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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 OSTI2023

Solution Processed Schottky Diodes Enabled by Silicon Carbide Nanowires for Harsh Environment Applications

Silicon carbide nanowires (SiC NWs) exhibit promising features to allow solution-processable electronics to be deployed in harsh environments. By utilizing a nanoscale form of SiC, we were able to disperse the material into liquid solvents, while maintaining the resilience of bulk SiC. Here, this letter reports the fabrication of SiC NW Schottky diodes. Each diode consisted of just one nanowire with an approximate diameter of 160 nm. In addition to analyzing the diode performance, the effects of elevated temperatures and proton irradiation on the current–voltage characteristics of SiC NW Schottky diodes were also examined. The device could maintain similar values for ideality factor, barrier height, and effective Richardson constant upon proton irradiation with a fluence of 10 16 ion/cm 2 at 873 K. As a result, these metrics have clearly demonstrated the high-temperature tolerance and irradiation resistance of SiC NWs, ultimately indicating that they may provide utility in allowing solution-processable electronics in harsh environments.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Standard Modular Architecture for Consumer End Plug and Play Interfaces

The growth in load and generation sources at the edge of the grid is driving the innovation in power electronic (PE) grid interfaces at the consumer end to improve the grid resiliency, reliability, security, and cost of the future infrastructure. Novel PE technologies are key enablers for the future grid infrastructure to handle the issues that arise because of the projected growth. This paper introduces a novel fundamental building block (FBB) architecture with plug-and play features. An example framework that enables co-ordination of multiple FBBs hierarchically to provide various grid functions is also presented. The FBBs are designed to be equipped with advanced features like online health monitoring, embedded intelligence, and decision-making capability for enhancing the metrics of consumer end plug and play interfaces. The paper elaborates on the proposed architecture and the developed framework i.e., the controls, communication, protection, and the corresponding timing requirements for the building blocks. The architecture and the framework have been validated through simulations and the communication framework has been validated with a control hardware in the loop platform.

Chinthavali, Madhu Sudhan↗
DOE OSTI2025

PhotonIDs: ML-Powered Photon Identification System for Dark Count Elimination

Reliable single photon detection is the foundation for practical quantum communication and networking. However, today's superconducting nanowire single photon detector(SNSPD) inherently fails to distinguish between genuine photon events and dark counts, leading to degraded fidelity in long-distance quantum communication. In this work, we introduce PhotonIDs, a machine learning-powered photon identification system that is the first end-to-end solution for real-time discrimination between photons and dark count based on full SNSPD readout signal waveform analysis. PhotonIDs ~demonstrates: 1) an FPGA-based high-speed data acquisition platform that selectively captures the full waveform of signal only while filtering out the background data in real time; 2) an efficient signal preprocessing pipeline, and a novel pseudo-position metric that is derived from the physical temporal-spatial features of each detected event; 3) a hybrid machine learning model with near 98% accuracy achieved on photon/dark count classification. Additionally, proposed PhotonIDs ~ is evaluated on the dark count elimination performance with two real-world case studies: (1) 20 km quantum link, and (2) Erbium ion-based photon emission system. Our result demonstrates that PhotonIDs ~could improve more than 31.2 times of signal-noise-ratio~(SNR) on dark count elimination. PhotonIDs ~ marks a step forward in noise-resilient quantum communication infrastructure.

Linne, Karl C. [Chicago U.] (ORCID:000900091870358↗
DOE OSTI2025

Recent Advances and Future Perspectives of Low-Concentration CO 2 Enrichment and Emerging Applications

Low-concentration carbon dioxide (LCCO 2 ) streams, ranging from ambient air to industrial flue gas, represent a significant yet underutilized carbon resource. This review examines recent advances in technologies for enriching LCCO 2 and enabling its integration into a wide range of applications. It covers emerging materials and processes for CO 2 capture, including immobilized amines, metal oxides, polymers, porous carbons, and zeolites, with a focus on performance metrics, regeneration strategies, and process intensification. Beyond capture, the review explores the direct utilization of LCCO 2 through reactive capture pathways enabled by novel catalysts and processes, as well as nonreactive applications, such as greenhouse cultivation, enhanced weathering, and sustainable agriculture. Additionally, this review highlights the role of LCCO 2 in enhancing critical metal recovery. Finally, the review outlines crucial future research directions necessary to fully realize LCCO 2 enrichment’s potential within circular carbon economies and critical material supply chains, paving the way toward a more energy-innovative and resource-resilient industrial sector. The findings are significant for researchers in academia, industry, and government, as well as the public interested in extending the value of LCCO 2 .

atmospheric chemistry↗
DOE OSTI2022

A Data Quality-Aware Framework to Reliably Forecast Photovoltaic Generation and Consumer Load for an Improved Resilience of Microgrids

Photovoltaic (PV) power and consumer load forecasting plays a critical role to ensure operational resilience of the electric grid. Most data-driven forecasting algorithms rely heavily on the continuous availability of good quality data for periodic training and validation. When deployed at the grid’s edge, prolonged disruptions to communications during extreme events degrade data quality. Factors such as missing observations, epistemic uncertainties, data drift, and concept drift are manifestations of data quality that impact the generalization of such field-deployed forecasting models. Currently, there exists no mechanism in the literature to dynamically switch between models under varying degrees of data quality as quantified by certain metrics for each factor highlighted above. This paper addresses this shortcoming by conceptually introducing a data qualityaware framework for reliable PV generation and consumer load forecasting. The framework’s design incorporates components of missing values, divergence tests, and continuous monitoring of generalization performance to detect changes in data quality caused by communications disruptions and trigger specific classes of forecasting models grouped under three use cases (UC1- UC3). As a first step towards validating this framework, real data collected from an actual field microgrid system is used to demonstrate the viability of the three use cases. Results show that the performance is the best in UC1 with an unadjusted R-square value of 0.954, followed by 0.939 for UC2 and 0.757 for UC3.

Sundararajan, Aditya↗
DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Techno-Economic Analysis: Best Practices and Assessment Tools

A team at Sandia National Laboratories (SNL) recognized the growing need to maintain and organize the internal community of Techno - Economic Assessment analysts at the lab . To meet this need, an internal core team identified a working group of experienced, new, and future analysts to: 1) document TEA best practices; 2) identify existing resources at Sandia and elsewhere; and 3) identify gaps in our existing capabilities . Sandia has a long history of using techno - economic analyses to evaluate various technologies , including consideration of system resilience . Expanding our TEA capabilities will provide a rigorous basis for evaluating science, engineering and technology - oriented projects, allowing Sandia programs to quantify the impact of targeted research and development (R&D), and improving Sandia's competitiveness for external funding options . Developing this working group reaffirms the successful use of TEA and related techniques when evaluating the impact of R&D investments, proposed work, and internal approaches to leverage deep technical and robust, business - oriented insights . The main findings of this effort demonstrated the high - impact TEA has on future cost, adoption for applications and impact metric forecasting insights via key past exemplar applied techniques in a broad technology application space . Recommendations from this effort include maintaining and growing the best practices approaches when applying TEA, appreciating the tools (and their limits) from other national laboratories and the academic community, and finally a recognition that more proposals and R&D investment decision s locally at Sandia , and more broadly in the research community from funding agencies , require TEA approaches to justify and support well thought - out project planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2019

Health Management and Prognostics for Electric Aircraft Powertrain

W and c Any air borne vehicle needs incorporating safety as key parameter of measure, and inclusion of autonomy raises the critical need for safety under autonomous operations. Management of faults and component degradation is key as complexity in autonomous operations grow over the period of time. Therefore, in addition to basic operational requirements, an autonomous electric vehicle should be able to make accurate estimates of its current system health and take the correct decisions to complete its mission successfully. Real-time safety and state-awareness tools are therefore essential for the vehicle to be able to reach its destination in a safe and successful manner. The need for safety assurance and health management capabilities is particularly relevant for aircraft electric propulsion systems, which are relatively new and with limited historical to learn. They are critical systems requiring high power density along with reliability, resilience, efficient management of weight, and operational costs. A model- based fault diagnosis and prognostics approach of complex critical systems can successfully accomplish the safety and state awareness goal for such electric propulsion systems, enabling autonomous decision making capability for safe and efficient operation. To identify critical components in the system a Qualitative Bayesian approach using FMECA is implemented. This requires the assessment of some quantities representing the state of the electric unmanned aerial systems (e-UAS), as well as look-ahead forecasts of such states during the entire flight, presented in form of safety metrics (SM). In-service data and performance data gathered from degraded components sup- ports diagnostic and prognostic methods for these systems, but this data can be difficult to obtain as weight and packaging restrictions reduce redundancy and instrumentation on-board the vehicle. Therefore, an model-based framework should be capable or operating with limited data. In addition to data scarcity, the variability of such complex critical systems re- quires the model-based framework to reason in the presence of uncertainty, such as sensor noise, and modeling imperfections. Quantification of errors and uncertainties in the measured states and quantities is therefore a fundamental step for a precise estimation of such SMs; un-modeled uncertainty may result in erroneous state assessment and un- reliable predictions of future states of e-UAVs. Typical, centralized model-based schemes suffer from inherent disadvantages such as computational complexity, single point of failure, and scalability issues, and therefore may fail in such a complex scenario. This paper presents a methodology for developing a system level diagnostics and prognostics approach using a Qualitative Bayesian FMECA approach along with a formal uncertainty management framework for an e-UAS. In this work we demonstrate the efficacy of the framework to predict effects of sub-system level degradation on vehicle operation incorporating uncertainty management to predict future behavior under different operating conditions.

Kulkarni, Chetan↗
DOE OSTI2021

A Comprehensive Framework for Valuation of Ecosystem Services in the Context of Hydropower

While models to value ecosystem services exist, they suffer from a lack of “portability” in the sense that a single modelling framework developed for one site cannot be imported and applied to another. Additionally, benefit estimations are often fragmented, incomplete, incomparable, and in heterogenous metrics, thus preventing aggregation to arrive at one comprehensive value. In the context of hydropower, the methods, as well as the metrics used to value the associated ecosystem services, vary between stakeholders (e.g., competing water users, agencies, etc.), detracting from the ability to assess the total value of a hydropower project, water management schemes, or comparing value between competing water users. Further, current methods to value water neglect non-market ecosystem services’ values by failing to account for externalities, resulting in misallocation of costs (e.g., obligatory stakeholder payments for water use) and misinterpretation of hydropower benefits. The benefit-cost ratios in hydropower development are often incomplete because non-market benefits are excluded (e.g., benefits, such as fish habitat resiliency and agricultural water reliability, derived from hydropower development). An approach to enable consistent, standardized hydropower benefit-cost analyses does not currently exist. This paper conceptualizes a methodology to standardize ecosystem services valuation in the context of hydropower projects through the integration of the U.S. Environmental Protection Agency’s existing Final Ecosystem Goods and Services Classification System framework and economic valuation techniques, and demonstrates the applicability of this methodology through a limited proof-of-concept application (the New Waddell Plant, a pumped hydroelectric energy storage facility in Phoenix, AZ). The beauty of our proposed framework lies in its ability to be imported and applied to any other pumped storage facility after accounting for site-specific features.

13 HYDRO ENERGY↗
DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Wildfire Risk Evaluation Framework for Grid Operations and Planning

The United States (US) environmental protection agency's (EPA's) climate change indicators for wildfires show a long-term trend of increased annual wildfire activity, larger wildfire size, and more variable dynamics in wildfire behavior. This has caused more frequent preemptive public safety power shutoff (PSPS) events in the regions with recognized high wildfire risk. These preemptive power shutoffs attempt to prevent the ignition of a wildfire but it nonetheless renders the transmission line non-operational, which often sheds load of downstream communities. As wildfires and PSPS events become more frequent, it is crucial to find the communities most at risk of load shedding. To that end, this paper proposes the wildfire risk evaluation of the system (WiRES) framework, which is a performance-based framework that translates extreme weather-related and PSPS event probabilities into a cumulative probability of a non-operational transmission line. This study also provides a geospatial visualization tool that breaks down the entire western electrical coordinating council (WECC) region into 50 km grid cells which can be used to 1) filter out transmission lines with higher than a threshold outage probability, and 2) graphically discover the affected regions and their biophysical and socioeconomic metrics. %such as the social vulnerability index, population density, gross domestic product, etc. Lastly, an impact assessment study is conducted which connects the results of the proposed framework to python and powerworld-based contingency analysis to highlight the applicability of the framework.

Wildfire, Resilience, Extreme Events, Grid Reslien↗
DOE OSTI2023

Building water resilience in the face of cascading wildfire risks

Severe wildfire is altering the natural and the built environment and posing risks to environmental and societal health and well-being, including cascading impacts to water systems and built water infrastructure. Research on wildfire-resilient water systems is growing but not keeping pace with the scale and severity of wildfire impacts, despite their intensifying threat. In this study, we evaluate the state of knowledge regarding wildfire-related hazards to water systems. We propose a holistic framework to assess interactions and feedback loops between water quality, quantity, and infrastructure hazards as determinants of post-fire water availability and access. Efforts to address the evolving threat of wildfires to water systems will require more interdisciplinary research on the complex relationships shaping wildfire’s threat to water availability and access. To support this, we need reliable long-term data availability, consistent metrics, greater research in shared contexts, more extensive research beyond the burn area, and multistakeholder collaboration on wildfire risks to water systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Understanding implications of cathode architecture on energy density of solid-state batteries

Next generation solid-state batteries (SSB) will need to leverage high voltage cathodes, as well as metallic anodes to achieve the realistic performance targets necessary to replace liquid electrolyte-based batteries in cutting-edge applications including electric vehicles. However, limitations arising from mass and charge transports, kinetics and chemo-mechanical degradation at the electrode | electrolyte interface limit the performance of present day SSBs. Optimizing composite cathode architecture, which is an integral part of solid-state batteries, is vital to realize the high-energy density and high-performance goals for next-generation solid-state batteries. Additionally, cathode architecture needs to be optimized for high loadings of active material, well-percolated ion and electron transport pathways and increased resilience against electrochemical stresses. This paper provides a first report of framework for geometric modeling of composite cathode architectures and evaluates the impact of cathode architecture on cell-level energy density using hierarchical models. Packing around primary and secondary active material particles are simulated for a range of active material particle size and solid electrolyte size distributions in the composite cathode. Impact of packing architecture on processing parameters of a given cathode composition and thickness, as well as on achievable energy density is evaluated for a range of commonly used solid electrolyte and cathode materials. Overall, the proposed framework offers a facile exploratory methodology for establishing initial metrics for scalable processing of practical and competent SSBs.

25 ENERGY STORAGE↗
DOE OSTI2022

Deployment Readiness Framework Subtask 1.1 (Literature Review)

Island and remote coastal communities face some of the most challenging environments for building, operating, and maintaining energy infrastructure, as well as the highest costs for electricity, fuels, and other essential energy sources. As sea-levels rise and storms become more intense and frequent, these communities and the energy infrastructure that supports coastal lives and livelihoods are also at increasing risk from natural hazards. To address these challenges, many coastal communities are envisioning energy solutions that will support the triple bottom line goals of the blue economy: economic growth, environmental sustainability, and social equity. Yet, island and remote coastal communities often face limited resources and capacity to tackle complex energy and coastal resilience issues. To support community-driven energy transitions in island and remote communities, and to better understand relationships between energy, community, and ecosystem resilience, the Department of Energy’s Water Power Technologies Office (WPTO) has initiated the development of a Deployment Readiness Framework (DRF). The objective of the work is to co-produce and test practical tools and approaches that assess the readiness of coastal communities for marine energy demonstration, deployment, and operation. The DRF aims to build on and support the Energy Transitions Initiative Partnership Program (ETIPP) and other community-oriented energy transition programs. This project is jointly led by Pacific Northwest National Laboratory (PNNL) and the National Renewable Energy Laboratory (NREL). The development of the DRF includes three main phases: 1) a learning phase involving stakeholder engagement and literature review to synthesize metrics of community readiness to advance through an energy transition and to understand the state of the research and practice of participatory science-policy processes in various sectors, 2) a design phase to define readiness approaches and tools that will be developed as part of the DRF, and 3) an implementation phase to create the applications and interfaces for WPTO and the national laboratories to interact with the DRF. All three phases include close collaboration with communities and end-users of the framework, first to identify gaps in the science and tools needed to achieve community-driven energy transition goals and second, to test and improve the framework iteratively. Through technical assistance programs like ETIPP and utilizing the completed DRF to understand the influencing factors which motivate or deter energy transitions, WPTO hopes to engage a number of near-term marine energy demonstration opportunities. Here we report on the results from the literature review (Subtask 1.1) to inform the stakeholder engagement (Subtask 1.2) and design phase (Task 2) of the project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2016

Risk of Performance Decrements and Adverse Health Outcomes Resulting from Sleep Loss, Circadian Desynchronization, and Work Overload

Sleep loss, circadian desynchronization, and work overload occur to some extent for ground and flight crews, prior to and during spaceflight missions. Ground evidence indicates that such risk factors may lead to performance decrements and adverse health outcomes, which could potentially compromise mission objectives. Efforts are needed to identify the environmental and mission conditions that interfere with sleep and circadian alignment, as well as individual differences in vulnerability and resiliency to sleep loss and circadian desynchronization. Specifically, this report highlights a collection of new evidence to better characterize the risk and reveals new gaps in this risk as follows: Sleep loss is apparent during spaceflight. Astronauts consistently average less sleep during spaceflight relative to on the ground. The causes of this sleep loss remain unknown, however ground-based evidence suggests that the sleep duration of astronauts is likely to lead to performance impairment and short and long-term health consequences. Further research is needed in this area in order to develop screening tools to assess individual astronaut sleep need in order to quantify the magnitude of sleep loss during spaceflight; current and planned efforts in BHP's research portfolio address this need. In addition, it is still unclear whether the conditions of spaceflight environment lead to sleep loss or whether other factors, such as work overload lead to the reduced sleep duration. Future data mining efforts and continued data collection on the ISS will help to further characterize factors contributing to sleep loss. Sleep inertia has not been evaluated during spaceflight. Ground-based studies confirm that it takes two to four hours to achieve optimal performance after waking from a sleep episode. Sleep inertia has been associated with increased accidents and reduced performance in operational environments. Sleep inertia poses considerable risk during spaceflight when emergency situations necessitate that crewmembers wake from sleep and make quick decisions. A recently completed BHP investigation assesses the effects of sleep inertia upon abrupt awakening, with and without hypnotics currently used in spaceflight; results from this investigation will help to inform strategies relative to sleep inertia effects on performance. Circadian desynchrony has been observed during spaceflight. Circadian desynchrony during spaceflight develops due to schedule constraints requiring non-24 operations or 'slam-shifts' and due to insufficient or mis-timed light exposure. In addition, circadian misalignment has been associated with reduced sleep duration and increased medication use. In ground-based studies, circadian desynchrony has been associated with significant performance impairment and increased risk of accidents when operations coincide with the circadian nadir. There is a great deal of information available on how to manage circadian misalignment, however, there are currently no easily collected biomarkers that can be used during spaceflight to determine circadian phase. Current research efforts are addressing this gap. Work overload has been documented during current spaceflight operations. NASA has established work hour guidelines that limit shift duration, however, schedule creep, where duty requirements necessitate working beyond scheduled work hours, has been reported. This observation warrants the documentation of actual work hours in order to improve planning and in order to ensure that astronauts receive adequate down time. In addition to concerns about work overload, ground based evidence suggests that work underload may be a concern during deep space missions, where torpor may develop and physically demanding workload will be exchanged for monitoring of autonomous systems. Given that increased automation is anticipated for exploration vehicles, fatigue effects in the context of such systems needs to be further understood. Performance metrics are needed to evaluate fitness-for-duty during spaceflight. Although ground-based evidence supports the notion that sleep loss, circadian desynchronization and work overload lead to performance impairment, inconsistency in the measures used to evaluate performance during spaceflight make it difficult to evaluate the magnitude of performance impairment during spaceflight. Work is underway to standardize measures of performance evaluation during spaceflight. Once established, such performance indicators need to be correlated with operational performance. Individual differences in sleep need and circadian preference, phase shifting ability and period have been documented in ground-based studies. Individual differences in response to sleep loss and circadian misalignment have also been documented and are presumed to be associated with genetic polymorphisms. No studies have systematically reported individual differences in sleep or circadian-related outcomes during spaceflight. More work is needed in this area in order to identify genetic or phenotypic biomarkers that predict resilience or vulnerability to sleep loss in order to personalize countermeasure strategies and mitigate performance impairment during spaceflight. Two laboratory and field investigations specific to this topic are currently ongoing; additional efforts, including an effort to mine existing biological data from spaceflight relative to sleep and circadian outcomes, are planned. Sex differences in sleep need and circadian period and phase have been reported in ground-based studies. The impact of these sex differences on performance is unclear. Sex differences in sleep need and circadian rhythms have not been systematically studied during spaceflight, presumably due to the small number of women that have flown in space. More research is needed in this area to evaluate whether any of the observed sex differences in physiology lead to altered performance in spaceflight and on the ground.

Flynn-Evans, Erin↗
DOE OSTI2021

Quantifying the Solar Energy Resource for Puerto Rico

After Hurricane Maria, multiple U.S. Department of Energy laboratories studied the state of the electric grid in Puerto Rico and analyzed grid resilience and grid integration of renewable energy. As part of the work done at the National Renewable Energy Laboratory, researchers created new solar resource data, conducted a technical potential and supply curve analysis, and studied the interannual variability of the solar resource. A new methodology was developed to downscale solar resource data from the National Solar Radiation Data Base (NSRDB) from a 4-km x 4-km spatial and 30-minute temporal resolution to a 2 km x 2 km and 5-minute resolution. This methodology primarily used simple physical principles to develop high-resolution cloud properties which were then used to compute solar radiation. The high-resolution datasets were validated against ground measurements and the error metrics were found to be similar to the original lower resolution dataset. Using 20 years of downscaled data from the NSRDB multi-year capacity factors for photovoltaics (PV) were developed for both single-axis tracking and fixed latitude-tilt configurations. Use of the multi-year data provides the ability to understand variability in capacity factors due to variability in weather over a long period of time. For Puerto Rico the coastal regions were found to have significant higher capacity factors than inland. Using land-use and terrain information a technical potential analysis was conducted for Puerto Rico. This analysis restricted single PV plant development to a maximum of 100 MW nameplate capacity. The nameplate capacity for each municipality were then determined. Based on our assumptions, 56 of the 78 total municipalities of Puerto Rico contain some level of solar capacity. Most of the interior municipalities did not have any capacity because of the geographic exclusions used in this study. The lowest capacity for a PV plant observed in a municipality was 10 MW. The maximum capacity within a county was 2,000 MW. Further a supply curve analysis was conducted by taking the results of the technical potential and quantifying system and transmission costs. The levelized cost of energy (LCOE) was calculated for each theoretical PV plant site, and the levelized cost of transmission was added to the LCOE to produce a total cost estimate for each site. The results of the supply curve analysis allow for a relative comparison of the cost for integrating new PV capacity into the grid. This analysis indicates that cheaper total LCOE sites tend to be larger in capacity. The total capacity in this study was found to be far beyond the maximum peak load for the island. However, this study does not consider the economic and market potential for development. The cumulative capacity presented in this study assumes that the best locations are developed first and ignores the complex decision paths for new power plant development. Therefore, this analysis can only be treated as illustrative. Finally this study investigates the impact of inter-annual variability of resource using a variety of metrices including probability of exceedance and variation in capacity factor and LCOE. This study demonstrates that the capacity factor or LCOE could vary by over 10% year to year. This clearly indicates the risks involved in using any particular year of data and clearly points to the use of multi-year data to reduce some of the risks related to variability in weather.

14 SOLAR ENERGY↗
DOE OSTI2022

Home Energy Upgrades as a Pathway to Home Decarbonization in the US: A Literature Review

This work aims to characterize how home energy upgrade projects and programs in the US have evolved over the past decade. It also identifies what changes are needed to drive expansion of the US energy retrofit market in such a way that addresses carbon emissions from buildings, improves resilience and upgrades the housing stock. This review focuses on whole-home energy upgrades, targeting deep energy retrofit savings of >30%. The topics we cover include trends in home electrification, US and European home energy upgrade programs, energy upgrade measure costs, business economics, and health effects. Key changes in project design noted in this review include: (1) the electrification of dwellings with rapidly improving heat pump systems and low-cost solar photovoltaic technology; and (2) a shift away from high-cost building envelope strategies and towards more traditional home performance/weatherization envelope upgrades. Promising program design strategies covered include: (1) end-use electrification programs; (2) novel financing approaches; (3) the use of carbon-based program and project metrics; and (4) “one-stop shop” programs. Based on the existing market barriers, we suggest that the industry should adopt new project performance metrics. Additionally, market drivers are needed to spur widespread energy upgrades in the US housing stock. Costs must be reduced, and projects designed to appeal to homeowners and contractors.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

A Field-Deployable Magnetic Resonance Imaging Rhizotron for Modeling and Enhancing Root Growth and Biogeochemical Function

A collaborative team from Texas A&M AgriLife Research, ABQMR Inc., the Soil Health Institute, the Athinoula A. Martinos Center for Biomedical Imaging, and NIST developed low-field magnetic resonance imaging (LF-MRI) instrumentation capable of imaging intact soil-root systems. The system measured root biomass, architecture, 3D mass distribution, and growth rates, providing a non-destructive means to evaluate ideal plant characteristics based on root metrics. It also successfully generated three-dimensional images of soil water content, a key property influencing root growth and exploration. Operating much like an MRI used in a medical setting, the system functioned in field conditions without damaging plants, overcoming the limitations of traditional methods such as trenching, soil coring, and root excavation. Over the course of the project, the team designed and built three functional prototype systems. These prototypes provided new insights into root–water–soil interactions that drive processes such as nutrient uptake, water use, and carbon management. This information contributed to efforts to optimize plants for carbon sequestration without sacrificing economic yield. The project also supported the identification of desirable traits for energy sorghum, including high root growth rates, more vertical root angles, and enhanced drought resilience under water-limiting conditions.

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