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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 235 records · Page 13

All sources NASA NTRS DOE OSTI

NASA NTRS1993

Investigation of alternate power source for Space Shuttle Orbiter hydraulic system

This investigation consists of a short-term feasibility study to determine whether or not an alternate electrical power source would trade favorably from a performance, reliability, safety, operation, and weight standpoint in replacing the current auxiliary power unit subsystems with its attendant components (water spray boiler, hydrazine fuel and tanks, feed and vent lines, controls, etc.), operating under current flight rules. Results of this feasibility study are used to develop recommendations for the next step (e.g., to determine if such an alternate electrical power source would show an advantage given that the current operational flight mode of the system could be modified in such a way as not to constrain the operational capability and safety of the vehicle). However, this next step is not within the scope of this investigation. This study does not include a cost analysis, nor does it include investigation of the integration aspects involved in such a trade, except in a qualitative sense for the determination of concept feasibility.

Simon, William E.↗

DOE OSTI2022

Evaluation of mitigation strategies for radioactive fission gases in fluoride-salt-cooled high-temperature reactors

Here, the Fluoride-salt-cooled High-temperature Reactor (FHR) is a promising Generation IV nuclear reactor due to its improved safety design, near atmospheric working pressure, and high electric power conversion efficiency. However, control of radioactive fission gases, such as tritium, is a critical issue in FHRs due to the significantly larger production rate and potentially larger leakage rate of tritium compared to those in Light Water Reactors (LWRs). Therefore, this study proposes two mitigation strategies for tritium in FHRs: Double-Wall Heat eXchanger with a Tritium Carrier (DWHX-TC) option and Single-Wall Heat eXchanger with a Tritium Barrier (SWHX-TB) option. For the DWHX-TC option, two tube surface configurations, i.e., plain and fluted tubes, and four tritium carriers, i.e., helium, FLiBe, FLiNaK, and KF-ZrF 4 , are initially considered, among which the best tube configuration and promising tritium carrier are then selected and used for design optimization of the DWHX. For the SWHX-TB option, two cases, tritium barriers with and without cracks, are investigated in this study. In addition, the two mitigation strategies are evaluated by an in-house, one-dimensional, coupled HEat and MAss Transfer (HEMAT) code developed to predict the leakage rate of tritium in nuclear systems. Our analysis shows that (1) a double-wall configuration using fluted tubes as both the inner and outer tubes is better than other double-wall configurations investigated; (2) helium, as a tritium carrier, is superior to other candidates, including FLiBe, FLiNaK, and KF-ZrF 4 ; (3) the leakage rate of tritium (into the atmosphere) is reduced from 3400 Ci/day, taking the Advanced High-Temperature Reactor (AHTR) as a reference, to 4.0 Ci/day for an optimum design of the DWHX-TC option using helium as the tritium carrier and 1.7 Ci/day for an optimum design of the SWHX-TB option using 30-µm thick silicon carbide (SiC) as the tritium barrier (no cracks); and (4) potential cracks in tritium barriers may significantly deteriorate mass transfer performance of the SWHX-TB option. For example, a cracking rate of 0.1% leads to an increase of 694.1% in the leakage rate under the conditions investigated.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Mechanically induced thermal runaway severity analysis of Li-ion batteries and continuous energy release monitoring

The large-scale deployment of Li-ion batteries in stationary energy storage and electrical vehicle applications demands a strong focus on safety, particularly on the thermal runaway risk and severity evaluation. A standardized single-side mechanical indentation test protocol was developed to induce an internal short-circuit (ISC) and evaluate cells' thermal runaway severity at different state of charge (SOC). The observed hazard severity (OHS in five categories) and evaluated scores in this work have a comprehensive consideration of each cell's capacity, initial voltage, SOC, temperature and voltage change, allowing a better evaluation of the cells' thermal runaway potential. This method was applied to about 200 Li-ion batteries in order to build an extensive thermal runaway database covering various SOCs, capacities and chemistries. In this study, we monitored the transitions of stored electrochemical energy and applied mechanical energy into both thermal energy and acoustic emissions (AE). The surface temperature and mechanical failures were monitored by infrared imaging and AE to capture critical events within battery cells throughout the mechanical indentation tests. Furthermore, the initial temperature maps can predict two types of follow-up events: thermal runaway or gradual heat release via conduction. Analyzing each cell's severity, AEs, and leveraging the evolving database offer insights into predicting occurrences of thermal runaway. The test method, thermal runaway severity evaluation and prediction, and the corresponding database provide battery designers, manufacturers, and end-users a clear overview of Li-ion batteries' thermal runaway potential under mechanical abuse, advancing the safety design of Li-ion batteries.

Acoustic emission↗

DOE OSTI2021

Energy and flow effects of optimal automated driving in mixed traffic: Vehicle-in-the-loop experimental results

Not provided.

Transportation↗

DOE OSTI2024

Capturing Infrastructure Interdependencies for Power Outages Prediction During Extreme Events

As extreme weather events such as hurricanes, severe thunderstorms, and floods grow in frequency and intensity, the disruption of power grid systems poses significant challenges, including widespread electrical outages, economic losses, and threats to public safety. This paper presents a forward-looking approach that leverages geographical graph-based machine learning models to predict county-level maximum power outages during such events. By capturing the intricate interdependencies within power system networks, our approach aims to provide precise and actionable predictions that can optimize emergency response efforts and enhance grid resilience. Through the integration of real-world data, including hurricane advisories and power outage records, we have trained and benchmarked multiple machine learning models, demonstrating the feasibility and potential of this method. While our initial results are promising, this paper also charts a course for advancing these models, addressing the remaining challenges, and ultimately transforming how we anticipate and respond to the impacts of extreme weather on power systems.

Lee, Sangkeun (Matt) [ORNL] (ORCID:000000021317511↗

DOE OSTI2026

Performing Numerical Analysis of Cybersecurity Options Using Dynamic Risk Analysis Tool EMRALD

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Considering a cyber threat should involve defense-in-depth methods and a quantitative or numerical evaluation of overall effectiveness against dynamic, time-dependent attacks to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related safety is a requirement set by North American Electric Reliability and the U.S. Nuclear Regulatory Commission. They are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks may focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want to know business reliability and recovery from those threats, and that requires modeling physical behavior of the targets. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with different tools having issues such as state-base explosion. Dynamic modeling enables time and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic numerical risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies. Keywords: cyber modeling; cyber-physical systems; numerical cyber modeling

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Composite Cathode Architectures Made By Freeze-Casting for All Solid State Lithium Batteries (CRADA Final Report)

As part of a Battery 500 seedling project, MBRDNA partnered with LBNL and Montana State University (MSU) to develop solid-state batteries based on an Al-substituted LLZO (Li 7 La 3 Zr 2 O 12 ) ceramic ion-conductor. All solid-state lithium batteries are attractive contenders for use as power sources for electric vehicles because of their potential for better safety and higher energy density than state-of-the-art lithium-ion batteries, although they are still at early stages of development. Al-substituted LLZO is one of the most promising solid electrolytes for battery applications based on high ionic conductivity, a wide operating voltage window, and apparent stability vs. reduction by lithium. However, difficulties with processing thin (< 20 $\textit{um}$ and dense LLZO membranes have hampered development of devices based on this material. It is non-compressible and must be sintered at high temperatures to densify components. It is also difficult to maintain contact between the active cathode material and LLZO in the composite cathodes, which is critical for successful operation. This research project aimed to use novel processing methods to overcome these issues. The end result was the first ever-reported truly all solid-state battery based on LLZO operating at room temperature without application of exogenous pressure. During the course of the project, we also identified a number of issues that need to be addressed to improve the technology readiness level of this battery.

25 ENERGY STORAGE↗

DOE OSTI2026

Flexible Natural Gas/Hydrogen CHP System

This project included design, development, and demonstration of a cost-effective and scalable, unique-to-the-industry 2.0 MWe fuel-flexible Caterpillar G3516 CHP genset that can run on 100% hydrogen, 100% natural gas, and up to 25% blends of hydrogen in natural gas (volume basis). This was accomplished based on over two years of extensive R&D work done on a single-cylinder engine for developing innovative hydrogen fueling, combustion, air, and electronic control systems design and strategies. Findings from the single-cylinder engine work were scaled up and applied for modifications of and lab testing and development of a 2.0 MWe 16-cylinder G3516 fuel-flexible engine-genset and electronic controls systems. During this testing and development in the engine lab, the genset was run on 100% hydrogen, 100% natural gas, and up to 25% blends of hydrogen in natural gas (volume basis) and its steady-state and transient performance and emission were documented.

03 NATURAL GAS↗

NASA NTRS1981

The Mod-2 wind turbine development project

A major phase of the Federal Wind Energy Program, the Mod-2 wind turbine, a second-generation machine developed by the Boeing Engineering and Construction Co. for the U.S. Department of Energy and the Lewis Research Center of the National Aeronautics and Space Administration, is described. The Mod-2 is a large (2.5-MW power rating) horizontal-axis wind turbine designed for the generation of electrical power on utility networks. Three machines were built and are located in a cluster at Goodnoe Hills, Washington. All technical aspects of the project are described: design approach, significant innovation features, the mechanical system, the electrical power system, the control system, and the safety system.

Linscott, B. S.↗

NASA NTRS1987

Speculations on future opportunities to evolve Brayton powerplants aboard the space station

The Space Station provides a unique, low-risk environment in which to evolve new capabilities. In this way, the Space Station will grow in capacity, in its range of capabilities, and its economy of operation as a laboratory and as a center for space operations. Although both Rankine and Brayton cycles, two concepts for solar dynamic power generation, now compete to power the station, this paper confines its attention to the Brayton cycle using a mixture of He and Xe as its working fluid. Such a Brayton powerplant to supply the station's increasing demands for both electric power and heat has the potential to gradually evolve higher and higher performance by exploiting already-evolved materials (ASTAR-811C and molten-Li heat storage), its peak cycle temperature rising ultimately to 1500 K. Adapting the station to exploit long tethers (200 to 300 km long) could yield increases in payloads to LEO, to GEO, and to distant destinations in the solar system. Such tethering of the Space Station would not only require additional power for electric propulsion but also would so increase nuclear safety that nuclear powerplants might provide this power. From an 8000-kWt SP-100 reactor, thermoelectric power generation could produce 300 kWe, or adapted solar-Brayton cycle, 2400 to 2800 kWe.

English, Robert E.↗

NASA NTRS1987

Speculations on future opportunities to evolve Brayton powerplants aboard the space station

The Space Station provides a unique, low risk environment in which to evolve new capabilities. In this way, the Space Station will grow in capacity, in its range of capabilities, and its economy of operation as a laboratory and as a center for space operations. The Brayton cycle using a mixture of He and Xe as its working fluid is examined herein. Using a Brayton powerplant to supply the station's increasing demands for both electric power and heat has the potential to gradually evolve higher and higher performance by exploiting already evolved materials (Ta alloy and molten-Li heat storage), its peak cycle temperature rising ultimately to 1500 K. Adapting the station to exploit long tethers (200 to 300 km) could yield increases in payloads to LEO, to GEO, and to distant destinations in the solar system. Such tethering of the Space Station would not only require additional power for electric propulsion but also would so increase nuclear safety that nuclear powerplants might provide this power. From an 8000 kWt SP-100 reactor, thermoelectric power generation could produce 300 kWe, or adapted solar-Brayton cycle, 2400 to 2800 kWe.

English, Robert E.↗

NASA NTRS2008

Halbach Magnetic Rotor Development

The NASA John H. Glenn Research Center has a wealth of experience in Halbach array technology through the Fundamental Aeronautics Program. The goals of the program include improving aircraft efficiency, reliability, and safety. The concept of a Halbach magnetically levitated electric aircraft motor will help reduce harmful emissions, reduce the Nation s dependence on fossil fuels, increase efficiency and reliability, reduce maintenance and decrease operating noise levels. Experimental hardware systems were developed in the GRC Engineering Development Division to validate the basic principles described herein and the theoretical work that was performed. A number of Halbach Magnetic rotors have been developed and tested under this program. A separate test hardware setup was developed to characterize each of the rotors. A second hardware setup was developed to test the levitation characteristics of the rotors. Each system focused around a unique Halbach array rotor. Each rotor required original design and fabrication techniques. A 4 in. diameter rotor was developed to test the radial levitation effects for use as a magnetic bearing. To show scalability from the 4 in. rotor, a 1 in. rotor was developed to also test radial levitation effects. The next rotor to be developed was 20 in. in diameter again to show scalability from the 4 in. rotor. An axial rotor was developed to determine the force that could be generated to position the rotor axially while it is rotating. With both radial and axial magnetic bearings, the rotor would be completely suspended magnetically. The purpose of this report is to document the development of a series of Halbach magnetic rotors to be used in testing. The design, fabrication and assembly of the rotors will be discussed as well as the hardware developed to test the rotors.

Gallo, Christopher A.↗

NASA NTRS2017

Physics-of-Failure Approach to Prognostics

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of the electrical components present in the system. In case of electric vehicles, computing remaining battery charge is safety-critical. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. In this presentation our approach to develop a system level health monitoring safety indicator for different electronic components is presented which runs estimation and prediction algorithms to determine state-of-charge and estimate remaining useful life of respective components. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Kulkarni, Chetan S.↗

NASA NTRS2018

Systems Health Monitoring and Prognostics Using Model Based Approach

In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. Our research approach is to develop a system level health monitoring safety indicator which runs estimation and prediction algorithms to estimate remaining useful life predictions at system as well as subsystem levels. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. In addition a digital twin concept is being implemented in the framework to demonstrate verification and validation of developed algorithms.

Electronics Prognostics↗

NASA NTRS2020

Model Based Diagnostics and Prognostics Framework for Systems Health Management

In order to tackle and solve the system health prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. This presentation will cover a physics based-modeling approach implemented for case-studies in battery and composite structures for prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Banerjee, Portia↗

NASA NTRS2020

Prognostics for Systems Health Management - Model and Hybrid Based Approaches. Where are We Heading?

To facilitate and solve the prediction problem, awareness of the current state and health of the system is key, since it is necessary to perform condition-based system health predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditional. In case of next generation electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current health state of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operational conditions and flight profiles for accurate estimation of end-of-discharge (EOD) for the batteries. Similar framework can be implemented to other complex systems and subsystems. Our research approach is to develop a system level health monitoring safety indicator which runs estimation and prediction algorithms to estimate remaining useful life predictions at system, subsystem swell as component levels. Given models of the current and future system behavior, a general approach of model-based prognostics is discussed as a solution to the prediction problem and further for decision making. Data driven prognostics approaches have been equally used with good results in the past, where respective approaches have their own challenges to tackle. This limits their applicability to complex real-world domains: (a) high complexity or incompleteness of physics-based models and (b) limited representativeness of the training dataset for data-driven models. With the advent of internet of things for data collection and increased use of ML algorithms, hybrid approaches are the next avenue to reduce the challenges and achieve better results. An hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, we use physics-based performance models to infer unobservable model parameters related to the system's components health solving a calibration problem.

Prognostics↗

NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly in case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Batteries↗

NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Battery Prognostics↗