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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

In situ Visible Light and Thermal Imaging Data from a Laser Powder Bed Fusion Additive Manufacturing Process Co-Registered to X-ray Computed Tomography and Fatigue Data

This dataset is comprised of in situ sensing data collected during a laser-based powder bed fusion additive manufacturing process, as well as rasterized scan path information, post-build X-ray computed tomography (XCT), and fatigue test results. A total of 64 cylinders, approximately 15 mm in diameter and 102 mm tall, were printed out of stainless steel 316H on a Colibrium Additive Concept Laser M2 Series 5 machine. Parameters known to produce dense material were used to construct 56 of these cylinders, while the remaining 8 cylinders were printed with relatively high energy density parameters prone to producing keyhole pores. In addition, two spatter generation blocks were constructed upstream of the 64 cylinders such that ejecta produced during the melting of the spatter generators were stochastically seeded onto the 64 cylinders. Based on previous experiments, these spatter particles were theorized to produce stochastic lack-of-fusion pores. During the construction of the build, high-resolution images of reflected light in the visible spectrum were captured both before and after recoating for each print layer. Additionally, temporally integrated thermal imaging in the near infrared spectrum produced integrated sum and max images on a layerwise basis. The multimodal in situ data has been co-registered to the build plate coordinate system, allowing for identification of process anomalies (e.g., spatter particles) apparent in the two sensors. Following construction of the build, the cylinders were subjected to XCT to identify internal flaws, and the resulting data have also been registered to the build plate coordinate system. Finally, 60 of the 64 cylinders were machined into fatigue coupons conforming to ASTM E466 and subsequently subjected to either high- or -low-cycle fatigue testing. The results of the fatigue tests have also been included in the dataset, and the XCT data corresponded to the approximate location of the gauge sections of the machine fatigue specimen geometry.

42 ENGINEERING↗
DOE OSTI2014

Quantitative multi-modal NDT data analysis

A single NDT technique is often not adequate to provide assessments about the integrity of test objects with the required coverage or accuracy. In such situations, it is often resorted to multi-modal testing, where complementary and overlapping information from different NDT techniques are combined for a more comprehensive evaluation. Multi-modal material and defect characterization is an interesting task which involves several diverse fields of research, including signal and image processing, statistics and data mining. The fusion of different modalities may improve quantitative nondestructive evaluation by effectively exploiting the augmented set of multi-sensor information about the material. It is the redundant information in particular, whose quantification is expected to lead to increased reliability and robustness of the inspection results. There are different systematic approaches to data fusion, each with its specific advantages and drawbacks. In our contribution, these will be discussed in the context of nondestructive materials testing. A practical study adopting a high-level scheme for the fusion of Eddy Current, GMR and Thermography measurements on a reference metallic specimen with built-in grooves will be presented. Results show that fusion is able to outperform the best single sensor regarding detection specificity, while retaining the same level of sensitivity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Indoor Occupancy Sensing via Networked Nodes (2012–2022): A Review

In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called “sensor node deployment density” through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes’ occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps.

Emad-Ud-Din, Muhammad (ORCID:0000000279515538)↗
DOE OSTI2014

Pattern Search in Multi-structure Data: A Framework for the Next-Generation Evidence-based Medicine

With the advent of personalized and evidence-based medicine, the need for a framework to analyze/interpret quantitative measurements (blood work, toxicology, etc.) with qualitative descriptions (specialist reports after reading images, bio-medical knowledge-bases) to predict diagnostic risks is fast emerging. Addressing this need, we pose and address the following questions (i) How can we jointly analyze both qualitative and quantitative data ? (ii) Is the fusion of multi-structure data expected to provide better insights than either of them individually ? We present experiments on two bio-medical data sets - mammography and traumatic brain studies to demonstrate architectures and tools for evidence-pattern search.

data fusion↗
DOE OSTI2017

Canopus: A Paradigm Shift Towards Elastic Extreme-Scale Data Analytics on HPC

Scientific simulations on high performance computing (HPC) platforms generate large quantities of data. To bridge the widening gap between compute and I/O, and enable data to be more efficiently stored and analyzed, simulation outputs need to be refactored, reduced, and appropriately mapped to storage tiers. However, a systematic solution to support these steps has been lacking on the current HPC software ecosystem. To that end, this paper develops Canopus, a progressive JPEGlike data management scheme for storing and analyzing big scientific data. It co-designs the data decimation, compression and data storage, taking the hardware characteristics of each storage tier into considerations. With reasonably low overhead, our approach refactors simulation data into a much smaller, reduced-accuracy base dataset, and a series of deltas that is used to augment the accuracy if needed. The base dataset and deltas are compressed and written to multiple storage tiers. Data saved on different tiers can then be selectively retrieved to restore the level of accuracy that satisfies data analytics. Thus, Canopus provides a paradigm shift towards elastic data analytics and enables end users to make trade-offs between analysis speed and accuracy on-the-fly. We evaluate the impact of Canopus on unstructured triangular meshes, a pervasive data model used by scientific modeling and simulations. In particular, we demonstrate the progressive data exploration of Canopus using the “blob detection” use case on the fusion simulation data.

Lu, Tao↗
DOE OSTI2023

Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions

Abstract Wetlands are the largest natural source of methane (CH 4 ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH 4 , but interpreting its spatiotemporal variations is challenging due to the co‐occurrence of CH 4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data‐model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data‐constrained model—iPEACE—reasonably reproduced CH 4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH 4 production appeared to be the most important process, followed by oxidation in explaining inter‐site variations in CH 4 emissions. Based on a sensitivity analysis, CH 4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant‐mediated transport appeared to be the major pathway for CH 4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (<20%) periods. The lag time between CH 4 production and CH 4 emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH 4 production, plant‐mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH 4 emissions across biomes. These processes and associated parameters for CH 4 emissions among and within the wetlands provide useful insights for interpreting observed net CH 4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH 4 fluxes.

Ueyama, Masahito↗
DOE OSTI2020

MFNets: Multifidelity data-driven networks for Bayesian learning and prediction

This paper presents a multifidelity uncertainty quantification framework called MFNets. We seek to address three existing challenges that arise when experimental and simulation data from different sources are used to enhance statistical estimation and prediction with quantified uncertainty. Specifically, we demonstrate that MFNets can (1) fuse heterogeneous data sources arising from simulations with different parameterizations, e.g simulation models with different uncertain parameters or data sets collected under different environmental conditions; (2) encode known relationships among data sources to reduce data requirements; and (3) improve the robustness of existing multi-fidelity approaches to corrupted data. MFNets construct a network of latent variables (LVs) to facilitate the fusion of data from an ensemble of sources of varying credibility and cost. These LVs are posited as explanatory variables that provide the source of correlation in the observed data. Furthermore, MFNets provide a way to encode prior physical knowledge to enable efficient estimation of statistics and/or construction of surrogates via conditional independence relations on the LVs. We highlight the utility of our framework with a number of theoretical results which assess the quality of the posterior mean as a frequentist estimator and compare it to standard sampling approaches that use single fidelity, multilevel, and control variate Monte Carlo estimators. We also use the proposed framework to derive the Monte Carlo-based control variate estimator entirely from the use of Bayes rule and linear-Gaussian models -- to our knowledge the first such derivation. Finally, we demonstrate the ability to work with different uncertain parameters across different models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment

Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. This is particularly true for zoonotic diseases that infect humans, agricultural animals, and wildlife. Within the subset of zoonoses, vector-borne pathogens are changing more rapidly with climate change, and have a complex epidemiology, which may allow them to take advantage of a changing environment. Most mosquito-borne infectious diseases are transmitted by mosquitoes in three genera: Aedes, Anopheles, and Culex, and the expansion of these genera is well documented. There is an urgent need to study vector-borne diseases in response to climate change and to produce a generalizable approach capable of generating risk maps and forecasting outbreaks. Here, we provide a strategy for coupling climate and epidemiological models for zoonotic infectious diseases. We discuss the complexity and challenges of data and model fusion, baseline requirements for data, and animal and human population movement. Disease forecasting needs significant investment to build the infrastructure necessary to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions. These investments can contribute to building a modeling community around the globe to support public health officials so as to reduce disease burden through forecasts with quantified uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Human Factors Research Needs for In-Time Aviation Safety Management Systems (IASMS) Design: Enabling the NASA “Sky for All” Future Airspace Vision

Integrated safety management will be paramount for safely enabling the envisioned transformations of the future National Airspace System. Addressing the increasing need for advanced data analytics and fusion of aviation safety data, managed by human decision-makers, is essential for realizing the vision. The proposal, if accepted, will discuss safety management system challenges and how the concept of In-time Aviation Safety Management Systems addresses the need. It will also discuss human factors challenges involved in future integrated safety management, including trust, over-reliance, human-optimized data visualization, human-autonomy teaming, training, communication and dissemination of data, situation awareness, task load, and accountability.

Lawrence J Prinzel III↗
NASA NTRSDate not supplied

The Adaptable and Resilient Safety System: The Human Factor in Future In-Time Aviation Safety Management Systems

In-time integrated safety management will be paramount for safely enabling the envisioned transformations of the future National Airspace System (NAS). The path for realizing the vision includes addressing the increasing need for advanced data analytics and fusion of aviation safety data, managed by human decision-makers. The paper describes safety management systems and its’ challenges, and how the concept of In-time Aviation Safety Management Systems addresses the need to ensure an adaptable and resilient future safety system in the envisioned transformed NAS. Finally, it discusses potential human factors challenges, including new human roles and responsibilities, new information and cognitive requirements, new intelligent technologies that change human-system interaction and coordination, and new design paradigms for human system integration and teaming.

L. Prinzel↗
NASA NTRSDate not supplied

The Adaptable and Resilient Safety System: The Human Factor in Future In-Time Aviation Safety Management Systems

In-time integrated safety management will be paramount for safely enabling the envisioned transformations of the future National Airspace System (NAS). The path for realizing the vision includes addressing the increasing need for advanced data analytics and fusion of aviation safety data, managed by human decision-makers. The paper describes safety management systems and its’ challenges, and how the concept of In-time Aviation Safety Management Systems addresses the need to ensure an adaptable and resilient future safety system in the envisioned transformed NAS. Finally, it discusses potential human factors challenges, including new human roles and responsibilities, new information and cognitive requirements, new intelligent technologies that change human-system interaction and coordination, and new design paradigms for human system integration and teaming.

Aviation Safety↗
NASA NTRS2008

NASA C-17 Usage Overview

The usage and integrated vehicle health management of the NASA C-17. Propulsion health management flight objectives for the aircraft include mapping of the High Pressure Compressor in order to calibrate a Pratt and Whitney engine model and the fusion of data collected from existing sensors and signals to develop models, analysis methods and information fusion algorithms. An additional health manage flight objective is to demonstrate that the Commercial Modular Aero-Propulsion Systems Simulation engine model can successfully execute in real time onboard the C-17 T-1 aircraft using engine and aircraft flight data as inputs. Future work will address aircraft durability and aging, airframe health management, and propulsion health management research in the areas of gas path and engine vibration.

Miller, Christopher R.↗
DOE OSTI2021

Synergistic data analytics for electromechanical oscillation in electric power systems

Accurate real-time estimation of the four electromechanical oscillation properties, i.e., dominant oscillation modes, mode shapes, participation factors, and coherent groups, is of great importance to assess and mitigate potential electromechanical oscillations in interconnected power systems. While eigenvalue analysis can realize such estimation, it requires precise linearized dynamic models and accurate parameters, which are highly difficult to obtain in practice. Data fusion-based modal estimation methods can extract the properties of electromechanical oscillations from measurement data without the power system model and parameters, but most of the time only one or two property assessments can be accomplished each time. To overcome this challenge, this paper presents a synergistic data analytics solution to characterize the dynamic behaviors of electromechanical oscillations from real-time measurement data. Here, the proposed method uses optimized variable projection, and it is capable of estimating all four electromechanical oscillation behavior properties from measured responses. Case studies are performed using the simulated measurement data of a 16-generator 68-bus test system and the field measurements collected by the PMUs deployed in the Yunnan Power Grid. The results demonstrate that the proposed synergistic data analytics solution can achieve satisfactory performance in capturing the properties of electromechanical oscillations from measurement data and exhibit strong robustness against measurement noise when compared with existing measurement-based methods.

42 ENGINEERING↗
DOE OSTI2024

A Gaussian process guide for signal regression in magnetic fusion

Extracting reliable information from diagnostic data in tokamaks is critical for understanding, analyzing, and controlling the behavior of fusion plasmas and validating models describing that behavior. Recent interest within the fusion community has focused on the use of principled statistical methods, such as Gaussian process regression (GPR), to attempt to develop sharper, more reliable, and more rigorous tools for examining the complex observed behavior in these systems. While GPR is an enormously powerful tool, there is also the danger of drawing fragile, or inconsistent conclusions from naive GPR fits that are not driven by principled treatments. Here we review the fundamental concepts underlying GPR in a way that may be useful for broad-ranging applications in fusion science. We also revisit how GPR is developed for profile fitting in tokamaks. We examine various extensions and targeted modifications applicable to experimental observations in the edge of the DIII-D tokamak. Finally, we discuss best practices for applying GPR to fusion data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

The advanced evolution of massive stars

The nuclear rates for reactions involving 12 C and 16 O are key to computing the energy release and nucleosynthesis evolution of massive stars during their advanced burning phases. Ultimately, these burning rates shape the stellar structure and evolution and influence the nature of the compact objects produced at the end of the stellar life. We explore the implications of new nuclear reaction rates from both experimental and theoretical studies for 12 C(α, γ) 16 ​O, 12 C+ 12​ C, 12 C+ 16 ​O, and 16 O+ 16 ​O reactions for massive stars. Our goal is to investigate how the chemical structure and nucleosynthesis evolve from the He-exhaustion stage to the O-burning phase and how these processes influence the ultimate stellar fate. We computed rotating and non-rotating models for stars of different masses at solar metallicity. We used the stellar evolution code GENEC, which includes a large network of nuclear reactions and isotopes involved in advanced phases, as well as updated rates for 12 C(α,γ) 16 O. For the three fusion reactions involving 12 C and 16 O, we considered new rates following a data-driven fusion suppression scenario (hereafter HIN(RES)) and new theoretical rates obtained with time-dependent Hartree-Fock (TDHF) calculations. The updated 12 C(α, γ) 16 ​O rates mainly impact the chemical structure evolution changing the 12 C/ 16 O ratio at He-exhaustion and have little effect on the CO core mass. This variation in the 12 C/ 16 O ratio is in some cases critical for predicting the final fate of the model, which is very sensitive to 12 C abundance, and in particular the 20 M ⊙ remnant may change from a black hole to a neutron star. The He-burning (C-burning) lifetime is also decreased (increased) by about −2% (+15%). The combined new rates for 12 C+ 12 ​C and 16 O+ 16 ​O fusion reactions according to the HIN(RES) model lead to shorter C- and O-burning lifetimes by ≈ − 10%, and −50%, respectively, and shift the ignition conditions to higher temperatures and densities. In contrast, the theoretical TDHF rates primarily affect C-burning, increasing its duration by about 30% and lowering the ignition temperature. These changes modify the chemical structure of the core, the size and duration of C-burning shells, and hence their compactness. They also impact the central and shell nucleosynthesis (by ±1 dex and by factors of ±2–10, respectively), while 12 C+ 16 ​O reaction rates variations remain the least important.

abundances↗
DOE OSTI2023

Measuring higher-order moments of neutron-time-of-flight data for cryogenic inertial confinement fusion implosions on OMEGA

Ion temperatures serve as an important diagnostic for inertial confinement fusion (ICF) implosions. In direct-drive ICF experiments on OMEGA, neutron-time-of-flight (nTOF) data are used to infer the ion temperature of the fusing plasma produced in the implosion experiment. The analysis of the nTOF data requires an assumption about the shape of the underlying source signal. Since the source nTOF signal is a near-replica of the neutron energy spectrum, an ideal Gaussian shape, corresponding to the neutron energy spectrum of a uniform temperature plasma, is routinely employed. However, spatial and temporal variations of the ion temperature in the plasma give rise to higher-order moments, which were first described by Munro [Nucl. Fusion 56, 036001 (2016)]. In this work, we show a simpler alternative analysis to derive moments of the neutron energy spectrum for a plasma with variations in ion temperature. We also present a revised analysis of measured nTOF signals that uses a model with an additional degree of freedom to take into account the effect of ion temperature variations on the shape of the spectrum. Compared to presently used nTOF analysis, the revised analysis yields on average ≈2× more accurate fits to the data and up to 15% higher ion temperatures for cryogenic experiments. As a result, we quantify the ion temperature inflation caused by radially symmetric fluid flows, which are present even in a symmetric implosion, and which serve as a lower bound on the ion temperature inflation in real implosions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Integrating field observations and process-based modeling to predict watershed water quality under environmental perturbations

Watersheds play a critical role in supplying water resources needed for human use and ecosystem health. Understanding and predicting how, when, and where changes in the quantity and quality of water resources occur under different environmental stresses including extreme events is crucial for sustainable management of water resources under a changing environment. However, few studies have attempted to quantify or identify the factors and process interactions controlling the impact of extreme events across water-shed systems. Only few large-scale studies include coordinated monitoring and modeling efforts, which limits our ability to assess the large-scale impact of extreme events on water supply and quality. Methods are lacking to propagate uncertainty in process understanding through an integrated hydro-biogeochemical model framework and evaluate its importance, thus failing to take full advantage of the information potentially available through transformative advances in characterization technologies from high-resolution mass spectrometry to airborne and satellite-based remote sensing. There are consequent risks to our nations water security and to human and ecosystem health that may become exacerbated with the increasing frequency of extreme events that is projected for the coming decades. This paper reviews the current status of watershed science for both water quantity and quality and identifies critical gaps in our current knowledge and modeling capability in addressing the emergent needs in predicting watershed hydrologic and biogeochemical responses (i.e., water quantity and quality) under natural and anthropogenic perturbations. We highlight the need to (1) understand how environmental perturbations including extreme events like floods and droughts propagate through watershed systems and assess their short- and long-term impacts on watershed biogeochemistry, water quality and their recovery pathways; (2) develop and improve a watershed water quality model that reflects the state of scientific understanding gained from observations; and (3) construct a data-model fusion system for watershed characterization, process identification, and mechanistic model parameterization. A large base of modeling, monitoring and data capabilities have been built by various federal government agencies given the relevance of water to their critical missions. An emerging need is to build an integrated national capability for watershed water availability and quality that can address water-related missions across multiple federal agencies.

13 HYDRO ENERGY↗