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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Effects of plasmaspheric ion heating due to ionospheric and magnetospheric sources

In an initial study, the He(+) observations from the Retarding Ion Mass Spectrometer on Dynamics Explorer 1 (RIMS/DE 1) was examined for more than 120 transits of the plasmasphere in the fall of 1981. The He(+) to H(+) ratio was determined as it varied spatially over portions of the DE 1 orbit, and its variation with solar and magnetic activities and with local time, focusing specifically on the inner plasmasphere. These variations were compared along the L = 2 field line with calculations made by the Field Line Interhemispheric Plasma (FLIP) code. In a recently submitted paper, the He(+) to H(+) density ratio was examined for all the available data from 1981 to 1984 from the RIMS on DE 1. There are two basic characteristics of the ratio: one is that the ratio decreases with radial distance in the plasmasphere, and the other is the strong dependence of the density ratio on solar activity. In addition to the He(+)/H(+) ratio research, a phenomenon has been studied in the topside ionosphere which relates to the thermal coupling of the ionosphere to the plasmasphere. There is little or no correlation with magnetic and solar activity here. Another study has been directed toward the relation of plasma properties to the density gradients forming the plasmapause. The study has followed a two-pronged approach. First, the observations have been analyzed to determine what happens to the plasma properties across these boundary layers (density gradients). Second, comparisons were made with FLIP model calculations to determine how well the model is able to treat these conditions. Among the significant lessons learned in these studies are two that bear directly on the direction of future investigations in this area. First, composition cannot be viewed independently of thermal structure. Second, solar and magnetic activity effects are real; but the causal relationship between activity and effects is frequently quite complicated because several different processes appear to be operating in different ways and on different time scales. Under these circumstances, large correlation coefficients should not be expected and are not generally found.

Comfort, Richard H.↗
NASA NTRS2016

Assurance of Fault Management: Risk-Significant Adverse Condition Awareness

Fault Management (FM) systems are ranked high in risk-based assessment of criticality within flight software, emphasizing the importance of establishing highly competent domain expertise to provide assurance for NASA projects, especially as spaceflight systems continue to increase in complexity. Insight into specific characteristics of FM architectures seen embedded within safety- and mission-critical software systems analyzed by the NASA Independent Verification Validation (IVV) Program has been enhanced with an FM Technical Reference (TR) suite. Benefits are aimed beyond the IVV community to those that seek ways to efficiently and effectively provide software assurance to reduce the FM risk posture of NASA and other space missions. The identification of particular FM architectures, visibility, and associated IVV techniques provides a TR suite that enables greater assurance that critical software systems will adequately protect against faults and respond to adverse conditions. The role FM has with regard to overall asset protection of flight software systems is being addressed with the development of an adverse condition (AC) database encompassing flight software vulnerabilities.Identification of potential off-nominal conditions and analysis to determine how a system responds to these conditions are important aspects of hazard analysis and fault management. Understanding what ACs the mission may face, and ensuring they are prevented or addressed is the responsibility of the assurance team, which necessarily should have insight into ACs beyond those defined by the project itself. Research efforts sponsored by NASAs Office of Safety and Mission Assurance defined terminology, categorized data fields, and designed a baseline repository that centralizes and compiles a comprehensive listing of ACs and correlated data relevant across many NASA missions. This prototype tool helps projects improve analysis by tracking ACs, and allowing queries based on project, mission type, domain component, causal fault, and other key characteristics. The repository has a firm structure, initial collection of data, and an interface established for informational queries, with plans for integration within the Enterprise Architecture at NASA IVV, enabling support and accessibility across the Agency. The development of an improved workflow process for adaptive, risk-informed FM assurance is currently underway.

Software Verification & Validation↗
DOE OSTI2025

AI-Assisted Conceptual Development of a Pre-Geometric Cosmological Model - An Exercise in AI-Assisted Conceptual Framework Generation, Paper II: Local Geometry and Metric Structure

This paper develops the geometric sector of the replication-driven cosmogenesis framework introduced in Paper I. Starting from a pre-geometric spectral substrate and a minimal set of replication axioms, we show how coherent self-replicating units generate a spatial adjacency graph whose continuum limit acquires an effective Riemannian structure. The replication dynamics determines a characteristic correlation length that seeds the local metric, while overlap relations among coherent units produce an isotropic neighborhood geometry with an emergent dimensionality $d_{\rm eff}\simeq 3$ across a broad range of replication factors. As replication slows and causal order stabilizes, a limiting signal speed $c_\ast$ appears, providing the basis for the Lorentzian structure of spacetime without assuming a pre-existing light cone. We derive conditions under which the adjacency graph converges to a smooth three-dimensional manifold, describe the transition from Euclidean to Lorentzian propagation, and identify geometric invariants controlled by the replication parameters. This work establishes the geometric and causal layer of the replication cosmogenesis program, bridging the spectral axioms of Paper I to the cosmological dynamics explored in Paper III.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Active causal learning for decoding chemical complexities with targeted interventions

Abstract Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have become standard for predictions, but they face challenges when applied across different datasets due to reliance on correlations between molecular representation and target properties. These approaches typically depend on large datasets to capture the diversity within the chemical space, facilitating a more accurate approximation, interpolation, or extrapolation of the chemical behavior of molecules. In our research, we introduce an active learning approach that discerns underlying cause-effect relationships through strategic sampling with the use of a graph loss function. This method identifies the smallest subset of the dataset capable of encoding the most information representative of a much larger chemical space. The identified causal relations are then leveraged to conduct systematic interventions, optimizing the design task within a chemical space that the models have not encountered previously. While our implementation focused on the QM9 quantum-chemical dataset for a specific design task—finding molecules with a large dipole moment—our active causal learning approach, driven by intelligent sampling and interventions, holds potential for broader applications in molecular, materials design and discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Population structure limits the use of genomic data for predicting phenotypes and managing genetic resources in forest trees

There is overwhelming evidence that forest trees are locally adapted to climate. Thus, genecological models based on population phenotypes have been used to measure local adaptation, infer genetic maladaptation to climate, and guide assisted migration. However, instead of phenotypes, there is increasing interest in using genomic data for gene resource management. We used whole-genome resequencing and common-garden experiments to understand the genetic architecture of adaptive traits in black cottonwood. We studied the potential of using genome-wide association studies (GWAS) and genomic prediction to detect causal loci, identify climate-adapted phenotypes, and inform gene resource management. We analyzed population structure by partitioning phenotypic and genomic (single-nucleotide polymorphism) variation among 840 genotypes collected from 91 stands along 16 rivers. Most phenotypic variation (60 to 81%) occurred among populations and was strongly associated with climate. Population phenotypes were predicted well using genomic data (e.g., predictive abilityr> 0.9) but almost as well using climate or geography (r> 0.8). In contrast, genomic prediction within populations was poor (r< 0.2). We identified many GWAS associations among populations, but most appeared to be spurious based on pooled within-population analyses. Hierarchical partitioning of linkage disequilibrium and haplotype sharing suggested that within-population genomic prediction and GWAS were poor because allele frequencies of causal loci and linked markers differed among populations. Given the urgent need to conserve natural populations and ecosystems, our results suggest that climate variables alone can be used to predict population phenotypes, delineate seed zones and deployment zones, and guide assisted migration.

Science & Technology - Other Topics↗
DOE OSTI2024

Modular flow in JT gravity and entanglement wedge reconstruction

It has been shown in recent works that JT gravity with matter with two boundaries has a type II ∞ algebra on each side. As the bulk spacetime between the two boundaries fluctuates in quantum nature, we can only define the entanglement wedge for each side in a pure algebraic sense. As we take the semiclassical limit, we will have a fixed long wormhole spacetime for a generic partially entangled thermal state (PETS), which is prepared by inserting heavy operators on the Euclidean path integral. Under this limit, with appropriate assumptions of the matter theory, geometric notions of the causal wedge and entanglement wedge emerge in this background. In particular, the causal wedge is manifestly nested in the entanglement wedge. Different PETS are orthogonal to each other, and thus the Hilbert space has a direct sum structure over sub-Hilbert spaces labeled by different Euclidean geometries. The full algebra for both sides is decomposed accordingly. From the algebra viewpoint, the causal wedge is dual to an emergent type III 1 subalgebra, which is generated by boundary light operators. To reconstruct the entanglement wedge, we consider the modular flow in a generic PETS for each boundary. We show that the modular flow acts locally and is the boost transformation around the global RT surface in the semiclassical limit. It follows that we can extend the causal wedge algebra to a larger type III 1 algebra corresponding to the entanglement wedge. Within each sub-Hilbert space, the original type II ∞ reduces to type III 1 .

2D Gravity↗
DOE OSTI2021

Experimental test of a predicted dynamics–structure–thermodynamics connection in molecularly complex glass-forming liquids

Significance The physical origin of cooperative activated relaxation in glass-forming liquids, which underlies the huge growth of viscosity and suppression of diffusion with cooling, remains elusive. Understanding this problem is of fundamental scientific interest and can assist in the design of new materials. We combine experimental relaxation time and equation-of-state data of molecular liquids to provide strong evidence for the recent theoretical prediction that activated relaxation, though causally driven by dynamically coupled cage-scale hopping and nonlocal collective elasticity quantified by local structural correlations, is well captured based on a thermodynamic description with dimensionless compressibility as the key variable. A mechanistic deduction is the crossover from fragile to strong (e.g., inorganic networks) relaxation behavior arises from the irrelevance of long-range elasticity effects.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A framework for detection and attribution of regional precipitation change: Application to the United States historical record

Abstract Despite the emerging influence of anthropogenic climate change on the global water cycle, at regional scales the combination of observational uncertainty, large internal variability, and modeling uncertainty undermine robust statements regarding the human influence on precipitation. Here, we use output from global climate models in a perfect-data sense to develop a framework for conducting regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is lower than in other regions. Our unified approach can simultaneously detect systematic trends in mean and extreme precipitation, attribute trends to anthropogenic forcings, compute the effects of forcings as a function of time, and map the effects of individual forcings. Model output is used to conduct a set of tests that yield a parsimonious representation for characterizing seasonal precipitation over the CONUS for the historical record (1900 to present day), which ensures our D&A is insensitive to structural uncertainty. Our framework is developed using synthetic data in a Pearl-causal perspective wherein causality can be identified using intervention-based simulations. While the hypothesis-based framework and accompanying generalized D&A formula we develop should be widely applicable, we include a strong caution that the hypothesis-guided simplification of the formula for the historical climatic record of CONUS as described in this paper will likely fail to hold in other geographic regions and under future warming.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Observation of Relativistic Electron Microbursts in Conjunction with Intense Radiation Belt Whistler-Mode Waves

We present multi-satellite observations of large amplitude radiation belt whistler-mode waves and relativistic electron precipitation. On separate occasions during the Wind petal orbits and STEREO phasing orbits, Wind and STEREO recorded intense whistler-mode waves in the outer nightside equatorial radiation belt with peak-to-peak amplitudes exceeding 300 mV/m. During these intervals of intense wave activity, SAMPEX recorded relativistic electron microbursts in near magnetic conjunction with Wind and STEREO. This evidence of microburst precipitation occurring at the same time and at nearly the same magnetic local time and L-shell with a bursty temporal structure similar to that of the observed large amplitude wave packets suggests a causal connection between the two phenomena. Simulation studies corroborate this idea, showing that nonlinear wave.particle interactions may result in rapid energization and scattering on timescales comparable to those of the impulsive relativistic electron precipitation.

Kersten, K.↗
DOE OSTI2025

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Rupe, Adam [Pacific Northwest National Laboratory ↗
NASA NTRS2004

Investigation Organizer

Contents include the folloving:Overview of the Application; Input Data; Analytical Process; Tool's Output; and Application of the Results of the Analysis.The tool enables the first element through a Web-based application that can be accessed by distributed teams to store and retrieve any type of digital investigation material in a secure environment. The second is accomplished by making the relationships between information explicit through the use of a semantic network-a structure that literally allows an investigator or team to "connect -the-dots." The third element, the significance of the correlated information, is established through causality and consistency tests using a number of different methods embedded within the tool, including fault trees, event sequences, and other accident models. And finally, the evidence gathered and structured within the tool can be directly, electronically archived to preserve the evidence and investigative reasoning.

Panontin, Tina↗
DOE OSTI2023

Operation Optimization using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1979

Globally baryon symmetric cosmology, GUT spontaneous symmetry breaking, and the structure of the universe

Grand unified theories (GUT) such as SU(5), with spontaneous symmetry breaking, can lead more naturally to a globally baryon symmetric big bang cosmology with a domain structure than to a totally asymmetric cosmology. The symmetry is broken at random in causally independent domains, favoring neither a baryon nor an antibaryon excess on a universal scale. Because of the additional freedom in the high-energy physics allowed by such GUT gauge theories, new observational tests may be possible. Arguments in favor of this cosmology and various observational tests are discussed.

Stecker, F. W.↗
NASA NTRS2001

Multiwavelength Monitoring of Active Galactic Nuclei

By intensive monitoring of AGN variability over a large range in wavelength, we can probe the structure and physics of active galactic nuclei on microarcsecond angular scales. For example, multi-wavelength variability data allow us (a) to establish causal relationships between variations in different wavebands, and thus determine which physical processes are primary and which spectral changes are induced by variations at other wavelengths, and (b) through reverberation mapping of the UV/optical emission lines, to determine the structure and kinematics of the line-emitting region, and thus accurately determine the central masses in AGNs. Multiwavelength monitoring is resource-intensive, and is difficult to implement with general-purpose facilities. As a result, virtually all programs undertaken to date have been either sparsely sampled, or short in duration, or both. The potentially high return on this type of investigation, however, argues for dedicated facilities for multiwavelength monitoring programs.

Peterson, Bradley M.↗
NASA NTRS2004

Flynn Creek Impact Structure: New Insights from Breccias, Melt Features, Shatter Cones, and Remote Sensing

The Flynn Creek impact structure is located in Tennessee, USA (36 deg.17 min.N, 85 deg.40 min.W). The structure was first mapped as a crypto-volcanic by Wilson and Born in 1936 [1]. Although they did not properly identify the stratigraphy within the crater or the causal mechanism, they did correctly define the horizontal extent of the crater. More detailed surface and subsurface research by Roddy (1979) accurately described the crater as being an impact structure with a diameter of 3.8 km. It formed around 360 Ma, which corresponds to the interval between the deposition of the Nashville Group and the Chattanooga Shale. Although there is limited rock outcrop in the area, there are exposed surface faults, folds, and large outcrops of impact breccia within the crater.

Evenick, J. C.↗
DOE OSTI2024

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. Additionally, an error compensation mechanism is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The result show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92% - 95% from one hour to one week ahead forecasting.

14 SOLAR ENERGY↗
DOE OSTI2021

Multisource Data Fusion Outage Location in Distribution Systems via Probabilistic Graphical Models

Efficient outage location is critical to enhancing the resilience of power distribution systems. However, accurate outage location requires combining massive evidence received from diverse data sources, including smart meter (SM) last gasp signals, customer trouble calls, social media messages, weather data, vegetation information, and physical parameters of the network. This is a computationally complex task due to the high dimensionality of data in distribution grids. In this paper, we propose a multi-source data fusion approach to locate outage events in partially observable distribution systems using Bayesian networks (BNs). A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method. Our method can radically reduce the computational complexity of outage location inference in high-dimensional spaces. The graphical structure of the proposed BN is established based on the network’s topology and the causal relationship between random variables, such as the states of branches/customers and evidence. Utilizing this graphical model, accurate outage locations are obtained by leveraging a Gibbs sampling (GS) method, to infer the probabilities of de-energization for all branches. Compared with commonly-used exact inference methods that have exponential complexity in the size of the BN, GS quantifies the target conditional probability distributions in a timely manner. As a result, a case study of several real-world distribution systems is presented to validate the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2006

What Happened, and Why: Toward an Understanding of Human Error Based on Automated Analyses of Incident Reports

The objective of the Aviation System Monitoring and Modeling (ASMM) project of NASA s Aviation Safety and Security Program was to develop technologies that will enable proactive management of safety risk, which entails identifying the precursor events and conditions that foreshadow most accidents. This presents a particular challenge in the aviation system where people are key components and human error is frequently cited as a major contributing factor or cause of incidents and accidents. In the aviation "world", information about what happened can be extracted from quantitative data sources, but the experiential account of the incident reporter is the best available source of information about why an incident happened. This report describes a conceptual model and an approach to automated analyses of textual data sources for the subjective perspective of the reporter of the incident to aid in understanding why an incident occurred. It explores a first-generation process for routinely searching large databases of textual reports of aviation incident or accidents, and reliably analyzing them for causal factors of human behavior (the why of an incident). We have defined a generic structure of information that is postulated to be a sound basis for defining similarities between aviation incidents. Based on this structure, we have introduced the simplifying structure, which we call the Scenario as a pragmatic guide for identifying similarities of what happened based on the objective parameters that define the Context and the Outcome of a Scenario. We believe that it will be possible to design an automated analysis process guided by the structure of the Scenario that will aid aviation-safety experts to understand the systemic issues that are conducive to human error.

Maille, Nicolas P.↗