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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 541 records · Page 30

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NASA NTRS1985

Some remarks about simulation of cosmic ray phenomena with use of nuclear interaction models based on the current SPS proton-antiproton data

The x-y controversy is studied by introducing models with as many features (except for x and y distributions) in common, as possible, to avoid an extrapolation problem, only primary energies of 500 TeV are considered. To prove the point, Monte Carlo simulations are performed of EAS generated by 500 TeV vertical primary protons. Four different nuclear interaction models were used. Two of them are described elsewhere. Two are: (1) Model M-Y00 - with inclusive x and y distributions behaving in a scaling way; and (2) Model M-F00 - at and below ISR energies (1 TeV in Lab) exactly equivalent to the above, then gradually changing to provide the distributions in rapidity at 155 TeV as given by SPS proton-antiproton. This was achieved by gradual decrease in the scale unit in x distributions of produced secondaries, as interaction energy increases. Other modifications to the M-Y00 model were made.

Wrotniak, J. A.↗

DOE OSTI2024

A flexible class of priors for orthonormal matrices with basis function-specific structure

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure a priori. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system’s internal variability.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1998

An Object-Oriented Interface to the CCSDS Ground Telecommand Services

The Telecommand Data Routing and Channel Services defined by the Consultative Committee for Space Data Systems (CCSDS) are flexible enough to support a myriad of commanding models. Because the standard is so broad, the traditional approach has been to implement only the portion of the standard needed by the particular spacecraft being tested/operated. Tasked with providing Telecommand Services for an entire class of spacecraft, where each spacecraft may choose any valid CCSDS commanding model, NASA Code 584 designed a common architecture capable of handling the full CCSDS protocol. The solution uses another CCSDS standard - the Standard Formatted Data Unit (SFDU) as the interface to the Telecommand Services. SFDUs provide a consistent way of labelling data objects, as well as allowing data objects to encapsulate other data objects. The resulting interface is: - Flexible: The full Data Routing and Channel Services are available via a single interface. The client (i.e. the command source) may enter commands at any layer within the protocol stack, specify any of the data aggregation or segmentation methods, and dynamically set any configuration parameter defined in the standard. - Object-oriented: Each object specifies both the data and the actions to be performed with the data. An object may contain other objects. - Expandable: New capabilities are added by defining new objects. Objects pass thru the protocol layers until they reach the applicable layer. The resulting design is: - Modular: The logic for each protocol layer is contained in a separate Application Program Interface (API). The objects used for the external interface are also used for communication between layers. - Distributable: The design can be split along any layer boundary for distribution across multiple machines. The objects ensure data consistency across platforms. This paper describes the SFDU-based interface and the resulting protocol implementation. The implementation is currently used by NASA (National Aeronautics and Space Administration) for integration & test of the microwave Anisotropy Probe (MAP) and Earth observer-I (EO-l) spacecraft. It will be used for post-launch operations of these spacecraft as well as the Imager for Magnetopause to Aurora Global Exploration (IMAGE) spacecraft.

Ray, Timothy Joseph↗

DOE OSTI2021

Extended thermodynamic model for high salinity produced waters

To support the development of desalination processes for high salinity produced waters, we developed a comprehensive thermodynamic model that reliably predicts the thermodynamic properties and phase behavior of produced water. Here this study extends a previously developed thermodynamic model for the aqueous hexary oceanic salt system by incorporating the Sr 2+ and Ba 2+ ions. Based on the electrolyte nonrandom two-liquid theory, the model requires two adjustable binary interaction parameters for each water-electrolyte and electrolyte-electrolyte pair that has a common ion. The binary interaction parameters for the electrolyte-electrolyte pairs involving Sr 2+ and Ba 2+ ions were identified using thermodynamic and salt solubility data. The model was validated for temperatures from 273.15 K to 473.15 K and electrolyte concentrations from infinite dilution to salt saturation. Extension of the model to include HCO 3 − , CO 3 2− , and CO 2 is currently in progress.

42 ENGINEERING↗

DOE OSTI2024

Modeling Distributed Generation in California

In support of analysis for the biennial Integrated Energy Policy Report, the California Energy Commission and the National Renewable Energy Laboratory have partnered to study the growth of distributed energy resources in California. This study involves the use of National Renewable Energy Laboratory's Distributed Generation Market Demand model, available at https://www.nrel.gov/analysis/dgen/, to project statewide adoption of distributed photovoltaics and paired storage. Key outcomes of the collaboration include: • Improved representation of California building stock, load profiles, historical adoption, and tariffs, including the net billing tariff, in the dGen model; • Trained CEC staff members to use and adapt the dGen model for their specific needs; • Developed a methodology for representing emerging consumer segments to potentially adopt distributed energy resources, including low-income, multifamily, and renter-occupied buildings; • Forecasted solar photovoltaic and paired storage growth in California using a common set of modeling parameters. This report describes the multiyear effort, which includes a discussion of: • Methodology and data employed in adapting the Distributed Generation Market Demand model for California to forecast solar photovoltaic and storage statewide through 2040; • Steps taken to modify the base model to forecast solar photovoltaic adoption in emerging market segments such as multifamily or renter-occupied homes or both; • Future enhancements of the model.

14 SOLAR ENERGY↗

NASA NTRS2002

Best Practices for Crash Modeling and Simulation

Aviation safety can be greatly enhanced by the expeditious use of computer simulations of crash impact. Unlike automotive impact testing, which is now routine, experimental crash tests of even small aircraft are expensive and complex due to the high cost of the aircraft and the myriad of crash impact conditions that must be considered. Ultimately, the goal is to utilize full-scale crash simulations of aircraft for design evaluation and certification. The objective of this publication is to describe "best practices" for modeling aircraft impact using explicit nonlinear dynamic finite element codes such as LS-DYNA, DYNA3D, and MSC.Dytran. Although "best practices" is somewhat relative, it is hoped that the authors' experience will help others to avoid some of the common pitfalls in modeling that are not documented in one single publication. In addition, a discussion of experimental data analysis, digital filtering, and test-analysis correlation is provided. Finally, some examples of aircraft crash simulations are described in several appendices following the main report.

Fasanella, Edwin L.↗

NASA NTRS1972

The microwave spectrum and nature of the subsurface of Mars.

Expected microwave spectra of Mars are computed using an improved thermal model and accurate aspect geometry. It is found that when seasonal polar cap effects are included in the calculations, the observable spectrum of Mars is flat from 0.1-21 cm to within the accuracy of present data. The spectra obtained from this model are consistent with all the data and are obtainable from a relatively simple model (homogeneous, dry, smooth dielectric sphere). This result differs from that predicted by the analytical theory in common use which is in apparent conflict with the observed spectra. A range of electrical loss tangents, covering the extreme limits for likely dry particulate geological materials, is employed. The case of a lunar-like subsurface is completely consistent with all present data.

Cuzzi, J. N.↗

DOE OSTI2024

The DESI One-percent Survey: Evidence for Assembly Bias from Low-redshift Counts-in-cylinders Measurements

Abstract We explore the galaxy-halo connection information that is available in low-redshift samples from the early data release of the Dark Energy Spectroscopic Instrument (DESI). We model the halo occupation distribution (HOD) from z = 0.1 to 0.3 using Survey Validation 3 (SV3; a.k.a., the One-Percent Survey) data of the DESI Bright Galaxy Survey. In addition to more commonly used metrics, we incorporate counts-in-cylinders (CiC) measurements, which drastically tighten HOD constraints. Our analysis is aided by the Python package, galtab , which enables the rapid, precise prediction of CiC for any HOD model available in halotools . This methodology allows our Markov chains to converge with much fewer trial points, and enables even more drastic speedups due to its GPU portability. Our HOD fits constrain characteristic halo masses tightly and provide statistical evidence for assembly bias, especially at lower luminosity thresholds: the HOD of central galaxies in z ∼ 0.15 samples with limiting absolute magnitude M r < −20.0 and M r < −20.5 samples is positively correlated with halo concentration with a significance of 99.9% and 99.5%, respectively. Our models also favor positive central assembly bias for the brighter M r < −21.0 sample at z ∼ 0.25 (94.8% significance), but there is no significant evidence for assembly bias with the same luminosity threshold at z ∼ 0.15. We provide our constraints for each threshold sample’s characteristic halo masses, assembly bias, and other HOD parameters. These constraints are expected to be significantly tightened with future DESI data, which will span an area 100 times larger than that of SV3.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2012

Cloud Computing for Mission Design and Operations

The space mission design and operations community already recognizes the value of cloud computing and virtualization. However, natural and valid concerns, like security, privacy, up-time, and vendor lock-in, have prevented a more widespread and expedited adoption into official workflows. In the interest of alleviating these concerns, we propose a series of guidelines for internally deploying a resource-oriented hub of data and algorithms. These guidelines provide a roadmap for implementing an architecture inspired in the cloud computing model: associative, elastic, semantical, interconnected, and adaptive. The architecture can be summarized as exposing data and algorithms as resource-oriented Web services, coordinated via messaging, and running on virtual machines; it is simple, and based on widely adopted standards, protocols, and tools. The architecture may help reduce common sources of complexity intrinsic to data-driven, collaborative interactions and, most importantly, it may provide the means for teams and agencies to evaluate the cloud computing model in their specific context, with minimal infrastructure changes, and before committing to a specific cloud services provider.

cloud computing↗

NASA NTRS2016

Materials Outgassing Rate Decay in Vacuum at Isothermal Conditions

As a laboratory for scientific research, the International Space Station has been in Low Earth Orbit for nearly 20 years and is expected to be on-orbit for another 10 years. The ISS has been maintaining a relatively pristine contamination environment for science payloads. Materials outgassing induced contamination is currently the dominant source for sensitive surfaces on ISS and modeling the outgassing rate decay over a 20 to 30 year period is challenging. Materials outgassing is described herein as a diffusion-reaction process using ASTM E 1559 rate data. The observation of -1/2 (diffusion) or non-integers (reaction limited) as rate decay exponents for common ISS materials indicate classical reaction kinetics is unsatisfactory in modeling materials outgassing. Non-randomness of reactant concentrations at the interface is the source of this deviation from classical reaction kinetics. A diffusion limited decay was adopted as the result of the correlation of the contaminant layer thicknesses on returned ISS hardware, the existence of high outgassing silicone exhibiting near diffusion limited decay, and the confirmation of non-depleted material after ten years in the Low Earth Orbit.Keywords: Materials Outgassing, ASTM E 1559, Reaction Kinetics, Diffusion, Space Environments Effects, Contamination

Huang, Alvin Y.↗

NASA NTRS2017

Summary of Data from the Sixth AIAA CFD Drag Prediction Workshop: CRM Cases 2 to 5

Results from the Sixth AIAA CFD Drag Prediction Workshop Common Research Model Cases 2 to 5 are presented. As with past workshops, numerical calculations are performed using industry-relevant geometry, methodology, and test cases. Cases 2 to 5 focused on force/moment and pressure predictions for the NASA Common Research Model wing-body and wing-body-nacelle-pylon configurations, including Case 2 - a grid refinement study and nacelle-pylon drag increment prediction study; Case 3 - an angle-of-attack buffet study; Case 4 - an optional wing-body grid adaption study; and Case 5 - an optional wing-body coupled aero-structural simulation. The Common Research Model geometry differed from previous workshops in that it was deformed to the appropriate static aeroelastic twist and deflection at each specified angle-of-attack. The grid refinement study used a common set of overset and unstructured grids, as well as user created Multiblock structured, unstructured, and Cartesian based grids. For the supplied common grids, six levels of refinement were created resulting in grids ranging from 7x10(exp 6) to 208x10(exp 6) cells. This study (Case 2) showed further reduced scatter from previous workshops, and very good prediction of the nacelle-pylon drag increment. Case 3 studied buffet onset at M=0.85 using the Medium grid (20 to 40x10(exp 6) nodes) from the above described sequence. The prescribed alpha sweep used finely spaced intervals through the zone where wing separation was expected to begin. Although the use of the prescribed aeroelastic twist and deflection at each angle-of-attack greatly improved the wing pressure distribution agreement with test data, many solutions still exhibited premature flow separation. The remaining solutions exhibited a significant spread of lift and pitching moment at each angle-of-attack, much of which can be attributed to excessive aft pressure loading and shock location variation. Four Case 4 grid adaption solutions were submitted. Starting with grids less than 2x10(exp 6) grid points, two solutions showed a rapid convergence to an acceptable solution. Four Case 5 coupled aerostructural solutions were submitted. Both showed good agreement with experimental data. Results from this workshop highlight the continuing need for CFD improvement, particularly for conditions with significant flow separation. These comparisons also suggest the need for improved experimental diagnostics to guide future CFD development.

Tinoco, Edward N.↗

DOE OSTI2021

Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection

Deep neural networks (DNNs) have achieved state-of-the-art performance across a variety of traditional machine learning tasks, e.g., speech recognition, image classification, and segmentation. The ability of DNNs to efficiently approximate high-dimensional functions has also motivated their use in scientific applications, e.g., to solve partial differential equations and to generate surrogate models. In this paper, we consider the supervised training of DNNs, which arises in many of the above applications. We focus on the central problem of optimizing the weights of the given DNN such that it accurately approximates the relation between observed input and target data. Devising effective solvers for this optimization problem is notoriously challenging due to the large number of weights, nonconvexity, data sparsity, and nontrivial choice of hyperparameters. To solve the optimization problem more efficiently, we propose the use of variable projection (VarPro), a method originally designed for separable nonlinear least-squares problems. Our main contribution is the Gauss--Newton VarPro method (GNvpro) that extends the reach of the VarPro idea to nonquadratic objective functions, most notably cross-entropy loss functions arising in classification. These extensions make GNvpro applicable to all training problems that involve a DNN whose last layer is an affine mapping, which is common in many state-of-the-art architectures. In our four numerical experiments from surrogate modeling, segmentation, and classification, GNvpro solves the optimization problem more efficiently than commonly used stochastic gradient descent (SGD) schemes. Finally, GNvpro finds solutions that generalize well, and in all but one example better than well-tuned SGD methods, to unseen data points.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗

DOE OSTI2025

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Mulayim, Ozan Baris↗

NASA NTRS2023

A Community Convention for Ecological Forecasting: Output Files and Metadata Version 1.0

This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast communication, distribution, validation, and synthesis. For output files, we first describe the convention conceptually in terms of global attributes, forecast dimensions, forecasted variables, and ancillary indicator variables. We then illustrate the application of this convention to the two file formats that are currently preferred by the EFI, netCDF (network common data form), and comma-separated values (CSV), but note that the convention is extensible to future formats. For metadata, EFI's convention identifies a subset of conventional metadata variables that are required (e.g., temporal resolution and output variables) but focuses on developing a framework for storing information about forecast uncertainty propagation, data assimilation, and model complexity, which aims to facilitate cross-forecast synthesis. The initial application of this convention expands upon the Ecological Metadata Language (EML), a commonly used metadata standard in ecology. To facilitate community adoption, we also provide a Github repository containing a metadata validator tool and several vignettes in R and Python on how to both write and read in the EFI standard. Lastly, we provide guidance on forecast archiving, making an important distinction between short-term dissemination and long-term forecast archiving, while also touching on the archiving of code and workflows. Overall, the EFI convention is a living document that can continue to evolve over time through an open community process.

Michael C. Dietze↗

NASA NTRS1972

Sequence data - Magnitude and implications of some ambiguities.

A stochastic model is applied to the divergence of the horse-pig lineage from a common ansestor in terms of the alpha and beta chains of hemoglobin and fibrinopeptides. The results are compared with those based on the minimum mutation distance model of Fitch (1972). Buckwheat and cauliflower cytochrome c sequences are analyzed to demonstrate their ambiguities. A comparative analysis of evolutionary rates for various proteins of horses and pigs shows that errors of considerable magnitude are introduced by Glx and Asx ambiguities into evolutionary conclusions drawn from sequences of incompletely analyzed proteins.

Holmquist, R.↗

NASA NTRS1993

Variational data assimilation with a semi-Lagrangian semi-implicit global shallow-water equation model and its adjoint

An adjoint model is developed for variational data assimilation using the 2D semi-Lagrangian semi-implicit (SLSI) shallow-water equation global model of Bates et al. with special attention being paid to the linearization of the interpolation routines. It is demonstrated that with larger time steps the limit of the validity of the tangent linear model will be curtailed due to the interpolations, especially in regions where sharp gradients in the interpolated variables coupled with strong advective wind occur, a synoptic situation common in the high latitudes. This effect is particularly evident near the pole in the Northern Hemisphere during the winter season. Variational data assimilation experiments of 'identical twin' type with observations available only at the end of the assimilation period perform well with this adjoint model. It is confirmed that the computational efficiency of the semi-Lagrangian scheme is preserved during the minimization process, related to the variational data assimilation procedure.

Li, Y.↗

NASA NTRS2022

MLtool: Universal Supervised Machine Learning Tool to Model Tabulated Data

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine learning↗