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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

CO2 Plume Imaging with Accelerated Deep Learning-based Data Assimilation Considering Multiple Realizations: Application to the Illinois Basin-Decatur Carbon Sequestration Project

We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site. Unlike the previous work, this study incorporates the impact of spatial heterogeneity using multiple realizations. In the proposed workflow, a neural network model utilizes available monitoring data such as downhole pressure measurements as input and predicts the propagating pressure ‘front’ using the diffusive time of flight (DTOF) map which is considered as representative reservoir image of the flow field. The DTOF is the arrival time of pressure front propagation, which can be computed by the Fast Marching Method rapidly without flow simulations. Reservoir model calibration can be implemented by selecting the training data samples that describe the predicted DTOF map based on observed data. The power and efficacy of our workflow is demonstrated by application to the Illinois Basin-Decatur Project.

CO2 plume imaging↗
DOE OSTI2021

Early Warning Signs of a Mental Health Tsunami: A Coordinated Response to Gather Initial Data Insights From Multiple Digital Services Providers

The immediate impact of coronavirus 2019 (COVID-19) on morbidity and mortality has raised the need for accurate and real-time data monitoring and communication. The aim of this study is to document the initial observations from multiple digital services providers during the COVID-19 crisis, especially those related to mental health and well-being. We used email and social media to announce an urgent call for support. Digital mental health services providers (N = 46), financial services providers (N = 4), and other relevant digital data source providers (N = 3) responded with quantitative and/or qualitative data insights. People with lived experience of distress, as service users/consumers, and carers are included as co-authors. This study provides proof-of-concept of the viability for researchers and private companies to work collaboratively toward a common good. Digital services providers reported a diverse range of mental health concerns. A recurring observation is that demand for digital mental health support has risen, and that the nature of this demand has also changed since COVID-19, with an apparent increased presentation of anxiety and loneliness. Following this study, we will continue to work with providers in more in-depth ways to capture follow-up insights at regular time points. We will also onboard new providers to address data representativeness. Looking ahead, we anticipate the need for a rigorous process to interpret insights from an even wider variety of sources in order to monitor and respond to mental health needs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

kynema-fmb [SWR-23-07]

Kynema-FMB (FKA: Kynema) is an open-source performance portable flexible multibody (FMB) dynamics solver designed for time-domain simulations. While originally tailored for wind turbine structural dynamics, the formulation and implementation are those of a general flexible-multidbody dynamics solver that can readily be applied to a wide range of systems. Kynema was designed with a narrow focus, namely to provide a lightweight, fast, accurate FMD solver for coupling to computational-fluid-dynamics (CFD) codes, especially the CFD codes in the Kynema suite, for fluid-structure-interaction (FSI) simulations. Kynema-FMB is equipped to model systems that can be represented as a collection of beams and rigid bodies that are connected through constraints. Degrees of freedom are defined in the inertial/global frame of reference and include displacements and rotations (formally as rotation matrices, but stored as quaternions). The underlying formulation is built on a Lie-group time integrator designed for index-3 differential-algebraic equations, which is second-order accurate in time (Bruls et al., 2012). Beam models are based on geometrically exact beam theory and are discretized as high-order spectral finite elements similar to those in BeamDyn (Wang et al., 2017). The governing equations for a FMD system like a wind turbine constitute a highly nonlinear system of constrained partial-differential equations. Kynema-FMB uses analytical Jacobians in the nonlinear-system solves in each time step. Linear systems use sparse storage and several third-party sparse-linear-system solvers are enabled. Ill conditioning of linear systems is mitigated with preconditioning described in Bottasso et al, 2008. Kynema-FMB is integrated with a simple open-source controller (ROSCO). There is an application programming interface (API) for coupling to geometry-resolved CFD (like that in Sharma et al., 2023) and actuator-force CFD (like that in Kuhn et al., 2025). In the latter, for actuator-line models, Kynema-FMB includes an internal blade-element solver that depends on user-provided lookup tables for coefficients of lift and drag, i.e., aerodynamic polars. Kynema-FMB is written in C++ and leverages Kokkos and Kokkos-Kernels (KokkosEcosystem) as its performance portability layer enabling simulations on both CPU and GPU systems. The repository is equipped with extensive automated testing at the unit and regression/system levels. The following describes the high-level development objectives conceived for Kynema: *Kynema will follow modern software development best practices, including test-driven development (TDD), version control, hierarchical automated testing, and continuous integration (CI) for a robust development environment. *The core data structures are memory efficient and enable vectorization and parallelization at multiple levels. *Data structures are data-oriented to exploit methods for accelerated computing including high utilization of chip resources (e.g., single instruction multiple data (SIMD) instruction sets) and parallelization using GP-GPUs. *The computational algorithms incorporate robust open-source libraries for mathematical operations, resource allocation, and data management. *The API design considers multiple stakeholder needs and ensure integration with existing and future ecosystems for data science, machine learning, and AI. *Kynema-FMB is written in modern C++ and leverages Kokkos as its performance-portability library with inspiration from the kynema stack.

Sprague, MichaelA.↗
DOE OSTI2021

A causal data fusion method for the general exposure and outcome

Abstract With the advent of the big data era, the need to combine multiple individual data sets to draw causal effects arises naturally in many medical and biological applications. Especially each data set cannot measure enough confounders to infer the causal effect of an exposure on an outcome. In this article, we extend the method proposed by a previous study to causal data fusion of more than two data sets without external validation and to a more general (continuous or discrete) exposure and outcome. Theoretically, we obtain the condition for identifiability of exposure effects using multiple individual data sources for the continuous or discrete exposure and outcome. The simulation results show that our proposed causal data fusion method has unbiased causal effect estimate and higher precision than traditional regression, meta‐analysis and statistical matching methods. We further apply our method to study the causal effect of BMI on glucose level in individuals with diabetes by combining two data sets. Our method is essential for causal data fusion and provides important insights into the ongoing discourse on the empirical analysis of merging multiple individual data sources.

Li, Hongkai↗
DOE OSTI2024

HyperKube: A Kubernetes Based System for the Automation of Processing and Analysis of Hyperspectral Data Obtained from Multiple Hyperspectral Imaging Systems

Hyperspectral imagery is an emerging field of technology that has enormous potential for remote and proximal sensing in numerous areas of research. The plant phenotyping community is applying this technology to advance the throughput and accuracy of plant phenotypes based on airborne and lab-based hyperspectral imaging technology. Here we report an automated processing and analysis pipeline for four different hyperspectral imaging platforms, discuss the data issues involved, and present a strategy for computing and data architecture to handle hyperspectral data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Association Between Injection and Microseismicity in Geothermal Fields With Multiple Wells: Data-Driven Modeling of Rotokawa, New Zealand, and Húsmúli, Iceland

Understanding injection-induced microseismicity in geothermal systems can provide insight into reservoir connectedness. However, fault and reservoir complexity are difficult to represent in simple analytical models, which makes it difficult to discern clear relationships from incidental associations. Here, we have used data-driven models to study how fluid injection and microseismicity are related in the Rotokawa (New Zealand) and Hellisheiði (Iceland) geothermal fields. We tested two classes of model: (a) lagged linear regression of seismicity rate as a function of well injection rates; and (b) systematic extraction of injection time series features that are then evaluated for associations with the seismicity. These models allowed us to determine which wells had the greatest correlation with microseismicity and to explain this association in a reservoir context. Finally, exploring different data types and transformations, we were unable to establish a link between rapid changes in injection rate and seismicity spikes, as suggested by some theoretical models.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Impact Mapping for Geospatial Reasoning and Decision Making

Objective The reported study evaluated a novel approach to aiding geospatial reasoning and decision making. Background Impact mapping aims to alleviate the cognitive demands of geospatial tasks in part by externalizing data in the form of an integrated decision surface. This is achieved by aggregating data across multiple sources of information and visualizing their combined utility rather than objective measurements or individual utility. Previous research has shown that geospatial decisions improve when aided in this manner, but it remains unknown if dynamic decision making, often plagued by fatigue and anchoring bias, would benefit similarly. Method The experiment implemented a systematic manipulation of the presence of a composite impact map and the number of attributes present in a two-stage disaster relief, resource allocation task to investigate when and how impact mapping is beneficial or deleterious to decision makers. Results The presence of the composite impact map increased the utility of selected sites, increased re-planning decisions, reduced information display views, and reduced workload. Generally, the effect of the composite impact map was greater when participants were asked to evaluate more attributes. Conclusion Composite impact maps appear to improve repeated geospatial reasoning and minimize anchoring bias because they alleviate the cognitive demands otherwise necessary to interpret and maintain information from multiple attributes. Application Data visualization techniques, such as impact mapping, can improve repeated geospatial decision making in environments that include high cognitive demand.

Illingworth, David A.↗
DOE OSTI2024

LTE Electrolyzer Data Collection

The goal for NREL is to collect, develop and publish performance metrics relative to low temperature electrolyzer installations. This will be done through the development of: Secure storage solution to house the collection of data from multiple projects Standardization of data to be collected and analyzed. This will be done using data templates developed with the help of partners involved with electrolyzer installations. Analysis that produces metrics of interest for all stakeholders Aggregation of results from multiple projects to view industry progress as a whole Publication of aggregated results in the form of composite data products (CDPs) Collaboration with Idaho National Lab and their work with high temperature electrolyzer installations will enable efficient use of storage and analysis tools.

data↗
DOE OSTI2021

Investigating Vegetation Responses to Underground Nuclear Explosions Through Integrated Analyses

Vegetation has the potential to respond to underground nuclear explosions, yet these links have not been fully explored. Given the lack of previously described signatures, the changes in vegetation are possibly subtle. The integration of multiple different data streams is potentially a useful approach to improve signal detection. Here, we investigate whether semi-arid vegetation growth patterns responded to eight legacy underground nuclear tests at the Nevada National Security Site in southern Nevada, USA. We tested for spatial and temporal changes in vegetation cover, tree growth patterns, and tree leaf spectral properties using ground-based measurements, including those from tree-rings and hyperspectral surface vegetation reflectance, as well as space-based measurements of Normalized Difference Vegetation Index (NDVI) from Landsat. Multiple data streams suggest a localized (<1.2 km) spatial pattern whereby tree growth is enhanced closer to the source of the underground test relative to sites further away. We also observed a more regional (>1.2–9 km) pattern whereby tree growth is suppressed coincident with a drought beginning 1 year before the 1989 tests, but continuing in the 5 years following the tests, which is anomalous relative to what is expected based on the response of tree growth to previous droughts. furthermore, quantification of the relative effects of the tests on vegetation remains a challenge due to the coincident drought and the potential for other disturbances to have impacted tree growth at this time, but the integration of these data reveals a more nuanced growth response than any other one data set indicates alone.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

Tsdat: An Open-Source Data Standardization Framework for Marine Energy and Beyond

Many organizations are tasked with the collection and processing of large quantities of data from various measurement devices. Data reported from these sources are often not interoperable with datasets and software used by analysts and other organizations in the same domain, introducing barriers for collaboration on large-scale projects. This poses a particular problem for cross-device comparisons and machine learning applications, which rely on large quantities of data from multiple sources. To address these challenges, the open-source Time-Series Data Pipelines (Tsdat) Python framework was developed by Pacific Northwest National Laboratory, with strategic guidance and direction provided by the National Renewable Energy Laboratory and Sandia National Laboratories to facilitate collaboration and accelerate advancements in the marine energy domain through the development of an open-source ecosystem of tools. This paper will describe the Tsdat framework and the data standards within which it operates. A beta version of Tsdat has been released and is being used by several projects in marine energy, wind energy, and building energy systems.

big data↗
DOE OSTI2026

Data for KETCHUP: Parameterizing of Large-Scale Kinetic Models Using Multiple Datasets with Different Reference States

Repository for Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP can use K-FIT [2] input files. Example K-FIT input files are located in the K-FIT repository at https://github.com/maranasgroup/K-FIT.

Metabolomics↗
DOE OSTI2020

Inversion of Many-Beam Bragg Intensities for Phasing by Iterated Projections: Removal of Multiple Scattering Artifacts from Diffraction Data

An iterated projection algorithm (N-Phaser) is developed that reconstructs a scattering potential from N-beam multiple Bragg scattered intensities. The method may be used to eliminate multiple scattering artifacts from electron diffraction data, solving the phase problem and increasing the thicknesses of samples used in materials science, solid-state chemistry, and small molecule crystallography. For high-energy transmission electron diffraction, we show that the algorithm recovers accurate complex structure factors from a wide range of thicknesses, orientations, and relativistic beam energies, and does not require known thickness or atomic-resolution data if sufficient multiple scattering occurs. Furthermore, extensions to Cryo-electron microscopy and Micro-electron diffraction are suggested.

36 MATERIALS SCIENCE↗
DOE OSTI2024

IER-518: Data Analysis for High Multiplication Subcritical Experiments [Slides]

This presentation begins with a discussion of goals & motivation. This lecture then covers: experimental configurations, computational models, experimental measurements and simulations. Finally, this presentation concludes with data analysis & results plus a conclusion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection

Generating top-down tandem mass spectra (MS/MS) for complex mixtures of proteoforms has become possible through improvements in fractionation, on-line separation, dissociation, and mass analysis. The algorithms to match tandem mass spectra to sequences have undergone a parallel evolution, with both spectral alignment and peak matching being paired with diverse methods for scoring proteoform-spectral matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification through three distinct challenges. The first is identifying a large yield of PrSMs while controlling false discovery rate (FDR) in identifying thousands of proteoforms from complex cell lysates via four software workflows: ProSight Proteome Discoverer, TopPIC, Informed Proteomics, and pTop. The second is the deconvolution of data from both Thermo Orbitrap-class and Bruker maXis Q-TOF instruments to produce consistent precursor charge and mass determinations while generating fragment mass lists to optimize identification. The third attempts to detect diverse post-translational modifications (PTMs) in proteoforms from cow milk and human ovarian tissue. The data demonstrate that existing software suites produce admirable sensitivity, in some cases identifying a third of collected tandem mass spectra with FDR controlled below 2%; the overlap in these PrSMs, however, illustrates real value in searching data with multiple search engines. Differences among identification workflows seem to result from each search algorithm incorporating its own deconvolution algorithm. By transmitting deconvolution data from multiple deconvolution routes (Thermo Xtract, Bruker Auto MSn, Mascot Distiller, TopFD, and FLASHDeconv) to the downstream TopPIC search algorithm, we were able to detect common causes of deconvolution disagreement. The detection of PTMs was very inconsistent among search algorithms, with some workflows suggesting as little as 1% of PrSMs from cow’s milk were singly-phosphorylated while other workflows found that 18% of PrSMs were singly-phosphorylated. Taken together, these results make a strong argument for top-down researchers to adopt a standard practice of analyzing each MS/MS experiment with at least two different search engines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Hazard-Based Model of Activity Generation Using Vehicle Trajectory Data

Travel behavior modeling suffers from lack of longitudinal data due to the high cost of procurement and difficulty with ensuring data quality. In the recent years ubiquitous data intercepted from social media, cell detail record, GPS traces are increasingly being explored to analyze human mobility. However, these data lack key demographic information which limits their use for developing causal models of traveler behavior. The primary contribution of the paper lies in using vehicle trajectory data from Ann Arbor along with multiple other data sources for developing a hazard-based activity generation model. The different category of data used in the analysis include (i) vehicle trajectory data (VTD), (ii) American Census Survey (ACS), (iii) land use data, (iv) household travel survey (HTS) data, and (v) smart location data from Environmental Protection Agency (EPA). Home and work are identified as the two most frequently visited locations from the VTD and validated against land use categories of the region. Next a random utility-based model is developed using HTS data to identify other activity purposes as a function of the polynomials of distance from home, activity duration and departure time. The developed model is then applied to identify the non-home activity purposes in the VTD. Demographic and accessibility information are appended to the VTD from ACS and smart location databases respectively. The fmal database is then used to develop a hazard-based activity generation model. (C) 2020 The Authors. Published by Elsevier B.V.

activity generation↗
DOE OSTI2023

Identification of Blue Horizontal Branch Stars with Multimodal Fusion

Blue Horizontal Branch stars (BHBs) are ideal tracers to probe the global structure of the milky Way (MW), and the increased size of the BHB star sample could be helpful to accurately calculate the MW's enclosed mass and kinematics. Large survey telescopes have produced an increasing number of astronomical images and spectra. However, traditional methods of identifying BHBs are limited in dealing with the large scale of astronomical data. A fast and efficient way of identifying BHBs can provide a more significant sample for further analysis and research. Therefore, in order to fully use the various data observed and further improve the identification accuracy of BHBs, we have innovatively proposed and implemented a Bi-level attention mechanism-based Transformer multimodal fusion model, called Bi-level Attention in the Transformer with Multimodality (BATMM). The model consists of a spectrum encoder, an image encoder, and a Transformer multimodal fusion module. The Transformer enables the effective fusion of data from two modalities, namely image and spectrum, by using the proposed Bi-level attention mechanism, including cross-attention and self-attention. As a result, the information from the different modalities complements each other, thus improving the accuracy of the identification of BHBs. The experimental results show that the F1 score of the proposed BATMM is 94.78%, which is 21.77% and 2.76% higher than the image and spectral unimodality, respectively. It is therefore demonstrated that higher identification accuracy of BHBs can be achieved by means of using data from multiple modalities and employing an efficient data fusion strategy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Integrated Large-Scale Data Management Platform for Photovoltaic Power Conversion Equipment (PCE) Reliability Data

To meet the demand for accuracy and real-time capability of PV system degradation evaluation, massive volume data is needed to run high-fidelity and high-efficiency simulations and perform advanced data analysis. However, PV farm operators have a series of difficulties with PV inverter data, such as data collection from multiple channels, massive data storage, data management and massive data analysis. To address these challenges, we developed an integrated data management platform capable of data acquisition, processing, storage, query, and performing big data analysis utilizing AI algorithms. The platform can also achieve data correctness verification and provide an effective distributed data management solution to retrieve massive data and establish a connection to distributed computational frameworks.

data management platform↗