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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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DOE OSTI2025

Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository

The Department of Energy's (DOE's) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and helping users access data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data. This paper provides an update on recent improvements made to the GDR's data lakes and automated data pipelines, including: (1) streamlining the data lake intake process, (2) better educating users on the process and requirements through a new data lakes page, (3) adding data lake direct access links to GDR data lake submission pages, (4) implementing a DAS data pipeline to convert DAS data uploaded in SEG-Y format to a standardized hierarchical data format v5 (HDF5), (5) extending this pipeline to encompass data in the GDR data lake, (6) adding metadata requirements for geospatial data, (7) making user interface/user experience (UX) enhancements to the data pipelines' documentation pages, and (8) improving the GDR's data standards and pipelines pages to better guide users in ensuring that their data is standardized by the GDR's automated data pipelines. 2024 Geothermal Resources Council. All rights reserved.

accessibility↗
DOE OSTI2017

Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of acoustic impedance, porosity and lithofacies

We propose a Bayesian approach for seismic inversion to estimate acoustic impedance, porosity and lithofacies within the reservoir conditioned to post-stack seismic and well data. The link between elastic and petrophysical properties is given by a joint prior distribution for the logarithm of impedance and porosity, based on a rock-physics model. The well conditioning is performed through a background model obtained by well log interpolation. Two different approaches are presented: in the first approach, the prior is defined by a single Gaussian distribution, whereas in the second approach it is defined by a Gaussian mixture to represent the well data multimodal distribution and link the Gaussian components to different geological lithofacies. The forward model is based on a linearized convolutional model. For the single Gaussian case, we obtain an analytical expression for the posterior distribution, resulting in a fast algorithm to compute the solution of the inverse problem, i.e. the posterior distribution of acoustic impedance and porosity as well as the facies probability given the observed data. For the Gaussian mixture prior, it is not possible to obtain the distributions analytically, hence we propose a Gibbs algorithm to perform the posterior sampling and obtain several reservoir model realizations, allowing an uncertainty analysis of the estimated properties and lithofacies. Both methodologies are applied to a real seismic dataset with three wells to obtain 3D models of acoustic impedance, porosity and lithofacies. The methodologies are validated through a blind well test and compared to a standard Bayesian inversion approach. Using the probability of the reservoir lithofacies, we also compute a 3D isosurface probability model of the main oil reservoir in the studied field.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods

Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. Here, the result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Bayesian calibration of terrestrial ecosystem models: A study of advanced Markov chain Monte Carlo methods

Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this study, a Differential Evolution Adaptive Metropolis (DREAM) algorithm was used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The DREAM is a multi-chain method and uses differential evolution technique for chain movement, allowing it to be efficiently applied to high-dimensional problems, and can reliably estimate heavy-tailed and multimodal distributions that are difficult for single-chain schemes using a Gaussian proposal distribution. The results were evaluated against the popular Adaptive Metropolis (AM) scheme. DREAM indicated that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identified one mode. The calibration of DREAM resulted in a better model fit and predictive performance compared to the AM. DREAM provides means for a good exploration of the posterior distributions of model parameters. Lastly, it reduces the risk of false convergence to a local optimum and potentially improves the predictive performance of the calibrated model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

Designing the Cloud-based DOE Systems Biology Knowledgebase

Systems Biology research, even more than many other scientific domains, is becoming increasingly data-intensive. Not only have advances in experimental and computational technologies lead to an exponential increase in scientific data volumes and their complexity, but increasingly such databases themselves are providing the basis for new scientific discoveries. To engage effectively with these community resources, integrated analyses, synthesis and simulation software is needed, regularly supported by scientific workflows. In order to provide a more collaborative, community driven research environment for this heterogeneous setting, the Department of Energy (DOE) has decided to develop a federated, cloud based cyber infrastructure - the Systems Biology Knowledgebase (Kbase). Pacific Northwest National Laboratory (PNNL) with its long tradition in data intensive science lead two of the five initial pilot projects, these two focusing on defining and testing the basic federated cloud-based system architecture and develop a prototype implementation. Hereby the community wide accessibility of biological data and the capability to integrate and analyze this data within its changing research context were seen as key technical functionalities the Kbase needed to enable. In this paper we describe the results of our investigations into the design of a cloud based federated infrastructure for: (1) Semantics driven data discovery, access and integration; (2) Data annotation, publication and sharing; (3) Workflow enabled data analysis; and (4) Project based collaborative working. We describe our approach, exemplary use cases and our prototype implementation that demonstrates the feasibility of this approach.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

PNNL-ANL Hydrometeorological Super Ensemble

The current dataset contains data upload links to the following **hydrologic (water balance), river routing (water management), and hydropower simulation** data over CONUS: * Climate Forcing: **Livneh** (https://www.nature.com/articles/sdata201542) * Simulation Scenario: **Historical** * Simulation Period: **1971-2013 (1972-2013 for Hydropower)** * Simulation Models: **VIC (Variable Infiltration Capacity)**, **mosartwmpy (Model for Scale Adaptive River Transport-Water Management in Python)**, and **PNNL B1Hydro** * Output Format: **NetCDF** and **CSV**

Tidwell, Vincent C [Pacific Northwest National Lab↗
DOE OSTI2025

PNNL-ANL Hydrometeorological Super Ensemble

The current dataset contains data upload links to the following hydrologic (water balance), river routing (water management), and hydropower simulation data over CONUS: Climate Forcing: ClimRR (https://climrr.anl.gov/climrrdata) Simulation Scenario: Historical, Mid-Century, End-Century Simulation Period: 1995-2004, 2045-2054, 2085-2094 Simulation Models: VIC (Variable Infiltration Capacity), mosartwmpy (Model for Scale Adaptive River Transport-Water Management in Python), and PNNL B1Hydro Output Format: NetCDF and CSV

Tidwell, Vincent C [Pacific Northwest National Lab↗
DOE OSTI2025

TGW Hydrology, River Routing, and Hydropower Simulation Datasets

he current dataset contains data upload links to the following hydrologic (water balance), river routing (water management), and hydropower simulation data over CONUS: Climate Forcing: TGW (https://tgw-data.msdlive.org/) Simulation Scenario: Historical, Mid-Century, End-Century Simulation Period: 1980-2024, 2020-2059, 2060-2099 Simulation Models: VIC (Variable Infiltration Capacity), mosartwmpy (Model for Scale Adaptive River Transport-Water Management in Python), and PNNL B1Hydro Output Format: NetCDF and CSV

Tidwell, Vincent C [Pacific Northwest National Lab↗
DOE OSTI2015

Resource Discovery for Extreme Scale Collaboration (RDESC) Final Report - RPI/TWC - Year 3

The amount of data produced in the practice of science is growing rapidly. Despite the accumulation and demand for scientific data, relatively little is actually made available for the broader scientific community. We surmise that the root of the problem is the perceived difficulty to electronically publish scientific data and associated metadata in a way that makes it discoverable. We propose to exploit Semantic Web technologies and practices to make (meta)data discoverable and easy to publish. We share our experiences in curating metadata to illustrate both the flexibility of our approach and the pain of discovering data in the current research environment. We also make recommendations by concrete example of how data publishers can provide their (meta)data by adding some limited, additional markup to HTML pages on the Web. With little additional effort from data publishers, the difficulty of data discovery/access/sharing can be greatly reduced and the impact of research data greatly enhanced.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Transportation Database Development Using Floating Car Data

In support of projects funded by the U.S. Department of Transportation and the U.S. Department of Energy, the National Renewable Energy Laboratory (NREL) has received on average 4 million floating car data (FCD) points each day over a 3-year period. To handle data sets of this size, the Transportation & Hydrogen Systems Center at NREL has developed a series of transportation databases (Transportation Secure Data Center and Fleet DNA projects) to support real-world vehicle and transportation infrastructure analysis on a national scale. To ensure the quality of the FCD received by NREL and to ensure that data can be accessed and applied quickly and easily across transportation research disciplines, a customized data processing routine and database were required. 'Floating car data' is a general term for location data recorded by a vehicle or by a person traveling in a vehicle. FCD are used to support real-time traffic information systems and have proven to be beneficial in a large number of research applications. For large-scale traffic information systems, FCD are logged from smart phones using either the GPS included in the device, or if GPS is disabled, triangulation from the network. Additionally, in scientific applications FCD are oftentimes collected on a smaller scale using onboard GPS-enabled data logging systems. Through the fusion of real-time data and historically logged results, up-to-date and accurate vehicle operating information are provided in near real-time to support routing and operation decisions. The approach outlined within this paper builds upon methods developed for advanced traffic information systems to link FCD to the street of travel and further extends the link to data recorded by components within a vehicle. Linking vehicle component performance to the infrastructure a vehicle is operating within provides the path to assess vehicle and infrastructure interactions, going far beyond providing information on traffic flow alone.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

A data integration framework of additive manufacturing based on FAIR principles

Abstract Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2015

Reproducible Data Processing Research for the CABRI R.I.A. experiments Acoustic Emission signal analysis

The CABRI facility is an experimental nuclear reactor of the French Atomic Energy Commission (CEA) designed to study the behaviour of fuel rods at high burnup under Reactivity Initiated Accident (R.I.A.) conditions such as the scenario of a control rod ejection. During the experimental phase, the behaviour of the fuel element generates acoustic waves which can be detected by two microphones placed upstream and downstream from the test device. Studies carried out on the last fourteen tests showed the interest in carrying out temporal and spectral analyses on these signals by showing the existence of signatures which can be correlated with physical phenomena. We want presently to return to this rich data in order to have a new point of view by applying modern signal processing methods. Such an antecedent works resumption leads to some difficulties. Although all the raw data are accessible in the form of text files, analyses and graphics representations were not clear in reproducing from the former studies since the people who were in charge of the original work have left the laboratory and it is not easy when time passes, even with our own work, to be able to remember the steps of data manipulations and the exact setup. Thus we decided to consolidate the availability of the data and its manipulation in order to provide a robust data processing workflow to the experimentalists before doing any further investigations. To tackle this issue of strong links between data, treatments and the generation of documents, we adopted a Reproducible Research paradigm. We shall first present the tools chosen in our laboratory to implement this workflow and, then we shall describe the global perception carried out to continue the study of the Acoustic Emission signals recorded by the two microphones during the last fourteen CABRI R.I.A. tests. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

High-Efficiency Quantum Dot Lasers as Comb Sources for DWDM Applications

The trend of data center transceivers is to increase bandwidth while simultaneously decreasing their size, power consumption, and cost. While data center links have previously relied on vertical-cavity surface-emitting lasers (VCSELs) or in-plane lasers using coarse wavelength division multiplexing (WDM) to encode data, recently, dense WDM (DWDM) has moved to the forefront for next-generation links. Several approaches exist as light sources for DWDM links; DFB arrays, nonlinear microcombs, and semiconductor mode-locked lasers (MLLs). This paper focuses on quantum dot MLLs (QDMLLs), which currently offer the best efficiency, simplicity, and size. The efficiency of optical combs generated by QDMLLs is analyzed in depth in this study.

Dumont, Mario (ORCID:000000023956612X)↗
DOE OSTI2022

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Severino, Joseph↗
DOE OSTI2014

Extracting data from the muck: deriving biological insight from complex microbial communities and non-model organisms with next generation sequencing

It is becoming increasingly clear that microbes within microbial communities, for which cultured isolates have not yet been obtained, have an immense, untapped reservoir of enzymes that could help address grand challenges in human health, energy, and sustainability. Despite the obstacles associated with culturing these microbes, recent advances in next-generation sequencing (NGS) have made it possible to explore complex microbial communities in their native context for the first time. Key to extracting meaning from rapidly growing NGS datasets are bioinformatics tools that assemble the sequence data, annotate homologous sequences and interrogate it to reveal regulatory patterns. Complementing this are advances in proteomics that can link NGS data to biological function. Finally, this combination of next generation sequencing, proteomics and bioinformatic analysis forms a powerful tool to study non-model microbes, which will transform what we know about these dynamic systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Emerging semantics to link phenotype and environment

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. Lastly, in this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Near-real-time environmental monitoring and large-volume data collection over slow communication links

Climate change studies are one of the most important aspects of modern science and related experiments are getting bigger and more complex. One such experiment is the Spruce and Peatland Responses Under Changing Environments conducted in northern Minnesota. The SPRUCE experimental mission is to assess ecosystem-level biological responses of vulnerable, high-carbon terrestrial ecosystems to a range of climate warming manipulations and an elevated CO 2 atmosphere. This manipulation experiment generates a lot of observational data and requires a reliable on-site data collection system, dependable methods to transfer data to a robust scientific facility, and real-time monitoring capabilities. Furthermore this publication shares our experience of establishing a near-real-time data collection and monitoring system via a satellite link using the not very well-known possibilities of PakBus protocol.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

SOLTHERM Thermodynamic Database for Geochemical Modeling

This data submission is a link to a thermodynamic database maintained by the University of Oregon. The data at this link are not 'data results' from sampling. The data at this link comprise a thermodynamic database for aqueous species, minerals, and gases, including data for stoichiometry, equilibrium constants log K(T,P), aqueous activity coefficients, fugacity coefficients, and water enthalpy. These data are derived from SOLTHERM as a reference for the user, showing balanced reactions and equilibrium constants log K(T,P) along the liquid-vapor saturation curve only, up to 350 degrees C, for aqueous species and minerals including REE, and gases. These data include REE aqueous species and minerals. These data are used by programs SOLVEQ-XPT, CHIM-XPT, and GEOCAL-XPT.

15 GEOTHERMAL ENERGY↗