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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Annual PM2.5 and cardiovascular mortality rate data: Trends modified by county socioeconomic status in 2,132 US counties

This article contains data on county-level socioeconomic status for 2132 US counties and each county's average annual cardiovascular mortality rate (CMR) and fine particulate matter (PM2.5) concentration for 21 years (1990e2010). County CMR, PM2.5, and socioeconomic data were obtained from the US National Center for Health Statistics, US Environmental Protection Agency's Community Multiscale Air Quality modeling system, and the US Census, respectively. Annual socioeconomic indices were created using seven county-level measures from the 1990, 2000, and 2010 US Census using factor analysis. Quintiles of this index were used to generate categories of county socioeconomic status. This national data set contains data for annual PM2.5 and CMR changes over a time-period when there was a significant reduction in US air pollutants (following the enactment of the 1970 Clean Air Act). These data are associated with the article “The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time” [1]. Data are stored in a comma separated value format and can be downloaded from the USEPA ScienceHub data repository (https://doi.org/10.23719/ 1506014).

Socioeconomic status↗
DOE OSTI2020

European residential wood pellet trade and prices dataset

The European market for wood pellets used in small-scale heating systems has been expanding significantly over the past decade. For an analysis of market efficiency in the Journal Energy with the title “The European wood pellets for heating market - price developments, trade and market efficiency“ wood pellet prices have been collected as well as trade flows downloaded for the trade relations between Austria, Germany, Italy and France. Only since January 2012 monthly wood pellet trade data is published by Eurostat. This, now monthly expanding data-set provides new opportunities for analysing the development of this important renewable energy commodity. Furthermore, national wood pellet prices published by national authorities and interest groups are improving in quality in the recent years. The collection and combination of these data-sets are a chance for novel econometric analysis. This paper presents valuable tools and processes to acquire and prepare this data and connects to a data and code repository for downloading the resources described in this and the related Journal Energy publication.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Field surveying data of low-cost networked flood sensors in southeast Texas

Floods are common natural disasters worldwide and pose substantial risks to life, property, food production, and natural resources. Effective measures for flood mitigation and warning are essential. Southeast Texas is still at significant risk of flooding, and Lamar University is assisting the region with asset management of a flood sensor network for flooding events. This network provides real-time water stage information. Lamar University developed a survey program to measure elevation and coordinates at each sensor site location to make this data more useful for flood monitoring and mapping. This paper overviews the measurement of the elevation and coordinates of 74 networked flood sensors and various flood stage thresholds at critical points that flood decision-makers can use for reference at each site. In the first phase of this program, these sensors were deployed throughout a 7-county region spanning nearly 6,000 square miles in Southeast Texas. The latitude and longitude of the sensors and their elevations were determined using survey-grade Global Navigation Satellite System (GNSS) technology. Various Continually Operating Reference Stations (CORS) were utilized for post-processing to achieve sub-inch resolution. The flood stage thresholds, water level sensors elevation, and the elevations and positions of other critical surrounding points are viewable to the public through two online repositories and a web-based sensor management dashboard. The data is used to aid with decisions related to road closures or modeling efforts by mitigation decision-makers, emergency managers, and the public, including the Texas Department of Transportation, Houston Transtar, the National Weather Service, and the Sabine River Authority of Texas (SRA).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Evaluating Impacts of CO2 Gas Intrusion Into a Confined Sandstone aquifer: Experimental Results

Deep subsurface storage and sequestration of CO2 has been identified as a potential mitigation technique for rising atmospheric CO2 concentrations. Sequestered CO2 represents a potential risk to overlying aquifers if the CO2 leaks from the deep storage reservoir. Experimental and modeling work is required to evaluate potential risks to groundwater quality and develop a systematic understanding of how CO2 leakage may cause important changes in aquifer chemistry and mineralogy by promoting dissolution/precipitation, adsorption/desorption, and redox reactions. Sediments from the High Plains aquifer in Kansas, United States, were used in this investigation, which is part of the National Risk Assessment Partnership Program sponsored by the US Department of Energy. This aquifer was selected to be representative of consolidated sand and gravel/sandstone aquifers overlying potential CO2 sequestration repositories within the continental US. In this paper, we present results from batch experiments conducted at room temperature and atmospheric pressure with four High Plains aquifer sediments. Batch experiments simulate sudden, fast, and short-lived releases of the CO2 gas as would occur in the case of well failure during injection. Time-dependent release of major, minor, and trace elements were determined by analyzing the contacting solutions. Characterization studies demonstrated that the High Plains aquifer sediments were abundant in quartz and feldspars, and contained about 15 to 20 wt% montmorillonite and up to 5 wt% micas. Some of the High Plains aquifer sediments contained no calcite, while others had up to about 7 wt% calcite. The strong acid extraction tests confirmed that in addition to the usual elements present in most soils, rocks, and sediments, the High Plains aquifer sediments had appreciable amounts of As, Cd, Pb, Cu, and occasionally Zn, which potentially may be mobilized from the solid to the aqueous phase during or after exposure to CO2. However, the results from the batch experiments showed that the High Plains sediments mobilized only low concentrations of trace elements (potential contaminants), which were detected occasionally in the aqueous phase during these experiments. Importantly, these occurrences were more frequent in the calcite-free sediment. Results from these investigations provide useful information to support site selection, risk assessment, and public education efforts associated with geological CO2 storage and sequestration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Short-term electricity load forecasting: Application-driven evaluation of machine learning models across spatial and temporal scales

As we transition towards a decarbonized economy, the integration of variable renewable energy resources and new demands (e.g., electric vehicles, heat pumps) into the electricity grid places unprecedented pressure on grid operators to effectively anticipate and manage peak load. In this context, machine learning algorithms are proving to be indispensable for accurate short-term load forecasting, a crucial task to address these challenges. This study benchmarks 6 machine learning algorithms, including three neural networks and three tree-based algorithms, across various levels of spatial aggregation and time horizons (1, 4, 8, 24, and 48 h). The central contribution of this work is the comparison and analysis of load forecasting models not only based on statistical metrics, but also based on a novel error metric, which evaluates the cost implications of forecast errors for power system stakeholders. Results show that tree-based models outperform neural networks, based on statistical metrics, and yield less skewed error distributions for most spatial scales. However, through the lens of the novel error metric, neural networks are the more competitive choice, especially for forecast horizons that exceed 8 h. The study concludes with actionable recommendations to grid operators and highlights the need for the development of error metrics that link forecasting accuracy to operational costs. To promote transparency and open science, the datasets and Python code are open-sourced via a supplementary repository.

Houben, Nikolaus↗
DOE OSTI2025

Experimental data for damage mechanics simulation challenge

While there are many computational approaches for simulating damage in rock and other materials, few have been ground truth tested with either known experimental data or with blind data sets. Here, in this work, we present a bench-mark laboratory data set for a damage mechanics challenge to compare computational approaches on damage evolution in brittle-ductile materials. The samples were fabricated through additive manufacturing to produce repeatable specimens designed to fail in controlled ways. The failure was induced in the samples using a 3-point bending test to produce different Modes such as Mode I and mixed Modes including I-II, I-III and I-II-III Modes to generate a calibration data set and a blind challenge data set. Data collected included spatial and temporal measurements from traditional digital load–displacement sensors, 2D digital image correlation measurement to map surface deformations, 3D X-ray microscopy to ground-truth the crack-failure geometry, and laser profilometry to capture surface roughness. The data sets are available, on a data repository, to the community to advance computational models to improve our ability to predict damage in brittle-ductile materials.

3-point bending↗
DOE OSTI2017

Potential human exposure to halogenated flame-retardants in elevated surface dust and floor dust in an academic environment

Most households and workplaces all over the world possess furnishings and electronics, all of which contain potentially toxic flame retardant chemicals to prevent fire hazards. Indoor dust is a recognized repository of these types of chemicals including polybrominated diphenyl ethers (PBDEs) and non-polybrominated diphenyl ethers (non-PBDEs). However, no previous U.S. studies have differentiated concentrations from elevated surface dust (ESD) and floor dust (FD) within and across microenvironments. We address this information gap by measuring twenty-two flame-retardant chemicals in dust on elevated surfaces (ESD; n=10) and floors (FD; n=10) from rooms on a California campus that contain various concentrations of electronic products. We hypothesized a difference in chemical concentrations in ESD and FD. Secondarily, we examined whether or not this difference persisted: (a) across the studied microenvironments and (b) in rooms with various concentrations of electronics. A Wilcoxon signed-rank test demonstrated that the ESD was statistically significantly higher than FD for BDE-47 (p=0.01), BDE-99 (p=0.01), BDE-100 (p=0.01), BDE-153 (p=0.02), BDE-154 (p=0.02), and 3 non-PBDEs including EH-TBB (p=0.02), BEH-TEBP (p=0.05), and TDCIPP (p=0.03). These results suggest different levels and kinds of exposures to flame-retardant chemicals for individuals spending time in the sampled locations depending on the position of accumulated dust. Therefore, further research is needed to estimate human exposure to flame retardant chemicals based on how much time and where in the room individuals spend their time. Such sub-location estimates will likely differ from assessments that assume continuous unidimensional exposure, with implications for improved understanding of potential health impacts of flame retardant chemicals. - Highlights: • Brominated flame retardants used in electronic products accumulate in room dust • Various chemical moieties of flame retardants leach differently into room dust • Flame retardant concentrations in dust differ in elevated surfaces compared to floors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

PINN surrogate of Li-ion battery models for parameter inference, Part I: Implementation and multi-fidelity hierarchies for the single-particle model

To plan and optimize energy storage demands that account for Li-ion battery aging dynamics, techniques need to be developed to diagnose battery internal states accurately and rapidly. Here, this study seeks to reduce the computational resources needed to determine a battery's internal states by replacing physics-based Li-ion battery models - such as the single-particle model (SPM) and the pseudo-2D (P2D) model - with a physics-informed neural network (PINN) surrogate. The surrogate model makes high-throughput techniques, such as Bayesian calibration, tractable to determine battery internal parameters from voltage responses. This manuscript is the first of a two-part series that introduces PINN surrogates of Li-ion battery models for parameter inference (i.e., state-of-health diagnostics). In this first part, a method is presented for constructing a PINN surrogate of the SPM. A multi-fidelity hierarchical training, where several neural nets are trained with multiple physics-loss fidelities is shown to significantly improve the surrogate accuracy when only training on the governing equation residuals. The implementation is made available in a companion repository (https://github.com/NREL/PINNSTRIPES). The techniques used to develop a PINN surrogate of the SPM are extended in Part II for the PINN surrogate for the P2D battery model, and explore the Bayesian calibration capabilities of both surrogates.

25 ENERGY STORAGE↗
DOE OSTI2026

Dynamic modeling of heat pipe integrated thermal battery latent heat storage system experiment validation

A heat pipe integrated thermal battery system has been constructed to investigate a high-temperature latent heat thermal energy storage technology that takes advantage of near isothermal operation of latent heat storage and heat pipes to potentially enable high-energy isothermal heat storage. A dynamic model constructed in Modelica has been validated, showing errors between 2.5 °C–39.7 °C across 10-h to 47-h simulations against experiment results, showing good prediction capability of experiment output, especially against phase change time. Model calibrations showing vessel heat-up capability of 3 kW and heat pipes combining to provide 600 W each during experiment operation validate experiment circumstances including reduced material loading and reduced power capability. The experiment configuration uses an Al-Mg-Zn eutectic metal as the storage material, heated via heat tape wrapped around the vessel and guide tubes to bring the system to operation range (>400 °C) and to simulate charging heat exchange, respectively, with heat rejection occurring through the surfaces of the material and facilitated via guide tubes with less insulation wrapping. The model is available in the open-source repository HYBRID on Github.

25 - ENERGY STORAGE↗
DOE OSTI2026

Neural Posterior Estimation for Scalable and Accurate Inverse Parameter Inference in Li-Ion Batteries

Diagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physics-based models to perform probabilistic parameter estimation via Bayesian calibration, diagnostics can account for the uncertainty due to model fitness, data noise, and the observability of any given parameter. However, Bayesian calibration in Li-ion batteries using electrochemical data is computationally intensive even when using a fast surrogate in place of physics-based models, requiring many thousands of model evaluations. A fully amortized alternative is neural posterior estimation (NPE). NPE shifts the computational burden from the parameter estimation step to data generation and model training, reducing the parameter estimation time from minutes to milliseconds, enabling real-time applications. The present work shows that NPE can infer parameters equally or more accurately than Bayesian calibration, even if it leads to higher voltage reconstruction errors. We also demonstrate that the higher computational costs for data generation are tractable even in high-dimensional cases (ranging from 6 to 27 estimated parameters). The NPE method also offers several interpretability advantages over Bayesian calibration, such as local parameter sensitivity to specific regions of the voltage curve. The NPE method is demonstrated using an experimental fast charge dataset, with parameter estimates validated against measurements of loss of lithium inventory and loss of active material. The implementation is made available in a companion repository (https://github.com/NatLabRockies/BatFIT).

25 ENERGY STORAGE↗
DOE OSTI2019

Development and application of a machine learning based multi-objective optimization workflow for CO 2 -EOR projects

Carbon dioxide-Enhanced Oil Recovery (CO 2 -EOR) is known as one of techniques for hydrocarbon production improvement as wells as an important candidate to reduce greenhouse gas emissions. Thus, an ideal development strategy for a CO 2 -EOR project would consider multiple objectives including to maximize oil recovery, CO 2 storage volume and project economic outcomes. This work proposes a robust computational framework that couples artificial neural network (ANN) and multi-objective optimizers to optimize the aforementioned objectives in CO 2 -EOR processes simultaneously. Expert ANN systems are trained and employed as surrogate models of the high-fidelity compositional simulator in the optimization workflow. The robustness of the development optimization protocol is confirmed via a synthetic injection-pattern-base case study. Afterward a field implementation to Morrow-B formation to optimize the tertiary recovery stage of the field development is discussed. This work compares the optimum solution found using an aggregate objective function and the solution repository (Pareto front) generated by the multi-objective optimization process. The comparison indicates the existence of potential multi-solutions satisfying certain criteria in a CO 2 -EOR project designing, which cannot be found using traditional weighted sum method. The optimization results provide significant insight into the decision-making process of CO 2 -EOR project when multiple objective functions are considered.

02 PETROLEUM↗
DOE OSTI2014

An investigation of reaction parameters on geochemical storage of non-pure CO2 streams in iron oxides-bearing formations

Hematite deposit that is the main FeIII-bearing mineral in sedimentary red beds was proposed as a potential host repository for converting CO2 into carbonate minerals such as siderite (FeCO3), when CO2–SO2 gas mixtures are co-injected. This work investigated CO2 mineral trapping using hematite and sensitivity of the reactive systems to different parameters, including particle size, gas composition, temperature, pressure, and solid-to-liquid ratio. Experimental and modelling studies of hydrothermal experiments were conducted, which emulated a CO2 sequestration scenario by injecting CO2-SO2 gas streams into a NaCl-NaOH brine hosted in iron oxide-containing aquifer. This study provides novel information on the mineralogical changes and fluid chemistry derived from the co-injection of CO2-SO2 gas mixtures in hematite deposit. It can be concluded that the amount of siderite precipitate depends primarily on the SO2 content of the gas stream. Increasing SO2 content in the system could promote the reduction of Fe3+ from the hematite sample to Fe2+, which will be further available for its precipitation as siderite. Moreover, siderite precipitation is enhanced at low temperatures and high pressures. The influence of the solid to liquid ratio on the overall carbonation reaction suggests that the conversion increases if the system becomes more diluted.

CO2 storage↗
DOE OSTI2023

Automated generation of scientific workflow generators with WfChef

Scientific workflow applications have gained significant importance, and their automated and efficient execution on large-scale computing platforms has been the subject of extensive research and development. For these efforts to be successful, a solid experimental methodology is needed to evaluate workflow algorithms and systems. A foundation for this methodology is the availability of realistic workflow instances. Although public repositories provide workflow instances for a few scientific applications, these are limited in scope, and workflow instances are not available for all application scales of interest. To address this limitation, previous work has developed generators of synthetic workflow instances of arbitrary scales. Despite being popular, the implementation of these generators is a manual and labor-intensive process that requires expert application knowledge. As a result, these generators only target a handful of applications, even though there are hundreds of workflow applications in production. Here, we introduce WfChef , a fully automated framework for constructing a synthetic workflow generator for any scientific application. Based on an input set of workflow instances for a particular application, WfChef automatically produces a synthetic workflow generator. To measure the realism of the generated workflows, we define and evaluate several metrics. Using these metrics, we compare the realism of the workflows generated by WfChef generators to that of the workflows generated by the previously available, hand-crafted generators. We find that WfChef generators not only require zero development effort (because they are automatically produced), but also generate workflows that are more realistic than those generated by hand-crafted generators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

The surface structure of α-uranophane and its interaction with Eu(III) – An integrated computational and fluorescence spectroscopy study

Uranophane is a rare U(VI) secondary silicate mineral formed in nature by the oxidation of the primary mineral uraninite. It is also relevant to the long-term geochemistry of nuclear waste repositories, where it can be formed under oxidizing conditions and has the potential to act as a secondary barrier to the migration of radionuclides through mineral sorption reactions. A combination of classical molecular dynamics and ab-initio density functional theory (DFT) has been employed to investigate the uranophane|water interface as well as the interfacial reactivity of the U(VI) silicate toward acidic conditions and radionuclide ion sorption. The sorption simulations have been complemented by experimental sorption studies and laser induced fluorescence spectroscopy to help identify the molecular structure of the surface sorbed species. Experimental distances and essential coordination numbers are properly captured by the simulation results within bulk uranophane, while interfacial water is found to orient primarily with the hydrogen-atoms directed towards the negatively charged surface. Sorption sites for water are observed to belong to 3 different groups: (1) those involving uranyl oxygen, (2) involving uranyl and silica hydroxyl oxygen atoms, and (3) involving hydroxyl hydrogen. The pKa of the surface -OH groups have been calculated using a variety of models, including a bond valence approach and utilization of the energetics of deprotonation within DFT. Under basic conditions, deprotonation of the Si-OH groups is likely responsible for uranophane dissolution. Finally, the stability and structure of surface sorbed Eu3+ has been examined, with a stable inner-sphere species being observed.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2024

Modeling gas migration through clay-based buffer material using coupled multiphase fluid flow and geomechanics with stress-dependent gas permeability

A model for gas migration through clay-based buffer material is developed for modeling gas generation and migration associated with deep geologic nuclear waste disposal. The model is based on a multiphase fluid flow and geomechanics simulator that is adapted to consider enhanced gas flow when gas pressure is high enough to approach the confining stress magnitude. A key feature in the model is a direct coupling between gas permeability and stress, through a non-linear stress-dependent permeability function. The model was first tested and calibrated by modelling two different laboratory gas migration tests on Wyoming (MX-80) bentonite samples. The calibrated model was then applied to model gas migration through a bentonite buffer of a large-scale gas injection test (Lasgit) conducted at the Äspö Hard Rock Laboratory in Sweden. Observed preferential gas migration along interfaces (between compacted blocks and along the canister surface) required explicit representation of such interfaces in the model. The model with the stress-dependent gas permeability accurately captured observed experimental responses in terms of gas breakthrough time, peak gas pressure, and cumulative gas flow rates. The calibrated model was finally applied to simulate migration of hydrogen gas generated within a breached nuclear waste canister over 10,000 years, involving migration of much larger gas volumes. For the considered gas generation rate and host rock properties, the generated gas could migrate through the bentonite buffer and released into the surrounding host rock at a maximum gas pressure somewhat higher than the initial total stress, though a significant amount of hydrogen remained within the buffer. This modelling sets the stage for further detailed analysis of the impact of hydrogen gas generation on the long-term performance of nuclear waste repositories.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Numerical simulation of coupled THM behaviour of full-scale EBS in backfilled experimental gallery in the Horonobe URL

Bentonite-based engineered barrier system (EBS) is a key component of many repository designs for the geological disposal of high-level radioactive waste. Given the complexity and interaction of the phenomena affecting the barrier system, coupled thermo-hydro-mechanical (THM) numerical analyses are a potentially useful tool for a better understanding of their behaviour. In this context, a Task (the Horonobe EBS experiment) was undertaken to study, using numerical analyses, the thermo-hydro-mechanical (and thermo-hydro) interactions in bentonite based engineered barriers within the international cooperative project DECOVALEX 2023. One full-scale in-situ experiment and four laboratory experiments, largely complementary, were selected for modelling. The Horonobe EBS experiment is a temperature-controlled non-isothermal experiment combined with artificial groundwater injection. The Horonobe EBS experiment consists of the heating and cooling phases. Six research teams performed the THM or TH (depended on research team approach) numerical analyses using a variety of computer codes, formulations and constitutive laws. Finally, for each experiment, the basic features of the analyses are described and the comparison between calculations and laboratory experiments and field observations are presented and discussed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Effect of pore fluid chemistry on the mechanical behavior of a divalent compacted bentonite, an experimental and constitutive study

Ongoing research in isolating high-level nuclear waste and spent fuel has highlighted compacted bentonite as a suitable material for engineered barrier systems in deep geological repositories due to its extraordinary swelling and retention properties. This research focuses on the chemo-mechanical behavior of compacted bentonite exposed to different pore fluids with different concentrations and loading conditions. The study involves swelling pressure and compressibility experiments along with mineralogy analysis employing X-ray diffraction (XRD) and Cation exchange. The tests were conducted on BCV (a Mg/Ca- bentonite) compacted at a dry density of 1.48 ± .02 Mg/m 3 . An advanced chemical-mechanical constitutive model for unsaturated highly expansive clays was adopted to simulate the material response and better understand its behavior. The model is able to account for the main phenomena at both macro and microstructural levels and the interactions between them. The model successfully replicated experimental observations. The XRD analyses support the macroscopic observation, indicating that salinity impacts crystalline swelling as demonstrated by the reduction of basal spacing from 19.27 Å to 15.68 Å when the osmotic suction increases from 0 MPa to 33 MPa. The results suggested that the osmotic pressure generated by the concentration in the pore fluids promotes a reduction in swelling pressures, swelling strains, and crystalline swelling of clay minerals. Also, it affects the pre-consolidation stress and the compressibility of the compacted samples. In conclusion, it was also observed that both solution type and solution concentration impact the clay swelling pressure.

Chemo-mechanical constitutive model↗
DOE OSTI2025

DECOVALEX-2023: An international collaboration for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems

The DECOVALEX initiative is an international research collaboration (www.decovalex.org), initiated in 1992, for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems. DECOVALEX stands for “DEvelopment of COupled Models and VALidation against EXperiments”. The creation of this international initiative was motivated by the recognition that prediction of these coupled effects is an essential part of the performance and safety assessment of geologic disposal systems for radioactive waste and spent nuclear fuel. DECOVALEX emphasizes joint analysis and comparative modeling of the complex perturbations and coupled processes in geologic repositories and how these impact long-term performance predictions. The most recent phase of the DECOVALEX Project, here referred to as DECOVALEX-2023, started in early 2020 and ended in late 2023. More than fifty research teams associated with 17 international DECOVALEX partner organizations participated in the comparative evaluation of eight modeling tasks covering a wide range of spatial and temporal scales, geological formations, and coupled processes. This Virtual Special Issue on DECOVALEX-2023 provides an in-depth overview of these collaborative research efforts and how these have advanced the state-of-the-art of understanding and modeling coupled THMC processes. While primarily focused on radioactive waste, much of the work included here has wider application to many geoengineering topics.

Coupled processes↗