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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modeling the Moisture Content and Dry Matter Loss in Dynamic Woody Biomass Storage Piles with Variable Extraction

The urgent need to mitigate climate change has spurred significant interest in renewable energy sources. This paper explores the storage and processing of woody biomass for biofuel production, considering the dynamic nature of biomass piles in real-world scenarios. A model has been developed to analyze moisture content changes and dry matter loss in woody biomass stored in piles prior to processing, taking into account varying extraction methods and environmental conditions. Case studies that examine the effects of different unpiling methods (FIFO, LIFO, and homogeneous) on moisture content and dry matter loss under various feedstock arrival rates and weather conditions are presented. Results indicate that unpiling methods significantly impact moisture content, with LIFO typically resulting in higher moisture content due to the utilization of fresher feedstock. Dry matter loss increases with pile size and time, emphasizing the importance of accurate modeling for assessing carbon emissions and feedstock quality. Furthermore, the model highlights the importance of process loading order and extraction methods in biomass storage, suggesting potential cost benefits associated with decreased moisture content. The difference between different extraction methods could vary the moisture content in the feedstock reaching the biofuel process by as much as 37.6%, however dry matter loss varies minimally for realistic pile changes. Overall, this research contributes to a better understanding of biomass storage dynamics and informs sustainable biofuel production practices.

Niska, Janel↗
DOE OSTI2023

Moisture ingress in commercial steel drums: Water content determination, diffusion modelling and predicted permeation rates

Commercial steel drums underpin the global economy, playing a pivotal role in the storage and transportation of critical materials. Transported and stored materials, such as food, chemical and nuclear waste, can be sensitive to ambient conditions, particularly moisture that can enhance negative effects such as corrosion and material degradation. Although international standards and regulations are in place for the qualification of steel drums, there are no current testing requirements, established limits or boundaries for the permeation of moisture into the drums during transportation or storage. This work aims to provide insights into the moisture ingress over time into properly sealed steel drums and provides estimated moisture ingress rates over time through extrapolation. Water vapour transmission rate (WVTR) measurements through the gasket material at 10–40°C were 0.11–2.1 g/m 2 /day resulting in a permeation activation energy of 30.2 kJ/mol. Water sorption measurements and Karl Fischer titration (KFT) on ethylene propylene diene monomer (EPDM) gasket material revealed a decrease in equilibrium moisture saturation with increasing temperature. KFT measurements also revealed the presence of moisture within the adhesive and drum wall after exposure to ambient conditions. KFT and Fourier transform infrared spectroscopy (FTIR) show that moisture will desorb from the EPDM and drum wall after exposure to desiccating conditions, although a minimal amount of moisture will remain present. When sealed to the manufacturer's recommendations, the steel drums are effective in minimizing moisture ingress. In conclusion, in sealed empty drums, moisture ingress rates for 19-L drums were 0.4–1.5 mg/day at 25°C 15% relative humidity (RH) and increased to 7.1–8.8 mg/day at 40°C 90% RH, and moisture ingress rates for 210-L drums were 2.5 and 3.5 mg/day at field deployment conditions of 15.5°C 51.5% RH and 23°C 40% RH, respectively.

42 ENGINEERING↗
DOE OSTI2025

Introduction to NETL Natural Gas LCA

This work presents an overview of NETL natural gas (NG) life cycle modeling. The content covers: introduction, model structure and unit processes, co-product management, and highlights from the recently published NETL NG baseline report.

life cycle analysis (LCA)↗
DOE OSTI2024

Understanding Generative AI Content with Embedding Models

The construction of high-quality numerical features is critical to any quantitative data analysis. Feature engineering has been historically addressed by carefully hand-crafting data representations based on domain expertise. This work views the internal representations of modern deep neural networks (DNNs), called embeddings, as an implicit form of traditional feature engineering. For trained DNNs, we show that these embeddings can reveal interpretable, high-level concepts in unstructured sample data. We use these embeddings in natural language and computer vision tasks to uncover both inherent heterogeneity in the underlying data and human-understandable explanations for it. In particular, we find empirical evidence that there is inherent separability between real data and those generated from AI models.

Vargas, Max↗
DOE OSTI2023

Measure This, Not That: Pareto Optimal Trade-Offs between Model-Based Information Content and Measurements Cost

The slides present a novel convex optimization formulation to compute the best set of measurements for multi-response dynamical systems with asynchronous time steps that maximize the Fisher information content subject to budget constraints. The trace (A-optimality) or determinant (D-optimality) of the Fisher Information Matrix (FIM) quantifies the information content. The framework supports arbitrary (positive semi-definite) variance and covariances between every pair of responses and their time steps.

Wang, Jialu↗
DOE OSTI2025

Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape: Modeling Archive

This modeling archive is in support of the Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) publication "Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape", by Murphy et al. (2025). This archive contains model input files and outputs from landscape-scale simulations conducted using ELM, the land model component of the Department of Energy’s Energy Exascale Earth System Model (E3SM), at the Council NGEE Arctic field site (Council Road mile marker 71) on Alaska’s Seward Peninsula. Input data and model output from two sets of ELM simulations are provided. The first set of simulations were conducted with the two default ELM Arctic plant functional types (PFTs; broadleaf deciduous boreal shrub and a C3 grass) and the second set of simulations were conducted with a set of nine Arctic-specific PFTs including nonvascular mosses and lichens, graminoids, forbs, evergreen dwarf shrubs, three height classes of deciduous shrubs (dwarf, low, and low to tall), and deciduous alder shrubs (Sulman et al., 2021). Parameter names and major parameter changes in the Arctic-specific PFT configuration are described in Sulman et al. (2021) and archived in the Sulman et al. (2021) dataset (see below). Simulations were spatially explicit, covering an approximately 6.4X3.3 km domain at the Council site with a spatial resolution of 100 m for a total of 2,112 simulated grid cells under each ELM PFT configuration. The modeling archive contains meteorological forcing (seven *.nc files and one *.txt file), a domain definition file (one *.nc files), land surface configuration files (two *.nc files), parameter files (two *.nc files), annual ELM output files spanning 1980-2014 (68 *.nc files), and a User’s Guide (*pdf file). Additional information on the provided files is in the “Modeling Archive Contents” section of the User’s Guide. Model outputs are aggregated to the column scale (i.e. PFT-specific outputs are not provided here).

Murphy, Bailey [ORNL] (ORCID:0000000203995221)↗
DOE OSTI2025

Prediction of Silicon Content in a Blast Furnace via Machine Learning: A Comprehensive Processing and Modeling Pipeline

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features.

Chemistry↗
DOE OSTI2023

Application of Interparticle Spacing Model to Maximize Filler Content in Cementitious Pastes

One of the most impactful ways to reduce embodied carbon of concrete in the near term is by partially replacing Portland cement with supplementary cementitious materials and/or fillers. This article describes an approach to reduce cement content in concrete through the development of high filler, low water (HFLW) cementitious pastes. Pastes with cement content as low as 50% (% weight of fines) were designed by applying models that maximize the packing density of the granular system and that consider the characteristics of the materials to calculate interparticle spacing (IPS), thereby allowing to obtain pastes with higher solids concentration and lower water demand. Strong correlations were found between IPS, the consistency index of the Herschel-Bulkley rheological model, and cement hydration kinetic parameters of binary and ternary pastes containing a Portland limestone cement and up to two different types of ground limestone. The approach showed to be feasible for the design of HFLW concrete paste fraction.

Antunes da Silva, Denise↗
DOE OSTI2023

Discerning Deception: An Empirically-Driven Agent-Based Model of Expert Evaluation of Scientific Content

Both human subject experiments and computational, modeling and simulations have been used to study detection of deception. This work aims to combine these two methods by integrating empirically-derived information (from human subject experiments) into agent-based models to generate novel insights into the complex problems of detection of disinformation content. Computational experiments are used to simulate across multiple scenarios for evaluation and decision-making regarding the validity of potentially deceptive scientific documents. Factors influencing the human agent behaviors in the model were identified through a human subject experiment that was conducted to evaluate and characterize decision making related to disinformation discernment. Correlation and regression analyses were used to translate insights from the human subjects experiment to inform the parameterization of agent features and scenario development. Three scenarios were evaluated with the agent-based models to help evaluate the replicability of the simulations (validation analysis) and assess the influence of human agent and document features (sensitivity analyses). A replication of the human participant experiment demonstrated that the agent-based simulations compare favorably to empirical findings. The agent-based modeling was then used to conduct sensitivity analysis on the accuracy of deception detection as a function of document proportions and human agent features. Results indicate that precision values are adversely impacted when the proportion of deceptive documents is lower in the overall sample, whereas recall values are more sensitive to changes in human agent features. These findings indicate important nuances in accuracy evaluations that should be further considered (including consideration of potential alternate metrics) in future agent-based models of disinformation. Additional areas for future exploration include extension of simulations to consider other ways to align the agent-based model design with psychological theory and inclusion of agent-agent interactions, especially as it pertains to sharing of scientific information within an organizational context.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Stone Content Influence on Land Surface Model Simulation of Soil Moisture and Evapotranspiration at Reynolds Creek Watershed

Considerable advancement in spatiotemporal resolution of remote sensing and ground-based measurements has enabled refinement of parameters used in land surface models for simulating surface water fluxes. However, land surface modeling capabilities are still inadequate for accurate representation of subsurface properties and processes, which continue to limit the accuracy of land surface model simulation. Our objective in this study was to examine the performance of the variously parameterized Noah-Multiphysics (Noah-MP) land surface model and to improve simulations of evapotranspiration (ET) and soil moisture dynamics in stony soils using verification from eddy covariance ET and in-situ soil moisture data. Meteorological and eddy covariance data from the Lower Sheep sub-catchment within the Reynolds Creek Experimental Watershed in Southwestern Idaho were employed to drive Noah-MP and to assess simulation results. We evaluated the performance of Noah-MP considering four different scenarios: (1) A one-layer soil profile with Noah-MP default soil hydraulic parameters, followed by five-layer soil profiles using -(2) Noah-MP default soil hydraulic parameters -(3) soil hydraulic parameters derived from a pedotransfer function using field observations, and -(4) hydraulic parameters from (3) which also accounted for stone content in each layer. Each modeling experiment was forced with the same set of initial conditions, atmospheric input and vegetation parameters. Our results indicated that enhanced representation of soil profile properties and stone content information noticeably improve the Noah-MP land surface model simulation of soil moisture content and evapotranspiration.

Land surface models, Soil hydrology, Noah-MP, Evap↗
DOE OSTI2022

Modification at the C2'-O-Position with 2-Methylbenzothiophene Induces Unique Structural Changes and Thermal Transitions on Duplexes of RNA and DNA

Oligonucleotides can be chemically modified for a variety of applications that include their use as biomaterials, in therapeutics, or as tools to understand biochemical processes, among others. This work focuses on the functionalization of oligonucleotides of RNA and DNA (12- or 14-nucleotides long) with methylbenzothiophene (BT), at the C2'-O-position, which led to unique structural features. Circular dichroism (CD) analyses showed that positioning the BT units on one strand led to significant thermal destabilization, while duplexes where each strand contained 4-BT rings formed a distinct arrangement with cooperativity/interactions among the modifications (evidenced from the appearance of a band with positive ellipticity at 235 nm). Interestingly, the structural arrays displayed increased duplex stabilization (>10 °C higher than the canonical analogue) as a function of [Na + ] with an unexpected structural rearrangement at temperatures above 50 °C. Density functional theory–polarizable continuum model (DFT-PCM) calculations were carried out, and the analyses were in agreement with induced structural changes as a function of salt content. A model was proposed where the hydrophobic surface allows for an internal nucleobase rearrangement into a more thermodynamically stable structure, before undergoing full denaturation, with increased heat. While this behavior is not common, B- to Z-form duplex transitions can occur and are dependent on parameters that were probed in this work, i.e., temperature, nature of modification, or ionic content. To take advantage of this phenomenon, we probed the ability of the modified duplexes to be recognized by Zα (an RNA binding protein that targets Z-form RNA) via electrophoretic analysis and CD. Interestingly, the protein did not bind to canonical duplexes of DNA or RNA; however, it recognized the modified duplexes, in a [monovalent/divalent salt] dependent manner. Overall, the findings describe methodology to attain unique structural motifs of modified duplexes of DNA or RNA, and control their behavior as a function of salt concentration. While their affinity to RNA binding proteins, and the corresponding mechanism of action, requires further exploration, the tunable properties can be of potential use to study this, and other, types of modifications. The novel arrays that formed, under the conditions described herein, provide a useful way to explore the structure and behavior of modified oligonucleotides, in general.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Full-waveform tomography reveals iron spin crossover in Earth’s lower mantle

Abstract Three-dimensional models of Earth’s seismic structure can be used to identify temperature-dependent phenomena, including mineralogical phase and spin transformations, that are obscured in 1-D spherical averages. Full-waveform tomography maps seismic wave-speeds inside the Earth in three dimensions, at a higher resolution than classical methods. By providing absolute wave speeds (rather than perturbations) and simultaneously constraining bulk and shear wave speeds over the same frequency range, it becomes feasible to distinguish variations in temperature from changes in composition or spin state. We present a quantitative joint interpretation of bulk and shear wave speeds in the lower mantle, using a recently published full-waveform tomography model. At all depths the diversity of wave speeds cannot be explained by an isochemical mantle. Between 1000 and 2500 km depth, hypothetical mantle models containing an electronic spin crossover in ferropericlase provide a significantly better fit to the wave-speed distributions, as well as more realistic temperatures and silica contents, than models without a spin crossover. Below 2500 km, wave speed distributions are explained by an enrichment in silica towards the core-mantle boundary. This silica enrichment may represent the fractionated remains of an ancient basal magma ocean.

58 GEOSCIENCES↗
DOE OSTI2024

Modeling the impact of high thermal conductivity paper on the performance and life of power transformers

Degradation of insulation paper is a key contributor to the failure of power transformers. Insulation degradation accelerates at elevated temperatures, which highlights the potential for better thermal management to prolong life. While several studies have analyzed the benefits of high thermal conductivity oil for reducing temperatures inside a transformer, this study is an initial assessment of the benefits of high thermal conductivity paper on transformer life. Blending particulates with cellulosic fibers offers a pathway for high thermal conductivity paper (with good dielectric properties), which can reduce internal temperatures. Presently, life extensions that can be achieved by the use of such thermally conducting papers were estimated, with the thermal conductivity of the paper being the key parameter under study. The analytical-numerical thermal model used in this study was validated against experimental measurements in a distribution transformer, adding confidence to the utility of the model. This model was then used to provide estimates of hot-spot temperature reduction resulting from the use of papers with higher thermal conductivity than baseline. Transformer life was predicted conventionally by tracking the degree of polymerization of paper over time, based on an Arrhenius model. Results indicate that increasing the thermal conductivity of paper from 0.2 W/mK (baseline) to 1W/mK reduces the hot spot temperature by 10°C. While degradation significantly depends on the moisture and oxygen content, the model shows that such a temperature reduction can increase life for all conditions, by as much as a factor of three.

42 ENGINEERING↗
DOE OSTI2022

On ( g – 2) μ from gauged $\mathrm{U(1)_X}$

We investigate an economical explanation for the (g – 2) μ anomaly with a neutral vector boson from a spontaneously broken U(1) X gauge symmetry. The Standard Model fermion content is minimally extended by 3 right-handed neutrinos. Using a battery of complementary constraints, we perform a thorough investigation of the renormalizable, quark flavor-universal, vector-like U(1) X models, allowing for arbitrary kinetic mixing. Out of 419 models with integer charges not greater than ten, only 7 models are viable solutions, describing a narrow region in model space. These are either L μ – L τ or models with a ratio of electron to baryon number close to –2. The key complementary constraints are from the searches for nonstandard neutrino interactions. Furthermore, we comment on the severe challenges to chiral U(1) X solutions and show the severe constraints on a particularly promising such candidate.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

SAVY-4000 Finite-Element Drop Test Analysis

PFE Auxiliary Systems conducted drop testing on SAVY-4000 containers to evaluate structural response under 12-foot drop conditions. In support of that effort, a finite-element modeling capability was developed to simulate drop response across multiple container sizes and impact orientations. The purpose of this work was to provide a consistent analysis framework that could support interpretation of testing, compare response trends across multiple configurations, and generate quantities of interest for later comparison with experimental data. More broadly, the analysis and testing were intended to assess whether the containers continued to perform their primary function after a 12-foot drop, namely maintaining structural integrity and containment of the contents. The modeling approach combined an implicit preload analysis with an explicit drop simulation so that each drop event began from a mechanically realistic assembled condition, including compression of the silicone O-ring. Separate models were developed for 2-quart, 5-quart, 12-quart, and 10-gallon containers. The results were evaluated in terms of strain-gauge response, collar-lid gap behavior, and accumulated plastic strain. In addition, parametric studies were performed on the 2-quart container to assess sensitivity to O-ring stiffness, friction, canister thickness, geometry tolerance, and mesh density. The simulations showed that predicted drop responses depended strongly on both container size and drop orientation. Gap metrics identified cases in which the predicted collar-lid opening exceeded the nominal O-ring cross-section threshold, while plastic strain metrics identified localized regions of elevated permanent deformation. Parametric studies showed that the predicted response was especially sensitive to the assumed O-ring stiffness and contact friction, while the geometry tolerance study produced smaller changes in the cases examined. The main value of this work was that it established a repeatable modeling and simulation workflow to support drop-test implementation, evaluate effects of future configuration changes, and understand modeling assumptions that most influenced predicted response. At the current stage, the results were viewed as preliminary model predictions rather than validated predictions. The next step would be to compare drop-test data to the model so that predictive values of the workflow could be refined and used with greater confidence to assess whether the containers maintained structural integrity and containment of the contents after a 12-foot drop.

42 ENGINEERING↗
DOE OSTI2023

Arctic ice-ocean interactions in an 8-to-2 kilometer resolution global model

In the last decades, the Arctic climate has changed dramatically, with the loss of multiyear sea ice one of the clearest consequences. These changes have occurred on relatively rapid timescales, and both accurate short-term Arctic prediction (e.g., 10 days to three months) and climate projection of future Arctic scenarios present ongoing challenges. Here we describe a representation of the Arctic ocean and sea ice in a ultrahigh resolution simulation in which the horizontal grid mesh reduces from 8 km at the equator to 2 km at the poles (UH8to2) for the years 2017-2020. We find the simulation reproduces observed distributions of seasonal sea-ice thickness and concentration realistically, although concentration is biased low in the spring and summer and low biases in thickness are found in the central and eastern basins in the fall. Volume, fresh water, and heat transports through key passages are realistic, lying within observationally determined ranges. Climatological comparisons reveal that the UH8to2 Atlantic Water is shallower, warmer, and saltier than the World Ocean Atlas 2018 climatology for 2005-2017 in the eastern basin. Our analysis suggests that these biases, combined with a lack of stratification in the upper 100 m of the simulated ocean, contribute to the winter biases in modeled sea ice thickness. This relationship between biases in the sea ice and ocean points to a potential positive feedback within the model, illuminating challenges for long term model predictive power in a changing Arctic climate.

58 GEOSCIENCES↗
DOE OSTI2023

Data from: Arctic ice-ocean interactions in an 8-to-2 kilometer resolution global model

This data collection represents all UH8to2 model data used for the publication "Arctic ice-ocean interactions in an 8-to-2 kilometer resolution global model." This includes ocean and sea ice characteristics from the 2017-2020 UH8to2 model run. Further details about the model setup, forcing, and our interpretation of these data can be found in the publication.

58 GEOSCIENCES↗