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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 OSTI2024

Digital risk analysis in nuclear engineering projects: Designing for safety, performance, reliability, and security

Cyber-informed engineering and security-by-design frameworks are important in promoting the need to identify cybersecurity concerns early in the systems engineering lifecycle so risks from adversarial cyber-attacks can be eliminated or reduced through engineering design practices. In addition to adversarial risk, risk in operational technology systems also includes non-adversarial and unintentional risk from other factors such as human performance errors, environmental conditions, design flaws, and device degradation or failure. This paper introduces a new concept for characterizing digital risk, both adversarial and non-adversarial, and provides the basis for initial research into a novel digital risk analysis approach focused on incorporating attack difficulty into a multi-attribute analysis technique using robust decision-making. This digital risk characterization is also used to frame a discussion on the challenges of competing objectives and competing stakeholder requirements in an integrated energy system project that incorporates a small modular reactor and industrial facility.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Cyber-Informed Engineering for Nuclear Reactor Digital Instrumentation and Control

As nuclear reactors transition from analog to digital technology, the benefits of enhanced operational capabilities and improved efficiencies are potentially offset by cyber risks. Cyber-Informed Engineering (CIE) is an approach that can be used by engineers and staff to characterize and reduce new cyber risks in digital instrumentation and control systems. CIE provides guidance that can be applied throughout the entire systems engineering lifecycle, from conceptual design to decommissioning. In addition to outlining the use of CIE in nuclear reactor applications, this chapter provides a brief primer on nuclear reactor instrumentation and control and the associated cyber risks in existing light water reactors as well as the digital technology that will likely be used in future reactor designs and applications.

42 ENGINEERING↗
DOE OSTI2022

Next Generation System Analysis Model Recently Added Features and Future Plans - Abstract

The Nuclear Waste Policy Act of 1982, as amended (NWPA 1982), established the federal government’s responsibility to accept spent nuclear fuel (SNF) and high-level radioactive waste (HLW) from waste owners and generators for ultimate disposition. SNF generated by the current fleet of commercial nuclear reactors is being stored at the reactor sites in spent fuel pools (SFPs) and in dry independent spent fuel storage installations (ISFSIs). The US Department of Energy Office of Nuclear Energy (DOE-NE) is developing an Integrated Waste Management Program (IWMP) comprising a suite of options and supporting analyses to enable future informed choices. The IWMP is applying integrated waste management system architecture analysis, system engineering, and decision analysis principles to inform potential future decisions regarding potential nuclear waste management system architectures. Architecture analyses of the IWM system are being conducted to support the future deployment of a comprehensive system for managing nuclear waste that considers all major aspects of the back end of the nuclear fuel cycle (i.e., transportation, storage, and disposal). The Next Generation System Analysis Model (NGSAM) is an agent-based simulation software tool designed for the express purpose of modeling the IWM system. NGSAM imports data from the Oak Ridge National Laboratory (ORNL) Unified Database (e.g., historic assembly information, thermal profiles for assembly heat, at-reactor dry storage loadings) to ensure that the simulation initializes with a realistic representation of the state of commercial SNF in the United States. Recent major enhancements that have been implemented into NGSAM since NGSAM was last presented at the WM2019 conference include: • Tracking of railroad escort and buffer car acquisition. • Addition of heavy haul and barge routes for some sites, as well as support for user-defined inter-modal routes. • Updates to the logic that checks the thermal maps prior to package transport. • Addition of an allocation method that predicts when reactor sites will pack assemblies from their pools for dry storage and allocates packages to those reactor sites in the preceding periods, favoring direct transport packages and reducing the number of packages that reactor sites pack for dry storage at their ISFSIs. • Addition of reactor site family operational limits, which are used to limit the number of loads from the pool and from dry storage at a given reactor site per year. • Support has been added for multiple canister loading maps and packages having multiple compatible transportation overpacks. • Updates in the handling of non-commercial fuel, including a new database containing data to support the updates. • Support for repackaging at reactor sites. • Implementing additional output reports or modifying existing reports. • User edits can now be created and edited via the NGSAM website. • Ability to load packages for dry storage at ISF pools. • Same-type package blending at DOE sites. • Support for multi-mode transloading at reactor sites. These new features have improved NGSAM capabilities and/or improve the user experience with the model and will be discussed in more detail. The initial NGSAM requirements for advanced reactor fuels, reprocessing, treatment, and conditioning are preliminary and are described at a high level in this paper: analysts will provide more specific requirements to the NGSAM team in the future. Additionally, there are many data needs associated with modeling advanced reactors in NGSAM, but many of the data or plans are still in progress and/or yet to be fully defined. However, this document describes an initial exploration of the data relevant to this program. Advanced reactor data will likely require revision as concepts evolve and new considerations are made. This is a technical paper that does not take into account contractual limitations or obligations under the Standard Contract for Disposal of Spent Nuclear Fuel and/or High-Level Radioactive Waste (Standard Contract) (10 CFR Part 961). For example, under the provisions of the Standard Contract, spent nuclear fuel in multi-assembly canisters is not an acceptable waste form, absent a mutually agreed to contract amendment. To the extent discussions or recommendations in this paper conflict with the provisions of the Standard Contract, the Standard Contract governs the obligations of the parties, and this paper in no manner supersedes, overrides, or amends the Standard Contract. This paper reflects technical work which could support future decision making by DOE. No inferences should be drawn from this paper regarding future actions by DOE, which are limited both by the terms of the Standard Contract and Congressional appropriations for the Department to fulfill its obligations under the Nuclear Waste Policy Act including licensing and construction of a spent nuclear fuel repository.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Wet biofuel compression ignition

A compression ignition engine system allows use of hydrous fuels, in particular hydrous biofuels, with high water content (e.g., 20-85% water). The hydrous fuel is pressurized, and also preferably heated via the engine's exhaust gas, to increase its enthalpy, and is then directly injected into the engine cylinder(s) near top dead center. The system provides brake thermal efficiency increases of 20% or more versus a comparable system using conventional diesel fuel, while allowing the use of inexpensive undistilled or lightly distilled biofuels.

Wickman, David Darin↗
DOE OSTI2020

Enabling a Flexible Grid with Increased Penetration of DER: Techno-economic Analysis of Metal Hydride Thermochemical Energy Storage Integrated with Stirling Engine for Grid Energy Storage Applications

This report summarizes the results of a techno-economic assessment of the capital and operational expenses for 3 scenarios utilizing a TES/Stirling engine system which is charged by an electric heater to provide grid power and energy storage. While there are several integration possibilities and material choices that could be utilized in such systems, three difference scenarios were chosen for this assessment to reflect an anticipated deployment of the technology where each might require different TES system configurations. Additionally, an enhanced version of the HTMH is considered to demonstrate the expected impact on costs with further technology development. The predicted capital and operational LCOS for the system configurations range from $0.0198/kWhr e - $0.0734/kWhr e which compare positively to the LCOS for lithium ion battery storage ranging from $0.087/kWhr e – $0.329/kWhr e . The key drivers for cost improvements to the system are material property enhancements in the HTMH primarily related to reducing the costs associated with the HTMH vessel and heat exchangers. This analysis demonstrates the potential benefits and flexibility of integrating a Stirling engine with a metal hydride-based TES system and suggests that the MH TES technology, at its current level of development, provides a highly competitive alternative to lithium ion batteries for large scale grid energy storage applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Artificial Intelligence a D and D Enabler - 20552

The paper addresses the development of a specific search engine dedicated to Decommissioning and Dismantling (D and D) projects. The solution combines advanced model based system engineering methodologies and a data centric approach. It is based on an appropriate use of the last Natural Language processing techniques, the most innovative artificial intelligence algorithms and open source framework to handle large volume and diversity of data met in the case of a nuclear infrastructure we have to dismantle. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Is there a Perfect D and D Plan - 20100

Problems with many Deactivation and Demolition (D and D) projects arise not necessarily from the technical difficulty of the actions required in a given facility, but rather from lack of integration of the stove piped planning efforts; engineering, operations, environmental, radiological, human resources, etc. This paper will cover Polestar Technical Service, Inc. (Polestar) systems engineering approach to D and D planning based on the company's experience over the past 27 years. This systematic, four-pronged approach to planning is applicable to any D and D project and addresses several diverse topics in a cohesive and integrated manner. The four areas to be discussed are project scope, requirements, facility end state, work force and base operations. This approach progresses a facility through the typical DOE life-cycle from operations, through deactivation, into surveillance and maintenance (S and M) and to eventual demolition and site remediation. To achieve a final end state the approach brings together the efforts associated with identifying and managing the right set of requirements, detailing the scope of work, establishing the baseline, redefining the work processes, aligning the workforce resources with the work processes, and maintaining the base operations activities as the project progresses through the various life-cycle stages. Getting these building blocks right provides a solid framework for executing any D and D project. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation

The more easily available system performance data and advances in data analytics have provided us with opportunities to optimize maintenance programs for engineered systems, for example nuclear power plants. One key task in maintenance optimization is to obtain an accurate model for system degradation. Here, in this research, we propose a Bayesian method to address this problem. Noting that systems usually exhibit multiple states and that the actual state of a system usually is not directly observable, in the method we first model the system degradation process and the observation process based on a hidden Markov model. Then we develop a sequential Bayesian inference algorithm based on importance sampling and the forward algorithm to infer the posterior distributions of the transition rates in the hidden Markov model based on available observations. The proposed Bayesian method allows us to take advantage of evidence from multiple sources, and also allows us to perform Bayesian inference sequentially, without the need to use the entire history of observations every time new observations are collected. We demonstrate the proposed method using both synthetic data for a nuclear power plant feedwater pump and realistic data for a nuclear power plant chemistry analytical device.

42 ENGINEERING↗
DOE OSTI2024

Cyber-Informed Engineering (CIE) Guidance to Defeat Systematic OT Weaknesses

This research summary outlines the University of Illinois Information Trust Institute (ITI) team's evaluation of whether applying the 12 Cyber-Informed Engineering (CIE) Principles could reduce or eliminate weaknesses identified by the SEI-ETF in engineered systems. ITI's findings suggest that applying CIE principles to weaknesses in MITRE CWE View 1358 can potentially mitigate or eliminate those vulnerabilities.

42 ENGINEERING↗
DOE OSTI2020

NRIC EBR-II Test Bed Pre-Conceptual Design Report

Authorized by the Nuclear Energy Innovation Capabilities Act (NEICA), the National Reactor Innovation Center (NRIC) provides private sector technology developers access to strategic infrastructures and assets for commercial nuclear energy research, development, demonstration, and deployment activities. The mission is to support a timely and cost-effective path to the licensing and commercialization of new nuclear energy systems. To meet these needs, NRIC is developing two test beds at Idaho National Laboratory (INL). The ZPPR Test bed (ZTB) and the EBR-II Test bed (ETB). The EBR-II test bed will support the demonstration of systems that operate at less than 10 MWt. The baseline objective is for the EBR-II Dome to act as a safety significant containment structure capable of siting reactors that utilize Safeguards Category 4 material for operations. The major areas addressed in the pre-conceptual design include: • Installation of an access door • Electrical Power • Heat Removal • Ventilation in the Dome • Module handling system Along with the design for ETB a concept of operations (COP) has also been developed. The COP is intended to facilitate a common understanding of ideas, challenges, and issues. As systems continue to evolve in complexity System Engineers and Project Directors will utilize the COP to develop and sustain a common vision of the system for stakeholders.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

A High Specific Output Gasoline Low Temperature Combustion Engine

This report summarizes activities conducted in support of the project “A High Specific Output Gasoline Low Temperature Combustion Engine” under COOPERATIVE AGREEMENT NUMBER DE-EE0007788. This cooperative agreement was structured as a 51%/49% recipient/DOE NETL cost share with two Go / no-Go decision gates spanning three budget periods. The primary objective of the program is the development and demonstration of a downsized boosted, lean, low temperature gasoline combustion engine system capable of demonstrating a 15-17% fuel economy improvement relative to a contemporary naturally aspirated stoichiometric combustion engine consistent with relevant emissions constraints and the use of marketplace gasolines. The program focused on maximizing internal combustion engine fuel economy potential by combining the benefits of downsized boosted engine technology with next generation gasoline lean-burn, low temperature combustion and novel low temperature plasma ignition. The project achieved a 20.5% improvement from hot FTP cycle test and 18.9% improvement from cold FTP cycle test while meeting stringent SULEV30 emissions regulation for both test cycles (HOT FTP: 15.37 mg of NOx+HC; 0.16 g/mile of CO, COLD FTP: 23.1 mg of NOx+HC; 0.33 g/mile of CO).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Opportunities and data requirements for data-driven prognostics and health management in liquid hydrogen storage systems

During the past decade, Prognostics and Health Management (PHM) has become an important set of tools in various areas of industry and academic reliability engineering. PHM consists of a variety of mathematical and computational methods used to support data-driven decision-making to increase the safety, availability, and reliability of complex engineering systems. In particular, PHM can provide crucial insight into reliability and safety design improvements for developing technologies where historical performance and failure data are limited. This is the case of hydrogen fueling and storage technologies. This work presents a high-level approach for designing data-driven PHM applications for bulk liquid hydrogen (LH 2 ) storage systems for hydrogen fueling stations. This paper addresses core aspects of the design, development, and implementation of data-driven PHM applications that can improve the reliability assessment of hydrogen components. The analysis focuses on the relationship between data availability and diagnostic/prognostic capabilities; potential challenges; and integration schemes for current risk mitigation measures. We identify potential condition-monitoring data sources for key components in an LH 2 storage system, including storage tanks, piping, and pumps. Further, we determine that the short-term goals for the implementation of data-driven models in PHM frameworks in hydrogen systems should focus on developing adequate data collection and analysis strategies, as well as exploring the effect on reliability, safety, and regulations for hydrogen systems.

08 HYDROGEN↗
DOE OSTI2023

Deep Learning for Modeling Enhanced Geothermal Systems

Enhanced Geothermal Systems (EGS) offer a vast potential to expand the use of geothermal energy. Heat is extracted from this engineered system by injecting cold water into a subsurface fractures, which are in contact with the hot dry rock, and pulled through the production wells. Creating EGS requires improving the natural permeability of hot crystalline rocks. To develop economically viable EGS reservoirs, significant technical barriers (e.g., better stimulation technologies without adequate water and/or permeability) and non-technical barriers (e.g., land access and permitting) must be overcome. In this short conference paper, we present a workflow to address a part of this challenge – “How to develop economically viable EGS using existing technologies?”. Our workflow called the GeoThermalCloud (GTC) for EGS, leverages recent advances in machine learning, deep learning, and cloud computing. This GTC framework is open-source and available at https://github.com/SmartTensors/GeoThermalCloud.jl. The GTC framework provides trained deep learning (DL) models to estimate the net present value of a given EGS design scenario. The Geothermal Design Tool (https://github.com/GeoDesignTool/GeoDT.git), a fast and simplified multi-physics solver, is used to develop a database for training DL models. The database consists of EGS design parameters (inputs to DL model) and their net present value (output of DL model) in uncertain geologic systems. The EGS design parameters for constructing this training database are based on Utah FORGE but include the options of more wells and deeper depths. The DL models are trained by ingesting the EGS design parameters and estimating the corresponding net present value. Such an emulation allows us to screen various EGS designs quickly and identify good development strategies by coupling them with optimization techniques. Our preliminary results show promise in DL emulation of net present value. However, a lot more work is needed to improve the predictive capability of DL models (i.e., extensive hyperparameter tuning is necessary). This will be the primary focus of our future work.

artificial neural networks, geothermal↗
DOE OSTI2023

GeoThermalCloud for EGS – An Open-source, User-friendly, Scalable AI Workflow for Modeling Enhanced Geothermal Systems

Enhanced Geothermal Systems (EGS) offer a vast potential to expand the use of geothermal energy. Heat is extracted from this engineered system by injecting relatively cold water into subsurface fractures, which are in contact with hot dry rock, and brought back to surface through production wells. Creating EGS requires improving the natural permeability of hot crystalline rocks. In this short conference paper, we present a reproducible workflow for modeling EGS. Our workflow called the GeoThermalCloud (GTC) for EGS, leverages recent advances in machine learning, deep learning, and high-performance computing. This GTC framework is currently being made open-source, user-friendly, and reproducible through python scripts as well as Google Colab/Jupyter Notebooks. This GTC for EGS modeling scripts are made available at https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS and will constantly be updated to cater for geothermal community. Current GTC framework provides scripts to train deep learning (DL) models for techno-economics and data worth analysis. The Geothermal Design Tool (https://github.com/GeoDesignTool/GeoDT.git), a fast and simplified multi-physics solver, is used to develop a database for training DL models. This short paper provides details on the scripts to curate, process, and train DL models. The scripts can easily be modified to train on databases generated by other popular open-source simulators such as PFLOTRAN, STOMP, TOUGH, and GEOSX or commercial software such as ResFrac and COMSOL.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Improving Short Term Predictability of Hydrologic Models with Deep Learning

Focal Area: Focal Area 2. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection). Science Challenge: A major challenge exists in the lack of reliable predictive modeling of water-cycle extremes using macro-scale hydrologic models that are driven by atmospheric climate data. This causes a critical knowledge gap in understanding the magnitudes, probabilities, and severity of droughts, rainfall, and flooding. Improved understanding of these extreme events requires more accurate modeling at high spatial and temporal resolutions. This shortcoming is especially apparent when complex couplings between atmospheric quantities and engineered systems emerge, such as during extreme precipitation or drought events and at intersections of atmospheric-land-fluvial-ocean systems. Added complexity around representing operations of engineered infrastructure leads to a limited capability to analyze the temporal and spatial implications of compounding extreme events across inland and coastal regions. This results in profound challenges for water and power systems operators, agriculture production, and regional infrastructure planning. The science challenge can be summarized as How can we use AI/ML to improve short term predictability of extreme water-cycle events and associated risk, especially compounding events and extreme rainfall patterns that result in flooding, to inland and coastal communities under different climate scenarios?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Cyber-Informed Engineering Adoption in University Engineering Programs: An Overview of CIE Integration Successes at Nine U.S. Educational Institutions

This report examines the adoption of Cyber-Informed Engineering (CIE) in university engineering programs, driven by the need to protect critical energy infrastructure from adversarial threats. CIE equips current and future engineers and technicians with the necessary mindset, skills, and competencies to enhance the resilience of engineered systems against cyber attacks. This report highlights nine academic partners who are incorporating CIE into their curricula through various approaches, including lectures, courses, and certificates.

42 ENGINEERING↗
DOE OSTI2020

ZPPR Test Bed (ZTB) Pre-Conceptual Design Report

Authorized by the Nuclear Energy Innovation Capabilities Act (NEICA), the National Reactor Innovation Center (NRIC) provides private sector technology developers access to strategic infrastructures and assets for commercial nuclear energy research, development, demonstration, and deployment activities. The mission is to support a timely and cost-effective path to the licensing and commercialization of new nuclear energy systems. To meet these needs, NRIC is developing two reactor demonstration test beds at Idaho National Laboratory (INL), the ZPPR Test bed (ZTB) and the EBR-II Test bed (ETB). ZTB will support the demonstration of systems that operate at less than 500 kWt. The baseline objective is for the ZPPR Cell to act as a confinement structure capable of siting reactors that utilize Safeguards Category 1 material for operations. The major areas addressed in the pre-conceptual design include: • Installation of an access door • Electrical Power • Heat Removal • Ventilation in the Cell • Reactor Installation Along with the design for ZTB, a concept of operations (COP) has also been developed. The COP is intended to facilitate a common understanding of ideas, challenges, and issues. As systems continue to evolve in complexity System Engineers and Project Directors will utilize and update the COP to develop and sustain a common vision of the system for stakeholders.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗