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

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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

The Future of Air Conditioning for Buildings - Executive Summary

The Building Technologies Office (BTO), within the U.S. Department of Energy’s (DOE) Office of Energy Efficiency and Renewable Energy, works with researchers and industry to develop and deploy technologies that can substantially reduce energy consumption and greenhouse gas (GHG) emissions in residential and commercial buildings. Air conditioning (A/C) systems in buildings contribute to GHG emissions both directly through refrigerant emissions, as well as indirectly through fossil fuel combustion for power generation. BTO promotes pre-competitive research and development (R&D) on next-generation HVAC technologies that support the phase down of hydrofluorocarbon (HFC) production and consumption, as well as cost-effective energy efficiency improvements. Over the past several decades, product costs and lifecycle cooling costs have declined substantially in many global markets due to improved, higher-volume manufacturing and higher energy efficiency driven by R&D investments and efficiency policies including minimum efficiency standards and labeling programs.1 This report characterizes the current landscape and trends in the global A/C market, including discussion of both direct and indirect climate impacts, and potential global warming impacts from growing global A/C usage. The report also documents solutions that can help achieve international goals for energy efficiency and GHG emissions reductions. The solutions include pathways related to low-global warming potential2 (GWP) refrigerants, energy efficiency innovations, long-term R&D initiatives, and regulatory actions. DOE provides, with this report, a fact-based vision for the future of A/C use around the world. DOE intends for this vision to reflect a broad and balanced aggregation of perspectives. DOE brings together this content in an effort to support dialogue within the international community and help keep key facts and objectives at the forefront among the many important discussions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Thermal-Hydraulic Results for the Boiling Water Reactor Dry Cask Simulator.

The thermal performance of commercial nuclear spent fuel dry storage casks is evaluated through detailed numerical analysis. These modeling efforts are completed by the vendor to demonstrate performance and regulatory compliance. The calculations are then independently verified by the Nuclear Regulatory Commission (NRC). Carefully measured data sets generated from testing of full sized casks or smaller cask analogs are widely recognized as vital for validating these models. Recent advances in dry storage cask designs have significantly increased the maximum thermal load allowed in a cask in part by increasing the efficiency of internal conduction pathways and by increasing the internal convection through greater canister helium pressure. These same canistered cask systems rely on ventilation between the canister and the overpack to convect heat away from the canister to the environment for both aboveground and belowground configurations. While several testing programs have been previously conducted, these earlier validation attempts did not capture the effects of elevated helium pressures or accurately portray the external convection of aboveground and belowground canistered dry cask systems. The purpose of this investigation was to produce validation-quality data that can be used to test the validity of the modeling presently used to determine cladding temperatures in modern vertical dry casks. These cladding temperatures are critical to evaluate cladding integrity throughout the storage cycle. To produce these data sets under well-controlled boundary conditions, the dry cask simulator (DCS) was built to study the thermal-hydraulic response of fuel under a variety of heat loads, internal vessel pressures, and external configurations. An existing electrically heated but otherwise prototypic BWR Incoloy-clad test assembly was deployed inside of a representative storage basket and cylindrical pressure vessel that represents a vertical canister system. The symmetric single assembly geometry with well-controlled boundary conditions simplified interpretation of results. Two different arrangements of ducting were used to mimic conditions for aboveground and belowground storage configurations for vertical, dry cask systems with canisters. Transverse and axial temperature profiles were measured throughout the test assembly. The induced air mass flow rate was measured for both the aboveground and belowground configurations. In addition, the impact of cross-wind conditions on the belowground configuration was quantified. Over 40 unique data sets were collected and analyzed for these efforts. Fourteen data sets for the aboveground configuration were recorded for powers and internal pressures ranging from 0.5 to 5.0 kW and 0.3 to 800 kPa absolute, respectively. Similarly, fourteen data sets were logged for the belowground configuration starting at ambient conditions and concluding with thermal-hydraulic steady state. Over thirteen tests were conducted using a custom-built wind machine. The results documented in this report highlight a small, but representative, subset of the available data from this test series. This addition to the dry cask experimental database signifies a substantial addition of first-of-a-kind, high-fidelity transient and steady-state thermal-hydraulic data sets suitable for CFD model validation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Development of Bismuth and Platinum Bi-Metallic Nanoparticles to Enhance Melt Wire Temperature Resolution

Advanced manufacturing (AM) based on direct-write (DW) technologies has emerged as the predominant enabler for the fabrication of active and passive sensors to be deployed in harsh operating environments seen in a nuclear reactor. Recently, Idaho National Laboratory and Boise State University have recently established capabilities to incorporate advanced manufacturing (AM) methods to accelerate, modernize and enhance functionality of nuclear sensors and instrumentation to achieve the goal of enhancing the safety and efficiency of nuclear reactors. A significant thrust of this work includes the development of nuclear relevant feedstock materials compatible with a variety of direct-write processes for the development, fabrication and testing of AM sensors for peak temperature detection and neutron flux monitoring. For this report, bismuth and bismuth/platinum (BiPt) bi-metallic nanoparticles were synthesized using wet chemical approaches to develop bi-metallic feedstock materials to enhance the sensitivity of melt wires for peak temperature detection. Nanoparticle characterization was performed with Transmission Electron Microscopy (TEM) for nanoparticle composition and size, Transmission X-Ray Diffraction Fluorescence (TXRF) for nanoparticle composition, and Differential Scanning Calorimetry to elucidate the melting point of BiPt bi-metallic nanoparticles. Preliminary results indicate bi-metallic BiPt nanoparticles as a viable pathway for fabricating high resolution AM melt wires.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Low-Cost High-Concentration Photovoltaic Systems for Utility Power Generation

Under DOE's Technology Pathway Partnership (TPP) program, Amonix, Inc. developed a new generation of high-concentration photovoltaic systems using multijunction technology and established the manufacturing capacity needed to supply multi-megawatt power plants built using the new Amonix 7700-series solar energy systems. For this effort, Amonix Collaborated with a variety of suppliers and partners to complete project tasks. Subcontractors included: Evonik/Cyro; Hitek; the National Renewable Energy Laboratory (NREL); Raytech; Spectrolab; UL; University of Nevada, Las Vegas; and TUV Rheinland PTL. The Amonix TPP tasks included: Task 1: Multijunction Cell Optimization for Field Operation, Task 2: Fresnel Lens R&D, Task 3: Cell Package Design & Production, Task 4: Standards Compliance and Reliability Testing, Task 5: Receiver Plate Production, Task 6: MegaModule Performance, Task 7: MegaModule Cost Reduction, Task 8: Factory Setup and MegaModule Production, Task 9: Tracker and Tracking Controller, Task 10: Installation and Balance of System (BOS), Task 11: Field Testing, and Task 12: Solar Advisor Modeling and Market Analysis. Amonix's TPP addressed nearly the complete PV value chain from epitaxial layer design and wafer processing through system design, manufacturing, deployment and O&M. Amonix has made progress toward achieving these reduced costs through the development of its 28%+ efficient MegaModule, reduced manufacturing and installation cost through design for manufacturing and assembly, automated manufacturing processes, and reduced O&M costs. Program highlights include: (1) Optimized multijunction cell and cell package design to improve performance by > 10%; (2) Updated lens design provided 7% increased performance and higher concentration; (3) 28.7% DC STC MegaModule efficiency achieved in Phase II exceeded Phase III performance goal; (4) New 16' focal length MegaModule achieved target materials and manufacturing cost reduction; (5) Designed and placed into production 25 MW/yr manufacturing capacity for complete MegaModules, including cell packages, receiver plates, and structures with lenses; (6) Designed and deployed Amonix 7700 series systems rated at 63 kW PTC ac and higher. Based on an LCOE assessment using NREL's Solar Advisor Model, Amonix met DOE's LCOE targets: Amonix 2011 LCOE 12.8 cents/kWh (2010 DOE goal 10-15); 2015 LCOE 6.4 cents/kWh (2015 goal 5-7) Amonix and TPP participants would like to thank the U.S. Department of Energy Solar Energy Technology Program for funding received under this program through Agreement No. DE-FC36-07GO17042.

14 SOLAR ENERGY↗
DOE OSTI2026

GridCoPilot for Thermal Events: An LLM-Based Platform for Power Grid Reliability Analysis

Large Language Models show promise for translating natural language into database queries, but deploying such systems in safety-critical domains requires high reliability. We present an application of GridCoPilot to thermal event analysis (heatwaves and coldwaves) that affect power grid reliability. Our approach uses a LangChain SQL Agent to translate natural language queries into auditable SQL statements, with deterministic visualization routines that parse the structured query results. We introduce structural framing as a design principle, we integrate a NERC-region-level event library with county-level meteorology and decompose the combined data into three relational tables (event metadata, county-level event details, and a county-to-NERC subregion mapping), using prompt-guided joins to direct the model toward correct multi-table queries. For two core analytical patterns (identifying worst events by region and by region-year), the system achieved 100% SQL accuracy across all 16 NERC subregions and both event types (64 queries total). These results validate the approach for target use cases, though performance on diverse natural language formulations requires further investigation. We discuss design trade-offs, failure modes including JSON output truncation, and pathways for extending this approach to other hazard domains.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Techno-Economic Evaluation of Strategies to Approach Net-Zero Carbon Sustainable Aviation Fuel via Woody Biomass Gasification and Fischer-Tropsch Synthesis

Reducing the carbon dioxide (CO2) emissions from the transportation industry is a key target for achieving global net-zero carbon goals. Wide-spread electrification, efficient engine design, and alternative fuel implementations have been introduced for light-duty vehicles and are projected to significantly reduce light-duty emissions in the near future. Conversely, the aviation sector contributes considerably towards transportation-based carbon emissions, but current projections do not show substantial reductions in carbon emissions over time. Challenges for the aviation sector include low compatibility with electrification, relative inflexibility to variations in fuel properties, and requirement of high energy density fuels. Thus, identifying pathways to decarbonize the aviation sector via liquid low- or net-zero carbon biofuels that are compatible with current aviation infrastructure is crucial. This work investigates the economic feasibility of approaching net-zero carbon sustainable aviation fuel (SAF) from woody biomass via gasification and Fischer-Tropsch synthesis. In doing so, this assessment identifies the economic opportunities and trade-offs of several carbon mitigation strategies coupled with renewable resource interventions required to approach net-zero carbon fuels via biomass. Renewable natural gas and green hydrogen utilization strategies are assessed to examine the impact of fuel yield improvements versus renewable resource cost. Additionally, both carbon capture and sequestration (CCS) and carbon capture and utilization (CCU) are considered in this analysis to help mitigate carbon loss to the atmosphere. Each scenario was assessed on cost, carbon efficiency, energy efficiency, and overall technology-readiness level (TRL). The results of this analysis show that renewable natural gas, green hydrogen, and CCS can be viewed as a low-cost, near-term carbon abatement strategies. However, in the long-term, deployment of more expensive and less technologically mature CCU technologies can make use of point sources of CO2 to boost overall fuel production.

aviation fuel↗
DOE OSTI2020

Investigating the interconnectedness of active transportation and public transit usage as a primer for Mobility-as-a-Service adoption and deployment

With the advent of Mobility-as-a-Service packages to reduce car usage and (by extension) greenhouse gas emissions, it is crucial that researchers and practitioners consider mutual determinants and outcomes that link the adoption of multiple alternative modes. This study therefore investigates the joint usage of active travel (walking, cycling, bike-sharing) and public transit (bus, rail) modes with respect to personal and situational contexts. Online survey data (n = 826) were collected across six Midwestern U.S. states using Amazon MTurk. Respondents indicated their average weekly usage of eight travel modes across three trip purposes so that multimodality could be assessed. Several psychological constructs were extracted via (a) the stages of change framework, used to indicate willingness to adopt new behavior, and (b) confirmatory factor analysis conducted on Likert scales rooted in theories of community, identity, norms, personality, and well-being. A multiple-indicators multiple-causes structural equation model is then employed to investigate the process of adopting a modality style that incorporates active and transit modes. The model confirms that compatible physical and social contexts, as well as navigational skills and openness to learning, are key primers of multimodalism. However, a path juncture stemming from neighborhood support for mobility innovation illustrates a potential polarity in outcomes between individuals and communities. In addition, the stage of active mobility adoption is linked to identity and norm activation, offering further guidance on what influences readiness for change. The seamless integration of mobility services is critical to matching the convenience and comfort of the private vehicle; understanding potential pathways to sustainable mobility, though, requires analyses and interventions that are driven by well-being outcomes and grounded in rigorous psychological frameworks. Here, the research findings offer practical guidance for identifying intervention opportunities to be linked with MaaS enrollment while demonstrating the need to illuminate how mobility might relate to social cohesion, identity expression, and various sources of satisfaction.

42 ENGINEERING↗
DOE OSTI2016

GoAmazon 2014/15 Thermal Desorption Chemical Ionization Mass Spectrometer (TDCIMS) Field Campaign Report

The Thermal Desorption Chemical Ionization Mass Spectrometer (TDCIMS) deployment to the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility T3 site in Manacapuru, Brazil, was motivated by two main scientific objectives of the Green Ocean Amazon (GoAmazon) 2014/15 field campaign. 1) Study the interactions between anthropogenic and biogenic emissions by determining important molecular species in ambient nanoparticles. To address this, TDCIMS data will be combined with coincident measurements such as gas-phase sulfuric acid to determine the contribution of sulfuric acid condensation to nucleation and growth. We can then compare that result to TDCIMS-derived nanoparticle composition to determine the fraction of growth that can be attributed to the uptake of organic compounds. The molecular composition of sampled particles will also be used to attribute specific chemical species and mechanisms to growth, such as the condensation of low-volatility species or the oligomerization of α-dicarbonyl compounds. 2) Determine the source of new ambient nanoparticles in the Amazon. The hypothesis prior to measurements was that potassium salts formed from the evaporation of primary particles emitted by fungal spores can provide a unique and important pathway for new particle production in the Amazon basin. To explore this hypothesis, the TDCIMS recorded the mass spectra of sampled ambient particles using a protonated water cluster Chemical Ionization Mass Spectrometer (CIMS). Laboratory tests performed using potassium salts show that the TDCIMS can detect potassium with high sensitivity with this technique.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Animal displacement from marine energy development: Mechanisms and consequences

For marine wave and tidal energy to successfully contribute to global renewable energy goals and climate change mitigation, marine energy projects need to expand beyond small deployments to large-scale arrays. However, with large-scale projects come potential environmental effects not observed at the scales of single devices and small arrays. One of these effects is the risk of displacing marine animals from their preferred habitats or their migration routes, which may increase with the size of arrays and location. Many marine animals may be susceptible to some level of displacement once large marine energy arrays are increasingly integrated into the seascape, including large migratory animals, non-migratory pelagic animals with large home ranges, and benthic and demersal mobile organisms with more limited ranges, among many others. Yet, research around the mechanisms and effects of displacement have been hindered by the lack of clarity within the international marine energy community regarding the definition of displacement, how it occurs, its consequences, species of concern, and methods to investigate the outcomes. This review paper leveraged lessons learned from other industries, such as offshore development, to establish a definition of displacement in the marine energy context, explore which functional groups of marine animals may be affected and in what way, and identify pathways for investigating displacement through modeling and monitoring. In the marine energy context, we defined displacement as the outcome of one of three mechanisms (i.e., attraction, avoidance, and exclusion) triggered by an animal's response to one or more stressors acting as a disturbance, with various consequences at the individual through population levels. The knowledge gaps highlighted in this study will help the regulatory and scientific communities prepare for mitigating, observing, measuring, and characterizing displacement of various animals around marine energy arrays in order to prevent irreversible consequences.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

DeFault: DEep‐Learning‐Based FAULT Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site

Abstract The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high‐quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, , specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain‐adaptation, allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using , the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of on a field case study involving injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning’s capacity to refine these methods. Ultimately, our work has significant implications for CCUS technology deployment, an essential strategy in combating climate change. Plain Language Summary In our quest to tackle climate change, we use a strategy known as carbon capture, utilization, and storage (CCUS) to keep greenhouse gases out of the atmosphere. This strategy relies heavily on our ability to understand what's happening deep under the earth's surface. To make sure we store super critical safely, we need to accurately map out the geological structure, especially faults, but this is tough without high‐quality data and complex computer programs. We've developed a new tool called “DeFault,” which uses advanced machine learning to improve how we find and map these underground features. “DeFault” is smart enough to learn from numerically simulated data and then apply what it’s learned to real‐world situations. It groups together seismic activity—tiny tremors and shifts in the earth—based on when and where they happen, which helps us spot where there might be cracks or faults. We tested “DeFault” in Illinois, where CO 2 is injected underground, and it successfully pinpointed where these tremors occurred and mapped out the faults, helping to prevent accidents accurately in the future. Our study shows that “DeFault” will be a powerful ally in making CCUS safer and more effective, especially for the Illinois Basin Decatur Project. Key Points Faults and fractures introduced by carbon storage can be monitored by passive seismicity DeFault algorithm enables an automatic process for accurate and efficient passive seismic event locating and clustering

58 GEOSCIENCES↗
DOE OSTI2014

A Sorghum bicolor expression atlas reveals dynamic genotype-specific expression profiles for vegetative tissues of grain, sweet and bioenergy sorghums

Background: Effective improvement in sorghum crop development necessitates a genomics-based approach to identify functional genes and QTLs. Sequenced in 2009, a comprehensive annotation of the sorghum genome and the development of functional genomics resources is key to enable the discovery and deployment of regulatory and metabolic genes and gene networks for crop improvement. Results: This study utilizes the first commercially available whole-transcriptome sorghum microarray (Sorgh-WTa520972F) to identify tissue and genotype-specific expression patterns for all identified Sorghum bicolor exons and UTRs. The genechip contains 1,026,373 probes covering 149,182 exons (27,577 genes) across the Sorghum bicolor nuclear, chloroplast, and mitochondrial genomes. Specific probesets were also included for putative non-coding RNAs that may play a role in gene regulation (e. g., microRNAs), and confirmed functional small RNAs in related species (maize and sugarcane) were also included in our array design. We generated expression data for 78 samples with a combination of four different tissue types (shoot, root, leaf and stem), two dissected stem tissues (pith and rind) and six diverse genotypes, which included 6 public sorghum lines (R159, Atlas, Fremont, PI152611, AR2400 and PI455230) representing grain, sweet, forage, and high biomass ideotypes. Conclusions: Here we present a summary of the microarray dataset, including analysis of tissue-specific gene expression profiles and associated expression profiles of relevant metabolic pathways. With an aim to enable identification and functional characterization of genes in sorghum, this expression atlas presents a new and valuable resource to the research community.

Shakoor, N↗
DOE OSTI2020

Cybersecurity Resiliency of Marine Renewable Energy Systems-Part 1: Identifying Cybersecurity Vulnerabilities and Determining Risk

Technology innovation, market demand, and the potential impacts of a changing climate are driving the marine renewable energy (MRE) industry to develop market-ready systems to provide low-carbon electricity for emerging, off-grid markets. The advanced operational and information technology devices used in MRE systems create a pathway for a cyber threat actor to gain unauthorized access to data or disrupt operation. To improve the resiliency of MRE systems as a predictable, affordable, and reliable source of energy from oceans and rivers, guidance was developed for an end users' organization that describes a framework for identifying and managing cybersecurity risk. The development of the cybersecurity guidance is based on standards described in the Risk Management Framework and Cybersecurity Framework developed by the National Institute of Standards and Technology (NIST). This paper is the first of a two-part series that describes an approach to determine the cybersecurity risk for MRE systems based on assessing potential cyber threats, identifying vulnerabilities (people, processes, and technology, including physical and operational environment), and evaluating the consequences a cyberattack would have on operation of the MRE system and impact on end users' mission and business objectives. MRE developers and stakeholders can use this approach to assess their current cybersecurity risk posture to incorporate appropriate cybersecurity controls to reduce the consequences and impacts from a cyberattack on MRE systems. This approach can be refined further as MRE systems are deployed and operational configurations are available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Green Ocean Amazon (GoAmazon) 2014/15. Semi-Volatile Thermal Desorption Aerosol Gas Chromatograph (SVTAG) Field Campaign Report

In areas where biogenic emissions are oxidized in the presence of anthropogenic pollutants such as SO2, NOx, and black carbon, it has become increasingly apparent that secondary organic aerosol (SOA) formation from biogenic volatile organic compounds (VOCs) is substantially enhanced. Research is urgently needed to elucidate fundamental processes of natural and anthropogenically influenced VOC oxidation and the contribution of these processes to SOA formation. GoAmazon 2014/15 afforded study of the chemical transformations in the region downwind of Manaus, Brazil, where local biogenic VOC emissions are high, and their chemical oxidation can be studied both inside and outside of the urban plume to differentiate the role of anthropogenic influence on secondary aerosol formation during oxidation of these natural VOC emissions. To understand the connection between primary biogenic VOC emissions and their secondary products that form aerosols, we made time-resolved molecular level measurements by deploying a Semi-Volatile Thermal Desorption Aerosol Gas Chromatograph (SV-TAG) and a sequential filter sampler during two intensive operational periods (IOPs) of the GoAmazon 2014/15 field campaign. The SV-TAG measured semi-volatile organic compounds in both the gas and particle phases and the sequential filter sampler collected aerosols on quartz fiber filters in four-hour increments used for offline analysis. SV-TAG employed novel online derivatization that provided chemical speciation of highly oxygenated or functionalized compounds that comprise a substantial fraction of secondary organic aerosols, yet are poorly characterized. It also provided partitioning of these compounds between the vapor and particle phases at sufficient time resolution to define the importance of competing atmospheric processes. These measurements were supported by offline analysis of the filters using two-dimensional gas chromatography (GC x GC) with high-resolution time-of-flight mass spectrometry (HR-TOF-MS) using both electron impact (EI) and soft vacuum ultraviolet (VUV) ionization with derivatization. Speciated chemical data from SV-TAG and filter measurements were used to elucidate the relative importance of potential oxidation pathways by providing detailed information on the product distribution from atmospheric reactions and the quantification of known tracers for various oxidation pathways. Together, these techniques provided unequivocal molecular identification of a wide range of atmospheric organic compounds spanning the volatile, semi-volatile, and non-volatile phases. This level of chemical characterization provided insight into the chemical and physical processes that control the atmospheric oxidation of biogenic VOC and subsequent formation of SOA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Synergies and trade-offs between storage, transmission, and sector coupling in high renewable energy systems

Energy storage, transmission, and sector coupling are some prominent flexibility solutions to support variable renewable energy (VRE) integration. However, investment cost uncertainties and public acceptance could hamper the deployment of these flexibility solutions. This raises questions about the development and cost-effectiveness of future energy systems, especially on how the dependence on local and cross-border solutions of flexibility would evolve if the uptake of these solutions is restricted. In this context, this paper identifies the synergies among flexibility options under restrictions on transmission expansion or increased costs of energy storage. It contributes to determining whether investments in energy storage and/or transmission expansion offer the least-cost transition and investigates the impact of sector coupling on these solutions. A long-term energy system planning and optimisation model towards 2050 is developed using the open-source energy system optimisation tool Balmorel, and a case study of the countries surrounding the Baltic Sea and the North Sea is established. Five cases with restrictions imposed on transmission expansion and higher energy storage technology costs are analysed at different levels of sector coupling. The results highlight the importance of transmission expansion at all levels of sector coupling. As the level of sector coupling increases, uncertainties around the cost of energy storage drive the least-cost pathways. Optimal investment solutions are found to have a mix of transmission and energy storage in capacity expansion at all levels of sector coupling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Role of climate goals and clean-air policies on reducing future air pollution deaths in China: a modelling study

Over 3 million people still die every year from diseases caused by exposure to outdoor PM 2.5 air pollution, and more than a quarter of these premature deaths occur in China. In addition to clean air policies that target pollution emissions, climate policies aimed at reducing fossil-fuel CO 2 emissions (e.g., to avoid 1.5°C of warming) may also dramatically improve air quality and public health. Yet there has been no comprehensive accounting of public health outcomes under different energy pathways and local clean air management decisions in China. In particular, further research is needed to understand the relationships among climate and clean air polices and future health burdens in China, where an aging population will further exacerbate the impacts of air pollution. Using a China-focused integrated assessment model (GCAM-China) and a dynamic emission projection model (DPEC), we project future Chinese air quality in scenarios spanning a range of global climate targets (i.e. 1.5°0C, 2°C, NDC, unambitious, baseline, and 4.5°C) as well as national clean air actions (i.e. 2015-pollution, current-pollution, and ambitious-pollution). We then evaluate the health impacts of PM2·5 air pollution of scenario matrix using the chemical transport model WRF-CMAQ and the latest epidemiological concentration–response (C-R) functions (i.e. GBD2019). We find that, without ambitious climate mitigation (e.g., under current NDC pledges), Chinese deaths related the PM2.5 air pollution do not substantially decrease—and often grow—by mid-century, regardless of clean air policies and air quality improvements. For example, in scenarios that track China’s current NDC pledge and deploy best-available pollution control technologies, PM 2.5 -related deaths in China decrease slightly by 2030 (to 1.2 million per year) but no further by mid-century (Ambitious-pollution-NDC-goals; 1.2 million deaths in 2050) despite substantial and continuous improvements in population-weighted air quality (27.2 to 16.0 µg/m 3 from 2030 to 2050). The contrary trends of improving air quality and increasing PM 2·5 -related deaths in many of our scenarios reveals the extent to which extra efforts are needed to compensate for the established fact of increasing age of China’s population in future. Substantial decreases in China’s PM 2.5 -related deaths and age-standardized death rates (e.g., decreasing by 0.3–0.5 million deaths and 10.2–14.2 per 100,000 population age-standardized death rates per year) thus require the sort of large-scale transition in energy sources entailed by scenarios that meet international climate goals to avoid 1.5°C and 2°C of warming (Ambitious-pollution-2°C- and 1.5°C-goals).

58 GEOSCIENCES↗
DOE OSTI2017

Eugenol specialty chemical production in transgenic poplar ( Populus tremula × P. alba ) field trials

Summary A foundational study assessed effects of biochemical pathway introduction into poplar to produce eugenol, chavicol, p ‐anol, isoeugenol and their sequestered storage products, from potentially available substrates, coniferyl and p ‐coumaryl alcohols. At the onset, it was unknown whether significant carbon flux to monolignols vs. other phenylpropanoid (acetate) pathway metabolites would be kinetically favoured. Various transgenic poplar lines generated eugenol and chavicol glucosides in ca . 0.45% (~0.35 and ~0.1%, respectively) of dry weight foliage tissue in field trials, as well as their corresponding aglycones in trace amounts. There were only traces of any of these metabolites in branch tissues, even after ~4‐year field trials. Levels of bioproduct accumulation in foliage plateaued, even at the lowest introduced gene expression levels, suggesting limited monolignol substrate availability. Nevertheless, this level still allows foliage collection for platform chemical production, with the remaining (stem) biomass available for wood, pulp/paper and bioenergy product purposes. Several transformed lines displayed unexpected precocious flowering after 4‐year field trial growth. This necessitated terminating (felling) these particular plants, as USDA APHIS prohibits the possibility of their interacting (cross‐pollination, etc.) with wild‐type (native plant) lines. In future, additional biotechnological approaches can be employed (e.g. gene editing) to produce sterile plant lines, to avoid such complications. While increased gene expression did not increase target bioproduct accumulation, the exciting possibility now exists of significantly increasing their amounts (e.g. 10‐ to 40‐fold plus) in foliage and stems via systematic deployment of numerous ‘omics’, systems biology, synthetic biology and metabolic flux modelling approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

FORCE Regression Testing

Via programs including the Light Water Reactor Sustainability and Integrated Energy Systems, the U.S. Department of Energy has invested in the Framework for Optimization of ResourCes and Economics (FORCE) software framework (Idaho National Laboratory 2024a) for the technical and economic analysis of nuclear-integrated energy systems (IES). Nuclear IES expand the use of nuclear from traditional baseload electricity generation to a flexible and adaptive source of combined heat and power. Nuclear heat can be used in the production of a variety of energy currencies such as hydrogen and ammonia as well as other heat applications including water desalination and district heating. FORCE is designed with the intent to provide interconnected analysis tools that enable the accurate technical and economic assessment of specific nuclear IES configurations for individual energy markets. FORCE consists of three main analysis pathways: HYBRID (Idaho National Laboratory 2024b), which contains high-resolution physical models for IES; Holistic Energy Resource Optimization Network (HERON) (Idaho National Laboratory 2024c), which analyzes IES long-term economic viability; and Optimization of Real-time Capacity Allocation (ORCA) (Idaho National Laboratory 2024d), designed for real-time control of IES via digital twins and optimal decision making, including autonomous and remote operation research. Development of the FORCE ecosystem is guided by three pillars: capability, which assures that the computational requirements of IES analysis are met by the software tools; reliability, which provides for consistent code performance and expected behaviors; and accessibility, which lowers the barrier to entry for using the software and accelerates analysis by users beyond the FORCE primary developers. Reliability of the FORCE ecosystem is established according to the American Nuclear Society?s Nuclear Quality Assurance (NQA-1) program [American Society of Mechanical Engineers 1982], with specific levels of software quality assurance (SQA) within NQA-1 applied to each software tool in FORCE. As the tools within FORCE have matured, some integration algorithms to accurately connect the software tools for holistic analysis have been developed and deployed within the FORCE software repository. In accordance with NQA-1 standards, regression tests are required to guarantee the software performs consistently even when new capabilities are added to the software. In this report, we document the deployment of both unit tests, which test the consistent behavior of small pieces of the FORCE code base, as well as integration tests, which test the consistent performance of full use cases for the FORCE integration algorithms. We further document the encapsulation of these tests within a test harness, which collectively checks for each successful test completion on demand. Finally, we document the automation of the test harness using GitHub Actions [GitHub 2024], which require all tests succeed before any new capability or other changes can be added to the FORCE integration software

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Distributed optical fiber sensing: Review and perspective

Over the past few decades, optical fibers have been widely deployed to implement various applications in high-speed long-distance telecommunication, optical imaging, ultrafast lasers, and optical sensors. Distributed optical fiber sensors characterized by spatially resolved measurements along a single continuous strand of optical fiber have undergone significant improvements in underlying technologies and application scenarios, representing the highest state of the art in optical sensing. This work is focused on a review of three types of distributed optical fiber sensors which are based on Rayleigh, Brillouin, and Raman scattering, and use various demodulation schemes, including optical time-domain reflectometry, optical frequency-domain reflectometry, and related schemes. Recent developments of various distributed optical fiber sensors to provide simultaneous measurements of multiple parameters are analyzed based on their sensing performance, revealing an inherent trade-off between performance parameters such as sensing range, spatial resolution, and sensing resolution. This review highlights the latest progress in distributed optical fiber sensors with an emphasis on energy applications such as energy infrastructure monitoring, power generation system monitoring, oil and gas pipeline monitoring, and geothermal process monitoring. This review aims to clarify challenges and limitations of distributed optical fiber sensors with the goal of providing a pathway to push the limits in distributed optical fiber sensing for practical applications.

Lu, Ping (ORCID:0000000292010956)↗