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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 OSTI2024

Accelerating Residential Building Decarbonization: Market Guidance to Scale Zero-Carbon-Aligned Buildings

The US buildings sector faces a confluence of challenges, including a clear necessity to decarbonize the built environment to mitigate climate change, a need for greater resilience in the face of more frequent extreme weather events, a dearth of affordable housing, and flat or declining construction productivity that hinders the sector’s ability to adapt. Better data and guidance on new and existing residential buildings can outline paths forward for the market. These can help clarify stakeholder priorities and highlight applications for new (or newly relevant) technologies and approaches that have the potential to break traditional barriers, bridge technical gaps, reduce costs, create added value, and enable decarbonization of the national residential building stock. Decarbonizing the national building stock before 2050 will require massive increases in zero-carbon retrofits and new construction in this decade. By 2030, whole-home retrofit activity must increase several fold, and virtually all new construction will need to be zero carbon. It is difficult to imagine achieving this transformation without substantial changes in how buildings are constructed and retrofitted.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Industrial Assessment Center

Established in 1990, San Diego State University’s (SDSU) Industrial Assessment Center (IAC) is proud of its years of service. During this period, it has served over 620 small and medium-sized manufacturing plants in Southern California. SDSU/IAC’s efforts to transfer state-of-the-art technologies to industry have increased revenues, cultivated creativity, improved energy efficiencies, and benefited the environment. The Center has contributed to the region's economic growth and stability by assisting small and medium size companies to better compete in the global market. It has helped mitigate climate change by reducing greenhouse gas emissions. IAC activities have fostered productive relationships between the University and local industry, assisted industrial sectors to improve their energy efficiency and enhance their manufacturing productivity, in turn impacting the material and working conditions of their employees. In addition to financial savings and environmental benefits, we have trained tens of students who became energy specialists in various companies. Thus, a substantial benefit of the IAC has been the ongoing training of engineering faculty and students. All IAC graduates were offered jobs before or within weeks of their graduation. The activities of the SDSU/IAC have expanded the institutional expertise of the College and improved the knowledge base of the faculties involved leading to several related publications, master’s theses, and senior student projects. Significant number of peer-reviewed publications of the IAC director at SDSU have greatly benefitted from the experience of the Center. As a result of this extensive exposure to manufacturing processes, the SDSU/IAC has grown to be an integral component of SDSU’s engineering research and training. We have successfully built upon these established achievements and academic excellence. IAC service to industry is particularly vital in Southern California, a region with one of the highest manufacturing concentrations in the country. SDSU/IAC has understood and implemented the overall objectives of DOE’s IAC program and guidelines except for the pandemic years when the country’s manufacturing sector was put in dire stress. In addition to student training and service to industry, IAC’s contribution to state and local governments as well as utility companies to assess energy policies and design rebate and incentive strategies cannot be undermined.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Final Technical Report

The capture of CO2 and its simultaneously conversion to useful chemical fuels driven by solar energy represents one of the best solutions to resolve our growing energy and environmental concerns. The most critical challenge to this endeavor is the rational design of a photocatalytic architecture that can effectively couple a given photosensitizer (PS) with an appropriate catalyst, thereby enabling efficient photosensitization of a multi-electron reduction catalysis. This research program aims to address this challenge using an interdisciplinary approach that combines innovative material design and synthesis, fundamental mechanistic studies, and photocatalytic performance evaluation. The strategies include 1) constructing and investigating a novel class of 2D COF hybrid photocatalysts with an effective photoactive organic building block as PS and a precisely incorporated CO2 reduction molecular catalyst (MC); and 2) mechanistic origins of CO2 photoreduction using a set of complementary time-resolved and in situ spectroscopic techniques. The novelty of the proposed hybrid system lies in the unprecedented combination of the unique advantage of porous crystalline COF PS with the precise catalytic function of MC for photocatalytic CO2 reduction. In the periods of the support (09/01/2019-12/31/2022), we have made research progress in four projects: 1) Exploring 2D COFs with incorporated Mn complex for light driven CO2 reduction; 2) The dependence of excited state and charge transfer dynamics on monomer structure of 2D COFs; and 3) Control over Charge Separation by Imine Structural Isomerization in Covalent Organic Frameworks with Implications on CO2 Photoreduction; and 4) The impact of monomer structure on the photoluminescence properties of COFs. We found that both monomer structure and linker chemistry can effectively impact the excited state dynamics, charge transfer properties, and photoluminescence quantum yields, the important properties that dictate their applications in photocatalysis. In addition, we found that the direction of imine linker determines charge transfer direction and thus controls the types of catalytic reactions (e.g. water oxidation or CO2 reduction reactions). The result from these fundamental studies provides important information for correlating the structure of the COF photocatalysts with their photophysical properties and catalytic functions, paving the way for their novel application in photocatalysis. We expect that our findings will contribute to addressing current shortcomings of semiconductor- and molecular-based photocatalytic systems that suffer from inefficient light harvesting and charge separation and poor adsorption and activation of reactants. In turn, this research will contribute towards the development of novel photocatalytic systems for CO2 reduction to generate renewable chemical fuels and simultaneously address the problem of mitigating climate change due to CO2 accumulation. In addition, the experimental approaches employed in this research can be easily transferred to other energy technologies and are expected to broadly impact fields involving photocatalysis, optoelectronic devices, and solar energy conversion. The proposed research has also been integrated with educational activities and serve as a basis to raise awareness around the critical issues of global energy production and consumption, and to develop the next generation of solar energy scientists.

14 SOLAR ENERGY↗
DOE OSTI2024

Community Sentiment Analysis with Focus on CCS

This research assesses community sentiment towards Carbon Capture and Storage (CCS) as depicted in digital media, focusing on themes such as emissions, transport, and storage. Utilizing the VADER sentiment analysis model, the study analyzed titles and descriptions from online news articles to capture immediate impressions and sentiments. Results indicate a predominantly positive sentiment towards CCS, with significant regional variations. States like Alabama and Texas exhibit high positive sentiment, likely due to economic ties to the energy industry, while states like Delaware and Utah show negative sentiment, potentially driven by environmental concerns. The significance of this study lies in its potential to inform policy and communication strategies for CCS implementation. Understanding public sentiment is crucial for the success of CCS projects as it helps to identify and address community concerns. Media influence plays a significant role in shaping public opinion, and this study highlights the need for effective communication strategies to promote the benefits of CCS while addressing any misconceptions. By providing insights into regional differences in sentiment, this research supports the development of tailored interventions that can enhance public acceptance and support for CCS initiatives, ultimately contributing to the success of efforts to mitigate climate change through reduced greenhouse gas emissions.

White, Casey↗
DOE OSTI2024

Evaluating the Nation's Pipeline Infrastructure with NETL's Advanced Infrastructure Integrity Model (AIIM)

This poster is a part of BIL-EDX4CCS Task 36: Advanced Infrastructure Integrity Modeling to Evaluate Existing Energy Infrastructure Reusability and Risk, the goal of which is to produce a smart tool that will assess existing energy infrastructure reusability and risk using the Advanced Infrastructure Integrity Model (AIIM). This model forecasts lifespan and potential risk using a multitude of factors such as incidents reports, structural characteristics, and the surrounding environment. The project aims to provide scientific insights for a better understanding of carbon storage (CS), potential to support CS stakeholder needs, national decarbonization, and mitigating climate change. AIIM will utilize an energy infrastructure database as its input, developed by acquiring publicly available data as well as NETL derived products. These resources include incidents, geohazards, and infrastructure variables. Soil data in the form of rasters and pipeline incident reports were processed and a script was developed to count the number of times features such as roads, railroads, and rivers intersected with pipeline segments which were then converted to points. Distance to oil and natural gas wells, petroleum ports, intermodal freight facilities, and geologic structures were also calculated. After data preparation and quality control was completed, the data was integrated into the pipeline points. Once models are complete, a smart tool will be created in the form of an online dashboard.

Malay, Caleb↗
DOE OSTI2024

A Workflow for Characterizing Legacy Wells as Potential Leakage Pathways for Integration to NRAP-Open-IAM

Carbon capture and storage is a crucial component of climate change mitigation strategies, involving the capture of carbon dioxide (CO2) from point sources and its injection into permeable subsurface formation. Many suitable CO2 storage sites coincide with legacy wells since the conditions that kept hydrocarbons in-situ for thousands of years are also ideal for storage of carbon dioxide. To protect underground sources of drinking water (USDW) during greenhouse gas injection, the Environmental Protection Agency (EPA) mandates area of review evaluations. These evaluations ensure that drinking water sources would not be contaminated by injected fluids. They include identification of legacy wellbores, integrity assessments, and implementing any necessary corrective action. Previous assessment approaches of legacy wells include high-level scoring of regional data and well construction and abandonment evaluation. This work describes a novel methodology that evaluates well construction and abandonment, ranks them based on complexity, and performs a risk assessment with NRAP-Open-IAM. A workflow of the methodology is presented, highlighting its capabilities and limitations.

Wise, Jarrett↗
DOE OSTI2023

Increasing CO 2 Capture Rate in Liquid-Solvent Direct-Air Carbon Capture via Additive Manufacturing

Carbon capture is essential to meeting climate change mitigation goals. One approach currently being commercialized utilizes liquid-based solvents to capture CO 2 directly from the atmosphere but is limited by slow absorption of CO 2 into the liquid. Improved air/solvent liquid mixing increases CO 2 absorption rate, and this increased CO 2 absorption efficiency allows for smaller carbon capture systems with lower capital costs and better economic viability. In this project, we study the use of passive micromixers fabricated by metal additive manufacturing. The micromixer’s small-scale surface geometric features perturb and mix the liquid film to enhance mass transfer and CO 2 absorption. In this project, we evaluated this hypothesis through computational and experimental studies. Computational investigations focused on developing capabilities to simulate thin film (~ 100μm) fluid flow on rough surfaces. Such thin films are in a surface-tension dominated regime and simulations in this regime are prone to instabilities. Improvements to the Nalu code completed in this project resulted in a 10x timestep stability improvement for these problems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Design Requirements and Software Specification for the Autonomous Energy Management Software System for Small Commercial Buildings

Commercial buildings are responsible for approximately 20 percent of the total United States energy consumption and greenhouse gas emissions. Over 85 percent of these buildings lack building automation systems. Many of these buildings are small (<50,000 square feet), underserved, and use rooftop units for heating, ventilation, and air-conditioning needs. Because these buildings lack proper energy management systems, they have several operational deficiencies that lead to excess energy consumption. Studies have shown that managing the rooftop units heating and cooling set points, schedules, setbacks, and optimal start can result in 20 to 25 percent reduction in electricity consumption in small commercial buildings. In addition, improving demand flexibility of these buildings will result additional cost savings for the building owner. Therefore, the Department of Energy’s Building Technologies Office approved a project to address the needs for small commercial buildings. The project is led by Pacific Northwest National Laboratory (PNNL) with Intellimation LLC as the cooperative research and development agreement partner. The primary goal of the project is to develop and validate an autonomous energy management software (AEMS) system that will continuously optimize small commercial building operations by minimizing energy consumption and cost, while providing a solution for maximizing decarbonization benefits from electrification of buildings. The work will leverage the vast experience of PNNL research and development staff who have over two decades of experience in developing and successfully transferring software technologies to the private sector. This solution will be jointly developed with Intellimation, a company that plans to use it to scale their building energy efficiency (EE) and grid services offering. Widespread deployment of the AEMS system will improve the EE and demand flexibility of the building commercial building stock. It should also support cities and states in meeting their climate change mitigation goals. This document describes the various EE and grid service features of the AEMS system, infrastructure and data required to implement those features, and how the features should be automated. It also details how the various features will be tested and validated, including field validation. The document also details what flexibility the users have and how they will be able to leverage those capabilities exercise those. The intent is to create an AEMS system that would support scalable deployment, requires minimal configuration, and is easy to maintain over its expected lifespan. The initial alpha release of AEMS system is planned for March 2023, and the beta release is planned for the summer of 2023. The final release is planned for March 2024. Section 2 of the report documents the relevant building types that AEMS is suitable for. Section 3 documents EE features that will be supported. It will also include the data requirements, hardware requirements, implementation details, and how EE features will be tested and validated. Grid service features will be documented in section 4, including data requirements, hardware requirements, implementation details, and how the services will be tested and validated. Planned next steps are described in section 5.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

An ML-based terrestrial data fusion and augmentation framework to enable advanced understanding of the terrestrial carbon and water interactions

Soil moisture is essential to the terrestrial carbon and water cycles and land–atmosphere interactions. There are various types of soil moisture data, and each type has the distinct spatiotemporal strengths and limitations, depending on the diverse applications and retrieval methodologies of different data types (Li et al., in review; The PNNL-82151 FY23 Report). However, the limitations of different soil moisture data in terms of accuracy and spatiotemporal coverage hinder our ability to further understand the soil moisture dynamics across scales. To have a gap free soil moisture data product with a fine spatiotemporal coverage and vertical profiles, we train extreme gradient boosting (XGBoost) models by using (1) in-situ soil moisture measurements from the International Soil Moisture Network (ISMN), (2) soil moisture from the ECMWF reanalysis (ERA) at the 9 km and sub-daily spatiotemporal resolution, (3) the Daymet meteorological fields, and (4) data products that characterize surface conditions, including soil texture, organic content, topography, vegetation type, and rooting depth. We use the trained XGBoost models that have consistent performance across seven soil layers, i.e., 0–5 cm, 5–10 cm, 10–20 cm, 20–40 cm, 40–60 cm, 60–100 cm, and 100–200 cm, and the gridded model predictors to generate a soil moisture data at the 1 km and daily spatiotemporal resolution for the Continental United States (CONUS) from 2001–2020. This dataset can be broadly used for Earth system model benchmark, monitoring extreme weathers, making informed decisions regarding agriculture, water resource management, climate change mitigation, and ecosystem preservation.

58 GEOSCIENCES↗
DOE OSTI2024

Optimizing Carbon Capture, Transport, and Storage: Overcoming Challenges with Machine Learning and Cost-Benefit Analysis

Carbon Capture, Utilization, and Storage (CCUS) is a critical strategy for reducing CO₂ emissions and mitigating climate change. However, its widespread deployment faces numerous challenges across the capture, transport, and storage phases. These challenges include the technical complexity of predicting subsurface behaviors during CO₂ injection, ensuring long-term storage integrity, optimizing transportation networks, and balancing the economic and environmental trade-offs. Addressing these issues requires an integrated approach combining advanced subsurface modeling with system-level analyses to assess costs, risks, and benefits. This presentation provides an overview of studies conducted by the National Energy Technology Laboratory (NETL) to tackle these challenges. NETL’s efforts encompass cutting-edge research in subsurface fluid behavior machine learning predictions, alongside the development of innovative tools for system optimization and economic evaluation. By bridging technical expertise and strategic analysis, NETL aims to advance the deployment of CCUS technologies to support global decarbonization efforts. Presented at the Carnegie Mellon University CEE IESS Student Seminar October 4, 2024.

Shih, Chung Yan↗
DOE OSTI2025

Floating photovoltaic power plants: A review of energy yield, reliability, and operation and maintenance

Photovoltaic (PV) systems are essential for the transition to sustainable energy, reducing fossil fuel dependence and mitigating climate change. Although PV requires minimal land area — PV can meet the European Union's energy needs using only 0.26% of its land — space for deployment is often scarce in densely populated regions. Floating photovoltaics (FPV) offer an effective solution to land-use challenges by installing PV systems on floating structures in water bodies. FPV is a growing niche within PV with a cumulative installed capacity reaching 7.7 GW globally by 2023. Almost 90% of the installed FPV capacity is in Asia, with close to 50% of in China alone, while the Netherlands and France are the largest markets outside Asia. FPV shows strong potential to support climate targets, but still faces challenges like regulatory barriers, cost competitiveness compared to ground-based PV (GPV), and uncertainties about environmental impacts and system reliability. FPV systems are currently installed mainly on sheltered inland waters, such as quarry lakes, irrigation ponds and reservoirs. FPV technical standards are still being developed. Guidelines have been published by the World Bank, DNV, and Solar Power Europe, and emerging national standards from South Korea, China, and Singapore address design, components, and safety. The International Electrotechnical Commission (IEC) is working on formal standards for floats, mooring systems, and electrical connectors. However, the published best practices lack quantitative guidance for yield modelling and reliability, which this report aims to address. It provides data-driven insights, models, and parameters essential for accurate energy yield, reliability, and maintenance predictions over FPV systems' lifetimes.

14 SOLAR ENERGY↗
DOE OSTI2025

Data-Enabled Fusion Technology (Final Scientific/Technical Report)

Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Greenhouse gas flux response in biochar- and compost-amended urban soils under simulated soil hydrologic dynamics

Understanding greenhouse gas emission dynamics in lawn soils is essential for improving climate change mitigation strategies in urban and suburban environments. This project measured fluxes of carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) fluxes from turfgrass soil columns amended with biochar, compost, biochar-compost blend, control (no amendment) in a controlled greenhouse mesocosm experiment. This project simulated contrasting water saturation regimes consisting of normal irrigation with sprinkler, transient half or full saturation by water table manipulation, and subsequent drying phase under both sod or seeded grass conditions. Compost-amended columns (compost or biochar-compost blend) exhibited higher average CO 2 fluxes (6 µmol m-2 s-1) compared to biochar or control columns (4.5 – 5.0 µmol m-2 s-1) across all saturation levels under sod conditions. Seeded grass conditions generally resulted in less CO 2 emissions. The CO 2 fluxes were positively correlated with air temperature and negatively correlated with soil moisture. Biochar-amended columns retained high soil moisture (~95 %) throughout experiments, demonstrating superior moisture retention compared to compost. CH 4 and N 2 O fluxes exhibited temporal increases (5-8 nmol m-2 s-1) during saturation and drainage phases, indicating their sensitivity to hydrologic conditions. These findings suggest that temperature and amendment types are primary driver of CO 2 emissions, while CH 4 and N 2 O fluxes are more responsive to water saturation dynamics in lawn soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Micromundos biominerales en las algas rojas

Coralline algae (rhodophyta) populate vast pinkish colour regions of the coast. If you step on them in your bare feet, they might hurt you, because they are hard and sharp. Many organisms find shelter and develop within their tiny branches. Photosynthesis of coralline algae conducts the formation of carbonates that exhibit a fascinating architecture. The alga and its associated microorganisms (microbiota) participate in the formation of these minerals, that accumulate and cement the materials that ultimately shape beaches and coastal lines. Carbonates are susceptible to acid-base chemistry; thus, their structural stability and their dissolution depend on the pH of the surrounding environment. Therefore, these biominerals and the marine organisms that build them (such as algae, corals, mollusks or equinoderms) are vulnerable to ocean acidification. By trying to see beyond our eyesight, we were able to understand that algal branches hide an amazing structural strength, where its microstructure and chemistry play a major role. We found minerals with a vast structural and thermal stability in the algal body, named algal thallus. Currently, basic science explores coralline red and green algae as interesting models to understand carbon sequestration in stable structures. Therefore, this research might inspire the development of technologies to mitigate climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

AssessCCUS: An Integrated Approach for Aggregating Resources to Enable Techno-Economic and Life Cycle Assessment of Carbon Management Technologies

Carbon capture, utilization, and storage (CCUS) - also sometimes known as carbon management - technologies are becoming an increasingly important part of the portfolio of technologies necessary to mitigate climate change and defossilize industrial production systems (Sick, 2021). These technologies capture carbon dioxide from industrial point sources or from the atmosphere directly and then either sequester it or use it as a carbon source in valuable products. Potential utilization pathways include, but are not limited to, concrete, fuels, and certain commodity chemicals, and sequestration pathways can include permanent geological storage or temporary storage in natural sinks ranging from forests to agricultural soil. Regardless of the pathway, assessment of the economic and environmental performance of the technologies is important for understanding their potential scalability and impact as well as developing plans to minimize life cycle costs and potential environmental trade-offs. A full discussion of potential trade-offs associated with CCUS is outside the scope of this article, but promoting assessment broadly helps to stimulate important conversations about the benefits and drawbacks of any particular technological choice.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Adapting the Technology Performance Level Integrated Assessment Framework to Low-TRL Technologies Within the Carbon Capture, Utilization, and Storage Industry, Part I

With the urgent need to mitigate climate change and rising global temperatures, technological solutions that reduce atmospheric CO 2 are an increasingly important part of the global solution. As a result, the nascent carbon capture, utilization, and storage (CCUS) industry is rapidly growing with a plethora of new technologies in many different sectors. There is a need to holistically evaluate these new technologies in a standardized and consistent manner to determine which technologies will be the most successful and competitive in the global marketplace to achieve decarbonization targets. Life cycle assessment (LCA) and techno-economic assessment (TEA) have been employed as rigorous methodologies for quantitatively measuring a technology's environmental impacts and techno-economic performance, respectively. However, these metrics evaluate a technology's performance in only three dimensions and do not directly incorporate stakeholder needs and values. In addition, technology developers frequently encounter trade-offs during design that increase one metric at the expense of the other. The technology performance level (TPL) combined indicator provides a comprehensive and holistic assessment of an emerging technology's potential, which is described by its techno-economic performance, environmental impacts, social impacts, safety considerations, market/deployability opportunities, use integration impacts, and general risks. TPL incorporates TEA and LCA outputs and quantifies the trade-offs between them directly using stakeholder feedback and requirements. In this article, the TPL methodology is being adapted from the marine energy domain to the CCUS domain. Adapted metrics and definitions, a stakeholder analysis, and a detailed foundation-based application of the systems engineering approach to CCUS are presented. The TPL assessment framework is couched within the internationally standardized LCA framework to improve technical rigor and acceptance. It is demonstrated how stakeholder needs and values can be directly incorporated, how LCA and TEA metrics can be balanced, and how other dimensions (listed earlier) can be integrated into a single metric that measures a technology's potential.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Adapting Technology Learning Curves for Prospective Techno-Economic and Life Cycle Assessments of Emerging Carbon Capture and Utilization Pathways

Comparisons of emerging carbon capture and utilization (CCU) technologies with equivalent incumbent technologies are necessary to support technology developers and to help policy-makers design appropriate long-term incentives to mitigate climate change through the deployment of CCU. In particular, early-stage CCU technologies must prove their economic viability and environmental reduction potential compared to already-deployed technologies. These comparisons can be misleading, as emerging technologies typically experience a drastic increase in performance and decrease in cost and greenhouse gas emissions as they develop from research to mass-market deployment due to various forms of learning. These changes complicate the interpretation of early techno-economic assessments (TEAs) and life cycle assessments (LCAs) of emerging CCU technologies. The effects of learning over time or cumulative production themselves can be quantitatively described using technology learning curves (TLCs). While learning curve approaches have been developed for various technologies, a harmonized methodology for using TLCs in TEA and LCA for CCU in particular is required. To address this, we describe a methodology that incorporates TLCs into TEA and LCA to forecast the environmental and economic performance of emerging CCU technologies. This methodology is based on both an evaluation of the state of the art of learning curve assessment and a literature review of TLC approaches developed in various manufacturing and energy generation sectors. Additionally, we demonstrate how to implement this methodology using a case study on a CO 2 mineralization pathway. Finally, commentary is provided on how researchers, technology developers, and LCA and TEA practitioners can advance the use of TLCs to allow for consistent, high-resolution modeling of technological learning for CCU going forward and enable holistic assessments and fairer comparisons with other climate technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗