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Recommendations for an Applicant to Calculate Activity Data for Greenhouse Gases Estimates

In 2009, the U.S. Nuclear Regulatory Commission (NRC) directed the NRC staff to address climate change issues and consider the impacts of the emissions of carbon dioxide (CO 2 ) and other greenhouse gases (GHGs) in its environmental reviews for major licensing actions (NRC 2009b). To implement this direction from the Commission, the staff issued guidance in 2011 and updated guidance in 2014 in Attachment 1 to Interim Staff Guidance COL/ESP-ISG-026 (NRC 2011; NRC 2014). This guidance provides a simpler method than the method described in RG 4.2 Rev. 3, that an applicant can use to meet the plant parameter envelope (PPE) value from the Generic Environmental Impact Statement for Licensing of New Nuclear Reactors (NR GEIS). NRC staff estimated the 97-year lifecycle GHG emissions from a reference 1000 megawatt electrical (MWe) light-water reactor (LWR) for various activities associated with construction, operation (including uranium fuel cycle), and decommissioning of nuclear power plants and presented the results in Appendix H of the NR GEIS. Appendix H of the NR GEIS includes estimates of direct emissions from construction equipment and emergency diesel engines in a nuclear facility and indirect emissions from workforce vehicular traffic, fuel transportation and the uranium fuel cycle. The NR GEIS Section 3.3 extended the estimates in Appendix H for the installation of two 1000 MWe nuclear reactors on the same site. Scaling factors were used to extrapolate the GHG emissions of a reference 1000 MWe reactor to a two-unit nuclear reactor plant (each reactor unit generating 1000 MWe). GHG emission estimates for building, operation, decommissioning and safe storage (SAFSTOR) for a two-unit nuclear reactor plant would be based on the plant’s physical size, and therefore estimates for these source categories were assumed to be twice the value of the reference 1000 MWe reactor. However, GHG emissions from the fuel cycle (including fuel transportation) were scaled upward by a factor of 3, based on plant efficiencies greater than the 80 percent assumption in Appendix H. Table 1 below shows the PPE emissions for two 1000 MWe nuclear reactors as provided in NR GEIS. The total GHG emissions for two 1000 MWe reactors were calculated as 2,534,000 metric tons (MT) of CO 2 equivalent (CO 2 (e)) based on a 97 year GHG life cycle period. The GHG emissions lifetime of 97 years for a reference nuclear reactor includes a 7-year building phase, 40 years of operation, 10 years of active decommissioning, and 40 years of SAFSTOR operations (NRC 2024). Construction equipment and vehicular traffic from workers commute would contribute to the GHG emissions during a 7-year building phase. Uranium fuel cycle, vehicular traffic, fuel and waste transportation, and testing of standby diesel generators would contribute to GHG emissions during the 40-year operations phase. While NRC’s regulations allow up to 60 years of reactor facility decommissioning, Appendix H estimated that most of the GHGs would occur over an estimated 10-year period during which to the licensee would engage in significant demolition and earth-moving activities, as discussed in Supplement 1 to NUREG-0586 (NRC 2002). Vehicular traffic by the workforce during a 40-year SAFSTOR period would additionally contribute GHG emissions. The carbon footprint for a 40-year SAFSTOR period was separately analyzed from the decommissioning activities as provided in Table YYYY-2 of the staff issued guidance in 2011 (NRC 2011).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

RandONets: Shallow networks with random projections for learning linear and nonlinear operators

Deep neural networks have been extensively used for the solution of both the forward and the inverse problem for dynamical systems. However, their implementation necessitates optimizing a high-dimensional space of parameters and hyperparameters. This fact, along with the requirement of substantial computational resources, pose a barrier to achieving high numerical accuracy, but also interpretability. Here, to address the above challenges, we present Random Projection-based Operator Networks (RandONets): shallow networks with random projections and tailor-made numerical analysis methods that learn accurately and fast linear and nonlinear operators. Building on previous works, we prove that RandOnets are universal approximators of linear and nonlinear operators. Due to their simplicity, RandONets provide a one-step transformation of the input space, facilitating interpretability. For the evaluation of their performance, we focus on operators of PDEs. We show, that RandONets outperform by several orders of magnitude, both in terms of numerical approximation accuracy and computational cost, the “vanilla” DeepONets. Hence, we believe that our method will trigger further developments in the field of scientific machine learning, for the development of new ‘’light”schemes that will provide high accuracy while reducing dramatically the computational cost. A MATLAB toolbox for RandONets, including demos, is available on GitHub at https://github.com/GianlucaFabiani/RandONets.

Interpretable machine learning↗

Selected Lessons Learned over the ISS Design, Development, Assembly, and Operations: Applicability to International Cooperation for Standardization

This slide presentation reviews some of the lessons learned in the sphere of international cooperation during the development, assembly and operation of the International Space Station. From the begining all Partners shared a common objective to build, operate and utilize a crewed laboratory in low orbit as an international partnership. The importance of standards is emphasized.

Hirsch, David B.↗

Intelligent Systems and Advanced User Interfaces for Design, Operation, and Maintenance of Command Management Systems

Historically Command Management Systems (CMS) have been large, expensive, spacecraft-specific software systems that were costly to build, operate, and maintain. Current and emerging hardware, software, and user interface technologies may offer an opportunity to facilitate the initial formulation and design of a spacecraft-specific CMS as well as a to develop a more generic or a set of core components for CMS systems. Current MOC (mission operations center) hardware and software include Unix workstations, the C/C++ and Java programming languages, and X and Java window interfaces representations. This configuration provides the power and flexibility to support sophisticated systems and intelligent user interfaces that exploit state-of-the-art technologies in human-machine systems engineering, decision making, artificial intelligence, and software engineering. One of the goals of this research is to explore the extent to which technologies developed in the research laboratory can be productively applied in a complex system such as spacecraft command management. Initial examination of some of the issues in CMS design and operation suggests that application of technologies such as intelligent planning, case-based reasoning, design and analysis tools from a human-machine systems engineering point of view (e.g., operator and designer models) and human-computer interaction tools, (e.g., graphics, visualization, and animation), may provide significant savings in the design, operation, and maintenance of a spacecraft-specific CMS as well as continuity for CMS design and development across spacecraft with varying needs. The savings in this case is in software reuse at all stages of the software engineering process.

Mitchell, Christine M.↗

Intelligent systems and advanced user interfaces for design, operation, and maintenance of command management systems

Historically, command management systems (CMS) have been large and expensive spacecraft-specific software systems that were costly to build, operate, and maintain. Current and emerging hardware, software, and user interface technologies may offer an opportunity to facilitate the initial formulation and design of a spacecraft-specific CMS as well as to develop a more generic CMS system. New technologies, in addition to a core CMS common to a range of spacecraft, may facilitate the training and enhance the efficiency of CMS operations. Current mission operations center (MOC) hardware and software include Unix workstations, the C/C++ programming languages, and an X window interface. This configuration provides the power and flexibility to support sophisticated and intelligent user interfaces that exploit state-of-the-art technologies in human-machine interaction, artificial intelligence, and software engineering. One of the goals of this research is to explore the extent to which technologies developed in the research laboratory can be productively applied in a complex system such as spacecraft command management. Initial examination of some of these issues in CMS design and operation suggests that application of technologies such as intelligent planning, case-based reasoning, human-machine systems design and analysis tools (e.g., operator and designer models), and human-computer interaction tools (e.g., graphics, visualization, and animation) may provide significant savings in the design, operation, and maintenance of the CMS for a specific spacecraft as well as continuity for CMS design and development across spacecraft. The first six months of this research saw a broad investigation by Georgia Tech researchers into the function, design, and operation of current and planned command management systems at Goddard Space Flight Center. As the first step, the researchers attempted to understand the current and anticipated horizons of command management systems at Goddard. Preliminary results are given on CMS commonalities and causes of low re-use, and methods are proposed to facilitate increased re-use.

Potter, William J.↗

Advanced Building Technologies for Energy Savings and Decarbonization

The building sector is a major consumer of energy, making it essential to explore innovative strategies for reducing its environmental impact. As we shift our focus toward decarbonization and electrification, the need for advanced building operation techniques and equipment becomes increasingly urgent. These advancements are crucial for maintaining or enhancing indoor environmental quality while simultaneously minimizing energy consumption. This Special Issue aims to showcase cutting-edge technologies in building energy management, alongside effective measurement and verification methods. It will also address fault detection and diagnosis approaches, leveraging both simulation and experimental studies. The ultimate goal is to highlight solutions that not only reduce CO₂ emissions but also improve indoor environmental quality, creating healthier and more sustainable living and working spaces. By presenting a diverse array of research contributions, this Special Issue will provide valuable insights into the latest advancements in building energy technologies. It will drive the discussion around effective strategies for energy efficiency and environmental sustainability in the building sector. These efforts can help transform buildings from energy consumers into more efficient spaces that contribute to reducing our overall carbon footprint.

Im, Piljae↗

A digital twin platform for building performance monitoring and optimization: Performance simulation and case studies

Advancements in sensor technology, data analytics, affordable compute, and communication infrastructure have paved the way for Digital Twin technology in optimizing building operations and controls. This study presents the development of an open and interoperable web-based Digital Twin platform for integrating diverse data streams and facilitating effective user interactions. The platform utilizes modern technologies for the web framework and time-series data management, ensuring scalability and responsiveness. The backend supports seamless integration of diverse data sources and emulators, incorporating data from building sensors and meters, external weather Application Programming Interfaces, and advanced EnergyPlus simulation models of the building and its energy systems including the Distributed Energy Resources that are formulated in Functional Mockup Units. A simulation case study was conducted with FlexLab, a test facility on Lawrence Berkeley National Laboratory campus. The case study includes normal operations, Distributed Energy Resource integration, and power outage scenarios, to illustrate the Digital Twin’s ability to provide critical insights into energy performance and thermal resilience. The results demonstrated the platform’s potential as a decision-support tool for optimizing building energy performance and enhancing resilience against extreme weather events. Future work will focus on deploying the Digital Twin platform to a real building for field validation, extending its capabilities to cover more scenarios such as bidirectional Electric Vehicle interactions, and enhancing user engagement.

EnergyPlus↗

Exploring Informal Learning at the Airlines

Airline pilot training is extensive, highly structured, and defined by aircraft and airspace system operating requirements, yet pilots describe a tradition of between-pilot knowledge transfer and self-directed learning. This learning supplements their approved training programs. While industry and regulators focus on “formal learning” systems, pilots report relying on “informal learning” to build operational expertise. The persistence of informal learning suggests gaps in how successfully formal learning prepares pilots to handle operational complexities. The community that researches learning has extensively studied informal learning, and its characteristics seem to align with how pilots report increasing their skills and knowledge informally. However, no research into informal learning practices among airline pilots seems to exist. In this paper we provide examples of informal learning in commercial aviation, how they fit into two existing frameworks for workplace learning, and propose that researching informal learning might help identify opportunities to improve formal aviation learning systems.

pilot learning↗

Emergency Lighting

A lighting system originally developed for NASA's Apollo and Skylab manned spacecraft resulted in a industrial spinoff and creation of a whole new company to produce and market the product line. The company is UDEC Corp., Waltham, Mass. UDEC's "Multi-Mode" electronic lighting systems are designed for plant emergency and supplemental use, such as night lighting, "always-on" stairwell lights and illuminated exit signs. Their advantages stem from the qualities demanded for spacecraft installation: extremely high fight output with very low energy drain, compactness, light weight, and high reliability. The Multi-Mode system includes long-life fluorescent lamps operated by electronic circuitry, a sealed battery that needs no maintenance for 10 years, and a solid-state battery charger. A typical emergency installation consists of a master module with battery and an eight watt lamp, together with four remote "Satellight" modules powered by the master's battery. As a night lighting system for maintenance or I security, UDEC fixtures can bypass the battery and 1 operate on normal current at a fraction of the energy 1 demand of conventional night lighting. Industrial customers have realized savings of better than ninety percent with UDEC night lights. UDEC started as a basement industry in 1972 but the company has already sold more than 1,000 lighting systems to building operators.

Source record↗

An open control sequence specification to scale building demand flexibility via analytics software

For over two decades, researchers and practitioners have showcased the ability of large commercial buildings to provide grid services by shedding or shifting load. Various utility demand response (DR) and virtual power plant (VPP) programs throughout the United States are presently utilizing these demand-side resources. However, growth of these programs have been limited, in part due to the high cost necessary to integrate the DR control strategies into the building automation system (BAS). Implementing these strategies involves adjusting control sequences, necessitating dozens of hours of customized programming per building, limiting their adoption to large organizations and progressive owners. Recent efforts by researchers and industry have demonstrated the capability of energy management and information systems (EMIS), originally designed for fault detection and diagnostics, to interface with existing BAS and perform supervisory control to optimize building operations. While these approaches are quickly being adopted by industry, demand flexibility (DF) control strategies remain limited in product offerings. One of the challenges is the lack of documented best-practice DF sequences, despite the rich literature on field implementations. This paper develops a new open-specification for a zone-based temperature adjustment shed strategy for commercial building HVAC systems, describing the specification’s implementation in two EMIS tools in both experimental and field settings. Both implementations successfully reduced electric load by at least 40% on average during the called event, while maintaining temperature limits. This study’s detailed process from specification to deployment shows the potential for scalability as well as highlights challenges related to integration with heterogeneous BAS products.

Granderson, Jessica↗

Human Factors Directions for Civil Aviation

Despite considerable progress in understanding human capabilities and limitations, incorporating human factors into aircraft design, operation, and certification, and the emergence of new technologies designed to reduce workload and enhance human performance in the system, most aviation accidents still involve human errors. Such errors occur as a direct or indirect result of untimely, inappropriate, or erroneous actions (or inactions) by apparently well-trained and experienced pilots, controllers, and maintainers. The field of human factors has solved many of the more tractable problems related to simple ergonomics, cockpit layout, symbology, and so on. We have learned much about the relationships between people and machines, but know less about how to form successful partnerships between humans and the information technologies that are beginning to play a central role in aviation. Significant changes envisioned in the structure of the airspace, pilots and controllers' roles and responsibilities, and air/ground technologies will require a similarly significant investment in human factors during the next few decades to ensure the effective integration of pilots, controllers, dispatchers, and maintainers into the new system. Many of the topics that will be addressed are not new because progress in crucial areas, such as eliminating human error, has been slow. A multidisciplinary approach that capitalizes upon human studies and new classes of information, computational models, intelligent analytical tools, and close collaborations with organizations that build, operate, and regulate aviation technology will ensure that the field of human factors meets the challenge.

Hart, Sandra G.↗

Analysis of strategies to meet ASHRAE S241 infectious aerosol control targets by space type and region using EnergyPlus™ simulations

Professional organizations, such as ASHRAE, have recently proposed new voluntary standards for controlling infectious aerosols. Specifically, ASHRAE Standard 241 (S241) defines equivalent clean air targets for a range of space types that can be met through combinations of mitigation measures. This paper seeks to inform the selection of measures by space type and climate zone through building simulations that quantify the impacts of increased outdoor air ventilation; increasing filtration and/or adding germicidal ultraviolet (GUV) in the central heating, ventilation, and air-conditioning (HVAC) systems; or using portable air cleaners (PACs), upper room GUV, or whole room GUV approaches to increase equivalent clean air delivery. The measures are assessed individually and in practical combinations for their ability to meet S241 in four space types (offices, classrooms, dining areas, and healthcare waiting rooms) using prototype buildings modeled using EnergyPlus. The mitigation measures are compared holistically against baseline building operations using metrics for equivalent clean air, energy, and comfort. The results in this study show that single measures can meet S241 for offices with minimal impacts on energy and comfort, while either upper room GUV systems or combinations of measures such as MERV 13 HVAC filtration with PACs are needed to meet S241 for classrooms. The dining area and healthcare waiting room targets cannot be met in this study when assuming design occupancy and MS2 as the challenge agent, even when combining multiple measures together. This paper provides valuable considerations when designing measures to meet S241 for a range of spaces and scenarios.

GUV↗

Rubisco activity and activation state dictate photorespiratory plasticity in Betula papyrifera acclimated to future climate conditions

Plant metabolism faces a challenge of investing enough enzymatic capacity to a pathway without overinvestment. As it takes energy and resources to build, operate, and maintain enzymes, there are benefits and drawbacks to accurately matching capacity to the pathway influx. The relationship between functional capacity and physiological load could be explained through symmorphosis, which would quantitatively match enzymatic capacity to pathway influx. Alternatively, plants could maintain excess enzymatic capacity to manage unpredictable pathway influx. In this study, we use photorespiration as a case study to investigate these two hypotheses in Betula papyrifera. This involves altering photorespiratory influx by manipulating the growth environment, via changes in CO 2 concentration and temperature, to determine how photorespiratory capacity acclimates to environmental treatments. Surprisingly, the results from these measurements indicate that there is no plasticity in photorespiratory capacity in B. papyrifera, and that a fixed capacity is maintained under each growth condition. The fixed capacity is likely due to the existence of reserve capacity in the pathway that manages unpredictable photorespiratory influx in dynamic environments. Additionally, we found that B. papyrifera had a constant net carbon assimilation under each growth condition due to an adjustment of functional rubisco activity driven by changes in activation state. These results provide insight into the acclimation ability and limitations of B. papyrifera to future climate scenarios currently predicted in the next century.

59 BASIC BIOLOGICAL SCIENCES↗

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗

Analysis of Frost Formation and Novel Defrost Techniques for Commercial Refrigeration Applications (NFE-19-07879)

The purpose of this Cooperative Research and Development Agreement (CRADA) was to test a heat exchanger defrosting approach using radiant energy, including ultraviolet-C, -B, and -A. A preliminary test conducted at Oak Ridge National Laboratory demonstrated the concept of ultraviolet-based defrosting, which melts frost on the fin surface of a heat exchanger without increasing its overall temperature, thereby offering a new defrosting method that does not interrupt heat exchanger operation. Building on this key finding, this CRADA outlines a plan for upgrading the test equipment and for further testing to bridge the gap between the concept and its feasibility for industrial applications. This report also provides a brief review of existing defrosting technologies, the current state of the experimental system, and future parametric experiment plans.

99 GENERAL AND MISCELLANEOUS↗

Analysis of Frost Formation and Novel Defrost Techniques for Commercial Refrigeration Applications

The purpose of this Cooperative Research and Development Agreement (CRADA) was to test a heat exchanger defrosting approach using radiant energy, including ultraviolet-C, -B, and -A. A preliminary test conducted at Oak Ridge National Laboratory demonstrated the concept of ultraviolet-based defrosting, which melts frost on the fin surface of a heat exchanger without increasing its overall temperature, thereby offering a new defrosting method that does not interrupt heat exchanger operation. Building on this key finding, this CRADA outlines a plan for upgrading the test equipment and for further testing to bridge the gap between the concept and its feasibility for industrial applications. This report also provides a brief review of existing defrosting technologies, the current state of the experimental system, and future parametric experiment plans.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Section 3 Reporting: Preserve America

Brookhaven National Laboratory (BNL) is a multidisciplinary laboratory with seven Nobel Prize-winning discoveries, 37 R&D 100 Awards, and countless advances in science and technology. For over 75 years, the Laboratory has played a leading role in the Department of Energy (DOE) Science and Technology mission and continues to contribute to the DOE’s missions in energy resources, environmental quality, and national security. The Laboratory is operated and managed by Brookhaven Science Associates (BSA), which was founded by the Research Foundation for the State University of New York on behalf of Stony Brook University, and Battelle, a non-profit applied science and technology organization. BNL is committed to longstanding partnerships with researchers, academic institutions, industry, students, teachers, and the surrounding community. BNL is located near the center of Suffolk County on Long Island, New York within Brookhaven Township, about 60 miles east of New York City. Most of BNL’s principal facilities are located near the center of the 5,265-acre (8.23 square mile) site (Figure 1). The developed area encompasses approximately 1,820 acres, consisting of: ▪ 500 acres originally developed by the Army (as part of WW II Camp Upton) and still used for offices and other operational buildings. ▪ 200 acres occupied by large, specialized research facilities. ▪ 520 acres occupied by outlying facilities, such as the Sewage Treatment Plant, research agricultural fields, housing facilities, and fire breaks. ▪ 400 acres of roads, parking lots, and connecting areas. ▪ 200 acres developed for the Long Island Solar Farm. The balance of the site, approximately 3,400 acres, is largely wooded and represents a native pine barrens ecosystem. In November 2000, DOE set aside 530 acres of undeveloped land at BNL as the Upton Ecological and Research Reserve. The Upton Reserve preserves this portion of the pine barrens ecosystem and provides an area for ecological research and education activities.

99 GENERAL AND MISCELLANEOUS↗

Extraction and Analysis of Time Series Data from Building Automation Systems Using Large Language Models

Semantic schemas like Haystack 4, Brick and ASHRAE standard 223 enable the structured, standardized, and machine-readable representation of building data, facilitating interoperability, data integration, and advanced analytics. However, extracting information from these models requires specialized expertise in SPARQL and other programming languages, skills that are not commonly found among building professionals. Recent advancements in Large Language Models (LLMs), such as ChatGPT, enable the construction of queries using natural language, making it easier for individuals to interact with these systems in a manner that resembles everyday speech. However, these methods have not yet been tested on building semantic ontologies. This paper introduces a novel workflow and tool for enabling users to ask questions about a specific building's data, using natural language and receive answers automatically generated by GPT-4o. Our approach integrates semantic ontologies with advanced LLM capabilities to automate three critical steps: (1) generating SPARQL queries to retrieve time series references from ontological models, (2) extracting the corresponding time series data from the Building Automation System, and (3) performing computations and visualizations tailored to the user's query. The proposed method simplifies access to BAS data, allowing both domain experts and non-specialists to conduct sophisticated analyses without needing extensive technical knowledge of semantic web technologies. By demonstrating this pipeline, we facilitate more accessible and scalable data-driven decision-making in building operations and management.

Mulayim, Ozan Baris↗