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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Parallelization of Rocket Engine System Software (Press)

The main goal is to assess parallelization requirements for the Rocket Engine Numeric Simulator (RENS) project which, aside from gathering information on liquid-propelled rocket engines and setting forth requirements, involve a large FORTRAN based package at NASA Lewis Research Center and TDK software developed by SUBR/UWF. The ultimate aim is to develop, test, integrate, and suitably deploy a family of software packages on various aspects and facets of rocket engines using liquid-propellants. At present, all project efforts by the funding agency, NASA Lewis Research Center, and the HBCU participants are disseminated over the internet using world wide web home pages. Considering obviously expensive methods of actual field trails, the benefits of software simulators are potentially enormous. When realized, these benefits will be analogous to those provided by numerous CAD/CAM packages and flight-training simulators. According to the overall task assignments, Hampton University's role is to collect all available software, place them in a common format, assess and evaluate, define interfaces, and provide integration. Most importantly, the HU's mission is to see to it that the real-time performance is assured. This involves source code translations, porting, and distribution. The porting will be done in two phases: First, place all software on Cray XMP platform using FORTRAN. After testing and evaluation on the Cray X-MP, the code will be translated to C + + and ported to the parallel nCUBE platform. At present, we are evaluating another option of distributed processing over local area networks using Sun NFS, Ethernet, TCP/IP. Considering the heterogeneous nature of the present software (e.g., first started as an expert system using LISP machines) which now involve FORTRAN code, the effort is expected to be quite challenging.

Cezzar, Ruknet↗
NASA NTRS2020

Advancing Methodologies for Applying Machine Learning and Evaluating Spatiotemporal Models of Fine Particulate Matter (PM 2.5 ) Using Satellite Data Over Large Regions

Reconstructing the distribution of fine particulate matter (PM 2.5 ) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM 2.5 health impacts. Flexible statistical methods for prediction have demonstrated the integration of satellite observations with other predictors, yet these algorithms are susceptible to overfitting the spatiotemporal structure of the training datasets. We present a new approach for predicting PM 2.5 using machine-learning methods and evaluating prediction models for the goal of making predictions where they were not previously available. We apply extreme gradient boosting (XGBoost) modeling to predict daily PM 2.5 on a 1 x 1 km 2 resolution for a 13 state region in the Northeastern USA for the years 2000–2015 using satellite-derived aerosol optical depth and implement a recursive feature selection to develop a parsimonious model. We demonstrate excellent predictions of withheld observations but also contrast an RMSE of 3.11 μg/m 3 in our spatial cross-validation withholding nearby sites versus an overfit RMSE of 2.10 μg/m 3 using a more conventional random ten-fold splitting of the dataset. As the field of exposure science moves forward with the use of advanced machine-learning approaches for spatiotemporal modeling of air pollutants, our results show the importance of addressing data leakage in training, overfitting to spatiotemporal structure, and the impact of the predominance of ground monitoring sites in dense urban sub-networks on model evaluation. The strengths of our resultant modeling approach for exposure in epidemiologic studies of PM 2.5 include improved efficiency, parsimony, and interpretability with robust validation while still accommodating complex spatiotemporal relationships.

air pollution↗
DOE OSTI2024

ANS Winter 2024 Summary: Optimizing the ATF-2Ramp Power Profile

When the Halden Boiling Water Reactor closed down in 2018, a need to restore the capability for in-reactor power ramp testing arose. Such testing is valuable for studying pellet-clad interaction phenomena in nuclear fuels. The data from these studies is of great interest to a number of research programs, including the accident-tolerant fuel (ATF) program at Idaho National Laboratory (INL). In 2022, Woolstenhulme et al. proposed several power ramp testing ideas using facilities at INL, including irradiation in the Transient Reactor Test Facility (better known as TREAT) and the Advanced Test Reactor (ATR) [1]. Worrall et al. [2] and Labossiere-Hickman et al. [3] subsequently performed feasibility studies for the ATR testing options in 2023. This summary further investigates the three-pin trefoil design (Fig. 1) for the proposed ATF-2Ramp Experiment discussed in Labossiere-Hickman et al. [3]. ATF-2Ramp is designed to operate in the center flux trap (CFT) of the ATR during a powered axial locator mechanism (PALM) cycle: a short, variable-powered cycle with an asymmetric power distribution. Previously, it was shown that tailoring the thickness of the hafnium (Hf) neutron shields (“mini-shrouds”) surrounding each pin offered a degree of control sufficient to achieve the programmatic linear heat generation rate (LHGR) targets for ATF-2Ramp during the high-power period of a PALM cycle. New work involves shortening the experiment test train for consistency with the fuel pins in ATF-2D [4] and then shaping the axial power profile of the three test pins.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Power Systems Resilience

As communities take steps toward identifying and achieving their clean energy goals, the topic of resilience will likely emerge. This presentation aims to help inform and empower communities as they explore how their clean energy projects can address evolving power systems reliability and resilience due to climate change and other factors. The training provides a basic introduction to power systems and the resilience challenges this traditional infrastructure faces, as well as strategies for enhancing this important quality of electric service.

communities↗
DOE OSTI2022

Forecasting distributed energy resources adoption for power systems

Failing to incorporate accurate distributed energy resource penetration forecasts into long-term resource and transmission planning can lead to cost inefficiencies at best and system failures at worst. We have developed an open-source tool that employs an advanced Bass specification to calibrate and forecast technology adoption. The advanced specification includes geographic clustering, exogenously estimated market size, and dynamic time steps. Training on historical adoption of rooftop photovoltaics at the U.S. county-level and using detailed techno-economic estimates, our model achieves a two-year average mean-absolute-percentage-error of 19% in predicting system counts at the county-level, weighted by population. Model error was negatively correlated with market maturity - the error was 12% for counties in states with at least 28 W-per-capita of installed capacity. The advanced specification significantly reduces unweighted forecasting percent error compared to a conventional Bass specification: from 196% to 25% for capacity and from 226% to 22% for system count.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

A data-driven method for modelling dissipation rates in stratified turbulence

We present a deep probabilistic convolutional neural network (PCNN) model for predicting local values of small-scale mixing properties in stratified turbulent flows, namely the dissipation rates of turbulent kinetic energy and density variance, $\varepsilon$ and $\chi$ . Inputs to the PCNN are vertical columns of velocity and density gradients, motivated by data typically available from microstructure profilers in the ocean. The architecture is designed to enable the model to capture several characteristic features of stratified turbulence, in particular the dependence of small-scale isotropy on the buoyancy Reynolds number $Re_b:=\varepsilon /(\nu N^2)$ , where $\nu$ is the kinematic viscosity and $N$ is the background buoyancy frequency, the correlation between suitably locally averaged density gradients and turbulence intensity and the importance of capturing the tails of the probability distribution functions of values of dissipation. Empirically modified versions of commonly used isotropic models for $\varepsilon$ and $\chi$ that depend only on vertical derivatives of density and velocity are proposed based on the asymptotic regimes $Re_b\ll 1$ and $Re_b\gg 1$ , and serve as an instructive benchmark for comparison with the data-driven approach. When trained and tested on a simulation of stratified decaying turbulence which accesses a range of turbulent regimes (associated with differing values of $Re_b$ ), the PCNN outperforms assumptions of isotropy significantly as $Re_b$ decreases, and additionally demonstrates improvements over the fitted empirical models. A differential sensitivity analysis of the PCNN facilitates a comparison with the theoretical models and provides a physical interpretation of the features enabling it to make improved predictions.

42 ENGINEERING↗
DOE OSTI2025

Polarized and unpolarized gluon PDFs: Generative machine learning applications for lattice QCD matrix elements at short distance and large momentum

Lattice quantum chromodynamics (QCD) calculations share a defining challenge by requiring a small finite range of spatial separation z between quark/gluon bilinears for controllable power corrections in the perturbative QCD factorization, and a large hadron boost p z for a successful determination of collinear parton distribution functions (PDFs). However, these two requirements make the determination of PDFs from lattice data very challenging. We present the application of generative machine learning algorithms to estimate the polarized and unpolarized gluon correlation functions utilizing short-distance data and extending the correlation up to z p z ≲ 14 , surpassing the current capabilities of lattice QCD calculations. We train physics-informed machine learning algorithms to learn from the short-distance correlation at z ≲ 0.36 fm and take the limit, p z → ∞ , thereby minimizing possible contamination from the higher-twist effects for a successful reconstruction of the polarized gluon PDF. We also expose the bias and problems with underestimating uncertainties associated with the use of model-dependent and overly constrained functional forms, such as x α ( 1 − x ) β and its variants to extract PDFs from the lattice data. We propose the use of generative machine learning algorithms to mitigate these issues and present our determination of the polarized and unpolarized gluon PDFs in the nucleon. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Combined Data and Deep Learning Model Uncertainties: An Application to the Measurement of Solid Fuel Regression Rate

In complex physical process characterization, such as the measurement of the regression rate for solid hybrid rocket fuels, where both the observation data and the model used have uncertainties originating from multiple sources, combining these in a systematic way for quantities of interest (QoI) remains a challenge. In this paper, we present a forward propagation uncertainty quantification (UQ) process to produce a probabilistic distribution for the observed regression rate r. We characterized two input data uncertainty sources from the experiment (the distortion from the camera U c and the non-zero-angle fuel placement U Y ), the prediction and model form uncertainty from the deep neural network (U m ), as well as the variability from the manually segmented images used for training it (U s ). Here, we conducted seven case studies on combinations of these uncertainty sources with the model form uncertainty. The main contribution of this paper is the investigation and inclusion of the experimental image data uncertainties involved, and how to include them in a workflow when the QoI is the result of multiple sequential processes.

42 ENGINEERING↗
NASA NTRS1982

Life and reliability models for helicopter transmissions

Computer models of life and reliability are presented for planetary gear trains with a fixed ring gear, input applied to the sun gear, and output taken from the planet arm. For this transmission the input and output shafts are co-axial and the input and output torques are assumed to be coaxial with these shafts. Thrust and side loading are neglected. The reliability model is based on the Weibull distributions of the individual reliabilities of the in transmission components. The system model is also a Weibull distribution. The load versus life model for the system is a power relationship as the models for the individual components. The load-life exponent and basic dynamic capacity are developed as functions of the components capacities. The models are used to compare three and four planet, 150 kW (200 hp), 5:1 reduction transmissions with 1500 rpm input speed to illustrate their use.

Savage, M.↗
NASA NTRS1991

Design of thrust vectoring exhaust nozzles for real-time applications using neural networks

Thrust vectoring continues to be an important issue in military aircraft system designs. A recently developed concept of vectoring aircraft thrust makes use of flexible exhaust nozzles. Subtle modifications in the nozzle wall contours produce a non-uniform flow field containing a complex pattern of shock and expansion waves. The end result, due to the asymmetric velocity and pressure distributions, is vectored thrust. Specification of the nozzle contours required for a desired thrust vector angle (an inverse design problem) has been achieved with genetic algorithms. This approach is computationally intensive and prevents the nozzles from being designed in real-time, which is necessary for an operational aircraft system. An investigation was conducted into using genetic algorithms to train a neural network in an attempt to obtain, in real-time, two-dimensional nozzle contours. Results show that genetic algorithm trained neural networks provide a viable, real-time alternative for designing thrust vectoring nozzles contours. Thrust vector angles up to 20 deg were obtained within an average error of 0.0914 deg. The error surfaces encountered were highly degenerate and thus the robustness of genetic algorithms was well suited for minimizing global errors.

Prasanth, Ravi K.↗
DOE OSTI2020

Cataloging accreted stars within Gaia DR2 using deep learning

The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ. Traditional selection methods that have been used to identify accreted stars typically rely on full 3D velocity, metallicity information, or both, which significantly reduces the number of classifiable stars. The approach advocated here is applicable to a much larger portion of Gaia DR2. Methods. A method known as “transfer learning” is shown to be effective through extensive testing on a set of mock Gaia catalogs that are based on the F IRE cosmological zoom-in hydrodynamic simulations of Milky Way-mass galaxies. The machine is first trained on simulated data using only 5D kinematics as inputs and is then further trained on a cross-matched Gaia /RAVE data set, which improves sensitivity to properties of the real Milky Way. Results. The result is a catalog that identifies ~767 000 accreted stars within Gaia DR2. This catalog can yield empirical insights into the merger history of the Milky Way and could be used to infer properties of the dark matter distribution.

Astronomy & Astrophysics↗
DOE OSTI2022

Finding quadruply imaged quasars with machine learning – I. Methods

ABSTRACT Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic ‘needle in a haystack’ problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range of 0.86–0.89. Recall is close to 100 per cent down to total magnitude i ∼ 21 indicating high completeness, while precision declines from 85 per cent to 70 per cent in the range i ∼ 17–21. The methods are extremely fast: training on 2 million samples takes 20 h on a GPU machine, and 108 multiband cut-outs can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Geometrical-Based Generative Adversarial Network to Enhance Digital Rock Image Quality

X-ray microcomputed tomography (micro-CT) is a common tool for the study of porous media structures and properties. High-quality micro-CT data are required to accurately capture pore structures. Acquiring high-quality micro-CT data, however, is not always possible, owing to application limitations and experimental constraints. Therefore, we propose a geometrical-based generative adversarial network (GAN) to rapidly restore noisy micro-CT images to their clean counterparts. The training data and related ground-truth (GT) data are scanned for 7 min and 9.5 h, respectively. To evaluate the performance of the geometrical-based GAN, a 6003 voxel image that has never been used for training is reconstructed and compared with the corresponding GT image. Histogram matching and linear normalization are implemented to adjust the histogram of the reconstructed image to that of the GT image. A watershed-based segmentation method is then applied to delineate pore and solid phases. Lastly, we measure the Minkowski functionals and petrophysical properties, including absolute permeability, pore size distribution, drainage capillary pressure-saturation curve, and imbibition relative permeability, to estimate the physical accuracy of the denoised image. The results show that the proposed geometrical-based GAN can accurately restore noisy micro-CT data. By reducing the scanning time from 9.5 h to 7 min, the expenditure of collecting micro-CT can be decreased significantly. This is particularly important for applications where time-lapse images of a dynamic process are required, high-throughput imaging is necessary for real-time data analysis, or where the quantification of large sample volumes is required.

58 GEOSCIENCES↗
DOE OSTI2022

SourceFinder: a Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies

High-throughput genome sequencing technologies enable the investigation of complex genetic interactions, including the horizontal gene transfer of plasmids and bacteriophages. However, identifying these elements from assembled reads remains challenging due to genome sequence plasticity and the difficulty in assembling complete sequences. In this study, we developed a classifier, using random forest, to identify whether sequences originated from bacterial chromosomes, plasmids, or bacteriophages. The classifier was trained on a diverse collection of 23,211 chromosomal, plasmid, and bacteriophage sequences from hundreds of bacterial species. In order to adapt the classifier to incomplete sequences, each complete sequence was subsampled into 5,000 nucleotide fragments and further subdivided into k-mers. This three-class classifier succeeded in identifying chromosomes, plasmids, and bacteriophages using k-mer distributions of complete and partial genome sequences, including simulated metagenomic scaffolds with minimum performance of 0.939 area under the receiver operating characteristic curve (AUC). This classifier, implemented as SourceFinder, has been made available as an online web service to help the community with predicting the chromosomal, plasmid, and bacteriophage sources of assembled bacterial sequence data (https://cge.food.dtu.dk/services/SourceFinder/).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models

One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time.

machine learning↗
DOE OSTI2020

Wattile: Probabilistic Deep Learning-based Forecasting of Building Energy Consumption [SWR-20-94]

Accurate energy forecasting is becoming critical due to many reasons: i ) optimal distributed energy resources operations and dispatch, ii) fault detection and diagnostics, and iii) meeting operational energy efficiency targets. Wattile uses deep learning (DL) for the building's short-term load forecasting application. Two specific types of neural networks called, Long Short Term Memory (LSTM) and Sequence-to-Sequence (S2S) models are used to make predictions. Forecasting models are trained using online historical weather and occupancy indicator data streams from the Intelligent Campus Program's data acquisition systems at the National Renewable Energy Laboratory (NREL) for main meters and sub-meters of multiple building types. These models use probabilistic methods to provide quantile-based forecasts in addition to nominal conditional median predictions of electricity consumption.

Frank, Stephen↗
DOE OSTI2024

Wind Energy Fundamentals for the Bangladesh Financial Sector [Slides]

Financial institutions in Bangladesh play an important role in accelerating the deployment of wind energy. The country's central bank, the Bangladesh Bank, has been a pioneer in developing green banking initiatives. This training seeks to provide the Bangladesh Bank and other stakeholders in the Bangladesh financial sector with fundamental knowledge about wind energy so that they can support its growth in the country.

17 WIND ENERGY↗