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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

All Optical Neural Networks for Low Power Edge Computing

We developed a simplistic physics-based model of an all-optical neural network that mimics the encoder part of an autoencoder neural network for image compression. Our approach relies on the generation of a MATLAB-based model for both data compression and decompression and utilizes MATLAB's built-in autoencoder networks in combination with simple propagation of optical fields between layers constituting phase elements via Fourier transform. We optimize the phase elements using the particle swarm optimization technique and using our model, we demonstrate a compression ratio of 25% for 2828-pixel input images containing numeric digits from 0 to 9.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

All-Digital Plug and Play Passive RFID Sensors for Energy Efficient Building Control

This is the final report for the project “All-Digital Plug and Play Passive RFID Sensors for Energy Efficient Building Control”, funded by DOE, and performed by Clemson University, Phase IV Engineering and Harvard University from October 1, 2016 to December 31, 2020. The main objective of this project is to develop, demonstrate and pre-commercialize a novel, plug & play, battery-free, wireless sensor technology to enable low-cost (<$10 per node) indoor and outdoor temperature and humidity measurement for energy efficient building controls and operations. The proposed technology is based on the novel concept of all-digital sensing and its seamless integration with the passive RFID technology. This project focuses on the design, fabrication, material optimization, interrogation electronics, validation, and demonstration of the novel sensor nodes for building applications. The specific objectives of this research program include: (1) Design, fabricate and optimize a compact, robust, and high-resolution digitizer, which could convert rotation angle into digital numbers. (2) Develop the multi-physics-based modeling and simulation of the temperature/humidity transducer to derive a rational design of the architecture, dimension, structure, materials (i.e., mechanical, electrical, thermal and hygroscopic) properties and functions of the sensor node. (3) Design, fabricate and optimize the bi-material based humidity sensitive coil which could linearly transduce the environmental relative humidity variations to rotation angles. (4) Design, fabricate and optimize an UHF RFID platform which could support long-range passive wireless communications of 8-bit digital numbers. (5) Design, fabricate and optimize the miniaturized all-digital sensor using MEMS technology. (6) Validate the integrated all-digital sensing system in a real building environment to test the system’s performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Evaluation of a CFD Commercial Package to the Modeling of Acoustic Wave Propagation in Bubbly-Liquid Column

This document reports on the considerations and development of an initial computational model based on the first principles for predicting acoustic propagation in a wellbore based on a logging-while-drilling (LWD) tool. This tool could be used to create a large database of simulated signals mimicking gas-influx-while-drilling events (so-called “kicks”). While other logging techniques, such as electrical resistivity and bulk density, are available in LWD tools, the acoustic logging method was identified in a complementary experimental endeavor as the most appropriate for early kick detection due to its high sensitivity to the presence of gas in liquids.

03 NATURAL GAS↗
DOE OSTI2024

Developing and Evaluating a Smart Curtailment Strategy Integrated with a Wind Turbine Manufacturer Platform

The Renewable Energy Wildlife Institute lead a team of scientists, wind developers, and turbine manufacturers in a study to develop and test a “smart curtailment” system intended to help reduce bat collisions with wind turbines. The Vestas Bat Protection System (VBPS) is a newly developed software module within the Supervisory Control and Data Acquisition (SCADA) system of Vestas turbines. The VBPS combines data from commercially available environmental sensors and the turbine’s built-in sensors with the Vestas SCADA system. VBPS is designed to receive environmental data from sensors on the turbine such as temperature, wind speed, wind direction, time of day, and time of year, relays that information to the SCADA system to determine whether to execute turbine curtailments at any given time. The goals of this study were to 1) develop a bat fatality risk model based on bat activity data and environmental data collected in year 1, and to 2) evaluate the VBPS, using the bat fatality risk model to implement curtailment, in comparison to “blanket curtailment” (turbines curtailed when wind speed is below 5.0 meters per second (m/s)) and “control” (normally operating, feathered below 3.0 m/s) turbines in year 2. The field study took place at a wind energy facility in Iowa during the fall bat migration seasons (July – October) in 2021 and 2022. For VBPS to succeed as a viable strategy for the minimization of bat fatalities, it should meet or exceed the performance of blanket curtailment. Specifically, the VBPS should meet the following performance targets to demonstrate whether it an effective, practical risk reduction measure: (1) Turbines operating VBPS should have equal or fewer bat fatalities compared to turbines operating with blanket curtailment, and significantly fewer bat fatalities compared to control turbines; and (2) Turbines operating VBPS should have greater power production compared to turbines operating with blanket curtailment. The study was completed in accordance with the Statement of Project Objectives and within the terms of the Budget Justification. This Final Report describes the progress, challenges, and outcomes of the study.

17 WIND ENERGY↗
DOE OSTI2025

Closure and transport theory for plasmas with multiple ion species

This report summarizes a DOE-funded research project on closure and transport theory for plasmas with multiple ion species. The work developed accurate moment-based closure models across the full range of collisionality, incorporating both kinetic and collisional effects. Key contributions include high-fidelity closure relations, multi-temperature models, and transport coefficients applicable to fusion, space, and astrophysical plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus

Cell-based mathematical models have previously been developed to simulate the immune system in response to pathogens. Mathematical modeling papers which study the human immune response to pathogens have predicted concentrations of a variety of cells, including activated and resting macrophages, plasma cells, and antibodies. This study aims to create a comprehensive mathematical model that can predict cytokine levels in response to a gram-positive bacterium, S. aureus by coupling previous models. To accomplish this, the cytokines Tumor Necrosis Factor Alpha (TNF-α), Interleukin 6 (IL-6), Interleukin 8 (IL-8), and Interleukin 10 (IL-10) are included to quantify the relationship between cytokine release from macrophages and the concentration of the pathogen, S. aureus, ex vivo. Partial differential equations (PDEs) are used to model cellular response and ordinary differential equations (ODEs) are used to model cytokine response, and interactions between both components produce a more robust and more complete systems-level understanding of immune activation. In the coupled cellular and cytokine model outlined in this paper, a low concentration of S. aureus is used to stimulate the measured cellular response and cytokine expression. Results show that our cellular activation and cytokine expression model characterizing septic conditions can predict ex vivo mechanisms in response to gram-negative and gram-positive bacteria. Our simulations provide new insights into how the human immune system responds to infections from different pathogens. Novel applications of these insights help in the development of more powerful tools and protocols in infection biology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media

Reservoir simulation models are the major tools for studying fluid flow behavior in hydrocarbon reservoirs. These models are constructed based on geological models, which are developed by integrating data from geology, geophysics, and petro-physics. As the complexity of a reservoir simulation model increases, so does the computation time. Therefore, to perform any comprehensive study which involves thousands of simulation runs, a very long period of time is required. Several efforts have been made to develop proxy models that can be used as a substitute for complex reservoir simulation models. These proxy models aim at generating the outputs of the numerical fluid flow models in a very short period of time. This research is focused on developing a proxy fluid flow model using artificial intelligence and machine learning techniques. In this work, the proxy model is developed for a real case CO2 sequestration project in which the objective is to evaluate the dynamic reservoir parameters (pressure, saturation, and CO2 mole fraction) under various CO2 injection scenarios. The data-driven model that is developed is able to generate pressure, saturation, and CO2 mole fraction throughout the reservoir with significantly less computational effort and considerably shorter period of time compared to the numerical reservoir simulation model.

Amini, Shohreh↗
DOE OSTI2020

Modeling biological nitrogen fixation in global natural terrestrial ecosystems

Biological nitrogen fixation plays an important role in the global nitrogen cycle. However, the fixation rate has been usually measured or estimated at a particular observational site. To quantify the fixation amount at the global scale, process-based models are needed. This study develops a biological nitrogen fixation model to quantitatively estimate the nitrogen fixation rate by plants in a natural environment. The revised nitrogen module better simulates the nitrogen cycle in comparison with our previous model that has not considered the fixation effects. The new model estimates that tropical forests have the highest fixation rate among all ecosystem types, which decreases from the Equator to the polar region. The estimated nitrogen fixation in global terrestrial ecosystems is 61.5 Tg N yr-1 with a range of 19.8–107.9 Tg N yr-1 in the 1990s. Our estimates are relatively low compared to some early estimates using empirical approaches but comparable to more recent estimates that involve more detailed processes in their modeling. Furthermore, the contribution of nitrogen made by biological nitrogen fixation depends on ecosystem type and climatic conditions. This study highlights that there are relatively large effects of biological nitrogen fixation on ecosystem nitrogen cycling. and the large uncertainty of the estimation calls for more comprehensive understanding of biological nitrogen fixation. More direct observational data for different ecosystems are in need to improve future quantification of fixation and its impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Planning horizon affects prophylactic decision-making and epidemic dynamics

The spread of infectious diseases can be impacted by human behavior, and behavioral decisions often depend implicitly on a planning horizon?the time in the future over which options are weighed. We investigate the effects of planning horizons on epidemic dynamics. We developed an epidemiological agent-based model (along with an ODE analog) to explore the decision-making of self-interested individuals on adopting prophylactic behavior. The decision-making process incorporates prophylaxis efficacy and disease prevalence with the individuals? payoffs and planning horizon. Our results show that for short and long planning horizons individuals do not consider engaging in prophylactic behavior. In contrast, individuals adopt prophylactic behavior when considering intermediate planning horizons. Such adoption, however, is not always monotonically associated with the prevalence of the disease, depending on the perceived protection efficacy and the disease parameters. Adoption of prophylactic behavior reduces the epidemic peak size while prolonging the epidemic and potentially generates secondary waves of infection. Lastly, these effects can be made stronger by increasing the behavioral decision frequency or distorting an individual's perceived risk of infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Deterring Nuclear Proliferation: The Importance of IAEA Safeguards: A TEXTBOOK

Nuclear terrorism and nuclear proliferation are among the most pressing challenges to international peace and security that we face today. Iran and Syria remain in non-compliance with the safeguards requirements of the NPT, and the nuclear ambitions of North Korea remain unchecked. Despite these challenges, the NPT remains a cornerstone of the nuclear non-proliferation regime, and the safeguards implemented by the International Atomic Energy Agency (IAEA) under the NPT play a critical role in deterring nuclear proliferation.How do they work? Where did they come from? And what is their future? This book answers these questions. Anyone studying the field of nuclear non-proliferation will benefit from reading this book, and for anyone entering the field, the book will enable them to get a running start. Part I describes the foundations of the international safeguards system: its origins in the 1930s - when new discoveries in physics made it clear immediately that nuclear energy held both peril and promise - through the entry into force in 1970 of the NPT, which codified the role of IAEA safeguards as a means to verify states NPT commitments not to acquire nuclear weapons. Part II describes the NPT safeguards system, which is based on a model safeguards agreement developed specifically for the NPT, The Structure and Content of Agreements between the Agency and States required in connection with the Treaty on the Non-Proliferation of Nuclear Weapons, which has been published by the IAEA as INFCIRC/153. Part III describes events, especially in South Africa, the DPRK, and Iraq in the early 1990s, that triggered a transformation in the way in which safeguards were conceptualized and implemented.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2019

Evaluation of Caprock Integrity of the Upper Ordovician Units within the CarbonSAFE Prefeasibility Study Region, Subtask 4.3 – Geological Characterization

In this report, the Indiana Geological and Water Survey (IGWS) evaluates the seal capacity of the Maquoketa Shale Group, a thick and heterogeneous sequence of carbonates, silts, and clay-rich rock units of Upper Ordovician age. This unit has been identified as a regional caprock that can effectively isolate the CO 2 stored in underlying reservoirs for secure sequestration. To accomplish this objective, the Maquoketa Group was assessed using two approaches: (1) development of a lithofacies/qualitative permeability model based on the combined wireline responses, and (2) analysis to characterize the pore size distribution and capillary entry pressures of the various lithologies in the unit using the mercury injection capillary pressure method.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Analysis of Electro-static Assisted Air Dehumidification

The removal of water vapor from the air to reduce relative humidity is a well-known indoor environmental comfort requirement. Common dehumidification approaches require a substantial amount of energy and usually involve the cooling of atmospheric humid air below its dew point or the use of absorbent/adsorbent materials to extract water vapor out of the air. More recently, researchers investigated the effect of electrostatic forces for enhancing water vapor condensation. However, the studies are limited, and there is a lack of correlations that can predict the dehumidification rate. Also, the findings from the literature focused mainly on small flow rates on the order of one to two cfm. Electrically-enhanced condensation consists of the use of highly charged particles, preferably highly charged water droplets, that attract polar water vapor molecules to their surfaces and promote condensation, a phenomenon known as dielectrophoresis. An effect of the electric charge is the reduction of the vapor pressure on the droplets' surface with respect to the saturated pressure predicted by the Kelvin equation. Consequently, the equilibrium between evaporation and condensation is shifted towards condensation. Following the application of the modified Kelvin-Thomson theory, we developed a preliminary physics-based model to predict an effective size range of the charged droplets for optimal dehumidification. The range resulted in about 2 to 4 μm in diameter, under few simplifying assumptions. The effect of the size and the charge of the electrosprayed droplets on the overall dehumidification rate was briefly discussed. The use of electrosprays to produce small but highly charged droplets was broadly discussed in this paper. The cone-jet mode was identified as the most suitable electrospray operational mode, and it generated droplets of small size and high electrical charge. The cone-jet stability was also analyzed in detail. The preliminary data of the present work and the model results indicated that several electrospray heads were required to achieve a 5% dehumidification rate for airflow rates of about 5 cfm.

Morcelli, Stefano↗
DOE OSTI2015

Systems-based approach to understanding Technetium immobilization in the subsurface - 15326

The purpose of this work is to identify the chemical and biological reactions that occur and potentially influence the mobility of Technetium (Tc-99) in the subsurface. Tc-99 is a by-product of nuclear fission processes and has a relatively long half-life (t{sub 1/2}=212,000 years); additionally, it is highly soluble and mobile in groundwater and is most often remediated through pump and treat systems. The oxidized form of Tc-99 [Pertechnetate, Tc(VII)O{sub 4}-] is highly soluble and very mobile, while the reduced form of Tc-99 [Technetium-oxide, Tc(IV)O{sub 2}·nH{sub 2}O] is insoluble. Historically, complex interactions within the subsurface have made clean-up efforts for Tc-99 challenging. It is thought that environmental bacteria play a role in reducing contaminants to less soluble forms but it is difficult to predict or estimate the interactions and impacts of the complex, mixed microbial communities found in the environment. We have set up controlled batch experiments to examine a selection of organisms (Anaeromyxobacter dehalogenans strain CP-C, Geobacter sulfurreducens strain PAC, Geobacter daltonii strain FRC-32, and Shewanella oneidensis strain MR1) that have known metal reducing capabilities, for their ability to reduce Tc-99 to less soluble forms. An additional organism, Cellulomonas strain ES6 was chosen based on its origin of isolation from the Hanford site. Organisms were evaluated under anaerobic conditions both as isolates and in mixed consortia with and without the presence of iron-bearing mineral substrate. Experiments were monitored using spectroscopy, qPCR, volatile organic acid (VOA) analysis, and inductively coupled plasma-optical emission spectroscopy (ICP-OES) and mass spectrometer techniques (ICP-MS). Initial data collected has shown that the presence and type of substrate may increase the reduction of Tc-99 to less mobile forms. Data collected from these experiments will allow for (a) a better understanding of the mobility of Tc-99 in the subsurface, (b) better predictions of contaminant fate and transport over time, and (c) the development of long term remediation strategies for Tc-99. Additionally, information collected will be used to develop a systems-based metabolic model with predictive capabilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

OT Operational Anomaly Detection (OAD) T&D + DER

The growth of utility-scale renewable energy resources, distributed energy resources (DER), and transportation electrification has increased uncertainty and cybersecurity risks in power grids. The Purdue Enterprise Reference Architecture model which is widely adopted by the utility industry is now insufficient to protect the power grid against cyber-attacks. There is a need to identify what cybersecurity model is effective on Energy Management System (EMS), Advanced Distribution Management System (ADMS), and DER Management System (DERMS) to address the fundamental cybersecurity challenges in the age of increasing renewable energy and DER share as well as consumer participation in the electric energy industry. The next generation of cybersecurity model for OT network should be able to detect inside attackers, mitigate the cybersecurity risks arising from the new grid participants including DER aggregators, electric vehicle owners, and behind-the-meter consumers outside the utility company, and develop the strategy to trust consumer measurement data. This panel will discuss the challenges and pathways for the development of an ensemble cybersecurity model based on predictive state estimation to detect cybersecurity anomalies in OT network including EMS, ADMS, and DERMS.

cybersecurity↗
DOE OSTI2021

Analysis of New Data of Electro-static Assisted Air Dehumidification Processes

The removal of water vapor from the air to reduce relative humidity is a well-known indoor environmental comfort requirement. Common dehumidification approaches require a substantial amount of energy and usually involve the cooling of atmospheric humid air below its dew point or the use of absorbent/adsorbent materials to extract water vapor out of the air. More recently, researchers investigated the effect of electrostatic forces for enhancing water vapor condensation. However, the studies are limited, and there is a lack of correlations that can predict the dehumidification rate. Also, the findings from the literature focused mainly on small flow rates on the order of one to two cfm. Electrically-enhanced condensation consists of the use of highly charged particles, preferably highly charged water droplets, that attract polar water vapor molecules to their surfaces and promote condensation, a phenomenon known as dielectrophoresis. An effect of the electric charge is the reduction of the vapor pressure on the droplets' surface with respect to the saturated pressure predicted by the Kelvin equation. Consequently, the equilibrium between evaporation and condensation is shifted towards condensation. Following the application of the modified Kelvin-Thomson theory, we developed a preliminary physics-based model to predict an effective size range of the charged droplets for optimal dehumidification. The range resulted in about 2 to 4 m in diameter, under few simplifying assumptions. The effect of the size and the charge of the electrosprayed droplets on the overall dehumidification rate was briefly discussed. The use of electrosprays to produce small but highly charged droplets was broadly discussed in this paper. The cone-jet mode was identified as the most suitable electrospray operational mode, and it generated droplets of small size and high electrical charge. The cone-jet stability was also analyzed in detail. The preliminary data of the present work and the model results indicated that several electrospray heads were required to achieve a 5% dehumidification rate for airflow rates of about 5 cfm.

Morcelli, Stefano↗
DOE OSTI2024

Multi-phenomenology Yield Characterization

This report serves as the first delivery of a four-year applied science effort to transform and advance the error bounds for the yield estimate of an explosion. Each year’s delivery will be in this form, culminating in the submission of this work for peer review to a scientific journal. Importantly, the yearly progress reports can then also be viewed as expanding drafts working towards a formal journal article submission. For the first tranche of funding, we collaborated with Air Force Technical Applications Center (AFTAC) scientists to identify unclassified real-world data that demonstrate and validate our advanced error propagation methods. Collaboration includes visits to AFTAC and telecons. For this development, we illustrate the fusion of seismic, acoustic, optical, and surface effect signatures from an explosion. The mathematics and code being adapted to this specific application (Williams et al., 2021) involves physics models of multiple sensor signatures. We have also identified related physics models and have integrated them into code. Current methods of underground explosion yield estimation for the Threshold Test Ban Treaty (TTBT) have served the US treaty monitoring mission well for decades. A research objective of the Defense Nuclear Nonproliferation Research and Development (DNN R&D) office of the National Nuclear Security Administration (NNSA) has always been to provide new technical capabilities for monitoring lower thresholds. The general model and error propagation code to be developed in this project is based on significant advances in error modeling and propagation needed to analyze data at lower detection thresholds. The second tranche of funding for this project began on May 1, 2022, and planned work for the second tranche includes: i) completing the integration of physical model code into the general error model framework; this code accommodates a wide range of linear/nonlinear source models, fixed/ random effects, and frequentist/Bayesian analyses (the purpose of which is not to dictate to users how to analyze data, but instead to allow users the maximum flexibility in their work); ii) illustrative application of code to identified data, and; iii) initial planning with AFTAC researchers on delivery of code to the Common Development Environment at AFTAC, and continued writing of the planned final journal article submission (year two of this progress report), with particular emphasis on descriptions of data identified for this effort.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

A Mass‐Conserving‐Perceptron for Machine‐Learning‐Based Modeling of Geoscientific Systems

Although decades of effort have been devoted to building Physical-Conceptual (PC) models for predicting the time-series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology can be used to develop models that are much more accurate. However, the difficulty of extracting physical understanding from ML-based models complicates their utility for enhancing scientific knowledge regarding system structure and function. Here, we propose a physically interpretable Mass-Conserving-Perceptron (MCP) as a way to bridge the gap between PC-based and ML-based modeling approaches. The MCP exploits the inherent isomorphism between the directed graph structures underlying both PC models and GRNNs to explicitly represent the mass-conserving nature of physical processes while enabling the functional nature of such processes to be directly learned (in an interpretable manner) from available data using off-the-shelf ML technology. As a proof of concept, we investigate the functional expressivity (capacity) of the MCP, explore its ability to parsimoniously represent the rainfall-runoff (RR) dynamics of the Leaf River Basin, and demonstrate its utility for scientific hypothesis testing. To conclude, we discuss extensions of the concept to enable ML-based physical-conceptual representation of the coupled nature of mass-energy-information flows through geoscientific systems.

58 GEOSCIENCES↗
DOE OSTI2020

Kepler Data Analysis: Non-Gaussian Noise and Fourier Gaussian Process Analysis of Stellar Variability

We develop a statistical analysis model of Kepler stellar flux data in the presence of planet transits, non-Gaussian noise, and stellar variability. We first develop a model for the Kepler noise probability distribution in the presence of outliers, which make the noise probability distribution non-Gaussian. We develop a signal likelihood analysis based on this probability distribution, in which we model the signal as a sum of the star variability and planetary transits. We argue that these components need to be modeled together if optimal signal is to be extracted from the data. For the stellar variability model we develop an optimal Gaussian process analysis using a Fourier-based Wiener filter approach, where the power spectrum is non-parametric and learned from the data. We develop high dimensional optimization of the objective function, where we jointly optimize all the model parameters, including thousands of star variability modes, and planet transit parameters. We apply the method to Kepler-90 data and show that it gives a better match to the stellar variability than the existing methods, and robustly handles noise outliers. As a consequence, the planet radii have a higher value than what the existing methods give, including splines and celerite.

79 ASTRONOMY AND ASTROPHYSICS↗