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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 163 records · Page 9
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↗
NASA NTRS1972

Robot, computer problem solving system

The development of a computer problem solving system is reported that considers physical problems faced by an artificial robot moving around in a complex environment. Fundamental interaction constraints with a real environment are simulated for the robot by visual scan and creation of an internal environmental model. The programming system used in constructing the problem solving system for the simulated robot and its simulated world environment is outlined together with the task that the system is capable of performing. A very general framework for understanding the relationship between an observed behavior and an adequate description of that behavior is included.

Becker, J. D.↗
NASA NTRS1977

NASA flight management research

The NASA Flight Management Research Program is committed to better understanding the aircrew role and human-system interface requirements within the air transportation system. Tradition pilot roles have been supplemented by the flight management concept where crew members become decision makers and monitors of aircraft status. The aircrew is viewed as skilled human operators in a complex environment. In a systems context, work is under way to better understand the theoretical basis for human performance. Applied efforts are aimed at investigating current and future problems which crews face as flight managers. Research areas include control-display development and evaluation; procedures for effective crew-system interface; and alternative, nonvisual communication techniques. These areas are investigated in laboratory, part-task simulation and full mission simulation studies.

Laveson, J. I.↗
NASA NTRS1982

A study of interactive control scheduling and economic assessment for robotic systems

A class of interactive control systems is derived by generalizing interactive manipulator control systems. Tasks of interactive control systems can be represented as a network of a finite set of actions which have specific operational characteristics and specific resource requirements, and which are of limited duration. This has enabled the decomposition of the overall control algorithm simultaneously and asynchronously. The performance benefits of sensor referenced and computer-aided control of manipulators in a complex environment is evaluated. The first phase of the CURV arm control system software development and the basic features of the control algorithms and their software implementation are presented. An optimal solution for a production scheduling problem that will be easy to implement in practical situations is investigated.

Source record↗
NASA NTRS1984

Study of stator-vane fluctuating pressures in a turbofan engine for static and flight tests

As part of a program to study the fan noise generated from turbofan engines, fluctuating surface pressures induced by fan-rotor wakes were measured on core- and bypass-stator outlet guide vanes of a modified JT15D-1 engine. Tests were conducted with the engine operating on an outdoor test stand and in flight. The amplitudes of pressures measured at fan-rotor blade-passage fundamental frequencies were generally higher and appeared less stable for the static tests than for the flight tests. Fluctuating pressures measured at the blade-passage frequency of the high-speed core compressor were interpreted to be acoustic; however, disturbance trace velocities for either the convected rotor wakes or acoustic pressures were difficult to interpret because of the complex environment.

Mueller, A. W.↗
NASA NTRS1984

A route generator concept for aircraft onboard fault monitoring

Because of the increasingly complex environments in which the flight crews of commercial aviation aircraft must operate, a research effort is currently underway at NASA Langley Research Center to investigate the potential benefits of intelligent cockpit aids, and to establish guidelines for the application of artificial intelligence techniques to advanced flight management concepts. The segment of this research area that concentrates on automated fault monitoring and diagnosis requires that a reference frame exist, against which the current state of the aircraft may be compared to determine the existence of a fault. This paper describes a computer program which generates the position of that reference frame that specifies the horizontal flight route.

Palmer, M. T.↗
NASA NTRS1987

The effects of display-control I/O, compatibility, and integrality on dual-task performance and subjective workload

The utility of speech technology was evaluated in terms of three dual task principles: resource competition between the time shared tasks, stimulus central processing response compatibility, and task integrality. Empirical support for these principles was reviewed. Two studies investigating the interactive effects of the three principles were described. Objective performance and subjective workload ratings for both single and dual tasks were examined. It was found that the single task measures were not necessarily good predictors for the dual task measures. It was shown that all three principles played an important role in determining an optimal task configuration. This was reflected in both the performance measures and the subjective measures. Therefore, consideration of all three principles is required to insure proper use of speech technology in a complex environment.

Tsang, Pamela S.↗
NASA NTRS1988

POPEYE: A production rule-based model of multitask supervisory control (POPCORN)

Recent studies of relationships between subjective ratings of mental workload, performance, and human operator and task characteristics have indicated that these relationships are quite complex. In order to study the various relationships and place subjective mental workload within a theoretical framework, we developed a production system model for the performance component of the complex supervisory task called POPCORN. The production system model is represented by a hierarchial structure of goals and subgoals, and the information flow is controlled by a set of condition-action rules. The implementation of this production system, called POPEYE, generates computer simulated data under different task difficulty conditions which are comparable to those of human operators performing the task. This model is the performance aspect of an overall dynamic psychological model which we are developing to examine and quantify relationships between performance and psychological aspects in a complex environment.

Townsend, James T.↗
NASA NTRS1986

Manual for obscuration code with space station applications

The Obscuration Code, referred to as SHADOW, is a user-oriented computer code to determine the case shadow of an antenna in a complex environment onto the far zone sphere. The surrounding structure can be composed of multiple composite cone frustums and multiply sided flat plates. These structural pieces are ideal for modeling space station configurations. The means of describing the geometry input is compatible with the NEC-BASIC Scattering Code. In addition, an interactive mode of operation has been provided for DEC VAX computers. The first part of this document is a user's manual designed to give a description of the method used to obtain the shadow map, to provide an overall view of the operation of the computer code, to instruct a user in how to model structures, and to give examples of inputs and outputs. The second part is a code manual that details how to set up the interactive and non-interactive modes of the code and provides a listing and brief description of each of the subroutines.

Marhefka, R. J.↗