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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

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↗
DOE OSTI2026

Advancing Concentrating Solar Thermal Modeling Using System Advisor Model (SAM)

Concentrating solar thermal (CST) technologies play a critical role in enabling dispatchable power and high-temperature industrial heat applications. Accurate and flexible modeling tools are essential for evaluating system performance, guiding technology research and development, and informing investment decisions. The National Laboratory of the Rockies's System Advisor Model (SAM) is a widely used techno-economic simulation platform for CST systems, providing detailed performance and financial modeling capabilities for multiple CST system configurations. SAM integrates physics-based performance models with financial analysis to simulate the behavior of complex energy systems under realistic operating conditions. For CST technologies (including tower, parabolic trough, and linear Fresnel), SAM enables hourly simulations using site-specific weather data that ensure feasible operating conditions and convergence of mass and energy between core system components (i.e., solar field, receiver, thermal energy storage, and power cycle). These capabilities allow researchers and developers to evaluate annual energy production, capacity factors, levelized cost of energy (LCOE), and system dispatch strategies. A key advantage of SAM lies in its flexibility for parametric analysis and large-scale computational studies. Users can vary system design parameters such as heliostat field layout, receiver dimensions, thermal energy storage capacity, power block sizing, and installation cost assumptions to investigate their impact on system performance and financial metrics. When combined with automated scripting through LK, SDKTool, or Python interfaces, SAM enables high-throughput simulation workflows that support sensitivity analysis, technology benchmarking, and optimization studies. These approaches are particularly valuable for next-generation CST concepts, where design spaces are large and system interactions are complex. Another important capability of SAM is its support for dispatch optimization and thermal energy storage modeling, which are central to the value proposition of CST technologies. The ability to simulate integrated storage and flexible power generation allows researchers to explore strategies that maximize grid value, improve capacity utilization, and enhance integration with variable resources such as photovoltaic and wind generation. This poster will present an overview of SAM's thermal system modeling capabilities including concentrating solar. Additionally, we will highlight new feature developments including: 1) implementing Google's OR-Tools optimization platform for faster and more robust dispatch optimization, 2) developing a new power load following controller for modeling behind-the-meter applications, 3) enabling direct modeling of CSP-PV hybrid systems with the inclusion of battery storage, and 4) developing a multi-receiver falling particle Gen3 system model.

14 SOLAR ENERGY↗
DOE OSTI2024

A Computational Review of Privacy-Preserving Mechanisms for the Smart Grid

Smart grid technologies have rapidly become one of the largest and most comprehensive sources of data for the modern utility. For the most part, data streams are seen as an essential tool that enable utilities to carry their day-to-day business operations, but they also create the need for efficient and secure data management strategies. In the context of the smart grid, ensuring data privacy is becoming an increasing concern due to a combination of factors that range from shifts in operational paradigms and rapid technology evolution to changes in legislation. Furthermore, researchers have highlighted the risks associated with improperly protected energy records. For example, energy consumption data from homes could be used to infer the behaviors and habits of home occupants through activity recognition or user profiling (Fan, 2017), which may lead to unfair service pricing, targeted advertising, or other personal security violations. Similarly, Electric Vehicles’ (EVs) charging metadata could be used to reveal private information about the owner such as their payment methods, preferred charging stations, and other locational and timing information that could be used to reconstruct the vehicle owner’s behaviors. The privacy of user data, even when used for statistical analysis or machine learning training processes, also needs to be carefully considered, as an individual’s private traits may still be vulnerable if their inclusion/exclusion greatly impacts the result or could be linked to a public dataset through cross-reference. The breach of user privacy also has severe impacts for organizations that store, transmit, or work on the data in the form of diminishing the public’s trust in them while potentially incurring legal consequences (e.g., fines and suspensions under the European Union General Data Protection Regulation, Health Insurance Portability and Accountability Act, etc.). Because of these risks, several privacy-preserving mechanisms are available to help organizations comply with privacy legislations and prevent the unauthorized and malicious use of user data. In light of these concerns, this report focuses on performing a computational review of privacy-preserving mechanisms that have received a significant amount of interest in literature. It specifically focuses on 1) homomorphic encryption, 2) zero-knowledge proofs, 3) differential privacy, and 4) federated learning. It is worth noting that although many of the methods presented in this document rely on cryptographic primitives, their intent is not to provide perfect secrecy, but rather to enable users to maintain privacy, and thus they shall not be compared or equated to other constructs that are aimed to address cybersecurity constructs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Hydrogen Education for a Decarbonized Global Economy (H 2 EDGE) (Final Technical Report)

The energy sector is undergoing rapid growth and transformation, creating urgent demand for a skilled workforce to support emerging technologies and resilient systems. Hydrogen is an emerging solution for “hard to abate” sectors, including heavy industry and transportation that also provides optionality for the electric system and enables long‑duration energy storage. As hydrogen scales from traditional industrial uses to new applications, success depends on knowledgeable engineers, technicians, planners, and operators safely deploying hydrogen technologies across the full value chain: production, delivery, storage, and end use. The H 2 EDGE initiative, funded by the U.S. Department of Energy’s Hydrogen and Fuel Cell Technologies Office from 2020 to 2025, was launched to prepare people for careers hydrogen and related technologies. Through its interactive approach, H 2 EDGE equipped stakeholders with knowledge and tools to build a reliable and sustainable energy future together. H 2 EDGE built a national network of academic and industry partners to deliver modular training, share open-access curricula, and engage diverse talent pipelines. The program leveraged EPRI’s extensive industry membership and expanded on the train-the-trainer model of the GridEd program to connect educators, employers, and community organizations across all regions of the U.S. New methods were introduced to more systematically map hydrogen competencies and assess curricula using traditional and AI-assisted approaches. Outcomes included delivery of professional short courses, developing university curriculum, sponsoring faculty and student projects, and engaging stakeholders through workshops, site tours, and webinars.

08 HYDROGEN↗
DOE OSTI2025

Station Impact Analysis 2025

As part of the U.S. DOE EVs@Scale consortium, the NextGen Profiles (NGP) project presents analysis and results from the study of High Power Charging Electric Vehicles and Battery Charging Infrastructure. High Power Charging equipment is capable of recharging electric vehicle traction batteries at power levels of 200KW and above. The intent of the project is to further understand the most recent technological capabilities of the electric mobility industry related to charging performance. The project aims to develop EV, EVSE, and Fleet characterization testing practices and comprehensive analysis with inputs from key industry stakeholders. The results published in this NextGen Profiles project report provide data and insight for use by numerous entities including modeling and simulation organizations, policy makers, fleet planners, industry stakeholders and the general public involved with the development, deployment and operation of electrified transportation technologies. The factors influencing Electric Vehicle (EV) Direct Current Fast Charging (DCFC), including EV battery specifications, temperature effects on lithium-ion battery and power electronics performance, lithium-ion battery SOC bounding and charging station design considerations are specifically investigated to analyze their impacts on charging station operation and recommendations are made to minimize charge station dwell time, reduce charging costs and mitigate electric grid and charge station congestion. Additional high-power charging results are anticipated in future publications in support of the U.S. DOE EVs@Scale consortium NextGen Profiles project.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Newberry SHR Demonstration Project – Bipartisan Infrastructure Law Enhanced Geothermal (EGS) Pilot Demonstration (Abstract)

This project will create an Engineered Geothermal System (EGS) comprising two or more wells drilled to a depth of 4.25 km into superhot rock (SHR) with a temperature of 425 °C at Newberry Volcano in Central Oregon. An EGS is a manufactured heat exchanger in which water is injected in a deep injection well, or injector, to extract heat from the hot rock at depth and steam is returned to the surface in a production well, or producer, to generate electricity. In this project, the SHR EGS will be made using new methods and technologies to stimulate and connect hydraulic and natural fractures to enable multiple flow pathways between wells, allowing for optimal heat mining from the reservoir rock. The new technologies are designed to operate at rock temperatures much higher than those encountered in traditional geothermal. Following EGS completion, water will be injected into the injector well and steam extracted from the producer well in a long-term connectivity flow test demonstrating SHR reservoir evolution with time and use. Success will be measured by demonstrating the efficacy of new technologies and by producing economic quantities of steam (>40 MWth).

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Efficient and Robust p-Type Transistor Based on Ultrawide-Bandgap Semiconductor

The p-type transistor is an indispensable component of semiconductor technology, enabling a complementary operation with n-channel transistors for computation, storage, and communication. Achieving both high robustness and high efficiency is highly desirable but challenging for p-type transistors due to the limited semiconductors with reliable hole transport and their high activation energies. Here, in this study, we achieved a robust yet efficient p-type transistor by heterogeneously integrating an ultrawide-bandgap semiconductor and a high-κ dielectric layer through van der Waals integration. The p-type transistor employs a two-dimensional hole channel on hydrogenated diamond (bandgap 5.6 eV) combined with a high-κ (30.5) SrTiO 3 perovskite membrane. At room temperature, the transistor exhibits stable operation with a high on-current (∼200 mA/mm), low subthreshold swing (70 mV/dec), high hole mobility (566 cm 2 /(V·s) to 572 cm 2 /(V·s)), and high on–off ratio (∼10 9 ). Furthermore, tuning the annealing temperature allows operation in either enhancement or depletion mode. The robust p-type transistor with high efficiency holds great potential for future power electronics, ultraviolet (UV) optoelectronics, and harsh-environment electronic applications.

high-κ membrane↗
DOE OSTI2024

A New Understanding of Decarbonizing Industrial Process Heat

Analysis conducted over the last few years has improved our understanding of how industrial process heat is used in the United States. These improvements are important for characterizing the possibilities for industrial decarbonization. However, this analysis has largely been conducted from technical perspective and has remained disconnected from the social processes that underlie how industrial firms may adopt and implement technologies to decarbonize their process heating operations. This presentation introduces the concept of generic and configurational technology systems, and outlines the how the incorporation of user requirements and local contexts are essential for successful implementation of configurational systems. Insights drawn from semi-structured interviews with representatives of industrial firms are used to support the hypothesis that industrial process heat technologies are configurational. The potential implications for strategies to decarbonize process heat are then discussed.

adoption↗
DOE OSTI2026

Conceptual design of a replacement 2.1 K cold box for the Spallation Neutron Source Central Helium Liquefier

Abstract After more than 20 years of operation, the cold compressor technology in use at the Spallation Neutron Source (SNS) is obsolete, and replacement parts and service are no longer available. SNS has partnered with Jefferson Lab to design and construct a replacement sub-atmospheric cold box outfitted with modern cold compressor technology. The general design follows from Jefferson Lab’s experience on other recent sub-atmospheric cold box projects. Design decisions are backed by thorough engineering analysis to ensure technical requirements are met in a cost-effective manner. Conceptual design of the replacement cold box has been completed, and will be summarized in this paper. It features five cold compressors with an operating flow range of 80-140 g/s, and includes piping and valving to support cold compressor maintenance without interrupting flow circulation to the load. The approach to thermal shielding, insulation, and integration of the upgraded cold compressor hardware into the existing SNS control system will also be addressed.

Mastracci, B [Thomas Jefferson National Accelerato↗
DOE OSTI2024

Liquid Piston with Spray Cooling Near-Isothermal Compressor

The goal of this project was to prototype and characterize the performance of a liquid-piston spray-cooled gas compressor. The working principle of the compressor enables optimized high-efficiency operation over a very wide range of operating conditions, unlike conventional compressors that are optimized for a narrow range of operating conditions. The compressor technology is suitable for many applications, such as gas pipeline transport, gas storage, and commercial and residential heat pumps. Both physical testing and computational fluid dynamics (CFD) modeling of the processes using the Oak Ridge National Laboratory high-performance computing center were completed. The experimental and CFD studies focused on a near-isothermal liquid-piston compressor (LPC) that uses propylene glycol to compress CO 2 . The first prototype demonstrated isothermal operation during several sequentially executed cycles of CO 2 compression and raised the temperature of the compressed CO 2 by only 2 K, compared with approximately 6 K when the gas was compressed non-isothermally. Isothermal operation was demonstrated at CO 2 flow rates of up to 2 L/min. The second prototype was designed with two compression chambers to allow continuous flow of high-pressure CO 2 . However, the design of the valve train to direct flow between the compression chambers was not sufficient to allow demonstration of CO 2 compression. Numerical simulations of the LPC in which the compression chamber was filled with propylene glycol injected from the bottom inlet were performed using large eddy simulation (LES) with the wall-adapting local eddy-viscosity subgrid-scale model coupled with the multiphase volume of fluid (VOF) model to simulate the transient interface between gas and liquid and to capture the heat and mass transfers within the compression chamber. In this effort, the effects of boundary conditions applied to the LES-VOF calculations (i.e., no wall, an adiabatic wall, and a wall with a heat flux subscribed) on the overall pressure and temperature of the CO 2 gas as well as the transient evolution of flow and heat transfer within the compression chamber were investigated and are discussed in this report. The LES calculation with no wall showed no dynamical flow patterns, and the volume-averaged temperature of CO 2 increased from 305 to 392.7 K, whereas LES calculations with a constant wall temperature or a wall heat flux had similar increases of CO 2 temperatures. The results of the LES simulation using a wall heat flux showed different stages in the compression process and revealed dynamical formation and interaction of CO 2 gas layers and circulation flow patterns within the chamber that contributed to the overall heat transfer between the solid wall, gas, and liquid surface in the compressor. Though an industrial partnership for commercializing the compressor was not secured, the technology was attractive for an industrial partner to use in two research proposals in response to US Department of Energy funding opportunity announcements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Preliminary Look at the LBEG & MPEG Beam Transmissions between HPSim and Operation Data

This report summarizes recent work on estimating the transmission of LANSCE H- beams from the end of the present DTL to both the PSR stripper foil (LBEG) and WNR target 4 (MPEG). The LBEG beam might be considered a more typical LINAC beam and lends itself to continuous monitoring of transmission through the various stages of the accelerator. For the MPEG, however, the widely-spaced micropulses and lack of the requisite sensitivity current monitors throughout the accelerator make these measurements extremely difficult and more uncertain. Therefore, to assist in estimating the MPEG beam transmission, beam-dynamics simulations using HPSim were employed. These beam transmission estimates from the end of the Drift Tube Linac (DTL) to Target 4 (MPEG) and the PSR stripper foil (LBEG) are vital to determine the charge requirements for LAMP’s front end. In this technote, we demonstrate three major efforts in determining the transmission: (1) Convert the WNR beamline lattice from TRANSPORT to HPSim for use in the simulation; (2) Simulate the optimization process in the Central Control Room (CCR) that brings down the losses by up to a factor of 4000 between the end of the initial physics-based tuneup phase and production beam operation; (3) Analyze operational data to deduce measured transmissions. Table 1 shows the estimated losses with HPSim and operational analysis. Finally, a better measurement and other improvements to refine the results are also proposed.

43 PARTICLE ACCELERATORS↗
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 OSTI2024

Mid-IR UAV-based sensing platform with deep learning to Identify and Quantify Gaseous Emission in Gas Flares

This report details the development and evaluation of a Mid-Infrared (Mid-IR) Unmanned Aerial Vehicle (UAV)-based sensing platform integrated with deep learning algorithms for the identification and quantification of gaseous emissions in gas flares. The project, spearheaded by Omega Optics, Inc., aimed to address environmental monitoring challenges by leveraging advanced photonic technologies and autonomous UAV operations. The research focused on designing, optimizing, and fabricating photonic crystal waveguides and grating couplers to enhance the sensitivity and accuracy of gas detection. A comprehensive drone-based system was developed, featuring a miniaturized sensor, GPS module, and microcontroller communication network for real-time gas concentration monitoring. The system's adaptive sampling algorithm, implemented using the Robot Operating System (ROS), enables autonomous detection and localization of gas emission sources. Preliminary results demonstrate the platform's capability to detect and monitor gas emissions with high precision, cost-effectiveness, and scalability. Future work will expand upon this foundation by introducing 3D wind model-based learning for dynamic environmental conditions and further enhancing the user interface and data processing algorithms to support broader environmental monitoring applications. Overall, this project represents a significant step forward in UAV-based environmental sensing technologies, offering robust solutions for detecting and mitigating the impacts of gaseous emissions on public health and safety.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

A Concept of a Convection–Cloud Chamber to Study Aerosol–Cloud–Drizzle Interactions

Understanding and quantifying the full chain of processes from aerosol activation to drizzle formation, and the associated feedbacks to the aerosol chemical and physical properties, all within a turbulent cloud are some of the toughest challenges in atmospheric chemistry and physics and are keys to the cloud–precipitation puzzle. This paper describes a concept for a new type of research facility consisting of a cloud chamber plus associated instrumentation and computational models, to explore aerosol–cloud interactions and processing, cloud optical properties, entrainment–cloud interactions, and quantitative assessment of drizzle onset. The envisioned design is for a 3 m × 3 m × 9 m chamber, such that the height is sufficient to achieve long lifetimes for aerosol processing and for significant drizzle growth by collision and coalescence. A suite of computational tools for simulating microphysical properties in the chamber provides a digital twin for designing the chamber and a range of example experiments. Theory and test results from novel remote sensing systems for exploring chemical and physical interactions and evolution of aerosols, cloud droplets, and drizzle within turbulent clouds are described. Testing of technology needed for the operation of a large-volume chamber, including aerosol generation methods and novel materials for water vapor boundary conditions, is described. Simulations suggest that spatially uniform turbulence and microphysical properties can be sustained in a steady state, with reasonable aerosol and water vapor fluxes, and that substantial drizzle can be produced through collision and coalescence of cloud droplets. Remaining challenges for more detailed engineering design and a discussion of possible first-light experiments are described.

54 ENVIRONMENTAL SCIENCES↗