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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Platform for Single-Cell Dual RNA Sequencing of Host-Pathogen Interactions

The aim of this project was to advance single-cell RNA-Seq methods toward the establishment of a platform that may be used to simultaneously interrogate the gene expression profiles of mammalian host cells and bacterial pathogens. Existing genetic sequencing methods that measure bulk groups of cells do not account for the heterogeneity of cell-microbe interactions that occur within a complex environment, have limited efficiency, and cannot simultaneously interrogate bacterial sequences. In order to overcome these challenges, separate biochemistry workflows were developed based on a No-So-Random hexamer priming strategy or libraries of targeted molecular probes. Computational tools were developed to facilitate these methods, and feasibility was demonstrated for single-cell RNA-Seq for both bacterial and mammalian transcriptomes. This work supports cross-agency national priorities on addressing the threat of biological pathogens and understanding the role of the microbiome in modulating immunity and susceptibility to infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Formal Experimentation and Analysis of Handheld RFID Readers as a Tool for Nuclear Material Accounting

Commercial, off-the-shelf Radio Frequency Identification (RFID) systems have been successfully deployed for inventory tracking in numerous industries, but their viability in the tracking of complex environments containing nuclear material is less understood. Of primary interest in this setting is a RFID tracking system for nuclear material accounting which can reliably identify as many tags as possible with high accuracy, while also reducing an operator’s exposure to radiation. In this work, we develop a formal statistical approach to identify relevant handheld RFID reader settings which optimize tagging performance. To achieve this goal, we design a full factorial split-plot experiment for a static shelf configuration scene with 50 randomly placed nuclear material containers affixed with RFID tags. We use Bayesian inference to fit a second-order response surface model which expresses the probability of a successful match for each container as a function of the experimental factors. Such effects are allowed to vary across individual containers and the containers’ population in its entirety to estimate overall effects. Uncertainties of estimates and predictions are quantified via their corresponding posterior distributions. Following extensive model checking and validation, the fitted model is used to identify experimental factors which maximize matching probabilities at both the container-level and for the full shelf configuration scene. We also analyze sensitivity of performance to relevant factors.

25 ENERGY STORAGE↗
DOE OSTI2024

Evaluating large scale aqueous organic redox flow battery performance with a hybrid numerical and machine learning framework

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low-capacity degradation in 10 cm$^2$ cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier to commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm$^2$ DHP-based AORFB by combining a physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. Such combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗
DOE OSTI2025

Applying deep learning methods to develop new models of molecular charge transfer, nonadiabatic dynamics, and nonlinear spectroscopy in the condensed phase

Photon- and field-induced charge transfer has central importance in the generation and storage of electricity, the novel properties of materials, photo-induced catalysis, and electro-optic activity (e.g., photovoltaic cells, fuel cells, and organic chromophores for use in optical fibers and light-emission diodes). These non-equilibrium electronic and chemical transformations are probed by ultrafast, nonlinear spectroscopies. Accurate simulations play a crucial role in our ability to understand, optimize, and control these transformations. This project applies modern deep learning and machine learning (ML) methods to dramatically improve models of electronic dynamics, electronic-nuclear dynamics, and spectroscopic measurements for improved simulations of chemistry in complex environments, far from equilibrium phenomena, and processes in extreme environments, such as materials exposed to strong or resonant fields. This project develops accurate neural net models that go beyond predictive capability to also provide new insight into the fundamental physics underlying electron and nuclear dynamics. To achieve its objectives, this project explores and develops customized versions of high-capacity deep learning algorithms/models. These techniques are developed with an emphasis on fundamental chemical insight, not just predictive accuracy, to assist the development of the next generation of quantum simulation methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The Dual-Axis Radiographic Hydrodynamic Test Facility Capability eXpansion (DCX) Strategy

This document details the strategy proposed to meet the National Nuclear Security Administration (NNSA) requirement for the Dual-Axis Radiographic Hydrodynamic Test (DARHT) Facility Sustainment and Modernization projects identified in multiple NNSA Stockpile Stewardship and Management Plan (SSMP) reports to Congress [NNSA, 2020; 2021; 2022; 2023; 2024]. DARHT has become indispensable for the certification of the primaries of U.S. nuclear weapons since the current U.S. moratorium halted underground nuclear testing more than 30 years ago. However, aging facilities, evolving technology, and other issues are limiting or threatening the capability of DARHT to meet NNSA’s expanding mission needs now and into the future. This DARHT Capability eXpansion (DCX) strategy was developed to address NNSA needs by extending DARHT’s reliability and resilience, increasing the quality and quantity of DARHT data, and enabling hydrodynamic measurements in complex environments. The strategy can be implemented through integration of line-item projects, acquisition of major items of equipment, and other actions coordinated with the execution of the SSMP. This strategy document is intended for U.S. Government officials, particularly authorizers, appropriators, and program leaders. It may also be furnished to DARHT users, customers, collaborators, stakeholders, and visitors as needed

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

CRiSPPy: An advanced hydropower scheduling tool for the Colorado River Storage Project

The Western Area Power Administration (WAPA) plays a vital role in delivering reliable and cost-effective hydroelectric power to millions of customers across the western United States. The Colorado River Storage Project (CRSP) carries out WAPA’s mission in Arizona, Utah, Colorado, New Mexico, Nevada, Wyoming and Texas. Achieving this mission requires effective management of the Colorado River system, and depends on the use of advanced analytical tools and modeling methodologies. For many years, CRSP has relied on the Generation and Transmission Maximization Superlite (GTMax SL) model for its mid-term and long-term hydroscheduling needs. However, the evolving energy market, power system operations, environmental rules, and hydrology conditions, coupled with advancements in computational capabilities, have necessitated the development of a more modern and robust solution. This report introduces the Colorado River Storage Project Python-based (CRiSPPy) model, a new, advanced hydropower scheduling tool developed to address CRSP ever-evolving challenges. CRiSPPy represents a significant leap forward in our ability to model and optimize the operation of the Colorado River system. It incorporates state-of-the-art optimization algorithms, enhanced data management capabilities, and an advanced graphical user interface, providing WAPA CRSP personnel with unprecedented insights and decision-making support. This document details the development, capabilities, and implementation of CRiSPPy. It is intended to serve as a comprehensive resource for WAPA staff, stakeholders, and anyone interested in the future of hydropower scheduling in the Colorado River Basin. We are confident that CRiSPPy will enhance WAPA's mission while adapting to the challenges of a dynamic and increasingly complex environment. The version of CRiSPPy described in this report is the version 2.3. New versions of CRiSPPy will be developed as the tool keeps evolving to address CRSP challenges.

13 HYDRO ENERGY↗
DOE OSTI2025

Abstract for CRADA between NETL and GlycoSurf, Inc.

The National Energy Technology Laboratory (NETL) and GlycoSurf, Inc. (Participant) will collaborate in the development of novel luminescent sensing materials for rare earth elements using chemically modified surfactants. Rare earth elements are economically critical metals that are used in many technologies relevant to both energy and national defense. Slow and expensive characterization methods for rare earth element analysis are a major pain point for domestic production of these metals; the development of low-cost optical sensing materials and platforms can significantly reduce the time and financial costs associated with rare earth element prospecting and process monitoring. NETL has extensive experience developing inexpensive and compact optical sensors for critical metals such as rare earths. GlycoSurf, Inc. has commercialized high performance surfactants for the selective extraction of rare earth elements in complex environments such as acid mine drainage. By modifying these surfactants with fluorescent functional groups, trace concentrations of rare earths may be detected through a process called “photosensitization,” where the sensing material induces element-specific emission bands that enable different rare earth elements to be detected and distinguished. This project will enable the development of sensing materials capable of selectively detecting trace quantities of valuable rare earths in challenging conditions, including high ionic strength, highly acidic matrices.

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

Acoustic Particle Velocity Measurements around a Tidal Current Turbine

Quantifying the underwater sound produced by tidal turbines is essential both to understand their potential environmental impacts and to understand how that sound might interfere with the intended application of the turbine (e.g., powering acoustic monitoring systems). In this project, we measured the sound radiated by a small-scale crossflow tidal turbine. The turbine was deployed from October 2023 to March 2024 in the tidal channel at the entrance to Sequim Bay, WA. Acoustic measurements were made with three different sensor packages: a commercial-off-the-shelf vector sensor (operated by PNNL), a vector sensor array (operated by Integral Consulting), and drifting hydrophones (operated by UW). Acoustic recordings from the three sensors highlight changes in the turbine acoustic signature over the course of a tidal cycle and throughout the 6-month turbine deployment. Our results also highlight the utility of acoustic vector sensors for localizing sound attributable to marine energy devices in acoustically complex environments.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Continual Learning for Production-Level Machine Learning in Particle Accelerators

Particle accelerators operate in complex environments where data distribution can change dynamically, leading to data drifts that significantly challenge Machine Learning (ML) models. These non-stationary conditions often cause ML models to deteriorate in performance, making it difficult to maintain reliable predictions in operation. The primary sources of data drifts are changes in accelerator settings and changes in equipment performance which cannot be measured directly. To bridge this gap between ML development and long-term deployment in operational settings, we identify key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. We will provide a practical guide on selecting the appropriate method given resource constraints and desired stability plasticity trade offs. As a concrete example, we will present a real-world use case for anomaly detection to predict errant beams at the Spallation Neutron Source accelerator, where continual learning has been employed to demonstrate stable performance on drifting data streams. We will present practical challenges, lessons learned, and the results from the deployed ML model.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2025

Computational Fluid Dynamics-Based Modeling of Methane Flows Around Oil and Gas Equipment

Recent studies estimate that emissions from oil and gas production facilities contribute between 20 and 50% of the total methane ( CH 4 ) emitted in the US; therefore, quantifying and reducing these emissions are crucial for achieving climate goals. Methane quantification depends on both measuring methane concentrations and converting them to emissions through a modeling framework. Currently, simple atmospheric dispersion models are primarily used to quantify emissions and concentrations, but these estimates are highly uncertain when quantifying emissions from complex aerodynamic sources, such as oil and gas facilities. This investigation used a CFD modeling approach, which can account for aerodynamic complexity but has hitherto not been used to model methane concentrations downwind of a methane release of a known rate, and compared it against in situ measurements. High-time-resolution (1 Hz) methane concentration and meteorological data were measured during experiments conducted at the METEC on 21 March and 11 July 2024. The METEC site configuration, measured wind data, and controlled emission rates were used as input for the CONVERGE CFD model to model downwind CH 4 concentration. The modeling was carried out between 20 and 70 m, from two different points of release in two separate controlled-release experiments, one from a separator and another from a wellhead. In these experiments, we found that the CFD model could predict the CH 4 concentrations downwind of the release to a good degree. The model was evaluated on multiple metrics to assess its performance in estimating methane concentrations at typical fence line distances (∼30 m). These results help us to understand external flows and the ability of CFD models to predict downwind concentrations in aerodynamically complex environments.

03 NATURAL GAS↗
DOE OSTI2024

Exploring the Photophysics of N-Type Polymer N2200

The polymer interphase is a complex environment comprised of polymer chains, solvent molecules, and electrolyte ions. However, the microenvironments that these ingredients produce is both poorly-understood and difficult to investigate given the system's inherent structural heterogeneity. Nonetheless, a fundamental understanding of the structure, electrochemical behavior, and excited-state dynamics is a key prerequisite to optimizing devices based on such a system - for instance, photoelectrochemical cells for the direct, light-driven production of solar hydrogen, which may minimize efficiency losses compared to sequential light harvesting and electrochemical hydrogen production in separate but linked devices. This work combines transient absorption (TA) spectroscopy, which is a powerful tool for the observation of a variety of excited states on a femto- to microsecond timescale, with time-resolved microwave conductivity (TRMC) spectroscopy, which selectively probes the dynamics of free charge carriers (FC), of a blend of a donor polymer (PTB7-Th) and acceptor polymer (N2200) in a bulk heterojunction. Initial results indicate an extension of FC lifetime and homogenization of the microenvironment on a whole-film level, not just at the surface of the polymer; however, many questions are still under active investigation, including the changes in energy of the involved polaron states, effective conjugation length, FC mobility and migration distance, etc. Additional experiments regarding the photoinitiated behavior of the blend in different environments and on different timescales are ongoing.

charge recombination↗
DOE OSTI2023

Impact of trivalent f-element complexation and chemical environment on the radiation-induced chemical reactivity of TODGA and HOPO ligands

Understanding the impact of ionizing radiation on ligands employed and proposed for the separation, recovery, and transport of actinide ions is of vital importance for the innovation of nuclear fuel cycle technologies. In the presence of ionizing radiation fields, complexing ligands are subject to radiolytic processes, that lead to their destruction and the formation of potentially detrimental degradation products. Consequently, mastery of the radiation-induced behavior of these ligands is essential for predicting and optimizing their effectiveness, longevity, and minimizing the negative impacts of radiolysis. Furthermore, this knowledge is critical for the design of more radiation resistant molecules to be incorporated into advanced separation technologies. That said, the radiation chemistry of most actinide complexants have only been studied in the absence of their targeted metal ion complexes, which can lead to inaccurate conclusions on their radiolytic robustness and impacts on performance. With this in mind, we present our findings on the impact of f-element complexation and chemical environment on the radiation-induced chemical reactivity of N,N,N',N'-tetraoctyl diglycolamide (TODGA) and the model octadentate hydroxypyridinone ligand 3,4,3-LI(1,2-HOPO) (abbreviated as HOPO), both promising candidates for actinide separations

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Deep Convection Initiation, Growth, and Environments in the Complex Terrain of Central Argentina during CACTI

This study characterizes the wide range of deep convective cloud life cycles and their relationships with ambient environments observed during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign near the Sierras de Córdoba (SDC) range in central Argentina. We develop a novel convective cell tracking database for the entire field campaign using C-band polarimetric radar observations. The cell tracking database includes timing, location, area, depth, merge/split information, microphysical properties, collocated satellite-retrieved cloud properties, and sounding-derived environmental conditions. Results show that the SDC exerts a strong control on convection initiation (CI) and growth. CI preferentially occurs east of the SDC ridge during the afternoon, and cells often undergo upscale growth through the evening as they travel eastward toward the plains. Larger and more intense cells tend to occur in more unstable and humid low-level environments, and surface-based cells are stronger than elevated cells. Midtropospheric relative humidity and vertical wind shear also jointly affect the size and depth of the cells. Rapid cell area growth rates exhibit dependence on both large environmental wind shear and low-level moisture. Evolution of convective cell macro- and microphysical properties are strongly influenced by convective available potential energy and low-level humidity, as well as the presence of other cells in their vicinity. This cell tracking database demonstrates a framework that ties measurements from various platforms centering around convective life cycles to facilitate process understanding of factors that control convective evolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Antioxidant Activity in Supramolecular Carotenoid Complexes Favored by Nonpolar Environment and Disfavored by Hydrogen Bonding

Carotenoids are well-known antioxidants. They have the ability to quench singlet oxygen and scavenge toxic free radicals preventing or reducing damage to living cells. We have found that carotenoids exhibit scavenging ability towards free radicals that increases nearly exponentially with increasing the carotenoid oxidation potential. With the oxidation potential being an important parameter in predicting antioxidant activity, we focus here on the different factors affecting it. This paper examines how the chain length and donor/acceptor substituents of carotenoids affect their oxidation potentials but, most importantly, presents the recent progress on the effect of polarity of the environment and orientation of the carotenoids on the oxidation potential in supramolecular complexes. The oxidation potential of a carotenoid in a nonpolar environment was found to be higher than in a polar environment. Moreover, in order to increase the photostability of the carotenoids in supramolecular complexes, a nonpolar environment is desired and the formation of hydrogen bonds should be avoided.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Computational fluid dynamics analysis of char conversion in Sandia’s pressurized entrained flow reactor

Design and analysis of practical reactors utilizing solid feedstocks rely on reaction rate parameters that are typically generated in lab-scale reactors. Evaluation of the reaction rate information often relies on assumptions of uniform temperature, velocity, and species distributions in the reactor, in lieu of detailed measurements that provide local information. This assumption might be a source of substantial error, since reactor designs can impose significant inhomogeneities, leading to data misinterpretation. Spatially resolved reactor simulations help understand the key processes within the reactor and support the identification of severe variations of temperature, velocity, and species distributions. In this work, Sandia’s pressurized entrained flow reactor is modeled to identify inhomogeneities in the reaction zone. Tracer particles are tracked through the reactor to estimate the residence times and burnout ratio of introduced coal char particles in gasifying environments. The results reveal a complex mixing environment for the cool gas and particles entering the reactor along the centerline and the main high-speed hot gas reactor flow. Furthermore, the computational fluid dynamics (CFD) results show that flow asymmetries are introduced through the use of a horizontal gas pre-heating section that connects to the vertical reactor tube. Computed particle temperatures and residence times in the reactor differ substantially from the idealized plug flow conditions typically evoked in interpreting experimental measurements. Furthermore, experimental measurements and CFD analysis of heat flow through porous refractory insulation suggest that for the investigated conditions (1350 °C, <20 atm), the thermal conductivity of the insulation does not increase substantially with increasing pressure.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Physically rigorous reduced-order flow models of fractured subsurface environments without explosive computational cost

Fractured media models comprise discontinuities of multiple lengths (e.g. fracture lengths and apertures, wellbore area) that fall into the relatively insignificant length scales spanning millimeter-scale fractures to centimeter-scale wellbores in comparison to the extensions of the field of interest, and challenge the conventional discretization methods imposing highly-fine meshing and formidably large numerical cost. By utilizing the recent developments in the finite element analysis of electromagnetics that allow to represent material properties on a hierarchical geometry, this project develops computational capabilities to model fluid flow, heat conduction, transport and induced polarization in large-scale geologic environments that possess geometrically-complex fractures and man-made infrastructures without explosive computational cost. The computational efficiency and robustness of this multi-physics modeling tool are demonstrated by considering various highly-realistic complex geologic environments that are common in many energy and national security related engineering problems.

42 ENGINEERING↗