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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Study of Photocatalytic Nano-Particle Effects on the Low Temperature Plasma Ionization Mass Spectrometry

Although the low temperature plasma mass spectrometry (LTP-MS) is widely used as an analysis tool for many biochemical samples, its application window is somehow limited to the analytes of low molecular mass and high volatility. For this reason, there have been attempts to enhance the ionization/desorption efficiencies with extra heating, for instance. In this study, another enhancement method was suggested using the photocatalytic nano-particles (NPs). In order to assess the NP effects on the LTP-MS, two fatty acid ethyl ester samples of ethyl myristate and ethyl palmitate were used, and the NP of titanium dioxide (TiO{sub 2}) was mainly employed. The results showed that the signal intensities of the LTP-MS were largely increased with the TiO{sub 2} addition for both samples. In addition, the cholesterol sample was analyzed using the TiO{sub 2} assisted LTP-MS, also resulting in the enhancement of the signal intensity. The overall results inferred that the photocatalytic NP confirmed its role as an effective assist tool for the LTP-MS, especially suitable because of the facile method and the heat-free nature. .

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

CFD design-load analysis of a two-body wave energy converter

Wave energy converters (WECs) must survive in a wide variety of conditions while minimizing structural costs, so as to deliver power at cost-competitive rates. Although engineering design and analysis tools used for other ocean systems, such as offshore structures and ships, can be applied, the unique nature and limited historical experience of WEC design necessitates assessment of the effectiveness of these methods for this specific application. This paper details a study to predict extreme loading in a two-body WEC using a combination of mid-fidelity and high-fidelity numerical modeling tools. The mid-fidelity approach is a time-domain model based on linearized potential flow hydrodynamics and the high-fidelity modeling tool is an unsteady Reynolds-averaged Navier–Stokes model. In both models, the dynamics of the WEC power take-off and mooring system have been included. For the high-fidelity model, two design wave approaches (an equivalent regular wave and a focused wave) are used to estimate the worst case wave forcing within a realistic irregular sea state. These simplified design wave approaches aim to capture the extreme response of the WEC within a feasible amount of computational effort. When compared to the mid-fidelity model results in a long-duration irregular sea, the short-duration design waves simulated in CFD produce upper percentile load responses, hinting at the suitability of these two approaches.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

A versatile and low-cost chip-to-world interface: enabling ICP-MS characterization of isotachophoretically separated lanthanides on a microfluidic device

Microfluidics offer novel and state-of-the-art pathways to process materials. Microfluidic systems drastically reduce timeframes and costs associated with traditional lab-scale efforts in the area of analytical sample preparations. The challenge arises in effectively connecting microfluidics to off-chip analysis tools to accurately characterize samples after treatment on-chip. Here, a fused silica microfluidic device (MFD) is connected to a fused-silica capillary in order to create a world-to-chip connection between the MFD and an inductively coupled plasma mass spectrometer (ICP-MS). Specifically, this is demonstrated by utilizing isotachophoresis (ITP) on a microfluidic chip to separate samples containing up to 14 lanthanides into elementally pure bands. The separated sample is successfully transferred across a 7 nL dead space at the microchip-capillary junction, such that separation resolution is maintained and even increased through the interface and into the ICP-MS, where the elemental composition of the separated sample is analysed. Lanthanide samples of varying composition are detected using ICP-MS, demonstrating this versatile and cost-effective approach that does not degrade the separation quality achieved on the MFD. This simple and inexpensive connection enables fast, low-cost sample preparation immediately prior to injection into an ICP-MS or other analytical instruments.

Lackey, Hope E.↗
DOE OSTI2017

Determining the minimum required uranium carbide content for HTGR UCO fuel kernels

There are three important failure mechanisms that must be controlled in high-temperature gas-cooled reactor (HTGR) fuel for certain higher burnup applications are SiC layer rupture, SiC corrosion by CO, and coating compromise from kernel migration. All are related to high CO pressures stemming from free O generated when uranium present as UO 2 fissions and the O is not subsequently bound by other elements. Furthermore, in the HTGR UCO kernel design, CO buildup from excess O is controlled by the inclusion of additional uranium in the form of a carbide, UC x . An approach for determining the minimum UC x content to ensure negligible CO formation was developed and demonstrated using CALPHAD models and the Serpent 2 reactor physics and depletion analysis tool. Our results are intended to be more accurate than previous estimates by including more nuclear and chemical factors, in particular the effect of transmutation products on the oxygen distribution as the fuel kernel composition evolves with burnup.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Standardized verification of the C YCLUS fuel cycle simulator

Many nuclear fuel cycle simulators can analyze transitions from once-through to advanced nuclear fuel cycles. Verification studies compare various fuel cycle analysis tools to test agreement and identify sources of difference. A recent verification study, Feng et al. (2016) established transition scenario test case specifications and accordingly evaluated national laboratory nuclear fuel cycle simulators, DYMOND, VISION, ORION, and MARKAL. This work verifies the performance of C YCLUS , the agent-based, open-source fuel cycle simulator, using the test case specifications in Feng et. al. We report, C YCLUS demonstrates agreement with the results from the previous verification study. Minor differences reflect intentional, detailed material tracking in the C YCAMORE reactor module. These results extend the example results in Feng et al. to further enable future verification of additional nuclear fuel cycle simulation tools.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Differentiable hybrid neural network approach for enhancing reactor dynamics simulations

Reactor dynamics simulations provide essential insights into the time-dependent behavior of nuclear reactors under various operating conditions. However, high-fidelity simulations can be computationally intensive, requiring significant computational resources. Here, to address this challenge, this study employs a differentiable hybrid model that utilizes neural networks as a corrector to enhance the performance of a low-fidelity simulation, aligning its predictions with those of a high-fidelity simulation. Low-fidelity and high-fidelity simulations were obtained by adjusting the mesh size in the System Dynamics Analysis Tool. The differentiable hybrid model was trained in two approaches: time-step-wise and sequence-wise. It was then applied to simulate various transients in a molten salt reactor. Its performance was evaluated by comparing its responses to transients against those of the high-fidelity simulation. An additional approach was performed using a data-driven model to correct the low-fidelity simulation. In comparison, the differentiable hybrid model showed significant improvements in transient prediction, effectively addressing the limitations of the low-fidelity simulations. The results highlighted the robustness of the differentiable hybrid model in both training approaches. It delivered simulations that were at least 3.8 times faster than high-fidelity models. In the time-step-wise approach, it achieved at least a 39% improvement in accuracy. In the sequence-wise approach, it showed at least an 81% accuracy improvement over the full transient. This approach offers a promising path for improving computational efficiency without compromising accuracy in nuclear reactor simulations, making it suitable for real-time digital twin applications.

42 - ENGINEERING↗
DOE OSTI2025

Physics-based hybrid machine learning for critical heat flux prediction with uncertainty quantification

Critical heat flux (CHF) is a key quantity in nuclear system modeling due to its impact on heat transfer, safety margins, and reactor performance. This study develops and validates an uncertainty-aware hybrid modeling approach that combines machine learning with physics-based models to predict CHF in cases of dryout. The Biasi and Bowring empirical correlations were paired with three ML uncertainty quantification (UQ) techniques: deep neural network (DNN) ensembles, Bayesian neural networks (BNNs), and deep Gaussian processes (DGPs). A pure ML model without a base model was evaluated for comparison. Model performance was assessed under plentiful (7,350 points) and limited (9 points) training data scenarios using parity, uncertainty distributions, and calibration curves. Results show that the Biasi hybrid DNN ensemble achieved the best overall performance, with a mean absolute relative error of 1.846%, and well-calibrated uncertainty estimates. The BNN-based hybrids showed slightly higher error (2.14%) but superior uncertainty calibration. DGP models underperformed, with over 6% error and poor uncertainty calibration. All hybrid models outperformed pure machine learning configurations, demonstrating resistance against data scarcity. These findings indicate that hybrid modeling significantly improves predictive accuracy, interpretability, and resilience to data scarcity. The integration of uncertainty awareness provides actionable confidence in CHF predictions, which is vital for safety-critical decisions in nuclear applications. This hybrid approach offers a viable pathway for deploying ML models in reactor analysis tools while preserving domain knowledge and physical consistency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

MADLens, a python package for fast and differentiable non-Gaussian lensing simulations

Here, we present MADLens a python package for producing non-Gaussian lensing convergence maps at arbitrary source redshifts with unprecedented precision. MADLens is designed to achieve high accuracy while keeping computational costs as low as possible. A MADLens simulation with only particles produces convergence maps whose power agrees with theoretical lensing power spectra up to within the accuracy limits of HaloFit. This is made possible by a combination of a highly parallelizable particle-mesh algorithm, a sub-evolution scheme in the lensing projection, and a machine-learning inspired sharpening step. Further, MADLens is fully differentiable with respect to the initial conditions of the underlying particle-mesh simulations and a number of cosmological parameters. These properties allow MADLens to be used as a forward model in Bayesian inference algorithms that require optimization or derivative-aided sampling. Another use case for MADLens is the production of large, high resolution simulation sets as they are required for training novel deep-learning-based lensing analysis tools. We make the MADLens package publicly available under a Creative Commons License

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

miR-130b targets NKD2 and regulates the Wnt signaling to promote proliferation and inhibit apoptosis in osteosarcoma cells

miR-130b was significantly up-regulated in osteosarcoma (OS) cells. Naked cuticle homolog 2 (NKD2) inhibited tumor growth and metastasis in OS by suppressing Wnt signaling. We used three miRNA target analysis tools to identify potential targets of miR-130b, and found that NKD2 is a potential target of miR-130b. Based on these findings, we hypothesize that miR-130b might target NKD2 and regulate the Wnt signaling to promote OS growth. We detected the expression of miR-130b and NKD2 mRNA and protein by quantitative Real-Time PCR (qRT-PCR) and western blot assays, respectively, and found up-regulation of miR-130b and down-regulation of NKD2 mRNA and protein exist in OS cell lines. MTT and flow cytometry assays showed that miR-130b inhibitors inhibit proliferation and promote apoptosis in OS cells. Furthermore, we showed that NKD2 is a direct target of miR-130b, and miR-130b regulated proliferation and apoptosis of OS cells by targeting NKD2. We further investigated whether miR-130b and NKD2 regulate OS cell proliferation and apoptosis by inhibiting Wnt signaling, and the results confirmed our speculation that miR-130b targets NKD2 and regulates the Wnt signaling to promote proliferation and inhibit apoptosis of OS cells. These findings will offer new clues for OS development and progression, and novel potential therapeutic targets for OS. - Highlights: • miR-130b is up-regulated and NKD2 is down-regulated in osteosarcoma cell lines. • Down-regulation of miR-130b inhibits proliferation of osteosarcoma cells. • Down-regulation of miR-130b promotes apoptosis of osteosarcoma cells. • miR-130b directly targets NKD2. • NKD2 regulates OS cell proliferation and apoptosis by inhibiting the Wnt signaling.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Single molecule insights into interfacial molecular recognition for model electrochemical DNA biosensors

Electrochemical sensors that use surface-immobilized DNA to bind analytes and transduce the binding into electrochemical signals, have the potential for rapid, specific, and sensitive detection of bioanalytes via a compact and portable platform. However, accessing the structure of these surfaces/interfaces at the relevant spatial scale (< 10 nm), which determines the interfacial interactions and ultimately sensing performance, remains an unsolved challenge. Here, we review studies that have used high resolution atomic force microscope imaging and spatial statistical analysis tools to understand crowding interactions between thiolated DNA probes immobilized on gold electrodes and how such interactions impact target binding. We also review related studies that attempt to control the nanoscale spatial arrangement of the immobilized recognition elements to optimize sensing performance. Furthermore, these efforts have led to new advances in understanding of the structure-function relationships of DNA-based electrochemical biosensors to move the field toward rational engineering of these biosensing interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Applications of XPS in the characterization of Battery materials

In this study, technological development requires reliable power sources where energy storage devices are emerging as a critical component. Wide range of energy storage devices, Redox-flow batteries (RFB), Lithium ion based batteries (LIB), and Lithium-sulfur (LSB) batteries are being developed for various applications ranging from grid-scale level storage to mobile electronics. Material complexities associated with these energy storage devices with unique electrochemistry are formidable challenge which needs to be address for transformative progress in this field. X-ray photoelectron spectroscopy (XPS) - a powerful surface analysis tool - has been widely used to study these energy storage materials because of its ability to identify, quantify and image the chemical distribution of redox active species. However, accessing the deeply buried solid-electrolyte interfaces (which dictates the performance of energy storage devices) has been a challenge in XPS usage. Herein we report our recent efforts to utilize the XPS to gain deep insight about these interfaces under realistic conditions with varying electrochemistry involving RFB, LIB and LSB.

25 ENERGY STORAGE↗
DOE OSTI2023

Component-Level Analysis of Heating and Cooling Loads in the U.S. Residential Building Stock

The residential building sector accounts for a substantial portion of total energy consumption in the United States and offers a significant opportunity for energy reduction and decarbonization through improvements in energy efficiency. Heating and air conditioning are the primary contributors to residential energy usage and electricity system peak demand. However, due to the diversity of the housing stock and the complexity of factors affecting heating and cooling demand, identifying the relative contributions to heating and cooling loads poses challenges. To address this, we applied the ResStock analysis tool to simulate 550,000 building energy models, providing statistical representation of residential buildings in the contiguous United States. We introduced outputs that quantified the heating and cooling influence of different components of a home, such as air leakage, envelope components (ceilings, walls, windows, foundations), internal heat gains from people, lighting, plug loads, and duct losses and gains. Leveraging the granularity of ResStock, we present a dataset to enable deeper understanding of the contributors to heating and cooling loads as a function of housing characteristics such as location, envelope efficiency, and building type. This work aims to support prioritization of research and development and informed decision-making for residential building decarbonization.

building simulation↗
DOE OSTI2023

Opportunities for green hydrogen production with land-based wind in the United States

Hydrogen (H 2 ) is an efficient energy carrier and storage mechanism that can supply both stationary and transport energy demand. Rapidly declining renewable energy generation costs; technology innovations in wind, solar, battery storage, and electrolysis; and a global push for more sustainable and secure energy have driven increased interest in green H 2 production. In this study, we develop an H 2 scenario analysis tool to assist in rapid, high-resolution insights into future, green H 2 pathways to achieve policy goals and market competitiveness. Using this tool, we estimate H 2 production and costs for U.S., off-grid scenarios given varying policy and cost scenarios from 2025–2035. Results indicate that achieving economically competitive green H 2 production (below $\$$2/kg) is possible in 2030 with no policy incentives (one site achieves this target), while increasing policy support to include wind and green H 2 production tax credits enables widespread economic viability sooner, with sub-$\$$2/kg LCOH targets achieved by 2025 and 51.7% of sites achieving this target by 2035. Maximizing policy support through prevailing wage and apprenticeship credit multipliers enable widespread economic viability, including sub-$\$$2/kg of green H 2 by 2025 and even negative pricing by 2035. Regions with lowest LCOH values correspond to high wind resource areas and capacity factors. Achieving decarbonization goals with green H 2 depends on technology cost reductions and policy support, with a maximum average LCOH reduction of $\$$23.10 between no and maximum policy support scenarios, and a maximum average LCOH reduction of $\$$25.86 between current, conservative technology costs and 2035 projected technology cost assumptions.

08 HYDROGEN↗
DOE OSTI2019

Context dependent fungal and bacterial soil community shifts in response to recent wildfires in the Southern Appalachian Mountains

Decades of fire suppression coupled with changing climatic conditions have increased the frequency and intensity of wildfires. Here, the Southern Appalachia region of the United States is predicted to be particularly susceptible to climatic changes, with predicted increases in fire severity and occurrence. Following the record breaking fire season in 2016 in Southern Appalachia, we examined wildfire impacts on soil chemistry and below ground communities (fungi and bacteria – Illumina MiSeq) within two substrates (duff and soil) at two adjacent locations with similar plant communities (Great Smoky Mountains National Park – ‘Chimney Top 2’ Fire (GRSM) and Nantahala National Forest – ‘Cliffside’ Fire (NNF)) from replicate plots representing a range of fire severities (unburned, low severity, moderate severity, severe). Differing fire severities changed community composition, and fire severity played a stronger role in structuring bacterial communities than in structuring fungal communities. Further, fire impacts on soil communities and functional guilds responses were location- and substrate-specific with NNF responding more strongly to fire than GRSM. Additionally, using a novel analysis tool (Axis Weighted Ordination Distance – AWOrD), domain and location specific responses to wildfire severity are demonstrated. Taken together, our results suggest context-dependency in microbial responses to fire that must be accounted for to generate ecosystem-wide recovery predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Validation of Phasor-Domain Transmission and Distribution Co-simulation Against Electromagnetic Transient Simulation

The rapid deployment of renewable energy resources has led to the widespread use of power electronics in modern power systems. As these systems transition from being dominated by large synchronous machines to increasingly incorporating inverter-based resources (IBRs), traditional methods are becoming inadequate. Addressing this challenge, this paper introduces a scalable phasor-domain T\&D co-simulation framework based on open-source software. It focuses on the framework's validation against the PSCAD Electromagnetic Transient (EMT) analysis tool. The validation results demonstrate the framework's high-fidelity and a computational time speed-up of 60 to 100 times, marking a pioneering validation effort in T\&D co-simulation research.

Inverter-based resources, co-simulation, Electroma↗
DOE OSTI2021

EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models

In this work, we present EPIsembleVis, a web-based comparative visual analysis tool for evaluating the consistency of multiple COVID-19 prediction models. Our approach analyzes a collection of COVID-19 predictions from different epidemiological models as an ensemble and utilizes two metrics to quantify model performance. These metrics include (a) prediction uncertainty (represented as the dispersion of predictions in each ensemble) and (b) prediction error (calculated by comparing individual model predictions with the recorded data). Through an interactive visual interface, our approach provides a data-driven workflow for (a) selecting and constructing the COVID-19 model prediction ensemble based on the spatiotemporal overlap of available predictions of multiple epidemiological models, (b) quantifying the model performance using both the uncertainty of each model prediction ensemble, and the error of each ensemble member that represents individual model predictions, and (c) visualizing the spatiotemporal variability in the projection performance of individual models using a suite of novel ensemble visualization techniques, such as the data availability map, a spatiotemporal textured-tile calendar, multivariate rose chart, and time-series leaflet glyph. We demonstrate the capability of our ensemble visual interface through a case study that investigates the performance of weekly COVID-19 predictions, which are provided through the COVID-19 Forecast Hub UMass-Amherst Influenza Forecasting Center of Excellence [47] for the United States and United States Territories. The EPIsembleVis tool is implemented using open-source web technologies and adaptive system design, rendering it interoperable with Elasticsearch and Kibana for automatically ingesting COVID-19 predictions from online repositories, and it is generalizable for analyzing worldwide projections from more epidemiological models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Solid-liquid phase equilibria of Fe-Cr-Al alloys and spinels

Ferritic FeCrAl alloys are candidate accident tolerant cladding materials. There is a paucity of data concerning the melting behavior for FeCrAl and its oxides. Analysis tools have therefore had to utilize assumptions for simulations using FeCrAl cladding. The focus of this study is to examine in some detail the solid-liquid phase equilibria of FeCrAl alloys and spinels with the aim of improving the accuracy of severe accident scenario computational studies.

36 MATERIALS SCIENCE↗