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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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DOE OSTI2025

Impact of Domain Knowledge on the Property Prediction of Specialized Machine Learning Models

Developing transferable machine learning models is trending in data-driven materials research. However, how to apply such models to a specific research domain remains unclear. Here, in this work, we choose high-entropy materials as a platform with a specialized data set containing 145,323 DFT-relaxed materials. This data set is used to explore the role of domain-specific knowledge in training effective models. Our tests with three representative graph neural network architectures indicate the model complexity has much smaller influence on performance than the data itself. Specifically, the consideration of low-energy atomic ordering, structures with diverse elemental coverage, and high-order interactions significantly influences the model performance. We also find that domain knowledge-driven sampling can greatly enhance unsupervised learning techniques. This research highlights that developing specialized data sets is more beneficial than further complicating deep learning architectures. Additionally, physics-inspired sampling algorithms are crucially needed for better machine learning models for a specific materials research domain.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A Dataset of 3D Structural and Simulated Transport Properties of Complex Porous Media

Physical processes that occur within porous materials have wide-ranging applications including - but not limited to - carbon sequestration, battery technology, membranes, oil and gas, geothermal energy, nuclear waste disposal, water resource management. The equations that describe these physical processes have been studied extensively; however, approximating them numerically requires immense computational resources due to the complex behavior that arises from the geometrically-intricate solid boundary conditions in porous materials. Here, we introduce a new dataset of unprecedented scale and breadth, DRP-372: a catalog of 3D geometries, simulation results, and structural properties of samples hosted on the Digital Rocks Portal. The dataset includes 1736 flow and electrical simulation results on 217 samples, which required more than 500 core years of computation. This data can be used for many purposes, such as constructing empirical models, validating new simulation codes, and developing machine learning algorithms that closely match the extensive purely-physical simulation. This article offers a detailed description of the contents of the dataset including the data collection, simulation schemes, and data validation.

3D images↗

DOE OSTI2025

Adversarial Binaries: AI-guided Instrumentation Methods for Malware Detection Evasion

Adversarial binaries are executable files that have been altered without loss of function by an AI agent in order to deceive malware detection systems. Progress in this emergent vein of research has been constrained by the complex and rigid structure of executable files. Although prior work has demonstrated that these binaries deceive a variety of malware classification models which rely on disparate feature sets, a consensus as to the best approach has not been reached, either in terms of the optimization algorithms or the instrumentation methods. Furthermore, although inconsistencies in the data sets, target classifiers, and functionality verification methods make head-to-head comparisons difficult, here we extract lessons learned and make recommendations for future research.

malware obfuscation↗

DOE OSTI2023

Validation of LOCA2 and STAR-ESDM Statistically Downscaled Products

The National Climate Assessment (NCA) is the preeminent national report examining current and future risks posed by climate change. Countless agencies, policymakers, stakeholders and other end-users rely upon guidance from the NCA to plan for an uncertain future. These groups all depend on modern curated data, provided alongside the NCA, to quantify the impact of climate change on metrics of relevance for their decision processes. In its fifth iteration (NCA5), two statistically downscaled ensemble products, each providing data at grid spacing of approximately 5km over the contiguous United States, were selected to accompany the report. These include LOCalized Analogs version 2 (LOCA2) and Seasonal Trends and Analysis of Residuals Empirical-Statistical Downscaling Model (STAR-ESDM). Both data products are produced through a process known as statistical downscaling, where relatively coarse Global Climate Model (GCM) data is refined to locally relevant scales through the application of scientifically-supported empirical and algorithmic relationships. In support of the NCA effort, this report provides an independent validation of these two products against historical observations, with a focus on precipitation and near-surface temperature variables. Based on the results of this validation, several recommendations are provided related to the use of these data products. The structure of this report is as follows: In section 2, we review three gridded observational products that are used as part of our intercomparison. In section 3, we describe the two statistical downscaling techniques and their corresponding datasets that are the focus of this study. In section 4, the methodology we employ for validation is described. Section 5 provides results of the validation, which in turn motivate our recommendations on the use of these data products. A brief summary is provided in section 6.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Data Structure Alchemy

In an increasingly more data-driven world, the project set out to uncover the first principles of data-structure design, chart the immense design space they form, and build automation that can synthesize an optimal structure, or even a whole storage engine, for any given workload, hardware platform, and cost target. Data structures are at the center of every computational system and are directly responsible for its performance. Two core technical thrusts were defined: 1) Mapping design spaces for key data-centric abstractions (filters, hash functions, storage-engine layouts, neural-network topologies, blockchain protocols, image layouts, etc.). 2) Developing search & synthesis algorithms, initially analytical cost models, later neural-guided bi-level optimisers that navigate sextillions of candidate designs in seconds and materialise the best one as ready‐to-run code. This report distills the key insights, accomplishments, and impact.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

The Design and Implementation of a Secure Datastore Based on Ethereum Smart Contract

In this paper, we present a secure datastore based on an Ethereum smart contract. Our research is guided by three research questions. First, we will explore to what extend a smart-contract-based datastore should resemble a traditional database system. Second, we will investigate how to store the data in a smart-contract-based datastore for maximum flexibility while minimizing the gas consumption. Third, we seek answers regarding whether or not a smart-contract-based datastore should incorporate complex processing such as data encryption and data analytic algorithms. The proposed smart-contract-based datastore aims to strike a good balance between several constraints: (1) smart contracts are publicly visible, which may create a confidentiality concern for the data stored in the datastore; (2) unlike traditional database systems, the Ethereum smart contract programming language (i.e., Solidity) offers very limited data structures for data management; (3) all operations that mutate the blockchain state would incur financial costs and the developers for smart contracts must make sure sufficient gas is provisioned for every smart contract call, and ideally, the gas consumption should be minimized. Our investigation shows that although it is essential for a smart-contract-based datastore to offer some basic data query functionality, it is impractical to offer query flexibility that resembles that of a traditional database system. Furthermore, we propose that data should be structured as tag-value pairs, where the tag serves as a non-unique key that describes the nature of the value. We also conclude that complex processing should not be allowed in the smart contract due to the financial burden and security concerns. The tag-based secure datastore designed this way also defines its applicative perimeter, i.e., only applications that align with our strategy would find the proposed datastore a good fit. Those that would rather incur higher financial cost for more data query flexibility and/or less user burden on data pre- and post-processing would find the proposed database too restrictive.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1985

Pattern recognition in the satellite temperature retrieval problem

Pattern recognition procedures have been developed in order to improve the first-guess fields for satellite temperature retrievals. The first procedure is used to select one or more historical radiosonde temperature profiles as analog estimates of ambient thermal structure. The second procedure is used to organize a priori data into shape-coherent pattern libraries using structural information inherent in the data itself. On the basis of independent tests of about 800 temperature retrievals, it was found that: (1) the pattern recognition techniques reduced first-guess profile errors by nearly 50 percent in comparison with traditional partitioning schemes; and (2) with regression and physical-iterative retrieval algorithms, however, the effect of pattern recognition on temperature retrieval error was insignificant. Analysis of individual retrieval errors showed that poor retrievals may outweigh the potential benefits of both pattern recognition techniques.

Thompson, O. E.↗

NASA NTRS1987

A numerical study of three-dimensional separated flow past a hemisphere cylinder

Separated and vortical flow about a hemisphere-cylinder body has been investigated. An algorithm featuring two implicit factors, and partial flux splitting has been used to solve the thin-layer Navier-Stokes equations. In analyzing the complex flow patterns, experimental data and topological concepts are used to complement the numerical results in interpreting the surface-flow patterns as well as the flowfield structures. Basic issues concerning the three-dimensional separation characteristics and the leeward vortical structures are examined.

Ying, Susan X.↗

NASA NTRS1989

Control/structure interaction methods for space station power systems

The Structural Dynamics Research Corporation and the NASA Lewis Research Center have been working together to develop tools and methods for the analysis of control/structure interaction problems related to the space station power systems. Flexible modes of the solar arrays below 0.1 Hz, suggest that even for relatively slow control systems, the potential for control/structure interaction exists. The emphasis of the effort has been to develop tools which couple NASTRAN's powerful capabilities in structural dynamics with EASY5's powerful capabilities in control systems analysis. One product is an interface software package called CO-ST-IN for COntrol-STructure-INteraction. CO-ST-IN acts to translate data between NASTRAN and EASY5, facilitating the analysis of complex coupled problems. Interfaces to SDRC I-DEAS and MATRIXx are also offered. Beside transferring standard modal information, CO-ST-IN implements a number of advanced methods. These include a modal ordering algorithm that helps eliminate uncontrollable or unobservable modes from the analysis, an implementation of the more accurate mode acceleration algorithm for recovery of element forces and stresses directly in EASY5 and an implementation of fixed interface modes in NASTRAN, which reduces the error in the closed-loop model due to the use of truncated mode sets.

Blelloch, Paul↗

NASA NTRS1997

Finding corner point correspondence from wavelet decomposition of image data

A time efficient algorithm for image registration between two images that differ in translation is discussed. The algorithm is based on coarse-fine strategy using wavelet decomposition of both the images. The wavelet decomposition serves two different purposes: (1) its high frequency components are used to detect feature points (corner points here) and (2) it provides coarse-to-fine structure for making the algorithm time efficient. The algorithm is based on detecting the corner points from one of the images called reference image and computing corresponding points from the other image called test image by using local correlations using 7x7 windows centered around the corner points. The corresponding points are detected at the lowest decomposition level in a search area of about 11x11 (depending on the translation) and potential points of correspondence are projected onto higher levels. In the subsequent levels the local correlations are computed in a search area of no more than 3x3 for refinement of the correspondence.

Manohar, Mareboyana↗

NASA NTRS2001

Convective Systems Observed and Simulated During TRMM Field Campaigns

Recently completed TRMM field campaigns (TEFLUN1998, SCSMEX-1998, TRMM.LBA-1999, and KWAJEX 1999) have obtained direct measurements of microphysical data associated with convective systems from various geographical locations. These TRMM field experiments were designed to contribute to fundamental understanding of cloud dynamics and microphysics, as well as for validation,, testing assumptions and error estimates of cloud-resolving models, forward radiative transfer models, algorithms used to estimate rainfall statistics and vertical structure of precipitation and latent heating from both surface-based radar and satellites.

Tao, Wei-Kuo↗

NASA NTRS1996

APMS: An Integrated Set of Tools for Measuring Safety

This is a report of work in progress. In it, I summarize the status of the research and development of the Aviation Performance Measuring System (APMS) for managing, processing, and analyzing digital flight-recorded data. The objectives of the NASA-FAA APMS research project are to establish a sound scientific and technological basis for flight-data analysis, to define an open and flexible architecture for flight-data-analysis systems, and to articulate guidelines for a standardized database structure on which to continue to build future flight-data-analysis extensions. APMS will offer to the air transport community an open, voluntary standard for flight-data-analysis software, a standard that will help to ensure suitable functionality, and data interchangeability, among competing software programs. APMS will develop and document the methodologies, algorithms, and procedures for data management and analyses to enable users to easily interpret the implications regarding safety and efficiency of operations. APMS does not entail the implementation of a nationwide flight-data-collection system. It is intended to provide technical tools to ease the large-scale implementation of flight-data analyses at both the air-carrier and the national-airspace levels in support of their Flight Operations and Quality Assurance (FOQA) Programs and Advanced Qualifications Programs (AQP). APMS cannot meet its objectives unless it develops tools that go substantially beyond the capabilities of the current commercially available software and supporting analytic methods that are mainly designed to count special events. These existing capabilities, while of proven value, were created primarily with the needs of air crews in mind. APMS tools must serve the needs of the government and air carriers, as well as air crews, to fully support the FOQA and AQP programs. They must be able to derive knowledge not only through the analysis of single flights (special-event detection), but through statistical evaluation of the performance of large groups of flights. This paper describes the integrated suite of tools that will assist analysts in evaluating the operational performance and safety of the national air transport system, the air carrier, and the air crew.

Statler, Irving C.↗

NASA NTRS1997

APMS: An Integrated Suite of Tools for Measuring Performance and Safety

This is a report of work in progress. In it, I summarize the status of the research and development of the Aviation Performance Measuring System (APMS) for managing, processing, and analyzing digital flight-recorded data. The objectives of the NASA-FAA APMS research project are to establish a sound scientific and technological basis for flight-data analysis, to define an open and flexible architecture for flight-data-analysis systems, and to articulate guidelines for a standardized database structure on which to continue to build future flight-data-analysis extensions . APMS will offer to the air transport community an open, voluntary standard for flight-data-analysis software, a standard that will help to ensure suitable functionality, and data interchangeability, among competing software programs. APMS will develop and document the methodologies, algorithms, and procedures for data management and analyses to enable users to easily interpret the implications regarding safety and efficiency of operations. APMS does not entail the implementation of a nationwide flight-data-collection system. It is intended to provide technical tools to ease the large-scale implementation of flight-data analyses at both the air-carrier and the national-airspace levels in support of their Flight Operations and Quality Assurance (FOQA) Programs and Advanced Qualifications Programs (AQP). APMS cannot meet its objectives unless it develops tools that go substantially beyond the capabilities of the current commercially available software and supporting analytic methods that are mainly designed to count special events. These existing capabilities, while of proven value, were created primarily with the needs of air crews in mind. APMS tools must serve the needs of the government and air carriers, as well as air crews, to fully support the FOQA and AQP programs. They must be able to derive knowledge not only through the analysis of single flights (special-event detection), but through statistical evaluation of the performance of large groups of flights. This paper describes the integrated suite of tools that will assist analysts in evaluating the operational performance and safety of the national air transport system, the air carrier, and the air crew.

Statler, Irving C.↗

NASA NTRS1997

APMS: An Integrated Suite of Tools for Measuring Performance and Safety

Statler, Irving C.↗

NASA NTRS1997

Hypersonic Flow Computations on Unstructured Meshes

A method for computing inviscid hypersonic flow over complex configurations using unstructured meshes is presented. The unstructured grid solver uses an edge{based finite{volume formulation. Fluxes are computed using a flux vector splitting scheme that is capable of representing constant enthalpy solutions. Second{order accuracy in smooth flow regions is obtained by linearly reconstructing the solution, and stability near discontinuities is maintained by locally forcing the scheme to reduce to first-order accuracy. The implementation of the algorithm to parallel computers is described. Computations using the proposed method are presented for a sphere-cone configuration at Mach numbers of 5.25 and 10.6, and a complex hypersonic re-entry vehicle at Mach numbers of 4.5 and 9.8. Results are compared to experimental data and computations made with established structured grid methods. The use of the solver as a screening tool for rapid aerodynamic assessment of proposed vehicles is described.

Bibb, K. L.↗

NASA NTRS2008

A Data Type for Efficient Representation of Other Data Types

A self-organizing, monomorphic data type denoted a sequence has been conceived to address certain concerns that arise in programming parallel computers. A sequence in the present sense can be regarded abstractly as a vector, set, bag, queue, or other construct. Heretofore, in programming a parallel computer, it has been necessary for the programmer to state explicitly, at the outset, what parts of the program and the underlying data structures must be represented in parallel form. Not only is this requirement not optimal from the perspective of implementation; it entails an additional requirement that the programmer have intimate understanding of the underlying parallel structure. The present sequence data type overcomes both the implementation and parallel structure obstacles. In so doing, the sequence data type provides unified means by which the programmer can represent a data structure for natural and automatic decomposition to a parallel computing architecture. Sequences exhibit the behavioral and structural characteristics of vectors, but the underlying representations are automatically synthesized from combinations of programmers advice and execution use metrics. Sequences can vary bidirectionally between sparseness and density, making them excellent choices for many kinds of algorithms. The novelty and benefit of this behavior lies in the fact that it can relieve programmers of the details of implementations. The creation of a sequence enables decoupling of a conceptual representation from an implementation. The underlying representation of a sequence is a hybrid of representations composed of vectors, linked lists, connected blocks, and hash tables. The internal structure of a sequence can automatically change from time to time on the basis of how it is being used. Those portions of a sequence where elements have not been added or removed can be as efficient as vectors. As elements are inserted and removed in a given portion, then different methods are utilized to provide both an access and memory strategy that is optimized for that portion and the use to which it is put.

James, Mark↗

DOE OSTI2023

Small-Signal Angle Stability-Oriented False Data Injection Cyber-Attacks on Power Systems

The small-signal angle stability (SSAS) of a power system is determined by the property of operation points. The widely applied false data injection (FDI) cyber-attack, however, is able to stealthily mislead the optimal power flow (OPF) and thus compromise operation points, leading to damages to the SSAS margin. Here, to provide insights for cyber defenders, this paper proposes and investigates a stealthy SSAS-oriented FDI cyber-attack focusing on two attacking purposes, i.e., the SSAS margin and operation cost, with higher priority on the former one. First, this paper establishes a novel bi-level model with an implicit SSAS constraint based on a structure preserving model to compromise operation points. Then, for the SSAS interarea mode in a typical two-area system, this paper formulates closed-form expressions of how the SSAS margin and operation cost behave with respect to stealthy injections. By comparison, for the SSAS local mode in general power systems, this paper proposes a moving target cyber-attack-based hierarchical solution algorithm. Simulation results on a two-area system, a Kundur 11 bus system, and a modified IEEE 14 bus system demonstrate the significant damaging effects of the proposed SSAS-oriented FDI cyber-attack and the conflict between the two attacking purposes.

Benders decomposition↗

NASA NTRS1994

Beyond maximum entropy: Fractal Pixon-based image reconstruction

We have developed a new Bayesian image reconstruction method that has been shown to be superior to the best implementations of other competing methods, including Goodness-of-Fit methods such as Least-Squares fitting and Lucy-Richardson reconstruction, as well as Maximum Entropy (ME) methods such as those embodied in the MEMSYS algorithms. Our new method is based on the concept of the pixon, the fundamental, indivisible unit of picture information. Use of the pixon concept provides an improved image model, resulting in an image prior which is superior to that of standard ME. Our past work has shown how uniform information content pixons can be used to develop a 'Super-ME' method in which entropy is maximized exactly. Recently, however, we have developed a superior pixon basis for the image, the Fractal Pixon Basis (FPB). Unlike the Uniform Pixon Basis (UPB) of our 'Super-ME' method, the FPB basis is selected by employing fractal dimensional concepts to assess the inherent structure in the image. The Fractal Pixon Basis results in the best image reconstructions to date, superior to both UPB and the best ME reconstructions. In this paper, we review the theory of the UPB and FPB pixon and apply our methodology to the reconstruction of far-infrared imaging of the galaxy M51. The results of our reconstruction are compared to published reconstructions of the same data using the Lucy-Richardson algorithm, the Maximum Correlation Method developed at IPAC, and the MEMSYS ME algorithms. The results show that our reconstructed image has a spatial resolution a factor of two better than best previous methods (and a factor of 20 finer than the width of the point response function), and detects sources two orders of magnitude fainter than other methods.

Puetter, Richard C.↗