV Benchmarking of TRISO Fuel Performance Models Under Accident Conditions Final Report

The Generation IV International Forum (GIF) is a co-operative international endeavor of fourteen members organized to carry out the research and development needed to establish the feasibility and performance capabilities of the next generation nuclear energy systems. GIF selected six reactor technologies, amongst which is the Very High Temperature Reactor (VHTR) that is primarily dedicated to the cogeneration of electricity and hydrogen. The technical basis for VHTR is the tristructural isotropic (TRISO)-coated particle fuel, the graphite as the core structure, helium coolant, as well as the dedicated core layout and lower power density to removal decay heat in a natural way. At the heart of safety features of the VHTR concept lie the TRISO fuel particles that are designed to keep their structural integrity and retain fission products at temperatures up to 1600°C. As part as the design and future operation of VHTRs, a key aspect is the accurate prediction of fuel performance under irradiation and accident conditions. Modeling and simulation allow prediction of TRISO fuel behavior when subject to neutron flux and in high temperature accident scenarios. The refinement of the fuel performance models and codes is performed by comparison to in-pile and out-of-pile experimental data that reproduce the expected irradiation conditions in high temperature gas-cooled reactors (HTGRs). Historically, the International Atomic Energy Agency (IAEA) developed a benchmark dedicated to the validation of predictive methods for fuel and fission product behavior through the Coordinated Research Program CRP-2 (IAEA, 1997). CRP-2 was later updated to cover fuel fabrication, quality assurance, irradiation performance, safety testing, and spent fuel. The scope of the resulting CRP-6 benchmarks focused on HTGR fuel performance and fission product release (IAEA, 2012). Taking advantage of additional TRISO fuel fabrication, irradiation, and safety testing campaigns, GIF launched a Generation IV Benchmarking of TRISO Fuel Performance Models under Accident Conditions in late 2015. This GIF benchmark is a three-year program steered by Idaho National Laboratory (INL, USA). The other participants include the Japan Atomic Energy Agency (JAEA, Japan) and the Korea Atomic Energy Research Institute (KAERI, Korea). The objectives of the benchmark are to: follow on the IAEA CRP benchmarks, (2) model fission product release under accident conditions, (3) compare results obtained by the fuel performance modeling codes of the benchmark participants, and (4) compare these code predictions to experimental data. Safety tests chosen for modeling include the first and second experiments of the Advanced Gas Reactor program (AGR-1 and AGR-2) and the High Flux Reactor (HFR) EU1bis experiment. This report presents the results obtained by the three research institutions using their respective fuel performance modeling codes. Comparisons of the corresponding fission product release predictions are made with experimental data from AGR-1, AGR-2, and HFR-EU1bis. The benchmark results show good agreements between all participants but also show a general trend of over-prediction of the experimental release data, which is mainly attributed to the use of over-estimated diffusion coefficients.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Deep representation learning improves prediction of LacI-mediated transcriptional repression

Significance The understanding of protein function increases with new experimental and evolutionary datasets. A major challenge is to apply machine learning to these datasets to capture essential features of protein function. Here, we analyze the experimentally determined repression function for tens of thousands of mutants of the LacI protein. This study provides a continuous, noncategorical repression value across a majority of all single mutations and for thousands of higher-order mutations. To develop a top-performing model for the prediction of repression by LacI, we compare several leading variant effect prediction algorithms. A deep representation learning paradigm, first trained across millions of proteins from all known protein families and then fine-tuned using LacI experimental data, offers the highest predictive performance of repression function.

42 ENGINEERING↗
NASA NTRSDate not supplied

Kinetic Model Development of the Carbon Vapor Deposition Reactor to Predict Performance Versus Design

To enable complete closure of a Sabatier-based atmosphere revitalization system where oxygen recovery is predicated by carbon dioxide reduction to methane and water, there must be a means of recovering the hydrogen from the byproduct methane. Honeywell’s carbon vapor deposition (CVD) reactor is a potential technology that provides a thermochemical means by which methane decomposes at high temperatures (> 1,000 °C) into hydrogen, solid carbon, and a small percentage of hydrocarbons. This advanced methane pyrolysis reactor utilizes a carbon fiber substrate to capture and immobilize the solid pyrolytic carbon rather than allowing continuation toward undesirable soot. The CVD reactor is a highly dynamic system whose characteristic geometry changes while operating. The reactor model was developed to provide predictive performance assessment capabilities at different operating conditions, geometries, and configurations to aid in technology development. Modeling of the CVD reactor considered the multitude of simultaneous and interdependent physicochemical processes that are entailed, including: (1) the homogeneous gas-phase pyrolysis reactions, (2) the heterogeneous deposition reactions, and (3) the transport of gaseous species in the reactor domains. A 1-D CVD reactor model was developed based on a general packed-bed, axial-dispersion plug flow reactor model with a solid substrate balance to account for carbon deposition. The model includes 59 species and 243 reactions constituting the methane pyrolysis reaction network, carbon deposition, and interconversions of oxygenate species. The reactor model was compared to brassboard test data, where it was shown to be able to capture the pertinent physicochemical phenomena as evidenced by its agreement with short-duration run data (i.e., looking at different temperature and flow rate conditions), as well as trend agreement with the brassboard reactor’s long-duration test data. Recommendations are provided on further model development steps as well as its use cases.

Air Revitalization↗
NASA NTRS2002

Modeling of the HiPco process for carbon nanotube production. II. Reactor-scale analysis

The high-pressure carbon monoxide (HiPco) process, developed at Rice University, has been reported to produce single-walled carbon nanotubes from gas-phase reactions of iron carbonyl in carbon monoxide at high pressures (10-100 atm). Computational modeling is used here to develop an understanding of the HiPco process. A detailed kinetic model of the HiPco process that includes of the precursor, decomposition metal cluster formation and growth, and carbon nanotube growth was developed in the previous article (Part I). Decomposition of precursor molecules is necessary to initiate metal cluster formation. The metal clusters serve as catalysts for carbon nanotube growth. The diameter of metal clusters and number of atoms in these clusters are some of the essential information for predicting carbon nanotube formation and growth, which is then modeled by the Boudouard reaction with metal catalysts. Based on the detailed model simulations, a reduced kinetic model was also developed in Part I for use in reactor-scale flowfield calculations. Here this reduced kinetic model is integrated with a two-dimensional axisymmetric reactor flow model to predict reactor performance. Carbon nanotube growth is examined with respect to several process variables (peripheral jet temperature, reactor pressure, and Fe(CO)5 concentration) with the use of the axisymmetric model, and the computed results are compared with existing experimental data. The model yields most of the qualitative trends observed in the experiments and helps to understanding the fundamental processes in HiPco carbon nanotube production.

Validation Studies↗
DOE OSTI2021

Growing grasses in unprofitable areas of US Midwest croplands could increase species richness

The US has large potential to grow perennial energy crops, but because these crops are rarely grown in current agricultural landscapes, it is unclear how biodiversity may be affected. Over time, as agriculture has increased, many grassland species have declined. In addition, not all agricultural land is profitable for growing annual crops. Unprofitable areas were responsible for a loss of approximately $110 million USD per year from 2013 to 2016. Based on this, we want to know how converting less-profitable portions of agricultural fields to switchgrass, a native prairie grass, would influence species occurrence. To address this question, we developed an alternative landscape in which clustered corn/soy acres with a low return on investment (ROI) were replaced with grassland. We also developed and validated species distribution models to predict changes in species occurrence for 28 avian species in Iowa in response to landscape management. Furthermore, we compared results for three different models: Random forest (RF), Stochastic gradient boosting (GBM), and Neural network (Nnet) and found that all models performed well and predicted similar species distribution. Predicted species richness increased by 3.66% (RF), 2.79% (GBM), and 7.51% (Nnet) when we simulated a change in management for ~3% of Iowa's low ROI corn/soybean areas to grassland. If harvested, these areas could generate approximately 7.6 million dry tons/year of switchgrass for bioenergy, thereby increasing farmers earnings. Unprofitable areas tended to occur along streams, which suggest that incorporating partially harvested riparian buffers can benefit avian biodiversity, while improving water quality and reducing unnecessary costs for farmers.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1985

Tests of Low-Concentration-Ratio Photovoltaic Elements

Report describes performance measurements on elements of low-concentrationratio solar arrays (LCRSA's) employing silicon and gallium arsenide photovoltaic cells. Measurements intended to verify predictions of performance based on mathematical models. Measured and predicted values found to agree closely for both normal and off-normal pointing of array toward Sun.

Mills, M. W.↗
NASA NTRS1989

Nickel cadmium battery performance modelling

The development of a model to predict cell/battery behavior given databases of temperature is described. The model accommodates batteries of various structural as well as thermal designs. Cell internal design modifications can be accommodated as long as the databases reflect the cell's performance characteristics. Operational parameters can be varied to simulate any number of charge or discharge methods under any orbital regime. The flexibility of the model stems from the broad scope of input variables and allows the prediction of battery performance under simulated mission or test conditions.

Clark, K.↗
NASA NTRS1992

Finite difference time domain grid generation from AMC helicopter models

A simple technique is presented which forms a cubic grid model of a helicopter from an Aircraft Modeling Code (AMC) input file. The AMC input file defines the helicopter fuselage as a series of polygonal cross sections. The cubic grid model is used as an input to a Finite Difference Time Domain (FDTD) code to obtain predictions of antenna performance on a generic helicopter model. The predictions compare reasonably well with measured data.

Cravey, Robin L.↗
NASA NTRS1966

Human transfer functions used to predict system performance parameters

Automatic, parameter-tracking, model-matching technique compares the responses of a human operator with those of an analog computer model of a human operator to predict and analyze the performance of mechanical or electromechanical systems prior to construction. Transfer functions represent the input-output relation of an operator controlling a closed-loop system.

Source record↗
NASA NTRS2016

Isolated Open Rotor Noise Prediction Assessment Using the F31A31 Historical Blade Set

In an effort to mitigate next-generation fuel efficiency and environmental impact concerns for aviation, open rotor propulsion systems have received renewed interest. However, maintaining the high propulsive efficiency while simultaneously meeting noise goals has been one of the challenges in making open rotor propulsion a viable option. Improvements in prediction tools and design methodologies have opened the design space for next generation open rotor designs that satisfy these challenging objectives. As such, validation of aerodynamic and acoustic prediction tools has been an important aspect of open rotor research efforts. This paper describes validation efforts of a combined computational fluid dynamics and Ffowcs Williams and Hawkings equation methodology for open rotor aeroacoustic modeling. Performance and acoustic predictions were made for a benchmark open rotor blade set and compared with measurements over a range of rotor speeds and observer angles. Overall, the results indicate that the computational approach is acceptable for assessing low-noise open rotor designs. Additionally, this approach may be used to provide realistic incident source fields for acoustic shielding/scattering studies on various aircraft configurations.

Nark, Douglas M.↗
DOE OSTI2022

Calibration, modeling, parameterization, and verification of the instrument transfer function of an interferometric microscope

Interferometric microscopes are used to measure surface roughness, from which the computed power spectral density function can be used to extract bandwidth-limited values of the various surface properties, such as root-mean-square (rms) height and slope errors. Measurements with a microscope equipped with different objectives that have an overlapping spatial frequency range usually give different rms results over the common frequency bandwidth. This is a result of different instrument transfer functions (ITFs) that attenuate spatial frequencies by different amounts over the overlapping range. We report on the use of binary pseudo-random array (BPRA) standards to characterize the ITF of an interferometric microscope with the various objectives. We use a simple model of a 1D binary Results show that the spectrum for an undersampled array is a cosine function, rather than a straight line, constant white noise spectrum. We have an analytical model of the ITF that includes the effects of an obscured aperture and defocus, in addition to the usual parameters of numerical aperture, wavelength, and sampling period. In addition, the model includes the effect of aliasing of spatial frequency components beyond the Nyquist back into the sub-Nyquist region. We compare the model PSD predictions to the measurements performed with different objectives. Departures of the measured PSDs from the model predictions indicate that there are higher-order ITF corrections yet to be identified and included in the model.

Takacs, PZ↗
DOE OSTI2021

History Matching and Performance Prediction of a Polymer Flood Pilot in Heavy Oil Reservoir on Alaska North Slope

The first-ever polymer flood pilot to enhance heavy oil recovery on Alaska North Slope (ANS) is ongoing. After more than 2.5 years of polymer injection, significant benefit has been observed from the decrease in water cut from 65% to less than 15% in the project producers. The primary objective of this study is to develop a robust history-matched reservoir simulation model capable of predicting future polymer flood performance. In this work, the reservoir simulation model has been developed based on the geological model and available reservoir and fluid data. In particular, four high transmissibility strips were introduced to connect the injector-producer well pairs, simulating short-circuiting flow behavior that can be explained by viscous fingering and reproducing the water cut history. The strip transmissibilities were manually tuned to improve the history matching results during the waterflooding and polymer flooding periods, respectively. It has been found that higher strip transmissibilities match the sharp water cut increase very well in the waterflooding period. Then the strip transmissibilities need to be reduced with time to match the significant water cut reduction. The viscous fingering effect in the reservoir during waterflooding and the restoration of injection conformance during polymer flooding have been effectively represented. Based on the validated simulation model, numerical simulation tests have been conducted to investigate the oil recovery performance under different development strategies, with consideration for sensitivity to polymer parameter uncertainties. The oil recovery factor with polymer flooding can reach about 39% in 30 years, twice as much as forecasted with continued waterflooding. Besides, the updated reservoir model has been successfully employed to forecast polymer utilization, a valuable parameter to evaluate the pilot test’s economic efficiency. All the investigated development strategies indicate polymer utilization lower than 3.5 lbs/bbl in 30 years, which is economically attractive.

Wang, Xindan↗