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NASA NTRSDate not supplied

Connecting Federal Agencies to Satellite Earth Observations: NASA’s Satellite Needs Working Group Assessment Process

The Satellite Needs Working Group (SNWG), part of the U.S. Group on Earth Observations (USGEO), surveys agencies across the U.S. Government to identify the satellite Earth observations each agency needs to fulfill its high-priority objectives and responsibilities. Around 20 civilian agencies participate in the SNWG survey every two years. After receiving the surveys, the National Aeronautics and Space Administration (NASA) conducts an in-depth evaluation of each agency's needs in collaboration with fellow satellite Earth data providers, the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS). This assessment process, which takes place over an eight-month period, is divided into distinct phases. NASA first assembles an assessment team with the necessary subject matter expertise to evaluate each submitted need. An in-depth interview with each submitting agency then follows, featuring discussion of current and upcoming satellite missions as well as potential new activities that NASA, NOAA, and/or USGS could undertake to meet the agency's needs. The assessment teams then further evaluate these potential activities or solutions to identify how many agencies would benefit and estimate how much their level of satisfaction would increase. Solutions expected to have broad-reaching and significant agency benefits are proposed by NASA for funding. SNWG agencies receive an assessment report for each submitted need, in which the tri-agency assessment teams provide a detailed evaluation of the agency need, information on relevant satellite missions, and links to specific datasets or training resources. The SNWG Management Office at NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT) contributes to NASA’s SNWG assessment in a variety of capacities, including statistical analysis of the survey responses to identify trends and similar needs across multiple agencies. The Management Office also provides analytics for the new proposed solutions, demonstrating and quantifying their potential value. For activities that receive funding, the Management Office manages the implementation process and works to maximize the benefit to SNWG agencies via a Stakeholder Engagement Program.

Katrina Virts↗

NASA NTRS1977

The utility of short wavelength (1mm) remote sensing techniques for the monitoring and assessment of hydrologic parameters

Remote sensing programs that respond to the requirements of the water resources management and hydrologic communities are reviewed. The major areas where the needs of water resources management are being met involve the mapping and monitoring of snowcovered areas, hydrologic landuse, and surface water area. In the case of snowcovered area mapping the transfer of technology process is now being accomplished in the Western United States in a cooperative effort involving 6 federal agencies and 3 state agencies along with NASA. A new collaborative effort of the U.S. Army Corps of Engineers and NASA involves the mapping of landuse by Landsat and its use in hydrologic engineering watershed models employed in flood control/waterworks planning, design, and management. Improved systems planned for implementation in the 1978-1981 time frame can be expected to result in increased utilization of visible and near infrared remote sensing observations.

Rango, A.↗

NASA NTRS2000

The Dynamics of a Semi-Arid Region in Response to Climate and Water - Use Policy

The objectives of this project were to determine the response of semi-arid ecosystems to the combined forcings of climate variability and anthropogenic stress. Arid and semi-arid systems encompass close to 40% of the worlds land surface. The ecology of these regions are principally limited by water, and as the water resources wax and wane, so should the health and vigor of the ecosystems. Water, however, is a necessary and critical resource for humans living in these same regions. Thus for many and and semi-arid regions the natural systems and human systems are in direct competition for a limited resource. Increasing competition through development of and and semi-arid regions, export of water resources, as well as potential persistent changes in weather patterns are likely to lead to fundamental changes in carrying capacity, resilience, and ecology of these regions. A detailed understanding of these systems respond to forcing on a regional and local scale is required in order to better prepare for and manage future changes in the availability of water. In the Owens Valley CA, decadal changes in rainfall and increased use of groundwater resources by Los Angles (which derives 60-70% of its water from this region) have resulted in a large-scale experiment on the impacts of these changes in semi-arid ecosystems. This project works directly with the Inyo County Water Department (local water authority) and the Los Angles Department of Water and Power (regional demand on water resources) to understand changes, their causes, and impacts. Very detailed records have been kept for a number of selected sites in the valley which provide essential ground truth. These results are then scaled up through remote sensed data to regions scale to assess large scale patterns and link them to the fundamental decisions regarding the water resources of this region. A fundamental goal is to understand how resilient the native ecosystems are to large changes in water resources. Are they are on a spring (remove and return resources, do the systems return to the original state) or a vector (when water returns have the systems fundamentally changed).

Mustard, John F.↗

NASA NTRS1989

NASA-evolving to Ada: Five-year plan. A plan for implementing recommendations made by the Ada and software management assessment working group

At their March 1988 meeting, members of the National Aeronautics and Space Administration (NASA) Information Resources Management (IRM) Council expressed concern that NASA may not have the infrastructure necessary to support the use of Ada for major NASA software projects. Members also observed that the agency has no coordinated strategy for applying its experiences with Ada to subsequent projects (Hinners, 27 June 1988). To deal with these problems, the IRM Council chair appointed an intercenter Ada and Software Management Assessment Working Group (ASMAWG). They prepared a report (McGarry et al., March 1989) entitled, 'Ada and Software Management in NASA: Findings and Recommendations'. That report presented a series of recommendations intended to enable NASA to develop better software at lower cost through the use of Ada and other state-of-the-art software engineering technologies. The purpose here is to describe the steps (called objectives) by which this goal may be achieved, to identify the NASA officials or organizations responsible for carrying out the steps, and to define a schedule for doing so. This document sets forth four goals: adopt agency-wide software standards and policies; use Ada as the programming language for all mission software; establish an infrastructure to support software engineering, including the use of Ada, and to leverage the agency's software experience; and build the agency's knowledge base in Ada and software engineering. A schedule for achieving the objectives and goals is given.

Source record↗

DOE OSTI2024

High throughput, accurate gene annotation through AI and HPC-enabled structural analysis

With the advances in next generation sequencing technologies, the number of sequenced genomes is growing exponentially, resulting in a technology bottleneck for the translation of sequence information into usable hypotheses about the function of each gene. We have proposed leveraging our leadership high-performance computing (HPC) resources to help break this annotation bottleneck. Here we design an HPC-based framework to infer gene function from gene sequence by incorporating information about protein structure and interactions predicted by deep learning approaches. Accurate functional prediction and gene annotation using computational methods will facilitate breakthroughs in the genomic sciences essential to understanding and harnessing life processes in bacteria, fungi and plants. The development and applications of the state-of-the-art deep neural networks to protein structural modeling, interaction prediction, sequence comparison, and quality assessment of protein structural models will be made possible by leadership computational resources. These HPC-enabled bioinformatics and molecular modeling tools will provide powerful insights into molecular functions of genes.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2003

From Oxygen Generation to Metals Production: In Situ Resource Utilization by Molten Oxide Electrolysis

For the exploration of other bodies in the solar system, electrochemical processing is arguably the most versatile technology for conversion of local resources into usable commodities: by electrolysis one can, in principle, produce (1) breathable oxygen, (2) silicon for the fabrication of solar cells, (3) various reactive metals for use as electrodes in advanced storage batteries, and (4) structural metals such as steel and aluminum. Even so, to date there has been no sustained effort to develop such processes, in part due to the inadequacy of the database. The objective here is to identify chemistries capable of sustaining molten oxide electrolysis in the cited applications and to examine the behavior of laboratory-scale cells designed to generate oxygen and to produce metal. The basic research includes the study of the underlying high-temperature physical chemistry of oxide melts representative of lunar regolith and of Martian soil. To move beyond empirical approaches to process development, the thermodynamic and transport properties of oxide melts are being studied to help set the limits of composition and temperature for the processing trials conducted in laboratory-scale electrolysis cells. The goal of this investigation is to deliver a working prototype cell that can use lunar regolith and Martian soil to produce breathable oxygen along with metal by-product. Additionally, the process can be generalized to permit adaptation to accommodate different feedstock chemistries, such as those that will be encountered on other bodies in the solar system. The expected results of this research include: (1) the identification of appropriate electrolyte chemistries; (2) the selection of candidate anode and cathode materials compatible with electrolytes named above; and (3) performance data from a laboratory-scale cell producing oxygen and metal. On the strength of these results it should be possible to assess the technical viability of molten oxide electrolysis for in situ resource utilization on the Moon and Mars. In parallel, there may be commercial applications here on earth, such as new green technologies for metals extraction and for treatment of hazardous waste, e.g., fixing heavy metals.

Khetpal, Deepak↗

DOE OSTI2026

Grid and Wind Transient Ride Through Assessment of Type-5 Synchronous Wind Turbine

The integration of the inverter-based renewable energy resources is increasing and replacing conventional synchronous generators in the evolving bulk power system (BPS), impacting the system inertia along the way. Also, the limited over-current capability of power electronic converters continues to deteriorate the grid strength of the evolving power systems. Type-5 wind turbines have the potential to mitigate these problems because this configuration interfaces with the grid through a synchronous generator; hence, its operation and consequent grid impacts might mimic the conventional synchronous generator. The drivetrain of this type of turbines, however, is different from the more common Type-3 and Type-4 turbines. A hydraulic torque converter and gearbox with torque limiting feature are integral parts of a Type-5 wind turbine drivetrain, to isolate the wind speed variations at the wind turbine rotor from the grid connected generator side. This supports the synchronous operation of the generator. However, high fidelity models of Type-5 wind turbine drivetrain including these core components are not openly and widely available for grid integration and transient stability studies. Moreover, Type-5 turbines are not commonly deployed and hence its power generation and frequency stability characteristics are not widely publicized. This hinders appropriate assessment of Type-5 wind power plant's contribution to bulk grid resilience. This work provides transient stability assessment of a standalone Type-5 wind turbine. Normal operation and grid transient studies are performed to see the step load change and wind speed disturbance impacts on the Type-5 drivetrain and generation dispatch. Preliminary results show that Type-5 wind turbines can support load changes of between 0.6 p.u to 1.15 p.u. Beyond this point, the synchronization of the generator starts to deviate from its set point.

17 - WIND ENERGY↗

NASA NTRSDate not supplied

Root Cause Analysis of the Data Refinement Process – Medical Conditions Capability Resource Tables

The medical system for spaceflight thus far has been designed to support missions in low earth orbit (LEO). Crew capabilities are limited and heavily dependent on the team of medical support staff at Mission Control Center (MCC) to guide diagnosis and management. However, missions to the Moon and Mars will suffer from several constraints that will make this ground support focused approach to care ineffective. In order to update and modify medical system design, NASA has relied on Probabilistic Risk Assessment (PRA) modeling to mitigate medical risk through trade space analysis. Specifically, capability resource tables (CRT’s) were developed to create a dataset of resources required to manage a list of accepted medical conditions significant in exploration spaceflight. With 120 conditions, this dataset contained hundreds of capabilities and thousands of resources with tens of thousands of cells of data. Initially these tables were built in excel for high throughput during development, but ultimately had to be transferred, managed, and modified into the Evidence Library database for modeling purposes. The process of collating and reviewing the Evidence Library revealed numerous errors in the dataset that had to be corrected through iterative changes. Several error types emerged during this process and can be broken into specific classifications defined as “input”, “transcription”, “structural”, “branching”, and “information”. In reviewing these error types through the root cause analysis (RCA) approach, we were able to identify the contributors to these errors which included single data review points, changing product end goals, limited software selection, time constraints and several others. By reviewing and evaluating the underlying causes we can provide possible system improvements that can be implemented for current and future data management in PRA model inputs.

A. Anderson↗

DOE OSTI2023

Cybersecurity Assessment for a Behind-the-Meter Solar PV System: A Use Case for the DER-CF

The world's energy production is shifting toward lower-cost, cleaner, more efficient, and sustainable sources. The increasing numbers of distributed energy resources (DERs) are allowing for the rapid transformation of electric grids toward achieving the goal of energy decarbonization. Along with cleaner and more efficient energy, however, we must also aim for a secure energy future. Solar photovoltaic (PV) systems are an important part of this transition. This paper discusses a cybersecurity risk assessment for behind-the-meter DERs using a solar PV system as a use case of the Distributed Energy Resource Cybersecurity Framework (DER-CF) developed by the National Renewable Energy Laboratory. This poster presents a conference paper on the risk assessment processes and summarizes the DER-CF's use case recommendations to strengthen the cybersecurity posture of the electric grid.

cybersecurity↗

DOE OSTI2024

LiAISON (Life-cycle Assessment Integration into Scalable Open-source Numerical models) [SWR-24-01]

We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE)7. We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming. Scientific publication available here: https://pubs.acs.org/doi/full/10.1021/acs.est.2c04246

Ghosh, Tapajyoti↗

DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Prospective Impact Analysis of Novel Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM (Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

decarbonizing↗

DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming.

decarbonizing↗

DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 degrees C or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming.

decarbonizing↗

DOE OSTI2024

Towards Prospective LCA Using Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) Framework for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM(Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

emissions↗

DOE OSTI2022

Oklahoma State University – Industrial Assessment Center (Final Report)

Our first primary objective is to provide industrial assessments to clients in Oklahoma, Arkansas, Kansas, and north and northwest Texas, including the Texas Panhandle (about 10% of the state of Texas). This geographical region extends about 400 miles north and south and about 600 miles east and west. In addition, the partnership links two universities, Oklahoma State University (OSU) and Wichita State University (WSU) within our region together in a significant collaboration focused on improving our region’s industrial competitiveness. Our second primary objective is to produce competent, motivated energy engineers. Student training will be a combination of classroom work, individual mentoring, and on-the-job training. The focus will be on energy conservation technologies, in conjunction with increased productivity, the economics of the same, and client relationships including client recruiting, on-site assessment, reporting, and client-related communications. Finally, we will work to provide resources and expertise to complement the first two primary objectives. The effectiveness and efficiency of our IAC will be measured, internally, by four broad criteria: (1) client benefits, (2) student development and acceptance after graduation, (3) regional/national contribution to lowering energy use and reducing pollutants, and (4) compliance with DOE/FM requirements. Client-related benefits include client contacts, timeliness of assessments and reports, the value of recommendations provided, and the value of recommendations implemented. Student development includes many students trained and the level of training provided in energy, waste, and productivity-related technologies as well as teamwork and communication skills. The contribution to lower national energy use and waste production is a natural outcome of the program. Compliance with DOE/FM requirements includes timeliness of reports, participation in Best Practices activities, contributions to EERE goals and objectives, and other requirements as communicated to the IAC.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Interpretable Data-Driven Probabilistic Power System Load Margin Assessment with Uncertain Renewable Energy and Loads

The increasing uncertainties caused by the high-penetration of stochastic renewable generation resources poses a significant threat to the power system voltage stability. To address this issue, this paper proposes a probabilistic deep kernel learning enabled surrogate model to extract the hidden relationship between uncertain sources, i.e., wind power and loads, and load margin for probabilistic load margin assessment (PLMA). Unlike other deep learning approaches, a kernel SHAP provides the sensitivity analysis as well as interpretability of the inputs to outputs influences. This allows identifying the critical factors that affect load margin so that corrective control can be initiated for stability enhancement. Numerical results carried out on the IEEE 118-bus power system demonstrate the accuracy and efficiency of the proposed data-driven PLMA scheme.

deep kernel learning↗

DOE OSTI2023

Overcoming persistent challenges in putting environmental flow policy into practice: a systematic review and bibliometric analysis

The implementation of environmental flows (e-flows) aims to reduce the negative impacts of hydrological alteration on freshwater ecosystems. Despite the growing attention to the importance of e-flows since the 1970s, actual implementation has lagged. Therefore, we explore the limitations in e-flows implementation, their systemic reasons, and solutions. We conducted a systematic review and a bibliometric analysis to identify peer-reviewed articles published on the topic of e-flows implementation research in the last two decades, resulting in 68 research and review papers. Co-occurrence of terms, and geographic and temporal trends were analyzed to identify the gaps in environmental water management and propose recommendations to address limitations on e-flows implementation. We identify the underlying causes and potential solutions to such challenges in environmental water management. The limitations to e-flow implementation identified were categorized into 21 classes. The most recognized limitation was the competing priorities of human uses of water (n = 29). Many secondary limitations, generally co-occurring in co-causation, were identified as limiting factors, especially for implementing more nuanced and sophisticated e-flows. The lack of adequate hydrological data (n = 24) and ecological data (n = 28) were among the most mentioned, and ultimately lead to difficulties in starting or continuing monitoring/adaptive management (n = 28) efforts. The lack of resource/capacity (n = 21), experimentation (n = 19), regulatory enforcement (n = 17), and differing authorities involved (n = 18) were also recurrent problems, driven by the deficiencies in the relative importance given to e-flows when facing other human priorities. In order to provide a clearer path for successful e-flow implementation, system mapping can be used as a starting point and general-purpose resource for understanding the sociohydrological problems, interactions, and inherited complexity of river systems. Secondly, we recommend a system analysis approach to address competing demands, especially with the use of coupled water-energy modeling tools to support decision-making when hydropower generation is involved. Such approaches can better assess the complex interactions among the hydrologic, ecological, socioeconomic, and engineering dimensions of water resource systems and their effective management. Lastly, given the complexities in environmental water allocation, implementation requires both scientific rigor and proven utility. Consequently, and where possible, we recommend a move from simplistic flow allocations to a more holistic approach informed by hydroecological principles. To ease conflicts between competing water demands, water managers can realize more 'pop per drop' by supporting key components of a flow regime that include functional attributes and processes that enhance biogeochemical cycling, structural habitat formation, and ecosystem maintenance.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

US Department of Energy, Office of Science High Performance Computing Facility Operational Assessment 2019 Oak Ridge Leadership Computing Facility

Oak Ridge National Laboratory's (ORNL's) Leadership Computing Facility (OLCF) continues to surpass its operational target goals: supporting users; delivering fast, reliable computational ecosystems; creating innovative solutions for high performance computing (HPC) needs; and managing risks, safety, and security associated with operating some of the most powerful computers in the world. The results can be seen in the cutting-edge science conducted by users and the praise from the research community. Calendar year (CY) 2019 was a big year as OLCF staff ran five world-class resources (the leadershipclass computers Titan and Summit, the large analysis cluster called Eos, and the massive parallel filesystems called Atlas and Alpine)) and also began power and cooling upgrades for a 2021 exascale system called Frontier. While continuing exceptional operation of Titan, Eos, and Rhea, the OLCF released the Summit supercomputer for production on January 1, 2019. Summit debuted as the most capable and efficient system in its class and has been recognized as the most powerful system in the world for its performance on both the high performance linpack (HPL) and conjugate gradient (HPCG) benchmark applications since June 2018 according to TOP500. Summit represents the culmination of a multiyear effort between the OLCF, IBM, NVIDIA, and Mellanox to deliver a system that is unmatched for modeling, simulation, data analysis, and learning. To hit the ground running with science-ready applications on day one, application teams worked closely with the OLCF through the Center for Accelerated Application Readiness (CAAR) program for years in advance of the Summit deployment. CY 2019 was filled with outstanding results and accomplishments: a very high rating from users on overall satisfaction for the sixth year in a row; a tremendous amount of core-hours delivered to researchers from two leadership-class systems; and success in delivering on the allocation split of roughly 60%, 30%, and 10% of core-hours offered for the Innovative and Novel Computational Impact on Theory and Experiment (INCITE), Advanced Scientific Computing Research Leadership Computing Challenge (ALCC), and Director's Discretionary (DD) programs, respectively (see Operational Performance section). These accomplishments, coupled with the high utilization rates (overall and capability usage), represent the fulfillment of the promise of both leadership-class machines: efficient facilitation of leadership-class computational applications. Table ES.1 presents a summary of the 2019 OLCF metric targets and the associated results. More information can be found in the Operational Performance section for each OLCF resource. The scientific accomplishments of OLCF users are a strong indication of long-term operational success, with publications this year in such notable journals and publications as Nature, Nature Physics, Nature Plants, Physical Review X, Journal of the American Physical Society, Cell, Nano Letters, and Trends in Biotechnology. Crucial domain-specific discoveries facilitated by resources at the OLCF are described in the High Performance Computing Facility Operational Assessment 2019 Oak Ridge Leadership Computing Facility (OAR) Strategic Results section. For example, researchers used Summit to pinpoint and understand the production of proteins from genetic information, including mutations and the functional expression of disease (Section 8.2).

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