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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 19 records

Inventory of Composable Elements (ICE) v6.0.0

The Inventory of Composable Elements (ICE) is an open source registry software platform for managing information about biological parts. It is capable of recording information about plasmids, microbial host strains and seeds, as well as DNA parts. Includes features such as DNA sequence visualization, editing and annotation, auto-aligning sequencing trace files against reference templates, SBOL XML/RDF support, and web-of-registries functionality. The web of registries functionality provides strong support for distributed interconnected use and enables sharing and transfer of biological parts across various independent ICE instances. ICE adopts modern software development principles, leveraging component-base frameworks, offering a REST API for convenient third-party integration and emphasizing scalability, security, and service integrations for dynamic content availability. The source code is hosted at https://github.com/JBEI/ice. A public instance is available at public-registry.jbei.org, where users can try out features, upload parts or simply use it for their projects.

Plahar, Hector↗

Development of an MC&A toolbox for liquid-fueled molten salt reactors with online reprocessing (Final Report)

A critical barrier to the deployment of MSRs is the absence of a well-defined nuclear material control and accounting (MC&A) approach, a vital prerequisite to meet NRC licensing requirements as well as facilitating future international exports. Liquid-fueled MSR variants (especially those incorporating online refueling or reprocessing) present a unique set of challenges to traditional nuclear material control and accountancy. Unlike solid-fueled light-water reactors, traditional item-counting methods cannot be applied as an accountancy strategy. Rather, MC&A approaches to MSR variants (including both uranium and thorium fueled designs) are more analogous to bulk material handling facilities (e.g., enrichment and reprocessing); yet further complicating matters is the fact that fuel medium is also highly radioactive. Meanwhile, the space of MSRs covers a broad range of design parameters, including thermal and fast spectra designs; operation in actinide breeder or burner modes, choice of actinide fuel used (e.g., 235 U, 232 Th / 233 U, denatured 233 U, etc.), pool or loop-type configuration, and even different salt chemistry. Each of these design choices introduce significant challenges to MC&A approaches within MSR facilities. We propose to bridge this gap for liquid-fueled MSRs by developing a modular, component-based test framework for evaluating viable process monitoring and MC&A techniques specifically suited to liquid-fueled MSR system variants employing online refueling or reprocessing. This test platform will consist of a toolbox of independent process modules representing discrete physical units (such as the reactor core, off-gas processing, decay tanks, and actinide separation units), each with its own self-contained physics responsive to the input mass flow, along with appropriate measurement models that can be coupled to key flow points. These dynamic physical signatures thus afford the ability to test the viability and efficacy of potential accountancy techniques under the full range of reactor operating conditions. As process modules are connected via mass flows, the result is a reconfigurable, generic MSR mass flow model capable of serving as an MC&A test platform for a broad spectrum of possible MSR configurations. The resulting MSR MC&A toolbox will enable robust assessment of accountancy strategies for this unique facility type, including analysis of physical feedbacks arising both from depletion of the fuel over time as well as from potential off-normal events, including those introduced by equipment failures (e.g., a pump failure) as well as by malicious action (i.e., attempts to divert material). The proposed toolbox both addresses a critical needs area for the MPACT analysis toolkit while leveraging existing MPACT-sponsored tools, especially with respect to simulation of measurement and accountancy techniques for advanced fuel cycle facilities. Beyond enabling new analysis capabilities for MSR systems, the design of this toolbox will be to such to maximize compatibility with existing MPACT tools, such to enhance existing facility MC&A analysis capabilities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Component based development of computer-aided tools for different applications

Computer-aided methods and tools are used at different stages of chemical process development. However new tools are needed to meet the current and future challenges through innovative and more sustainable solutions that the currently available tools may not be able to provide. This work focuses on component-based software architecture for development of tailor-made computer-aided tools for specific application objectives. The architecture is based on “components”, which can be model-objects that are stored in a library; collection of property models; databases; numerical solvers; design-algorithms (work-flows) etc. Furthermore, the main idea of these components is to incorporate a plug & play framework, where for a specific purpose, after the work-flow and associated data-flow are established, a specific computer-aided tool can be configured through a library of software components. Also, through the use of software components that are based on predictive model-based solution techniques, the final software can be reliably used to provide innovative and more sustainable solutions over a wide range of problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Geothermal Operational Optimization with Machine Learning

The Geothermal Operational Optimization with Machine Learning (GOOML) project has developed a generic and extensible component-based system modeling framework to study complex geothermal fields using a data-driven approach. Through building a digital twin of a geothermal steam field with the GOOML modeling framework, operators can analyze historical and forecasted power production, explore possible steam field configurations, and optimize real world operations, all in a cost-effective digital environment. The GOOML modeling software is based on a historical data-assimilation framework that uses first-principal thermodynamics to model steam field components using historical data, and a forecast framework that uses machine-learning-driven models of steam field components to predict future operations. This modeling framework creates countless new opportunities for digital exploration of steam field design and operations. To date, digital twins have been developed for several steam fields in New Zealand and the United States. These digital twins have been validated by comparing hindcast predictions against historical production data. Field design and operations have been explored using genetic optimization and reinforcement learning. Initial results show compelling and often surprising opportunities for improved design and operation of fields with 2 to 5 percent improvements in annual energy production. GOOML is driving a step-change in geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and a first-of-its-kind intelligent geothermal systems model.

40 EE - Geothermal Technologies Office (EE-4G)↗

GOOML - Finding Optimization Opportunities for Geothermal Operations: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach. We have used this framework to develop digital twins that provide steamfield operators with an operational environment to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management for real world applications. The GOOML modeling software is built on a generic component-based systems framework that allows for both historical and forecast analysis. A GOOML model can perform historical data-assimilation using first-principal thermodynamics to create a meaningful data model. Historical production data can then be coupled with a forecast framework to train machine-learning models of steamfield components to predict future outputs. This modeling environment enables digital exploration of steamfield design configurations and operational scenarios. GOOML digital twins have been developed for steamfields in New Zealand and the United States representing differing power generation and field conditions. These digital twins have been validated by comparing hindcast predictions against historical production data. Reinforcement learning experiments were conducted to demonstrate the ability to programmatically explore the operations space using machine learning agents. Our initial results are compelling; two to five percent increases in annual energy production were demonstrated by the GOOML models with no additional infrastructure build required. GOOML offers a new approach to geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and interaction with digital twins. Through application of these tools, operators will realize greater availability and higher net generation which will increase the cost effectiveness of geothermal energy projects.

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A hardware-based heat pump water heater design model for quasi-steady-state simulation

A storage heat pump water heater (HPWH) comprises a vapor compression system and a hot water tank. It can lead to more than 70% energy saving, compared to electric resistance heating. HPWH usually uses a wrapped-tank coil as the water heater. The wrapped coil has a strong interaction with the water tank and impose a new challenge for the engineering design and simulation study. Based on the US DOE/ORNL Heat Pump Design Model (HPDM), a component-based thermal system modelling platform, a segment-to-segment wrapped-tank coil model and a one-dimensional stratified water tank model were developed, which facilitates modeling and design HPWHs having various configurations and components. In the paper, the new HPWH system design model was used to investigate effects of compressor size, refrigerant charge, water draw pattern, and refrigerant type, etc., for predicting 24-hour energy factors and first-hour rating. The HPWH system model was proved to be an effective equipment design tool.

Shen, Bo↗

Parametric Studies of Heat Pump Water Heater Using Low GWP Refrigerants

A storage heat pump water heater (HPWH) comprises a vapor compression system and a hot water tank. It can lead to more than 70% energy saving, compared with electric resistance heating. HPWH usually uses a wrapped-tank coil as the water heater. The wrapped coil has a strong interaction with the water tank and impose a new challenge for the engineering design and simulation study. Based on the US DOE/ORNL Heat Pump Design Model (HPDM), i.e., a public-domain, component-based thermal system modelling platform, a segment-to-segment wrapped-tank coil model coupled with a one-dimensional stratified water tank model were developed, which facilitates modeling and design HPWHs having various configurations and components. Additionally, the hardware-based design model was used to evaluate refrigerants having lower global warming potentials to replace R-134a, including R-1234yf, R-1234ze, R-290, R-513A and R-450A, when effects of compressor size, refrigerant charge on 24-hour uniform energy factor (UEF) and first-hour rating (FHR) were investigated.In the drop-in replacements, R-1234ze achieved the best UEF, and R-290 resulted in the lowest UEF. When downsizing the compressor to maximize the UEFs, the UEFs of R-134a, R-290, R1234yf, R-450A and R-513A ascended to the range of 4.4 to 4.53, with R-513A having the highest UEF. The R-1234ze ended up having the lowest UEF of 4.27. Refrigerants having larger volumetric vaporization heat led to higher FHRs if using the fixed-size compressor, that R-290 resulted in the highest FHR.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

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CASTLE: Conflict Analysis Strategy Testing Laboratory Environment v.1.0.0

SAND2024-01743O The Conflict Analysis Strategy Testing Laboratory Environment (CASTLE) is a software framework that enables and simplifies building a novel, turn-based strategy game in which it can define its own rules, maps, pieces, and interactions. The software is for novice to experienced programmers with some knowledge of Unity3D, a tool used in game production. CASTLE includes a library of common game mechanics used for strategic wargames and traditional board games, such as cards, tokens, dice, and grid maps. It follows design principles popularized by the video game industry and uses singletons for managing portions of the code. CASTLE builds on Unity's component-based design and can respond to engine events during execution. Among the numerous user-friendly features: Build games quickly and cost-effectively Network in real-time and apply data to new games developed on the framework Host multiple participants online Connect rule- or machine learning-based agents to a CASTLE game to serve as opponents or to simulate games Collect data collection from players and in-game behaviors Create a survey to gather demographics or opinions from players Store data locally or save it to an external database through Representational State Transfer (REST) functions CASTLE, which was prototyped using Microsoft Azure, is also designed for easily distributing online games using popular cloud services. The multiplayer functionality includes an agent interface, allowing developers to construct AI players that can substitute for humans in any of the games. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Fabian, Nathan↗

Dual Context: Leveraging Structured Application Context for Code Generation and Runtime Feature Activation via Chat Interfaces

Integrating artificial intelligence (AI) capabilities into software applications typically involves two common paths. For developers, AI assists in generating and documenting source code and other related software engineering efforts. For users, AI assists them through question-and-answer exchanges via chatbots. Both approaches have their value, but neither effectively leverages the modularity of component-based architectures that modern web application frameworks offer. We implement a proof of concept within a centralized suite of applications used for the Atmospheric Radiation Measurement (ARM) Data Center Operational Tools, where we introduce a third integration path through the ARM Context Engine (ACE). ACE is a context driven system that uses structured contextual specifications to enable Large Language Models (LLMs) to render interactive and feature-rich user interface (UI) components directly within chat responses, alongside or in place of conventional text outputs. These specifications serve two important purposes across what we call code context and UI context. Code context provides AI-assisted development tools with structured application knowledge beyond raw code, including component relationships, architectural patterns and schematic information, enabling the generation of consistent, well-structured code. UI context defines the rules for enabling and rendering component features at runtime based on the user's natural language input, allowing end users to activate capabilities such as data export, filtering, and pagination within chat responses, without requiring code changes or redeployment. We demonstrate, through a comparative evaluation against general-purpose AI chatbots, that context-driven component rendering provides interactive capabilities that text-based responses cannot replicate, including deterministic component behavior, application-consistent design language, and on-demand feature activation. A development effort comparison further shows that features that traditionally require multi-step development cycles can be activated with a single naturallanguage request. In this ongoing work, we present ACE as an emerging approach to AI integration that positions modular, well-documented software architecture as the foundation for AI-ready applications. ACE treats context as a shared resource across both development and user-facing AI, bringing cohesion to conventionally disconnected efforts, bridging developer tooling and end-user capabilities within a single framework.

Tadimeti, Vijay [ORNL]↗

nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure

We present nbodykit, an open-source, massively parallel Python toolkit for analyzing large-scale structure (LSS) data. Using Python bindings of the Message Passing Interface, we provide parallel implementations of many commonly used algorithms in LSS. nbodykit is both an interactive and scalable piece of scientific software, performing well in a supercomputing environment while still taking advantage of the interactive tools provided by the Python ecosystem. Existing functionality includes estimators of the power spectrum, two- and three-point correlation functions, a friends-of-friends grouping algorithm, mock catalog creation via the halo occupation distribution technique, and approximate N-body simulations via the FastPM scheme. The package also provides a set of distributed data containers, insulated from the algorithms themselves, that enables nbodykit to provide a unified treatment of both simulation and observational data sets. nbodykit can be easily deployed in a high-performance computing environment, overcoming some of the traditional difficulties of using Python on supercomputers. We provide performance benchmarks illustrating the scalability of the software. The modular, component-based approach of nbodykit allows researchers to easily build complex applications using its tools. The package is extensively documented at http://nbodykit.readthedocs.io, which also includes an interactive set of example recipes for new users to explore. As open-source software, we hope nbodykit provides a common framework for the community to use and develop in confronting the analysis challenges of future LSS surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

Device Access Abstractions for Resilient Information Architecture Platform for Smart Grid

An open application platform distributes the intelligence and control capability to local endpoints (or nodes) reducing total network traffic, improving speed of local actions by avoiding latency, and improving reliability by reducing dependencies on numerous devices and communication interfaces. The platform must be multi-tasking and able to host multiple applications running simultaneously. Given such a system, the core functions of power grid control systems include grid state determination, low level control, fault intelligence and reconfiguration, outage intelligence, power quality measurement, remote asset monitoring, configuration management, power and energy management (including local distributed energy resources, such as wind, solar and energy storage) can be eventually distributed. However, making this move requires extensive regression testing of systems to prove out new technologies, such as phasor measurement units (PMU). Additionally, as the complexity of the systems increase with the inclusion of new functionality (especially at the distribution and consumer levels), hidden coupling issues becomes a challenge with possible N-way interactions known and not known by device and application developers. Therefore, it is very important to provide core abstractions that ensure uniform operational semantics across such interactions. Here in this paper, we describe the pattern for abstracting device interactions we have developed for the RIAPS platform in the context of a microgrid control application we have developed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

The Application of Coordination to Magnetic Measurement Automation: An SSW System Example

Magnetic measurements are an integral part of the development cycle of accelerator magnets, a part which provides necessary feedback to drive design and manufacturing improvements and corrections. Automation of these measurements allows for reduction of human errors and reproducibility of measurements. Realization of automation via coordination that is separated at the architectural level from the functional code provides flexibility in defining measurement procedures and substantial extensibility. The described Single Stretched Wire (SSW) system herein is an implementation of such a solution with the use of a component-based framework designed to build a family of measurement systems.

43 PARTICLE ACCELERATORS↗

Beyond Widgets: Validated Systems Energy Savings and Utility Custom Incentive Program Systems Trends

Energy efficiency upgrades to buildings to date through avenues such as utility incentive programs have been largely limited to component-based products (e.g., lamps, RTUs). While some utilities do provide ‘custom’ incentive programs with whole building and system level technical assistance, these programs require deeper levels of analysis, resulting in higher program costs. This results in custom programs being restricted to utilities with greater resources, and are typically applied mainly to large or energy-intensive facilities, leaving much of the market without cost effective access and incentives for these solutions. In addition, with increasingly stringent energy codes, cost effective component-based solutions that achieve significant savings are dwindling. Integrated building systems (e.g., integrated façade, HVAC and/or lighting solutions) can deliver higher savings (Regnier et al. 2017) that translate into large sector-wide savings if deployed at the scale of these programs. However, systems application poses a number of challenges – energy savings assessments are inherently more complex and are not easily suited to the deemed measure model, and implementation of systems can be more disruptive. This paper presents the outcomes of a project to develop validated utility incentive program packages for three specific integrated building systems, in collaboration with ComEd, a consortium of California Public Owned Utilities (CA POUs) (Northern California Power Agency(NCPA) and the Southern California Public Power Authority(SCPPA)), and Xcel Energy (CO, MN). Previous work described the potential for energy savings of these systems at the individual and market level, early test findings for the task-ambient lighting system, and described the work products to develop utility incentive programs (Regnier et al. 2016). This paper presents validated end use energy savings of all three systems showing energy savings of ~20% for automated shading combined with daylight dimming (does not include light fixture retrofit savings); 30-46% for task-ambient lighting systems combined with plug load occupancy controls, depending on technology package and office building size (no daylight dimming impacts); and ~94% in the south-façade daylit zone for workstation specific lighting systems with daylight dimming (including lighting retrofit and task tuning). Using a U.S. Department of Energy (DOE) reference building model (DOE 2018), this translates to whole building savings of 2-5% (large-medium commercial), 12-23% (large-small commercial), and 13-15% (medium-large commercial) for these systems respectively. Early findings from the utilities in deploying these systems through their incentive programs indicate that deployment methods such as direct install programs might be best suited for these integrated systems. Further, early findings from a scoping study across custom utility programs and federal retrofit programs indicate that component level retrofits are still prevalent even in these programs, pointing towards an opportunity for deeper energy savings deployment. High performance retrofit case studies demonstrate that deeper energy savings is possible through integrated approaches. Market stakeholder interviews indicate however that there is generally a low level of awareness of the potential for energy savings of these three integrated systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Beyond Widgets -- Systems Incentive Programs for Utilities

Utility incentive programs remain one of the most significant means of deploying commercialized, but underutilized building technologies to scale. However, these programs have been largely limited to component-based products (e.g., lamps, RTUs). While some utilities do provide ‘custom’ incentive programs with whole building and system level technical assistance, these programs require deeper levels of analysis, resulting in higher program costs. This results in custom programs being restricted to utilities with greater resources, and are typically applied mainly to large or energy-intensive facilities, leaving much of the market without cost effective access and incentives for these solutions. In addition, with increasingly stringent energy codes, cost effective component-based solutions that achieve significant savings are dwindling. Building systems (e.g., integrated façade, HVAC and/or lighting solutions) can deliver higher savings that translate into large sector-wide savings if deployed at the scale of these programs. However, systems application poses a number of challenges – baseline energy use must be defined and measured; the metrics for energy and performance must be defined and tested against; in addition, system savings must be validated under well understood conditions. This paper presents a sample of findings of a project to develop validated utility incentive program packages for three specific integrated building systems, in collaboration with Xcel Energy (CO, MN), ComEd, and a consortium of California Public Owned Utilities (CA POUs) (Northern California Power Agency(NCPA) and the Southern California Public Power Authority(SCPPA)). Furthermore, these program packages consist of system specifications, system performance, M&V protocols, streamlined assessment methods, market assessment and implementation guidance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Energy Cost Savings of Systems-Based Building Retrofits: A Study of Three Integrated Lighting Systems in Comparison with Component Based Retrofits

Most building retrofit projects are still component-based in that they typically address only one piece or type of equipment at a time. Systems-based retrofits that seek to address multiple components in an integrated manner have the potential to provide significantly greater energy savings. Lawrence Berkeley National Laboratory (LBNL) partnered with several utilities to develop and evaluate three different integrated retrofit packages involving lighting systems: automated shading with daylight dimming controls, workstation-specific lighting with daylight controls, and task/ambient lighting with plug load occupancy controls. Specifically, our analysis sought to quantify the marginal benefits of these systems (energy savings as well as lighting performance and visual comfort) relative to component based approaches. The analysis was based on a combination of measured performance data from LBNL’s FLEXLAB® test facility and energy simulations. All three systems were compared to a simple fluorescent-to-LED retrofit, which represents the component-based approach. While the simple LED upgrade provides significant lighting energy savings of 63%, the systems yielded energy savings of 81-93%, which equates to additional savings of 49-82% over the simple LED upgrade. All systems tested provided satisfactory visual comfort as well, indicating good potential for market acceptance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Core-Pedestal Plasma Configurations in Advanced Tokamaks

Here, several configurations for the core and pedestal plasma are examined for a predefined tokamak design by implementing multiple heating/current drive (H/CD) sources to achieve an optimum configuration of high fusion power in a noninductive operation while maintaining an ideally magnetohydrodynamic (MHD) stable core plasma using the IPS-FASTRAN framework. IPS-FASTRAN is a component-based lightweight coupled simulation framework that is used to simulate magnetically confined plasma by integrating a set of high-fidelity codes to construct the plasma equilibrium (EFIT, TOQ, and CHEASE), calculate the turbulent heat and particle transport fluxes (TGLF), model various H/CD systems (TORIC, TORAY, GENRAY, and NUBEAM), model the pedestal pressure and width (EPED), and estimate the ideal MHD stability (DCON). The TGLF core transport model and EPED pedestal model are used to self-consistently predict plasma profiles consistent with ideal MHD stability and H/CD (and bootstrap) current sources. In order to evaluate the achievable and sustainable plasma beta, varying configurations are produced ranging from the no-wall stability to with-wall stability regimes, simultaneously subject to the self-consistent TGLF, EPED, and H/CD source profile predictions that optimize configuration performance. The pedestal density, plasma current, and total injected power are scanned to explore their impact on the target plasma configuration, fusion power, and confinement quality. A set of fully noninductive scenarios are achieved by employing ion-cyclotron, neutral beam injection, helicon, and lower-hybrid H/CDs to provide a broad profile for the total current drive in the core region for a predefined tokamak design. These noninductive scenarios are characterized by high fusion gain (Q ~ 4) and power (P fus ~ 600 MW), optimum confinement quality (H 98 ~ 1.1), and high bootstrap current fraction (f BS ~ 0.7) for Greenwald fraction below unity. The broad current profile configurations identified are stable to low-n kink modes either because the normalized pressure β N is below the no-wall limit or a wall is present.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Oak Ridge National Laboratory Security by Design: A Plant Design and Component-Based Approach to Technical Solutions to Insider and Outsider Threat

Security costs for nuclear reactors have been shown to rise over the lifecycle of a nuclear facility as new threats, security weaknesses, and the generic threat profile of the plant location change over time. This paper conducts a review of the technological gaps that currently exist in Security by Design (SeBD) based on the literature available and attempts to close those gaps through proposing technological constructs and processes that can integrate safety and security in the design process of a plant, with a primary focus on SMR technology. Under the new modular and mass manufacturing regime, where plant designs attempt to reduce costs with potential economies of scale and modular construction, the current SeBD definitions focusing on state level regulators are not considered adequate since the licensing stage is often too late in the design process to integrate the true cost savings of SeBD. Thus, this paper attempts to remedy the current logic in order to allow for continuous implementation of security alongside the development of the safety concept for the plant. If adopted within the design process the establishment of this type of technology will allow for a technological ecosystem to evolve that will imbed security concepts into the physical design of the components of the plant.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗