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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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142 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Investigation of Causal Relationships of the Cross‐Scale Wave Coupling Through Information Theoretical Approach

On 2015 October 2, MMS spacecraft observed an electron micro-injection event near the southern hemispheric high-altitude cusp, coinciding with intense wave activity across several frequency bands. Here, we investigated the MMS magnetic field and plasma during this event to explore cross-scale coupling among the wave modes. Employing the Hilbert-Huang transform, we perform an empirical mode decomposition to extract frequencies and amplitudes of the intrinsic mode functions (IMFs). In this analysis, we establish both linear and nonlinear relationships and examine the information transfer between the IMFs. Notably, the transfer entropy suggests that high frequency ion cyclotron waves may be driven by the mirror mode structures. Our case study effectively demonstrates the utility of the information theory based tools for studying cross-scale wave coupling phenomena.

Rivera, Elmer C. [Andrews University, Berrien Spri↗
DOE OSTI2021

A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms

Abstract Increasing occurrence of harmful algal blooms across the land–water interface poses significant risks to coastal ecosystem structure and human health. Defining significant drivers and their interactive impacts on blooms allows for more effective analysis and identification of specific conditions supporting phytoplankton growth. A novel iterative Random Forests (iRF) machine-learning model was developed and applied to two example cases along the California coast to identify key stable interactions: (1) phytoplankton abundance in response to various drivers due to coastal conditions and land-sea nutrient fluxes, (2) microbial community structure during algal blooms. In Example 1, watershed derived nutrients were identified as the least significant interacting variable associated with Monterey Bay phytoplankton abundance. In Example 2, through iRF analysis of field-based 16S OTU bacterial community and algae datasets, we independently found stable interactions of prokaryote abundance patterns associated with phytoplankton abundance that have been previously identified in laboratory-based studies. Our study represents the first iRF application to marine algal blooms that helps to identify ocean, microbial, and terrestrial conditions that are considered dominant causal factors on bloom dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Two-tooth bosonic quantum comb for temporal-correlation sensing

We introduce a two-tooth bosonic quantum comb that captures the sequential interactions between a thermal absorber and a long-lived coherent probe. The comb provides a causal, multi-time description of coherence transport, tracking how the probe records both instantaneous fluctuations and their temporal correlations. Using a process-tensor formulation, we derive closed form expressions showing that interference between the two interaction windows generates a non-monotonic memory response that reflects a fundamental competition between the absorbers thermal population and its dynamical correlations. By sweeping the temporal separation between the interaction windows, the probe directly samples the absorbers population correlator, enabling bosonic noise spectroscopy that discriminates Markovian temperature noise from slow or spectrally structured fluctuations. The approach is readily compatible with circuit-QED platforms and offers a general method for probing fluctuating bosonic environments.

Zhu, Shaojiang [Fermilab]↗
DOE OSTI2021

Visual HPC Workflows for the Analysis of System Dynamics Models

Visual analytics supported by high performance computing (HPC) accelerates and enhances the discovery, exploration, and analysis of causal patterns in complex system dynamics (SD) models. We present a suite of visualization-assisted ensemble-based techniques for hypothesis generation and testing, and for sensitivity analysis. By employing HPC to provide parallel, on-demand simulation of SD models, one can “steer” an ensemble of simulated scenarios in real time as one first formulates and then informally tests those hypotheses: this provides rapid feedback for analysts to refine their understanding of the causal relationships emergent from a model. Such understandings can be followed and augmented by rigorous application of statistical methods, namely global variance-based sensitivity analysis, Monte-Carlo filtering, adaptive regional sensitivity analysis, and self-organized maps: here timely computation relies on HPC, while effective presentation emphasizes high-dimensional multivariate data visualization. Immersive visualization in virtual 3D environments provides an excellent adjunct to the traditional 2D graphics typically used for SD models, as it generates an embodied understanding of model behavior and facilitates an active, collaborative critique of model structure and output. Finally, we summarize prospects for HPC-enabled visual analytics applied to SD modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Manipulation of amorphous precursors to enhance zeolite nucleation

Crystallization in media comprised of amorphous precursors is becoming a more common phenomenon for numerous synthetic, biological, and natural materials that grow by a combination of classical and nonclassical pathways. Amorphous phases can exhibit a wide range of physicochemical properties that may evolve during the course of nucleation and crystal growth. This creates challenges for establishing causal relationships between amorphous precursor properties and their effect(s) on the selection of mechanistic pathways of crystallization and ultimately the properties of the crystalline product. In this study, we examine ways to manipulate the composition and colloidal stability of amorphous (alumino)silicate precursors that are prevalent in nanoporous zeolite syntheses. Changes in the amorphous precursor properties are evaluated on the basis of their ability to enhance rates of crystal formation. Here, we use fumed silica as the primary silicon source and examine the effects of infusing the source or growth medium with additional alkali metal, which serves as an inorganic structure-directing agent to facilitate the formation of porous crystal structures. We also assess the impact of adding a polymer additive, which reduces the colloidal stability of precursors, wherein we posit that the confined pockets of solution within the interstitial spaces of the precursor aggregates play an important role in regulating the rate of zeolite crystallization. Three commercially relevant zeolites (mordenite, SSZ-13, and ZSM-5) were selected for this study based on their diverse frameworks and methods of preparation. Here our findings reveal that alkali infusion significantly reduces the crystallization times for mordenite and SSZ-13, but has little impact on ZSM-5 synthesis. Conversely, we find that polymer addition markedly enhanced the rates of crystallization among all three zeolites, suggesting that this method may be a general approach to reduce zeolite synthesis times. Given the relatively high costs associated with commercial zeolite production, identifying new methods to improve the efficiency of hydrothermal syntheses can have significant practical implications beyond the fundamental benefits of developing new routes to tailor nonclassical crystallization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

An Examination of the Hydropower Licensing and Federal Authorization Process

Site-permitting and regulation are necessary to ensure hydropower projects (both original and relicensed) comply with statutory requirements and address multiple stakeholder priorities that consider a range of factors, including water quality, species protection, cultural resource impacts, and recreation. However, the time involved in acquiring a license for an individual hydropower project can be highly variable by project, leading to increased project costs, financial risks, and uncertainties. In part, this variability is the result of a regulatory structure that has evolved over time to include multiple approvals and compliance requirements administered by the Federal Energy Regulatory Commission (FERC), U.S. Army Corp of Engineers (USACE), federal land management agencies, federal and state resource agencies, and Indian Tribes. Ultimately, the time, benefits, costs, and risks to developers associated with hydropower regulatory processes and/or the preparation (e.g., studies) required for regulatory agency review are not well documented or synthesized in the public domain, which may increase uncertainty and variability within the process. This report addresses these needs by presenting results of a comprehensive examination of hydropower licensing including quantitative and qualitative analyses of timelines, causal factors, and their combined effect on risk and costs to developers.

13 HYDRO ENERGY↗
DOE OSTI2025

Evolution of Magnetohydrodynamic Turbulence in the Expanding Solar Wind: Residual Energy and Intermittency

We conduct 3D magnetohydrodynamic simulations of decaying turbulence in the context of the solar wind. To account for the spherical expansion of the solar wind, we implement the expanding box model. The initial turbulence comprises uncorrelated counterpropagating Alfvén waves and exhibits an isotropic power spectrum. Our findings reveal the consistent generation of negative residual energy whenever nonlinear interactions are present, independent of the normalized cross helicity σc and compressibility. The spherical expansion facilitates this process. The resulting residual energy is primarily distributed in the perpendicular direction, with S 2 (b) − S 2 (u) ∝ l ⊥ or equivalently –E r ∝ k$^{–2}_{⟂}$. Here S 2 (b) and S 2 (u) are second-order structure functions of magnetic field and velocity respectively. In most runs, S 2 (b) develops a scaling relation S 2 (b) ∝ $l$$^{1/2}_{⊥}$ (E b ∝ k$^{–3/2}_{⟂}$). In contrast, S 2 (u) is consistently shallower than S 2 (b), which aligns with in situ observations of the solar wind. We observe that the higher-order statistics of the turbulence, which act as a proxy for intermittency, depend on the initial σc and are strongly affected by the expansion effect. Generally, the intermittency is more pronounced when the expansion effect is present. Finally, we find that in our simulations, although the negative residual energy and intermittency grow simultaneously as the turbulence evolves, the causal relation between them seems to be weak, possibly because they are generated on different scales.

Interplanetary turbulence↗
DOE OSTI2023

Convoluted filtering for process cycle modeling

Principles of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process-structure–property-performance relationships hidden in materials data. The process-structure modeling can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process-structure relations. In this work, a novel convoluted model-filtering technique was exploited to build and successfully train the Convoluted Filter (CoFi) artifacts for Fe-based alloy heat treatment cycles. The artifacts were pre-trained to filter out deep models that change the surrogate microstructure state after the heat treatment at ambient conditions. Direct representation of the thermal cycle features within knowledge Graph facilitated development of meaningful data models for microstructure evolution, which reduce overfitting to limited datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Structural Characterization of Toxicologically Relevant Cd 2+ -L-Cysteine Complexes

The exposure of humans to Cd exerts adverse human health effects at low chronic exposure doses, but the underlying biomolecular mechanisms are incompletely understood. To gain insight into the toxicologically relevant chemistry of Cd 2+ in the bloodstream, we employed an anion-exchange HPLC coupled to a flame atomic absorption spectrometer (FAAS) using a mobile phase of 100 mM NaCl with 5 mM Tris-buffer (pH 7.4) to resemble protein-free blood plasma. The injection of Cd 2+ onto this HPLC-FAAS system was associated with the elution of a Cd peak that corresponded to [CdCl 3 ] - /[CdCl 4 ] 2- complexes. The addition of 0.1–10 mM L-cysteine (Cys) to the mobile phase significantly affected the retention behavior of Cd 2+ , which was rationalized by the on-column formation of mixed CdCysxCly complexes. From a toxicological point of view, the results obtained with 0.1 and 0.2 mM Cys were the most relevant because they resembled plasma concentrations. The corresponding Cd-containing (~30 μM) fractions were analyzed by X-ray absorption spectroscopy and revealed an increased sulfur coordination to Cd 2+ when the Cys concentration was increased from 0.1 to 0.2 mM. The putative formation of these toxicologically relevant Cd species in blood plasma was implicated in the Cd uptake into target organs and underscores the notion that a better understanding of the metabolism of Cd in the bloodstream is critical to causally link human exposure with organ-based toxicological effects.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Reliability modeling in a predictive maintenance context: A margin-based approach

Current system reliability methods (typically based on fault trees or reliability block diagrams) can effectively propagate reliability data from the asset to the system level in order to identify system critical points. However, employed asset reliability data are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). Sensor data, diagnostic assessments, and prognostic assessments are in fact not considered in plant reliability models used to inform system engineers on the most critical assets. In addition, the propagation of quantitative health data from the asset to the system level is a challenge given the diverse nature and structure of health data elements (e.g., vibration spectra, temperature readings, expected failure time). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating available health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Here, this paper is directly addressing these two goals by proposing a different approach for reliability modeling that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. The propagation of health data from the asset to the system level is performed through fault tree models not in probability terms, but in terms of margin where margin is the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated with asset performance, a margin-based approach focuses on the cause of an undesired asset performance (i.e., its health). Hence, thinking of reliability in terms of margins implies decision-making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical assets.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Wave Prediction using X-Band Radar (Final Summary Report)

Model Predictive Control (MPC) is the best controls framework available to wave energy today. It allows for the maximization of energy yield from a WEC system while respecting various constraints and losses in the system. This type of constrained optimization is a critical capability for the techno-economic optimization of WEC devices and is difficult to do effectively with causal controls frameworks. MPC requires a future prediction of wave excitation forces, requiring wave prediction on a future time horizon of up to 30 seconds. The future horizon required is highly dependent on the WEC and PTO type available. To our knowledge no-one has been able to implement MPC on a WEC device at sea, due to the fact that phase-resolved wave-prediction is not a capability that has been sufficiently developed to date. The key objectives of this project are focused on developing “industry-ready” wave prediction technology building blocks that can be applied to a wide range of different wave energy conversion topologies. Our collaborative work with device developers has demonstrated that the level of improvement attainable for any particular device is heavily dependent on the device and PTO topology chosen. The range of annual average power capture improvements is on the order of 10% to over 100%. However, the important aspect is that we can get within about 10% of theoretical upper limits even when considering errors in the wave prediction and more importantly, we can optimize performance while respecting various constraints such as motion amplitude or peak structural loads. The combination of improved performance while limiting structural loads has a net effect of reducing the levelized cost of energy from these emerging technologies and allows us to design control laws that meet an economic optimum. Efforts under a previous project on controls has focused on developing a set of wave-prediction algorithms that work by leveraging a network of wave sensing measurement buoys that are equipped with real-time telemetry. The present work is focusing primarily on using X-band radar to measure the wave-field around the WEC to predict the future excitation forces on the structure and combining the measurements of these different sources to create improvements in the wave-prediction accuracy.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Assessing the cumulative effects of nearshore habitat restoration actions for multiple populations of juvenile salmon in Whidbey Basin, Washington: foundation and approach for synthesis and evaluation

Ecosystem restoration is a common tool for re-establishing ecosystem processes, structures, and functions to improve biodiversity and services in coastal and estuarine ecosystems. In the Salish Sea, salmon habitats have been fragmented, reduced in size, and diminished in quality, and the ecosystem processes that form and sustain these habitats have been degraded and disrupted as well. This loss is especially prevalent in estuaries, where up to 90% of former salmon habitat has been lost or compromised. Salmon species are integral to the identities and cultures of people in the Pacific Northwest, yet salmon abundances remain at historic lows, especially in urbanized areas. Recent investments in restoration are creating rearing habitat and repairing lost ecosystem function. However, restoration efforts in this region have largely proceeded at the site scale, with less attention to big-picture thinking regarding how restoration will effectively recover degraded or lost habitats for target species. As a result, no landscape-scale evaluation program exists, and the cumulative benefits of multiple interventions are unknown. We describe innovative methods for science synthesis related to the evaluation of cumulative effects of ecosystem restoration for Pacific salmon, using years of existing, but disparate data. Building from previous work on cumulative effects evaluation and incorporating a hierarchy of hypotheses approach, we propose using causal inference across numerous hypotheses in a framework to assess the cumulative benefits to Pacific salmon from multiple estuarine restoration projects. We present the framework as a method that can be used to address many complex questions and provide examples from the Salish Sea where the approach is being implemented. The framework draws on science synthesis from numerous fields and uses a hierarchy of hypotheses, causal analysis at multiple scales, and a new hierarchy of synthesis for assessing multiple lines of evidence documenting restoration effects on Pacific salmon. We propose causal inference to synthesize dissimilar data streams, in our case, to identify various manifestations of cumulative effects of restoration and benefits to salmon, and to further inform restoration and recovery planning. A unifying framework would allow for the detection of thresholds at which restoration provides measurable improvement and would greatly advance understanding of the effects of restoration on ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Collagen Turnover in Relation to Risk Factors and Hemodynamics in Human Intracranial Aneurysms

Determinants for molecular and structural instability, that is, impending growth or rupture, of intracranial aneurysms (IAs) remain uncertain. To elucidate this, we endeavored to estimate the actual turnover rates of the main molecular constituent in human IA (collagen) on the basis of radiocarbon ( 14 C) birth dating in relation to IA hemodynamics. Collagen turnover rates in excised human IA samples were calculated using mathematical modeling of 14 C birth dating data of collagen in relation to risk factors and histological markers for collagen maturity/turnover in selected IA. Hemodynamics were simulated using image-based computational fluid dynamics. Correlation, logistic regression, and receiver operating characteristic analyses were performed. Collagen turnover rates were estimated in 46 IA (43 patients); computational fluid dynamics could be performed in 20 IA (20 patients). The mean collagen turnover rate (γ) constituted 126% (±1% error) per year. For patients with arterial hypertension, γ was greater than 2600% annually, whereas γ was distinctly lower with 32% (±1% error) per year for patients without risk factors, such as smoking and hypertension. There was a distinct association between histological presence of rather immature collagen in human IA and the presence of modifiable risk factors. Spatial-temporal averaged wall shear stress predicted rapid collagen turnover (odds ratio, 1.6 [95% CI, 1.0–2.7]). Receiver operating characteristic analysis demonstrated a good test accuracy (area under the curve, 0.798 [95% CI, 0.598–0.998]) for average wall shear stress with a threshold ≥4.9 Pa for rapid collagen turnover. Our data indicate that turnover rates and stability of collagen in human IA are strongly associated with the presence of modifiable risk factors and aneurysmal hemodynamics. Finally, these findings underline the importance of strict risk factor modification in patients with unruptured IA. Future should include more detailed risk factor data to establish a more causal understanding of hemodynamics and the rupture risk of individual IA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

New insights into supradense matter from dissecting scaled stellar structure equations

The strong-field gravity in general relativity (GR) realized in neutron stars (NSs) renders the equation of state (EOS) P(ε) of supradense neutron star matter to be essentially nonlinear and refines the upper bound for Φ ≡ P/ε to be much smaller than the special relativity (SR) requirement with linear EOSs, where P and ε are respectively the pressure and energy density of the system considered. Specifically, a tight bound Φ ≲ 0.374 is obtained by perturbatively anatomizing the intrinsic structures of the scaled Tolman–Oppenheimer–Volkoff (TOV) equations without using any input nuclear EOS. New insights gained from this novel analysis provide EOS-model-independent constraints on the properties (e.g., density profiles of the sound speed squared s 2 = dP/dε and trace anomaly Δ = 1/3 – Φ) of cold supradense matter in NS cores. Using the gravity-matter duality in theories describing NSs, we investigate the impact of gravity on supradense matter EOS in NSs. In particular, we show that the NS mass M NS , radius R, and compactness ξ ≡ M NS /R scale with certain combinations of its central pressure and energy density (encapsulating its central EOS). Thus, observational data on these properties of NSs can straightforwardly constrain NS central EOSs without relying on any specific nuclear EOS model.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗