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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Ballistic characterization of additively manufactured extrusion deposited thermoplastic composite plates

Additive manufacturing (AM) is rapidly emerging in high performance applications such as army ground vehicles, automotive and transportation. However, the response of AM parts/components to extreme loading such as high velocity impacts is less studied. In this work, the performance under ballistic impact of AM panels is evaluated using a medium velocity gas gun, generating projectile velocities up to 400 m/s. The preferential print orientation properties are considered in order to evaluate whether the panels exhibit isotropic or anisotropic behavior under impact. Surface morphology is investigated by milling the beads smooth on samples and comparing the impact on as-printed samples to those that are smoothed. The effect of nickel chromium micron (nichrome) wire embedded in the AM panels (during print) of polycarbonate-carbon fiber (PC-CF) and polycarbonate-glass fiber (PC-GF) are explored. Thermoplastic polyurethane-acrylonitrile butadiene styrene/carbon fiber (TPU-ABS/CF), Acrylonitrile butadiene styrene-carbon-fiber (ABS-CF) AM samples absorbed >50% of the impact energy. The ballistic performance was noted to be in the following order – ABS-CF > TPU-ABS/CF > PC. Scanning electron microscopy (SEM) was conducted to study the interface between the nichrome wire and the polymer-fiber matrix. This work is the first of its kind exploring into the capabilities of AM panels as ballistic materials. This study leads the way for developing AM panels that are easily manufactured and exhibit superior ballistic resistance.

Landrie, Dakota M.↗
DOE OSTI2022

Identification of microRNAs responsive to arbuscular mycorrhizal fungi in Panicum virgatum (switchgrass)

Background: MicroRNAs (miRNAs) are important post-transcriptional regulators involved in the control of a range of processes, including symbiotic interactions in plants. MiRNA involvement in arbuscular mycorrhizae (AM) symbiosis has been mainly studied in model species, and our study is the first to analyze global miRNA expression in the roots of AM colonized switchgrass ( Panicum virgatum ), an emerging biofuel feedstock. AM symbiosis helps plants gain mineral nutrition from the soil and may enhance switchgrass biomass production on marginal lands. Our goals were to identify miRNAs and their corresponding target genes that are controlling AM symbiosis in switchgrass. Results: Through genome-wide analysis of next-generation miRNA sequencing reads generated from switchgrass roots, we identified 122 mature miRNAs, including 28 novel miRNAs. By comparing miRNA expression profiles of AM-inoculated and control switchgrass roots, we identified 15 AM-responsive miRNAs across lowland accession “Alamo”, upland accession “Dacotah”, and two upland/lowland F 1 hybrids. We used degradome sequencing to identify target genes of the AM-responsive miRNAs revealing targets of miRNAs residing on both K and N subgenomes. Notably, genes involved in copper ion binding were targeted by downregulated miRNAs, while upregulated miRNAs mainly targeted GRAS family transcription factors. Conclusion: Through miRNA analysis and degradome sequencing, we revealed that both upland and lowland switchgrass genotypes as well as upland-lowland hybrids respond to AM by altering miRNA expression. We demonstrated complex GRAS transcription factor regulation by the miR171 family, with some miR171 family members being AM responsive while others remained static. Copper miRNA downregulation was common amongst the genotypes tested and we identified superoxide dismutases and laccases as targets, suggesting that these Cu-miRNAs are likely involved in ROS detoxification and lignin deposition, respectively. Other prominent targets of the Cu miRNAs were blue copper proteins. Overall, the potential effect of AM colonization on lignin deposition pathways in this biofuel crop highlights the importance of considering AM and miRNA in future biofuel crop development strategies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Additive Manufacturing of Energy Harvesting Material System for Active Wireless MEMS Sensors

Combustion systems make up a large sum of the world’s yearly energy production despite advances in renewable energy sources. Precise control of pressure and temperature in combustion chambers allows for lower carbon emissions, and overall higher efficiency. Wireless temperature and pressure sensors are critical for the operation of combustion chambers. Energy harvesters have emerged as a viable way to power wireless sensors when heat and mechanical energy are available. Lithium niobate (LNB), barium titanate (BTO), and lead zirconate titanate (PZT) pyroelectric ceramics were studied and fabricated through additive manufacturing (AM) for use as energy harvesting structures in combustion environments. The electric power generation potential of LNB and PZT ceramics was studied by varying the temperature from 50 to 60 °C and by introducing cyclic compression loads of 2000 N amplitude. It was found that thermal energy conversion had the highest output of 500 nW and that combined thermal and mechanical conversion did not increase the harvesting potential because of the competing contributions of both effects. Powder bed and slurry extrusion AM methods were used to fabricate BTO and PZT ceramic structures. Optimization of the powder-based binder jetting method produced BTO ceramics with a relative density of 36.77% a piezoelectric coefficient of 153 pC/N. The density of the ceramics increased up to 56% when increasing the saturation of binder in the powder to because of liquid phase sintering. Finally, PZT structures were manufactured through AM and fin features were added to the design to enhance the heat transfer along the material. The harvested power density of the flat samples was 3.643 μW/cm 3 and of the finned samples was 3.034 μW/cm3. This research paves the ways for the development of self-powered wireless sensors in critical areas of operation such as combustion.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Understanding the Structure and Dynamics of Conjugated Polymers by Advancing Deuteration Chemistry and Neutron Scattering (Final Report)

The overarching goal of the proposed work was to set up a partnership between the University of Southern Mississippi (USM) and Oak Ridge National Laboratory (ORNL) to develop novel approaches to measure the backbone rigidity of conjugated polymers (CPs) and understand the critical role of sidechains on the backbone conformation and the materials macroscopic property. The backbone rigidity greatly influences the electronic properties of CPs, which ultimately determines the functionality and performance of these materials. Improvements in the electronic properties of CPs would allow for enhanced charge transport in semiconductor devices, improved photovoltaic performance, recycling of waste heat in thermoelectrics, and discovery of new phenomena that will enable the next generation of energy technologies. Although significant progress has been made to optimize the optical and electronic properties of CPs, largely through Edisonian methodologies, it remains a challenge to experimentally characterize conjugated backbone conformation (chain rigidity, torsion, planarity, and short-range order) and relate these to the fundamental optical and electronic properties (electronic coupling, charge transport, etc.). This has left fundamental gaps in our knowledge of the most basic structure/property relationships within these systems, precluded the study of fundamental physical phenomena, and constrained the design and realization of new electronic and device functionalities. Thus, the major goal of this work is to use novel deuteration methodologies via systematic synthetic approaches, and neutron scattering techniques to comprehensively characterize the structural and dynamic properties of CPs in contrast-matching solvents. Our work would, for the first time, elucidate the relationship between backbone rigidity and macroscopic properties. They will also allow a rational formulation of design principles for next-generation CPs that are resilient to disorder through precise control of the delocalized electrons along the polymer backbone. Overall, this project will advance our understanding of the structure, dynamics, and fundamental physics of these materials, which is crucial for enabling the prediction, design, control, and manipulation of current and emerging material electronic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine Learning for Joint Quality Control

The use of lightweight material combinations has been highly demanded in manufacturing automotive structures. However, making robust dissimilar material joints of such lightweight materials is still challenging. A significant barrier to achieving high-quality and repeatable joint performance is a deficient understanding of the relationship between the welding process, joint attributes, and joint performance. In this context, welding factors refer to material, equipment, environment, and process parameters, while joint features comprise specific microstructural attributes of the weld such as nugget size, heat affected zone (HAZ) topology, intermetallic layer thickness, and sheet thickness reduction. Joint performance is quantified in terms of strength (e.g., tensile shear, coach peel, cross-tension), weld size, and hardness, among other factors. While there have been many attempts to establish this process-structure-property relationship by developing a model derived from the associated physics and first principles, the complexity of the joining processes compounded by the complex interactions with different materials in an automotive assembly line environment, has hindered the usefulness of such attempts. The complexity is further exacerbated using different stacking materials, especially comprising dissimilar material combinations. In practice, the common approach has been the laborious process of creating welds, characterizing them, and then physically testing them through experimentation. With the emergence of artificial intelligence (AI) methods, an alternative pathway to eliciting the desired process-structure-property relationship at an accelerated pace is to use a data-driven approach by employing machine-learning (ML) techniques. This approach is benefitted by the availability of large streams of data, generated through years of research and testing by original equipment manufacturers, in the form of material, process, environmental, equipment, microstructural, and bulk-scale performance information from multimodal, multiscale sensors making measurements from laboratory-scale to production-scale processes. During Phase I efforts, which ended in fiscal year (FY) 2021, the Oak Ridge National Laboratory and Pacific Northwest National Laboratory (ORNL/PNNL) team demonstrated the effectiveness of different ML/AI frameworks in modeling complex relationships between resistance spot welding (RSW) process parameters, weld attributes, and joint properties using a subset of data from General Motors (GM). In FY 2022, the project team further refined and expanded their respective ML models to analyze additional welds with new weld stack-ups and materials to enhance the ML model predictive capability. ORNL extended its unified deep neural networks (DNN) ML training and prediction framework with new data streams of process parameters, and PNNL extended its model describing RSW process parameters’ associations with weld attributes. In FY 2023, the project team completed the development of the AI/ML architecture for analyzing aluminum/steel joints manufactured by GM via RSW and transitioned into the inline welding quality monitoring task for steel/steel RSW joints provided by GM.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Report on the Creation and Progress of the Hydrogen Regulatory Research Review Group

The current U.S. nuclear generation fleet is increasingly recognized by governmental, scientific, public policy, and industrial communities as having a strategic role in support of the ongoing national transition to a clean energy future. Federal incentives and actions are aligning to expand the role of nuclear power as a viable and more flexible contributor to the evolving national clean energy mix through programs and initiatives such as nuclear power loan guarantees; the Inflation Reduction Act’s (IRA) clean nuclear electrical, steam, and hydrogen incentives; the Infrastructure Investment and Jobs Act (IIJA, also referred to as the Bipartisan Infrastructure Act or BIL); and near term Department of Energy (DOE) funding opportunities related to nuclear based hydrogen hubs and nuclear integrated hydrogen demonstration projects. . Additionally, leveraging clean nuclear electricity and steam is being explored by industries desiring to transition away from carbon-intensive energy sources. Even with all these emerging enablers, notable barriers remain for the widespread adoption of these opportunities within the U.S. nuclear fleet, including the following: • Assurance of the markets for alternate products needed to support decision-making for large capital modification investments • Electric utility mindset and business history centered solely on producing electricity • Design change complexity and regulatory uncertainty associated with plant modifications needed to support alternate product streams The DOE Light Water Reactor Sustainability (LWRS) Flexible Plant Operations and Generation (FPOG) Pathway is developing options to help U.S. nuclear power plants (NPP) in all these areas to enable NPPs designed for steady baseload operation to integrate with intermittent wind and solar capacity to assure reliable clean energy for the nation. Current and near-term laboratory research is focusing on the technical, regulatory, safety, demonstration, and economic elements in support of improving nuclear plant flexibility through hybrid production of electricity and other non-electric products such as hydrogen and energy arbitrage.

42 ENGINEERING↗
DOE OSTI2023

Direct Insertion Polymerization of Ionic Monomers: Rapid Production of Anion Exchange Membranes

The limited number of methods to directly polymerize ionic monomers currently hinders rapid diversification and production of ionic polymeric materials, namely anion exchange membranes (AEMs) which are essential components in emerging alkaline fuel cell and electrolyzer technologies. Herein, we report a direct coordination-insertion polymerization of cationic monomers, providing the first direct synthesis of aliphatic polymers with high ion incorporations and allowing facile access to a broad range of materials. We demonstrate the utility of this method by rapidly generating a library of solution processable ionic polymers for use as AEMs. We investigate these materials to study the influence of cation identity on hydroxide conductivity and stability. Furthermore, we found that AEMs with piperidinium cations exhibited the highest performance, with high alkaline stability, hydroxide conductivity of 87 mS cm –1 at 80 °C, and a peak power density of 730 mW cm –2 when integrated into a fuel cell device.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Optimizing mixed cool thermal storage systems across a connected community

A high level of electric demand flexibility must be integrated into our building infrastructure to enable greater renewable energy penetration in the grid. In the U.S., 9% of electricity generated is used to cool buildings in a periodic manner, making this end-use an ideal target for active management through cool thermal energy storage (CTES) technologies. Historic uses for CTES are designed around central chilled water plants, but these systems cool less than 25% of U.S. commercial floorspace. Emerging technologies are under development to serve the many smaller distributed cooling systems, such as rooftop units (RTUs), and have the potential to add CTES to an additional 66% of cooled commercial floorspace. However, these unitary thermal storage systems (UTSS) lack the modeling and analysis tools to evaluate them in the future interactive grid context. Here, this study develops the modeling and optimization tools necessary to simultaneously examine central and distributed ice storage systems within the multi-building, connected community context. An integrated simulation-optimization workflow is created to allow for rapid customized analysis. Results demonstrate the energy and flexibility tradeoffs of various implementations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

A Scalable Quantum Cryptography Network for Protected Automation Communication (Final Report)

This is the final report for a CEDS-funded project aimed at developing a new quantum technology for securing utility communication networks used to control and monitor electrical grid equipment. Securing these control networks represents a unique challenge as the performance of the security solution has a direct impact on the stability and reliability of the electrical grid. Traditional, software-based solutions - developed for information networks - are not suitable for utility control networks because they introduce latency, require burdensome maintenance and upgrades, are often incompatible with legacy equipment, and introduce operational complexity that reduces grid reliability. Consequently, many U.S. utilities do not use existing solutions and, instead, protect their critical control networks through the careful isolation and obscuration of their networked equipment. With more utilities embracing grid automation, the attack surface that utilities must defend from hackers has grown to an unmanageable size. To address this situation, Qubitekk and its partners proposed and developed a hardware-based solution that can secure critical control networks without negatively impacting grid performance. This new solution is based on quantum key distribution (QKD) techniques that guarantee secure key generation and distribution across a utility control network. Through deployment and field testing of a prototype QKD system, we have shown that this solution delivers long-term network security, is technically feasible to implement and maintain on a utility’s distribution substation network and does not negatively impact grid operations. In addition, the project has identified and solved key challenges associated with generating, transmitting, and measuring coherent photonic quantum states on a real-world fiber optic network. These additional findings are playing a critical role in advancing quantum networks for quantum computing applications. An overview of the QKD prototype development effort, field testing activities and results, and additional findings relevant to emerging quantum networks are presented in this report.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A Real-Time Operations Manual for the IEEE 118 Bus Transmission Model

This document presents a sample operations manual for the IEEE 118 Bus Model, which is a synthetic test case developed in 1962 from a section of the transmission grid operated by American Electric Power (AEP). The model is one of the most used synthetic test cases for development of power system applications and is referenced by over 9000 papers. However, the model lacks any context for use with real-time energy management system (EMS) applications, including common considerations, such as generator ramp rates, reactive capabilities, operating limits, and other information typically used by power system operators for real-time decision making. This manual divides the IEEE 118 Bus Model into three operating areas, defines various operating limits, and sets recommended operating procedures for responding to a few types of emergency operating conditions. The manual can be used in support of a wide variety of human-in-the-loop evaluation methodologies for new advanced power applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Study of Nucleon Resonance Electroexcitations at Jefferson Lab

Nucleons are the most fundamental three-body systems in Nature. During the last decade crucial progress has been achieved in the exploration of the spectrum and the structure of nucleon resonances. Studies of exclusive ?N, ?+??p, K?, and K? electroproduction off protons on the evolution of the electrocouplings with photon virtuality Q2 using the CLAS spectrometer has revealed a complex interplay between an inner core of three dressed quarks surrounded by an external meson-baryon cloud. CLAS12 is the only facility capable of extending these results on the electrocouplings into the unexplored Q2 range, spanning the domain of quark momenta where ? 50% of hadron mass is generated. The increase of the CEBAF energy and the upgrade of the CLAS12 spectrometer to measure exclusive electroproduction channels at higher luminosity and photon virtualities will offer a unique opportunity to explore how the dominant part of hadron mass and the bound three-quark structure of the resonant states emerge from QCD.

Achenbach, Patrick↗
DOE OSTI2025

wa-hls4ml and lui-gnn: A benchmark and GNN-based surrogate model for hls4ml resource and latency estimation

As machine learning (ML) increasingly serves as a tool for addressing real-time challenges in scientific applications, the development of advanced tooling has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as model synthesis, are now becoming limiting factors in the rapid iteration of designs. To reduce these emerging constraints, multiple efforts are being launched toward designing an ML-based surrogate model that estimates resource usage of synthesized accelerator architectures. This model would reduce the design iteration time, especially when designing within a set of given hardware constraints. This approach shows considerable potential, but as it stands, the effort is early and would benefit from coordination and standardization to assist future work as it emerges. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of more than 100,000 fully connected neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. In addition to the resource utilization and latency data provided, the dataset includes generated artifacts and log files for many of the synthesized neural networks, in order to support future research in ML-based code generation. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, as well as the average performance across a subset of the dataset. We measure the performance of a given predictor model through multiple metrics, including $R^2$ score and SMAPE on regression tasks, as well as inference time to further characterize the estimator under test. Additionally, we introduce the latency/utilization inference graph neural network (lui-gnn), a surrogate model that uses a graph neural network to represent input architectures in the form of a directed graph. This graph representation allows for a diverse set of model architectures to all be effectively handled by a surrogate model. We present the architecture and performance of the model, as evaluated by the new proposed benchmark, including SMAPE, $R^2$ score, and inference times, and find that lui-gnn generally predicts latency and utilization for the 75\% quantile within several percent of the synthesized resources on the synthetic test dataset, indicating that this approach of estimating resource and latency via a surrogate models has promise and warrants further research.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Accessibility and Availability of Gas Stations for Liquid Fuel Supply During Severe Weather Events

Energy infrastructure is a pivotal lifeline with wider inter-dependencies. Electric power is essential to the continued functioning of critical assets; liquid fuels and other physical primary energy commodities that are used to supply the local energy needs of these assets during extreme events when electricity infrastructure is compromised. During Superstorm Sandy, the disruption of fuel supplies in the Tri-state area necessitated increasing situational awareness about oil and natural gas (ONG) systems to redirect fuel supplies to first responders and for restoration efforts. During winter storm events over the past several years, the demand for ONG for heating and power generation has skyrocketed in the north-east, which subsequently had caused ONG price to hit their highest level in years. ONG is a major energy source for restoration and recovery activities. However, very little, if any, situational awareness information about ONG supply and demand is available to those coordinating emergency efforts. Often this lack of information is due to the decentralized nature of the ONG supply chain and proprietary data maintained by private entities. To understand the impact of fuel availability on response and recovery efforts, this research focuses on (i) determining the accessibility to gas stations for liquid fuel supply across space and time, and (ii) examining the spatio-temporal variation of fuel availability and demand during extreme weather events. The broader scope of the project focuses on providing spatio-temporal situational awareness information about ONG availability and demand across the fuel supply chain for positioning of resources for response activities.

Kar, Bandana↗
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 OSTI2023

Using SWMM for emergency response planning: A case study evaluating biological agent transport under various rainfall scenarios and urban surfaces

To assist in emergency preparedness for a biological agent terrorist attack or accidental pathogen release, potential contaminant levels and migration pathways of spores spread by urban stormwater were evaluated using a Storm Water Management Model (SWMM) of U.S. Coast Guard Base Elizabeth City, North Carolina. The high temporal-spatial resolution SWMM model was built using spore concentrations in stormwater runoff from asphalt, grass, and concrete collected from a point-scale field study. The subsequent modeled contamination scenarios included a notional plume release and point releases mimicking the field study under three rainfall conditions. The rainfall scenarios included a 6-hour natural rainfall event on Dec. 8, 2021 and two design storms (2-year and 100-year events). The observed spore concentrations from asphalt and concrete from the actual field experiment were applied to calibrate the washoff parameters in the SWMM model, using an exponential washoff function. The calibrated washoff coefficient (c 1 ) and exponent (c 2 ) were 0.01 and 1.00 for asphalt, 0.05 and 1.45 for grass, and 2.45 and 1.00 for concrete, respectively. The calibrated SWMM model simulated spore concentrations in runoff at times and magnitudes similar to the field study data. In the point release modeled scenario, the concrete surface generated 55.6% higher average spore concentrations than asphalt. Similarly, in the field experiment, a 175% (p < 0.05) higher average spore concentration in surface runoff was observed from concrete than from asphalt. Here, this study demonstrates how SWMM may be used to evaluate spore washoff from urban surfaces under different precipitation amounts, intensities, and durations, and how visualized spatial migration pathways in stormwater runoff may be used for emergency planning and remediation.

54 ENVIRONMENTAL SCIENCES↗