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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A survey on degradation modeling, prognosis, and prognostics-driven maintenance in wind energy systems

Wind energy generation proliferated over the past decades, introducing unique challenges and opportunities for failure prediction, operation and maintenance. Decision-makers are continuously looking into new methods to infer failure mechanisms and behaviors of wind turbine components to detect and intervene in the failures before they happen. Evidently, degradation modeling and prognosis become engaging topics for researchers and practitioners to prevent catastrophic failures. Prognostics-driven approaches predict the time of failure for the components (e.g., predicting remaining useful life), which provides significant insights for scheduling of operations and maintenance activities. Integrating these prognostics-driven insights into wind farm operations and maintenance presents a substantial challenge, demanding careful consideration of numerous factors such as accessibility, crew routing, and spare part logistics. This study provides state-of-the-art review for degradation modeling, prognosis, and prognostics-driven maintenance techniques for wind energy systems. The discussed techniques align with the United Nations' sustainable development goals, in particular Goal 7 (Affordable and Clean Energy), by enhancing effectiveness and sustainability of wind energy operations. This work also showcases open research questions related to degradation modeling, prognosis, and prognostics-driven maintenance.

Altinpulluk, Nur Banu↗
DOE OSTI2024

An ontology-based knowledge graph for representing interactions involving RNA molecules

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph (KG) encompassing biological knowledge about RNAs gathered from more than 60 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Theory of alpha heating in inertial fusion: Alpha-heating metrics and the onset of the burning-plasma regime

Here, a detailed and comprehensive 1-dimensional theory of alpha-heating metrics is developed to determine the onset of burning plasma regimes in inertial fusion implosions. The analysis uses an analytic model of the deceleration, stagnation, and burn phases of inertial confinement fusion implosions combined with the results from a database of radiation-hydrodynamic simulations. The onset of the burning-plasma regime occurs when the alpha-heating rate in the hot spot exceeds the compression power input and is represented by the parameter Q α = 1/2 α energy/PdV work. A second burning plasma regime is also identified, where the alpha-heating rate exceeds the compression input to the entire stagnated plasma, including the hot spot and confining shell, and is represented by Q tot α . It is shown that progress towards the burning-plasma regime is correlated with the yield enhancement caused by alpha-heating but is more accurately related to the fractional alpha energy f α = 1/2 α energy/hot-spot energy . In the analysis presented here, we develop a method to infer these intermediate metrics from experiments and show that the alpha power produced in National Ignition Facility High-Foot implosions is approximately 50% of the external input power delivered to the hot spot and 25% of the total external power (from compression) delivered to the stagnated core.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Cold Spray NDE for Porosity and Other Process Anomalies

This paper describes a technology review of non-destructive examination (NDE) methods that can be applied to cold spray coatings. Cold spray is a process for depositing metal powder at high velocity so that it bonds to the substrate metal without significant heating that would be likely to cause additional residual tensile stresses. Coatings in the range from millimeters to centimeters are possible at relatively high deposition rates. Cold spray coatings that may be used for hydroelectric components that are subject to erosion, corrosion, wear, and cavitation damage are of interest. The topic of cold spray NDE is treated generally however and may be considered applicable to virtually any cold spray application except where there are constraints of the hydroelectric component application that bear special consideration. Hydroelectric turbine components are primarily made from low carbon and austenitic stainless steels. They are typically very large components that are partially assembled on-site. Transport of these components within the hydroelectric facility and away from the hydroelectric facility is challenging due to the component’s size and weight, high cost of disassembly, and the confined space of the dam. One method for prolonging component life is application of cold spray and thermal powder metal coatings to the components most susceptible to erosion, corrosion, cracking, and mechanical wear. Process controls and visual inspections have historically been applied to cold spray application; however, quantitative NDE could detect anomalies not readily apparent from such a visual examination. Optical profilometry, Eddy current, ultrasound, and hardness tests are shown for one set of good, fair, and poor Nickle-Chrome (NiCr) on 304 stainless steel (304SS) cold spray samples to demonstrate inspection possibilities. The primary indicator of cold spray quality is the cold spray porosity that is most directly measured with witness-sample destructive examinations (DE) – mostly photo-micrographs. These DE generated porosity values are correlated with optical profilometry, eddy current, ultrasound, and hardness test NDE methods to infer the porosity and other Information of interest. These parameters of interest primarily include: • Porosity primarily caused by improper process conditions (temperature, gas velocity, spray standoff, spray angle, powder size, condition, surface cleanliness, surface oxide, etc.). • Presence/absence of the cold spray coating including possible over-sprayed voids • Coating thickness Optical profilometry measurements of surface roughness trended with porosity plus if compared with a reference measurement or reference drawing would provide information on the coating thickness. Ultrasound could provide similar surface profile information plus attenuation measurements trended with porosity. The ultrasound measurements however may be limited to geometries where the substrate back-wall is normal to the cold spray surface and not too thick. Eddy current showed a strong correlation with porosity. Eddy currents can also be sensitive to cracks and do not need fluid coupling to make measurements. Eddy currents however are not sensitive to coating thicknesses in most cases. Vickers hardness measurements also tracked well with porosity. These types of hardness measurements however are also not sensitive to coating thickness. An NDE program may include multiple measurements. Acknowledgement: This work is sponsored by Bonneville Power Administration (BPA) under their technology innovation project program. Such support does not constitute BPA endorsement of views expressed in this work.

Glass, Samuel W.↗
DOE OSTI2020

Basic factors for acquiring, correcting, and interpreting probe current-voltage characteristic in moderate-collisional plasma for determining electron energy distribution

Methods for inferring the electron distribution function (EDF) from a probe current–voltage trace are reviewed for moderate-collisional plasma. The explicit accounting of particle collisions, modeling sheath depletion, and electron refilling processes are rarely performed when analyzing probe measurements, yet they are critical for regime validity, EDF-profile accuracy, and space potential quantification precision. Furthermore, the advantages, limitations, and utilization of large-area wall probes in moderate-collisional plasma, capable of highly resolved indications of energetic electrons in plasma, are outlined.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Hydro-scaling of direct-drive cylindrical implosions at the OMEGA and the National Ignition Facility

Deceleration-phase Rayleigh–Taylor instability (RTI) growth during inertial confinement fusion capsule implosions significantly affects the performance as it mixes cold ablator material into the fuel. Precise measurements of such instability growth are essential for both validating the existing simulation codes and improving our predictive capability. RTI measurements on the inner surface of a spherical shell are limited and are often inferred indirectly at limited convergence. In contrast, cylindrical implosions allow for direct diagnostic access to the converging interface by imaging down the cylinder axis while retaining the effects of convergence. We have performed direct-drive cylindrical implosion experiments at both the OMEGA and the NIF laser facilities using scaled targets. RTI growth is demonstrated to be scale-invariant between the cylindrical targets at OMEGA and similar targets at the NIF, which are scaled up by a factor of three in the radial dimension. Single-mode (m = 0) instability growth factors of ~14 are measured at a convergence ratio (CR) ~ 2.5 with nearly identical mode growth at both scales. The measurements are in agreement with xRAGE radiation-hydrodynamics simulations. In addition, we have developed the Bayesian-inference-engine method to account for the variations in the target alignment, magnification, and the parallax effect in the measurement, allowing a more precise comparison between the experimental data and the simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Four-dimensional chromosome reconstruction elucidates the spatiotemporal reorganization of the mammalian X chromosome

Significance Breakthrough technological advances such as Hi-C have enabled large-scale sequencing of pairwise genomic contacts. However, translating the 2D contacts to 3D spatial models remains challenging. Here we develop the “4DHiC” modeling method to infer 3D information from 2D datasets across time and construct a 3D model of the inactive X-chromosome (Xi). Our model not only demonstrates a spatial phase-separation between A/B and S1/S2 compartments to form “hemispheres,” but also reveals persistent smaller-scale structures hidden under the Xi superstructure. We also simulate Xist RNA spreading dynamics and deduce that the RNA spreads between hemispheres via both surface and core to establish the Xi. Our 4DHiC will serve a growing demand for data-driven methodologies that extract spatial information from biochemical interaction data.

Lappala, Anna↗
DOE OSTI2020

Massive neutrinos and degeneracies in Lyman-alpha forest simulations

Using a suite of hydrodynamical simulations with cold dark matter, baryons, and neutrinos, we present a detailed study of the effect of massive neutrinos on the 1-D and 3-D flux power spectra of the Lyman-α (Lyα) forest. The presence of massive neutrinos in cosmology induces a scale- and time-dependent suppression of structure formation that is strongest on small scales. Measuring this suppression is a key method for inferring neutrino masses from cosmological data, and is one of the main goals of ongoing and future surveys like eBOSS, DES, LSST, Euclid or DESI . The clustering in the Lyα forest traces the quasi-linear power at late times and on small scales. In combination with observations of the cosmic microwave background, the forest therefore provides some of the tightest constraints on the sum of the neutrino masses. However there is a well-known degeneracy between Σmν and the amplitude of perturbations in the linear matter power spectrum. We study the corresponding degeneracy in the 1-D flux power spectrum of the Lyα forest, and for the first time also study this degeneracy in the 3-D flux power spectrum. We show that the non-linear effects of massive neutrinos on the Lyα forest, beyond the effect of linear power amplitude suppression, are negligible, and this degeneracy persists in the Lyα forest observables to a high precision. Here, we discuss the implications of this degeneracy for choosing parametrisations of the Lyα forest for cosmological analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy

Simulations of excited state properties, such as spectral functions, are often computationally expensive and therefore not suitable for high-throughput modeling. As a proof of principle, here we demonstrate that graph-based neural networks can be used to predict the x-ray absorption near-edge structure spectra of molecules to quantitative accuracy. Specifically, the predicted spectra reproduce nearly all prominent peaks, with 90% of the predicted peak locations within 1 eV of the ground truth. Besides its own utility in spectral analysis and structure inference, our method can be combined with structure search algorithms to enable high-throughput spectrum sampling of the vast material configuration space, which opens up new pathways to material design and discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Room-Temperature Mechanical Resonator with a Single Added or Subtracted Phonon

A room-temperature mechanical oscillator undergoes thermal Brownian motion with an amplitude much larger than the amplitude associated with a single phonon of excitation. This motion can be read out and manipulated using laser light using a cavity-optomechanical approach. By performing a strong quantum measurement (i.e., counting single photons in the sidebands imparted on a laser), we herald the addition and subtraction of single phonons on the 300 K thermal motional state of a 4 GHz mechanical oscillator. Further, to understand the resulting mechanical state, we implement a tomography scheme and observe highly non-Gaussian phase-space distributions. Using a maximum likelihood method, we infer the density matrix of the oscillator, and we confirm the counterintuitive doubling of the mean phonon number resulting from phonon addition and subtraction.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Estimation of RTT and Loss Rate of Wide-Area Connections Using MPI Measurements

Scientific computations are expected to be increasingly distributed across wide-area networks, and the Message Passing Interface (MPI) has been shown to scale to support their communications over long distances. These computations should account for certain network parameters to ensure an effective execution, for example, by avoiding highly congested and long connections. The execution times of MPI basic operations reflect the connection parameters, including the Round Trip Time (RTT) and loss rate. We describe five machine leaning methods to estimate the connection RTT and loss rate using execution times of MPI basic operations. We utilize execution time measurements of MPI Sendrecv operations collected over emulated 10 Gbps connections with 0-366 ms round-trip times, wherein the longest connection spans the globe, under up to 20% periodic losses. These methods provide disparate, namely, linear and non-linear, and smooth and non-smooth, estimates of RTT and loss rate. Our results show that accurate estimates can be generated at low loss rates but they become inaccurate at loss rates 10% and higher. Overall, these results constitute a case study of the strengths and limitations of machine learning methods in inferring network-level parameters using application-level measurements.

Rao, Nageswara S.↗
DOE OSTI2024

Revealing Decision Conservativeness Through Inverse Distributionally Robust Optimization

This paper introduces Inverse Distributionally Robust Optimization (I-DRO) as a method to infer the conservativeness level of a decision-maker, represented by the size of a Wasserstein metric-based ambiguity set, from the optimal decisions made using Forward Distributionally Robust Optimization (F-DRO). By leveraging the Karush-Kuhn-Tucker (KKT) conditions of the convex F-DRO model, we formulate I-DRO as a bi-linear program, which can be solved using off-the-shelf optimization solvers. Additionally, this formulation exhibits several advantageous properties. We demonstrate that I-DRO not only guarantees the existence and uniqueness of an optimal solution but also establishes the necessary and sufficient conditions for this optimal solution to accurately match the actual conservativeness level in F-DRO. Furthermore, we identify three extreme scenarios that may impact I-DRO effectiveness. Our case study applies F-DRO for power system scheduling under uncertainty and employs I-DRO to recover the conservativeness level of system operators. Numerical experiments based on an IEEE 5-bus system and a realistic NYISO 11-zone system demonstrate I-DRO performance in both normal and extreme scenarios. An extended version of this paper with additional analyses is available at li2024revealing.

distributionally robust optimization↗
DOE OSTI2023

pnnl/brain_ohsu

Light sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. We fine-tuned an existing model, TrailMap, using expert labeled data from axonal structures in neocortex. Without changing the network architecture, we implemented nnU-Net framework modifications in data augmentation, data foreground sampling, window learning rate, and the inference overlap method. The resulting model from these combined approaches yielded an improved F1 score

Oostrom, Marjolein↗
DOE OSTI2023

Canonical and Noncanonical Hamiltonian Operator Inference

SAND2024-08577O Canonical and Noncanonical Hamiltonian Operator Inference is research code that applies old and new model reduction algorithms to well-known and publicly available "toy" problems. It applies operator inference (OpInf) methods for model reduction to several benchmark hyperbolic partial differential equations: linear wave, Korteweg-de Vries, Benjamin-Bona-Mahoney, and linear elasticity. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2025

Cyber Knowledge Alignment

An automated workflow and introduces new approaches to curate existing cybersecurity knowledge from multiple sources. It also proposes AI/ML-based methods to infer knowledge alignments across different repositories.

Purohit, Sumit [Pacific Northwest National Laborat↗
DOE OSTI2020

Framework For Spatial Agricultural Crop Yield Prediction Model Development

This framework was developed to provide data preprocessing for spatiotemporal agricultural yield data and remote sensing data for modelling using artificial neural networks (ANNs) to predict subfield crop yield estimates. The software includes methods to train, validate, and test ANN models. It also include methods to infer on new remote sensing data.

Griffel, LloydM.↗
DOE OSTI2018

A stochastic and dynamical view of pluripotency in mouse embryonic stem cells

Pluripotent embryonic stem cells are of paramount importance for biomedical sciences because of their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory networks. The rapid growth of single-cell sequencing data has greatly stimulated applications of statistical and machine learning methods for inferring topologies of pluripotency regulating genetic networks. The inferred network topologies, however, often only encode Boolean information while remaining silent about the roles of dynamics and molecular stochasticity inherent in gene expression. Herein we develop a framework for systematically extending Boolean-level network topologies into higher resolution models of networks which explicitly account for the promoter architectures and gene state switching dynamics. We show the framework to be useful for disentangling the various contributions that gene switching, external signaling, and network topology make to the global heterogeneity and dynamics of transcription factor populations. We find the pluripotent state of the network to be a steady state which is robust to global variations of gene switching rates which we argue are a good proxy for epigenetic states of individual promoters. The temporal dynamics of exiting the pluripotent state, on the other hand, is significantly influenced by the rates of genetic switching which makes cells more responsive to changes in extracellular signals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Beam-ion Studies in NSTX and NSTX-U (Final Technical Report)

The confinement of high energy "fast ions" is crucial for the success of magnetic fusion as a practical energy source. The research performed by UC Irvine on NSTX and NSTX-U provided new information about this important topic in the configuration known as a "spherical tokamak." Injected neutral beams provided the fast ions (also known as "beam ions"). There were five overarching goals of the research. One objective was to measure the confinement of beam ions in order to ascertain if they were accomplishing their desired purpose of transferring their energy to the bulk plasma. A second goal was to better understand instabilities that are driven unstable by the fast ions. A related goal was to use the similarities and differences between NSTX and the DIII-D conventional tokamak to better understand the fast-ion driven instabilities. A fourth goal was to develop new instruments to measure fast ions and techniques that facilitate analysis of the data. The fifth objective was to measure and understand acceleration of beam ions by RF waves. Much was accomplished in all of these five areas. In the first, it was shown that the large magnetic moment of the beam ions did not harm confinement but instabilities known as the "sawtooth" and "long-lived mode" do. In the second area, much attention was devoted to the fast-ion instabilities known as "Alfvén eigenmodes." In particular, Alfvén eigenmode "avalanches" can cause ~ 30% of the fast ions to be lost in a single explosive burst. The comparative studies with DIII-D showed that an instability discovered on NSTX is also important in conventional tokamaks. In the fourth category, several instruments were developed, a number of effects that complicate interpretation of the data were understood, and progress toward a new method to infer the fast-ion distribution function from the data was made. In the fifth category, the measured profile of accelerated fast ions was initially much broader than theoretical predictions but subsequent improvements in the theoretical modeling achieved good agreement with the data.

43 PARTICLE ACCELERATORS↗