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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Framework for Effective Science Communication and Outreach Strategies and Dissemination of Research Findings for Marine Energy Projects

Marine energy is an emerging renewable energy industry with the potential to produce 2300 terawatt-hours per year from resources within the United States. As development and testing of marine energy devices advance, regulatory and permitting decision-makers are concerned about the uncertainty surrounding the potential environmental effects resulting from the introduction of these novel devices in coastal and riverine environments. The Triton Initiative researches and provides recommendations for environmental monitoring technologies and methods to inform industry stakeholders with the data necessary to permit the testing of marine energy systems. Effective dissemination of the research findings is essential for improving the accessibility of data to stakeholders who may use the results to inform policy decisions, yet few frameworks for conducting science communications for marine energy projects exist. In this paper, we present tools, channels, and tactics for developing a science communication framework for marine energy projects, or similar areas of study, using the Triton Initiative’s pilot science communication program as a case study. By leveraging existing bodies of work in disciplines such as communications theory, marketing, public relations, and social science, the presented framework includes audience identification and analysis; channel development, including a website, blog, newsletter, social media, and webinars and presentations; and metrics for determining success. Outcomes from one year of Triton’s case study are presented, including the most effective tactics and lessons learned.

communication framework↗
DOE OSTI2020

Performance and usability enhancements for continuous subgraph matching queries on graph-structured data

A query graph, which includes vertices and edges, represents a query on graph-structured data. The query graph is decomposed into query subgraphs. A network analysis tool performs continuous subgraph matching queries to facilitate analysis of computer network traffic, social media events, or other streams of data represented as a dynamic data graph (graph-structured data). This can help identify emerging trends in the data. Some features of the network analysis tool enhance performance by effectively utilizing distributed computing resources (including processing cores and memory at different nodes of a cluster) to speed up the process of updating the dynamic data graph and detecting matches of query subgraphs. Features of a query graph building tool enhance usability by providing intuitive ways to specify query graphs and their subgraphs. Features of a results visualization tool enhance usability by providing an intuitive way to present the results of continuous subgraph matching queries.

Choudhury, Sutanay↗
DOE OSTI2024

Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods

Artificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they introduce additional decision-making and processes that require thorough documentation and reporting. We address these limitations by providing an approach to hand labeling training data for supervised ML that integrates quantitative content analysis (QCA)—a method from social science research. The QCA approach provides a rigorous and well-documented hand labeling procedure to improve the replicability and reproducibility of supervised ML applications in Earth systems science (ESS), as well as the ability to evaluate them. Specifically, the approach requires (a) the articulation and documentation of the exact decision-making process used for assigning hand labels in a “codebook” and (b) an empirical evaluation of the reliability” of the hand labelers. In this paper, we outline the contributions of QCA to the field, along with an overview of the general approach. We then provide a case study to further demonstrate how this framework has and can be applied when developing supervised ML models for applications in ESS. With this approach, we provide an actionable path forward for addressing ethical considerations and goals outlined by recent AGU work on ML ethics in ESS.

58 GEOSCIENCES↗
DOE OSTI2021

Emerging Energy Market Analysis Initiative, Methodological Framework

Planning and operations of the electric power sector are undergoing radical changes. Climate change mitigation efforts have forced rapid changes to the technology mix. Technologies like wind and solar have experienced rapid growth, while investment in fossil sources has peaked or is declining. These foundational changes are forcing changes to energy systems. Demand-side adoption of electrified technologies, including electric vehicles, is changing load profiles and opening up new avenues for consumer participation in the power systems. The implications of an evolving power system pertain to more than environmental and technical dimensions. Changes to the generation mix and its consequent upstream and downstream impacts such as fuel production have significant and highly concentrated consequences on economies and employment. Shifts towards distributed (or decentralized) generating assets offer the potential to reshape economic and employment opportunities associated with the energy sector across space and socioeconomic groups. The Emerging Energy Market Analysis (EMA) initiative aims to identify sustainable, regionally acceptable, and high-value energy solutions that are secure and equitable. Unlike short-term, least-cost choices that can narrowly account for traditional options, EMA’s focus on emerging energy markets recognizes that new or adapted practices and technologies can alter the frontier of solutions and advance a community’s social, economic, and natural pathways. Such change requires a more comprehensive analysis of societal input, resources, capabilities, and infrastructure. These considerations lay the foundation for community decision-making models that are responsive to community values as well as the history and drivers. The result is a community-based decision and engagement model that will be valuable to decisionmakers and developers of advanced and emerging energy solutions, seeking a social license to operate prior to project development.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Unconventional Quantum Advantages for Computation (U-QuAC)

While quantum computing offers the promise of exponential advantages, limited quantum speedups are known, especially for practical applications. To open new avenues for quantum advantages, we propose Unconventional Quantum Advantages for Computation (U-QuACs), with respect to unconventional resources such as space (number of bits or quantum bits of memory required to solve a problem), accuracy of solution, communication, or energy consumption. We focus on space-efficient quantum algorithms, where we seek to design algorithms that solve a problem using much less space than the total size of the input. A natural setting in which space is critical is the streaming model of computation, where the input data arrives sequentially in pieces that must each be processed individually. Streaming is motivated by a variety of problems including analysis of internet traffic or social networks. We design the first exponential quantum space advantage for a natural streaming problem, which also constitutes the first quantum advantage for approximating a discrete optimization problem, albeit with respect to space.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Contextualizing Non-Powered Dam Site Selection for Archimedes Screw Turbines: A Methodology for Responsible Archimedes Screw Turbine Conversion at Existing Dams

Non-powered dams represent 97% of dams in the United States and their energy generation potential has not been fully realized. The use of an Archimedes screw turbine to generate power at non-powered dams offers a dual benefit; producing electricity, and acting as downstream fish passage, helping to reconnect previously separated ecosystems. In this study, we assess the technical, environmental, social, and economic feasibility of generating power at non-powered U.S. dam sites using Archimedes screw turbines by integrating mechanical constraints, social impact metrics, proximity to infrastructure, and environmental sensitivity data. Results account for future precipitation predictions and show, between 2024 and 2050, the number of sites where Archimedes screw turbines are viable decreases by one site, but overall generation capacity increases due to increased flow rates across persisting locations. Our analysis identified 82 non-powered dam sites with a mean generation capacity of 49 kW that meet the mechanical requirements for Archimedes screw turbine technology in 2024. Our analysis presents a framework for considering social, environmental, and economic impacts of specific turbine technologies to convert non-powered dams to generate power.

Archimedes screw turbine↗
DOE OSTI2022

Assessing Gender Bias in Particle Physics and Social Science Recommendations for Academic Jobs

We investigated gender bias in letters of recommendation as a possible cause of the under-representation of women in Experimental Particle Physics (EPP), where about 15% of faculty are female-well below the 60% level in psychology and sociology. We analyzed 2206 letters in EPP and these two social sciences using standard lexical measures as well as two new measures: author status and an open-ended search for gendered language. In contrast to former studies, women were not depicted as more communal, less agentic, or less standout. Lexical measures revealed few gender differences in either discipline. The open-ended analysis revealed disparities favoring women in social science and men in EPP. However, female EPP candidates were characterized as “brilliant” in nearly three times as many letters as were men.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Unraveling the Threads of Environmental Justice in Critical Mineral Extraction: A Framework for Regionalized Life Cycle Data

As we transition to a more sustainable energy system, the extraction and processing of critical minerals becomes increasingly important. However, these industries often raise concerns about environmental and social impacts, particularly in disadvantaged communities. To address these concerns, our research focuses on regionalizing environmental life cycle data to connect it with communities affected by mineral extraction. A framework was developed for collecting life cycle background data that supports the Justice40 Toolset, a market-based approach to evaluating net benefits and costs of critical mineral material recovery pathways. A goal was to alleviate public skepticism around mineral extraction processes, including secondary and unconventional feedstocks, by highlighting both environmental and social impacts. To achieve this, computational analysis and geospatial data science techniques were employed, such as within-scale and across-scale methods and proxy dataset usage. By doing so, we were able to develop a framework for identifying and disaggregating data down to regions small enough to support Justice40 goals. Our approach not only provides valuable tools fo insight into the environmental implications of mineral extraction but also helps policymakers evaluate the social impacts on local communities. This research contributes to a more just and equitable transition to a sustainable energy system, ensuring that marginalized voices are heard in decision-making process.

Davis, Tyler [NETL Site Support Contractor, Nation↗
DOE OSTI2022

The public health exposome and pregnancy-related mortality in the United States: a high-dimensional computational analysis

Racial inequities in maternal mortality in the U.S. continue to be stark. The 2015–2018, 4-year total population, county-level, pregnancy-related mortality ratio (PRM; deaths per 100,000 live births; National Center for Health Statistics (NCHS), restricted use mortality file) was linked with the Public Health Exposome (PHE). Using data reduction techniques, 1591 variables were extracted from over 62,000 variables for use in this analysis, providing information on the relationships between PRM and the social, health and health care, natural, and built environments. Graph theoretical algorithms and Bayesian analysis were applied to PHE/PRM linked data to identify latent networks. PHE variables most strongly correlated with total population PRM were years of potential life lost and overall life expectancy. Population-level indicators of PRM were overall poverty, smoking, lack of exercise, heat, and lack of adequate access to food. In this high-dimensional analysis, overall life expectancy, poverty indicators, and health behaviors were found to be the strongest predictors of pregnancy-related mortality. This provides strong evidence that maternal death is part of a broader constellation of both similar and unique health behaviors, social determinants and environmental exposures as other causes of death.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Coordinative Entities: Forms of Organizing in Data Intensive Science

Scientific collaboration is a long-standing subject of CSCW scholarship that typically focuses on the development and use of computing systems to facilitate research. The research presented in this article investigates the sociality of science by identifying and describing particular, common forms of organizing that researchers in four different scientific realms employ to conduct work in both local contexts and as part of distributed, global projects. This paper introduces five prototypical forms of organizing we categorize as coordinative entities: the Principal Group, Intermittent Exchange, Sustained Aggregation, Federation, and Facility Organization. Coordinative entities as a categorization help specify, articulate, compare, and trace overlapping and evolving arrangements scientists use to facilitate data intensive research. We use this typology to unpack complexities of data intensive scientific collaboration in four cases, showing how scientists invoke different coordinative entities across three types of research activities: data collection, processing, and analysis. Finally, our contribution scrutinizes the sociality of scientific work to illustrate how these actors engage in relational work within and among diverse, dispersed forms of organizing across project, funding, and disciplinary boundaries.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks

In this paper we consider the effects of corporate hierarchies on innovation spread across multilayer networks, modeled by an elaborated SIR framework. We show that the addition of management layers can significantly improve spreading processes on both random geometric graphs and empirical corporate networks. Additionally, we show that utilizing a more centralized working relationship network rather than a strict administrative network further increases overall innovation reach. In fact, this more centralized structure in conjunction with management layers is essential to both reaching a plurality of nodes and creating a stable adopted community in the long time horizon. Further, we show that the selection of seed nodes affects the final stability of the adopted community, and while the most influential nodes often produce the highest peak adoption, this is not always the case. In some circumstances, seeding nodes near but not in the highest positions in the graph produces larger peak adoption and more stable long-time adoption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Comparison of Socio-Technical Threat Models

Given the adoption of emerging technologies and the increasing complexity of managing such systems with a lifecycle much shorter than that of critical infrastructure systems, there is a practical need to be able to analyze sociotechnical dependencies and their associated evolving risks. Threat models based on social influence techniques can be used to implement adversarial tactics analogous to the cyber kill chain and attested to within the MITRE ATT&CK for ICS framework including Initial Access, Persistence, Collection, and Impact. Furthermore, as with cyber disruptions, the impact of social influence threat models can have an asymmetric impact that is not spatially-localized. Finally, unlike cyber attacks with a reasonably short duration (ransomware takes days to months), social influence based attacks have the potential to persist for much longer as they are based on long-term strategic infrastructure investments within the private sector. Given the increased importance of electric vehicle charging stations as a long-term, strategic infrastructure investment within the Energy and Transportation Sectors, we provide initial results that compare the impact of a Loss of Availability (T0826) realized through cyber and social influence based threat models. The analysis employs techniques from automated reasoning and measures of network complexity to understand evolving dominance of EV payment and charging networks within geographic region of interest. Within this context, we compare the impact of a loss of availability due to ransomware versus that of loss of support due to a merger and acquisition. Results across several different metro areas will be provided.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Analysis of Historical Power Outages of the United States and the National Risk Index

Several works have been documented in the literature to study the societal effect of power outages and to analyze their correlation with the Social Vulnerability Index (SVI). However, the relationship between National Risk Index (NRI) and power outages is yet to be explored. This work analyzes the NRI indices such as Risk, Expected Annual Loss, Social Vulnerability, and Community Resilience with several resilience metrics such as event duration, impact duration, recovery duration, impact level, impact rate, recovery rate, recovery to impact ratio, and area under the outage curves to see the correlation of NRI indices with the resilience metrics. The results show that NRI indices such as Risk and Expected Annual Loss increase with the increase of event duration, impact duration, and recovery duration. All Other metrics are indifferent to the change in the Risk and EAL ratings. The results also show that there is no strong relationship between all the metrics and community resilience and social vulnerability. This work also performed the sensitivity analysis of the extreme event selection process. This sensitivity analysis reveals that the way of identifying extreme events has a significant impact on the evaluation of the events.

Bhusal, Narayan↗
DOE OSTI2022

Conflicts of Greens’ in Renewable Energy Landscapes: Case Studies and a Planning Framework

Reducing greenhouse gas emissions (GHG) through renewable energy deployment is a goal for many cities and nations to mitigate the effects of a rapidly changing climate while securing energy needs. Given national and state level policies on green energy to achieve GHG reduction targets, localities are considering utility-scale renewable energy (USRE) facilities on remote lands, rural areas, oceans, coastal waters and large rivers to meet the energy needs for urban residents and industries. Although these USRE facilities generate much “greener” electricity than fossil fuel power plants, locating them can pose conflicts with wildlife, including endangered, threatened and/or special status species, and the habitats that support wildlife populations (Brunette et al., 2013; Gasparatos et al. 2016; Mulvaney 2017), which lead to a “Conflict of Greens” (Ko et al., 2011). In addition to ecological impacts, conflicts over social and cultural resources in local communities lead to concerns over environmental justice, in this context “energy justice,” where negative environmental, social, and economic impacts of energy projects fall more heavily upon marginalized, vulnerable, or indigenous communities. The Pacific Rim region is a center of major and growing economies, and it has become a battlefield for competing “green” objectives. In addition to progressive renewable energy goals, such as in California, Hawaii, and Taiwan, countries around the Pacific Rim have the majority of the growth in manufacturing of wind and solar energy devices and also some of the highest levels of renewables deployment with China, India, and the US leading the way in wind and solar installations. Developing renewable energy source in the ocean such as offshore wind, tidal, and wave energy, are beginning to add to this mix. Given this challenge, the APRU SCL energy working group provides six case studies that address questions about the “Conflict of Greens” across the Pacific Rim including South Korea, Taiwan, and the United States (California, Hawaii, and Massachusetts). The literature review highlights the emerging ecological, social, and economic aspects of conflicts over siting renewables on the landscape. Using case studies, we examine the trial and errors in least-conflict spatial planning, data collection and analysis, public participation in decision-making, mitigation, social planning for energy transitions, and multi-scalar approaches. Lastly, we recommend interdisciplinary policy, planning, and design actions for sustainable energy landscapes across the Pacific Rim and beyond.

green versus green, renewable energy↗
DOE OSTI2025

Circularity Futures Workshop Series: Summary Report

The aim of this report is to synthesize key feedback received from the three-part Circularity Futures workshop series held in Spring 2024. The workshop series was conducted by the National Renewable Energy Laboratory (NREL) on behalf of U.S. Department of Energy, Office Energy Efficiency and Renewable Energy (EERE), and was broken into three workshops: Workshop 1 - Circularity Analysis Needs and Priorities; Workshop 2 - Circularity Metrics and Indicators; and Workshop 3 - Circularity Data. Together, the workshops focused on identifying the existing priorities and gaps in the circularity modeling space, understanding different stakeholders' use and interpretation of circularity metrics and indicators, identifying common data gaps and data quality challenges, and assessing the robustness of available solutions. The workshop series brought a diverse group of stakeholders - including representatives from U.S. government offices, national labs, nonprofit organizations, industry, and academia - to collect first-hand feedback on needs, priorities, challenges and opportunities in the circularity modeling and analysis space. The workshop discussions highlighted numerous common needs, priorities and challenges among the interviewed groups. Several topics were frequently discussed, including: 1) Circularity as a pathway for sustainable economic growth: While circularity is generally defined in terms of resource conservation and reducing wasteful disposal of materials, participants agreed that circular strategies should serve broader economic, environmental, and social goals. It is therefore crucial for circularity analysis to look beyond waste reduction and instead evaluate a variety of impact metrics such as cost savings, job creation, air quality, and pollutant emissions. Mutli-criteria decision-making frameworks may be useful for making sense of disparate metrics and evaluating tradeoffs between impact categories.; 2) Economic and social factors are not well understood: Underdevelopment of existing end-of-life (EOL) management infrastructure, inconsistent standardization codes and policy space in reusing recycled content, and suboptimal collection and sorting strategies collectively contribute to uncertainty about the economic potential of circular pathways. The latter observation is consistent among all technologies but more emphasized for renewable energy systems. Social impacts of circularity practices are less understood and less researched than other sustainability aspects.; 3) Inconsistent methods for assessing emerging technologies: LCA and TEA results vary widely depending on the assumptions made with regards to market adoption of new technologies. Emerging technologies suffer limited availability of data needed to conduct a robust circularity analysis. Yet, understanding projected impacts of proposed nascent technology is a key need for different stakeholder groups.; and 4) Lack of temporally and geospatially explicit data: There is a need for open data that represents variations in circularity technologies over time and location. The lack thereof leads to aggregated and potentially misrepresented results in circularity analysis. Sensitivity analyses should be included to verify whether options perceived as more sustainable align with real-world practices.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Systematic Enterprise Risk Management by Integrating the RISMC Toolkit and Cost-Benefit Analysis (Final Report)

The goal of this research is to theorize and quantify the relationships between safety and the financial performance of nuclear power plants (NPPs). The Socio-Technical Risk Analysis (SoTeRiA) theoretical framework, which connects the social aspects (e.g., safety culture) and structural features (e.g., safety practices) of an organization with organizational safety and financial risks, is used to theorize the direct and indirect relationships between safety and the financial performance of NPPs. An Integrated Enterprise Risk Management (I-ERM) methodological framework is developed to operationalize SoTeRiA to quantify NPP safety and financial performance in a unified platform where their underlying physical degradation mechanisms, coupled with maintenance performance (considering human and organizational factors), are explicitly incorporated to depict the interconnections and dependencies between safety and financial performance. In this study, NPP safety refers to both occupational safety and system safety (estimated from Probabilistic Risk Assessment, PRA), and financial performance refers to the monetary values associated with NPP operation and maintenance (O&M) strategies. This report covers a case study demonstrating the feasibility of the I-ERM methodological framework. More detailed development of one of the I-ERM modules, i.e., Probabilistic Physics-of-Failure (PPoF) analysis, and its connection with other I-ERM modules is demonstrated in a second case study. The outcome of this research will help NPP decision-makers create cost-saving maintenance strategies while maintaining safety by providing cost- and risk-informed recommendations regarding maintenance work processes and operational strategies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Effect of realistic routing on the social burden metric

The distance people travel to reach critical services is a key input to the Social Burden metric used by Sandia’s Resilient Node Cluster Analysis Tool (ReNCAT) in the optimization’s objective function. By default, ReNCAT utilizes Euclidian distances between population blocks and critical facilities when calculating Social Burden. However, these straight-line distances do not reflect how most residents or goods would travel throughout the area. As distance is a vital input to the burden calculation, a more realistic distance calculation will yield more realistic burden values. This work uses real road networks and calculates the shortest distance path between population centers and critical facilities using a standard graph theory approach. These realistic route distances are then used to compute Social Burden for four areas of study. It was found that distances using real road routes are generally, but not always, longer than the Euclidean distance. The increased length increases the final Social Burden metric, however, the overall burden percent change ranged between 17% and 52%, which means the impact of realistic routes relies heavily upon the area’s road topology. It was found that rural locations within an area may have larger burden increases than urban areas as more dense road networks allow routes to more closely follow a straight-line path. Additionally, using the most straight forward routing algorithms requires high computational effort for areas with large road networks. While it is believed this process can be made more performant, that task is beyond this scope of work.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

CCS Opportunity Along the Gulf Coast Corridor

The Gulf Coast corridor, onshore and offshore, from Corpus Christ to the Mississippi River presents a high concentration of CO2 from industrial sources and excellent storage reservoirs in the underlying Cretaceous and Tertiary sands. Analysis of the technical, geologic, economic and social aspects of this CCS opportunity is necessary for successful deployment of CCS technology and eventual attainment of NetZero goals. Presentation at the Offshore Technology Conference (OTC) held in Houston, Texas, May 6-9, 2024.

Grant, Timothy↗