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

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A deep learning-based Bayesian framework for high-resolution calibration of building energy models

Calibrating building energy models (BEMs), i.e., closing discrepancy between modeling and field measurements, is of significance to support its applications in building sustainability and resilience analysis. However, as being widely used in practice, current Bayesian calibration is mostly performed in low-resolution (annual or monthly), instead of high-resolution (hourly or sub-hourly), which is crucial to support emerging BEM applications, such as building-renewable energy integration (demand response) and smart control. This is attributable to the gaps in current Bayesian calibration process, including (1) difficulty in supporting reliable high-resolution calibration with over-parameterization and multi-solution issues, (2) inadequacy of meta-model to capture temporal building dynamics in high-resolution, and (3) excessive computational burdens of covariance matrix calculation in Bayesian inference. Therefore, to close these gaps, this research proposes a novel deep learning-based Bayesian calibration framework, involving pre-calibration mechanism, Long Short-Term Memory as surrogate models, and simplified covariance matrix calculation, to calibrate BEMs in high temporal resolution (i.e., hourly) with enhanced accuracy and computational efficiency. Finally, the case study demonstrates its effectiveness to match modeling outcomes with measurements and realize CV-RMSE of < 30 % and NMBE of < 6 % in hourly resolution, as well as a significant reduction of calibration time (by > 99 %, from > 600 h to ~ 1.5 h).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

An Enhanced Computational Framework for Energy Storage Participation in Transmission Planning with Electricity Market Participation: Dynamic Market Participation Restriction Estimation

In the previous study and report, we developed a computational framework to explore the technical and economic viability of the dual use of pumped storage hydropower (PSH) for both transmission assets and market participation. The rationale for the dual-use application is to improve the resource utilization rate and reduce the cost recovery burden. A PSH-based transmission solution generally has a large capacity, which corresponds to a higher investment cost compared to a traditional line solution. Market participation may allow the PSH asset to offset part of its high investment cost. A critical question is whether it is economically and technically viable to allow dual use of a PSH-based transmission project without jeopardizing transmission system reliability. And a more specific follow-up question is how to improve market participation performance while maintaining transmission reliability. In this study, we extend the computational framework by updating the method to determine transmission service obligation requirements dynamically based on the possibility of the need for transmission service in operation. Specifically, we correlate the need for transmission service with the probability of thermal overloading on a monitored transmission line, which is essentially based on the probability distribution of power flow and its probability of exceeding the line rating based on system dispatch and operational uncertainties. In addition, the need depends on the level of risk that a system operator is willing to take. The higher the risk tolerance, the lower the requirement that the system operator would apply to the PSH project as a transmission asset. The requirements are updated every day before the PSH project determines its market participation strategy. With the updated transmission service requirements, it is expected that a PSH project can serve as a transmission asset when the transmission system is at risk and really needs it, while it can participate in a market with sufficient information and certain restrictions. In this way, the transmission service quality and market participation performance can both be improved.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

ATP biosensor reveals microbial energetic dynamics and facilitates bioproduction

Adenosine-5’-triphosphate (ATP), the primary energy currency in cellular processes, drives metabolic activities and biosynthesis. Despite its importance, understanding intracellular ATP dynamics’ impact on bioproduction and exploiting it for enhanced bioproduction remains largely unexplored. Here, we harness an ATP biosensor to dissect ATP dynamics across different growth phases and carbon sources in multiple microbial strains. We find transient ATP accumulations during the transition from exponential to stationary growth phases in various conditions, coinciding with fatty acid (FA) and polyhydroxyalkanoate (PHA) production in Escherichia coli and Pseudomonas putida, respectively. We identify carbon sources (acetate for E. coli, oleate for P. putida) that elevate steady-state ATP levels and boost FA and PHA production. Moreover, we employ ATP dynamics as a diagnostic tool to assess metabolic burden, revealing bottlenecks that limit limonene bioproduction. Our results not only elucidate the relationship between ATP dynamics and bioproduction but also showcase its value in enhancing bioproduction in various microbial species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Manufacturing and Additive Design of Electric Machines by 3D Printing (MADE3D) (Final Technical Report)

As the U.S clean energy transition hinges on the growth of offshore wind, this is expected to be a major driver for innovations in manufacturing, material and design advancements to enable robust and cost-competitive wind turbines. The race to build larger and taller turbines rated 10 megawatts (MW) and beyond, has intensified pressure on OEMs in terms of logistics of handling and transporting large wind components, securing critical raw material as well as scaling up necessary domestic production infrastructure to meet the demand. There is increased interest in building lightweight, more efficient, and high-power dense drivetrains that will ease the burden on installation, minimize the raw material demand, increasing domestic sourceability. Despite good efficiency and reliability, existing drivetrain technologies such as direct-drive permanent magnet generators are heavy (> 300 tons), expensive, and often rely on large quantities of rare-earth permanent magnets (PMs), steel and copper.

17 WIND ENERGY↗
DOE OSTI2021

Modeling State Transition and Head-Dependent Efficiency Curve for Pumped Storage Hydro in Look-Ahead Dispatch

As one of the most widely installed utility-scale storage facilities, pumped storage hydro (PSH) plays an essential role in providing flexibility for power systems worldwide. Thus, to accurately quantify the flexibility of PSH units in operational optimization problems is important. The conventional PSH models in the literature rarely consider detailed state transitions due to their hourly-based settings. However, it becomes imperative for operational optimization on short-term intervals, especially with increasing shares of renewable energy in power systems. To this end, this paper presents a novel deterministic PSH model that considers the transition time and trajectory between three states of PSH units in look-ahead dispatch. Moreover, to better characterize the varying efficiency of PSH units with water head and flow rate, this paper proposes to model detailed headdependent efficiency curves (hereafter called input-output curves) in look-ahead dispatch, which encounters heavy computational burdens when short time intervals are applied. In this work, a zig-zag piece-wise linear approximation method is used for input-output curve modeling. This can enable an accurate quantification for variable efficiency and head dependence in a computationally effective manner. Here, numerical results are presented to show performances of the proposed PSH model in both flexibility quantification and computation time.

13 HYDRO ENERGY↗
DOE OSTI2025

Anomaly Identification of Synchronized Voltage Waveform for Situational Awareness of Low Inertia Systems

Inverter-based resources (IBRs) such as photovoltaics (PVs), wind turbines, and battery energy storage systems (BESSs) are widely deployed in low-carbon power systems. However, these resources typically do not provide the inertia needed for grid stability, resulting in a low-inertia power system. IBRs and lack of inertia have been known to cause anomalies such as waveform distortions and wideband oscillations in power systems due to the limited inertia level, leading to increased generation trips and load shedding. Here, to achieve effective anomaly identification, this paper proposes a synchro-waveform-based algorithm utilizing real-time synchronized voltage waveform measurements from waveform measurement units (WMUs). In the proposed method, different physical characteristics, as well as statistical features, are extracted from synchronized voltage waveform measurements to filter anomalies. Then, the anomaly identification approach based on the random forest is developed and deployed into the FNET/GridEye system considering trade-offs among accuracy, computational burden, and deployment cost. Moreover, four WMUs are specially designed and deployed on Kauai Island to receive instantaneous synchronized voltage waveform measurements. To verify the performance of the proposed algorithm, different experiments are carried out with collected field test data. The result demonstrates that the performance of the proposed synchro-waveform-based anomaly categorization algorithm can accurately identify anomalies 95.35% of the time, which has comparable performance among benchmarking algorithms.

Situational awareness↗
DOE OSTI2024

Data-driven Quasi-static Surrogate Model for Nuclear-powered Integrated Energy Systems

The integration of nuclear power into energy systems presents a promising avenue to address the growing global energy demands while minimizing greenhouse gas emissions. In this paper, we introduce a data-driven quasi-static surrogate model for nuclear-powered Integrated Energy Systems (IES) that comprises various components, including a small modular reactor (SMR), energy manifold (EM), balance of plant (BOP), high-temperature steam electrolysis (HTSE), and district heating (DH) system. Traditional physics-based models for these components often entail significant computational overhead and time consumption, necessitating the development of efficient surrogate models. The development of a complete surrogate model for the IES involves the creation of individual surrogate models for each component, leveraging machine learning techniques and simulated data. These isolated surrogate models are subsequently integrated, enabling a holistic view of the IES and reducing the computational burden associated with detailed physics-based simulations. This paper outlines the development process, validation, and the performance evaluation of the surrogate models. The findings shed light on the accuracy and applicability of the surrogate models in practical scenarios, demonstrating their potential to expedite the analysis of nuclear-powered IES and inform future research and development efforts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Optimal Sizing of Movable Energy Resources for Enhanced Resilience in Distribution Systems: A Techno-Economic Analysis

This article introduces a techno-economic analysis aimed at identifying the optimal total size of movable energy resources (MERs) to enhance the resilience of electric power supply. The core focus of this approach is to determine the total size of MERs required within the distribution network to expedite restoration after extreme events. Leveraging distribution line fragility curves, the proposed methodology generates numerous line outage scenarios, with scenario reduction techniques employed to minimize computational burden. For each reduced multiple line outage scenario, a systematic reconfiguration of the distribution network, represented as a graph, is executed using tie-switches within the system. To evaluate each locational combination of MERs for a specific number of these resources, the expected load curtailment (ELC) is calculated by summing the load curtailment within microgrids formed due to multiple line outages. This process is repeated for all possible locational combinations of MERs to determine minimal ELC for each MER total size. For every MER total size, the minimal ELCs are determined. Finally, a techno-economic analysis is performed using power outage cost and investment cost of MERs to pinpoint an optimal total size of MERs for the distribution system. To demonstrate the effectiveness of the proposed approach, case studies are conducted on the 33-node and the modified IEEE 123-node distribution test systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Quantitative approaches for including equity in risk and resilience infrastructure planning analyses

Risk and resilience assessments for critical infrastructure focus on myriad objectives, from natural hazard evaluations to optimizing investments. Although research has started to characterize externalities associated with current or possible future states, incorporation of equity priorities at project inception is increasingly being recognized as critical for planning related activities. However, there is no standard methodology that guides development of equity-informed quantitative approaches for infrastructure planning activities. Here, to address this gap, we introduce a logic model that can be tailored to capture nuances about specific geographies and community priorities, effectively incorporating them into different mathematical approaches for quantitative risk assessments. Specifically, the logic model uses a graded, iterative approach to clarify specific equity objectives as well as inform the development of equations being used to support analysis. We demonstrate the utility of this framework using case studies spanning aviation fuel, produced water, and microgrid electricity infrastructures. For each case study, the use of the logic model helps clarify the ways that local priorities and infrastructure needs are used to drive the types of data and quantitative methodologies used in the respective analyses. The explicit consideration of methodological limitations (e.g., data mismatches) and stakeholder engagements serves to increase the transparency of the associated findings as well as effectively integrate community nuances (e.g., ownership of assets) into infrastructure assessments. Such integration will become increasingly important to ensure that planning activities (which occur throughout the lifecycle of the infrastructure projects) lead to long-lasting solutions to meet both energy and sustainable development goals for communities.

community-based↗
DOE OSTI2023

Toward DMC Accuracy Across Chemical Space with Scalable Δ-QML

In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. With O(N 3 ) scaling with the number of electrons N, DMC has the potential to be a reference method for larger systems that are not accessible to more traditional methods such as CCSD(T). Assessing the accuracy of DMC for smaller molecules becomes the stepping stone in making the method a reference for larger systems. We show that when coupled with quantum machine learning (QML)-based surrogate methods, the computational burden can be alleviated such that quantum Monte Carlo (QMC) shows clear potential to undergird the formation of high-quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: the fixed-node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons-set-based QML (AQML) models. Numerical evidence presented includes converged DMC results for over 1000 small organic molecules with up to five heavy atoms used as amons and 50 medium-sized organic molecules with nine heavy atoms to validate the AQML predictions. Finally, numerical evidence collected for Δ-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies with near chemical accuracy throughout chemical space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Comparing Capacity Credit Calculations for Wind: A Case Study in Texas

The degree to which wind energy can contribute to the capacity needed to meet resource adequacy requirements, also known as capacity credit (CC), varies regionally with wind resource and correlation to net load. CC is an important metric widely used for resource planning and resource adequacy assessments. However, there are multiple methods for computing and estimating CC, depending on specific needs, access to data, and computational burden. It is unclear the extent to which the CC computation method may influence the result. To address this, we use a probabilistic resource adequacy tool and multiple approximation methods to systematically assess the CC of wind for near-term wind deployment under a case study in Texas. We find that proper consideration of transmission constraints is important; some approximation methods may overestimate the CC of wind due to a lack of consideration of transmission constraints, while other approximation methods may underestimate the CC by not capturing the ability of wind to be shipped to neighboring regions. In this case study, we find that several approximation methods do come close to the CC calculated by more robust probabilistic methods. However, the best approximation method may vary on a case-by-case basis, depending on system-specific considerations.

17 WIND ENERGY↗
DOE OSTI2023

Source Contributions to Fine Particulate Matter and Attributable Mortality in India and the Surrounding Region

Fine particulate matter (PM 2.5 ) is a leading mortality risk factor in India and the surrounding region of South Asia. This study evaluates the contribution of emission sectors and fuels to PM 2.5 mass for 29 states in India and 6 surrounding countries (Pakistan, Bangladesh, Nepal, Bhutan, Sri Lanka, and Myanmar) by combining source-specific emission estimates, stretched grid simulations from a chemical transport model, high resolution hybrid PM 2.5 , and disease-specific mortality estimates. We find that 1.02 (95% Confidence Interval (CI): 0.78-1.26) million deaths in South Asia attributable to ambient PM 2.5 in 2019 were primarily from three leading sectors: residential combustion (28%), industry (15%), and power generation (12%). Solid biofuel is the leading combustible fuel contributing to the PM 2.5 -attributable mortality (31%), followed by coal (17%), and oil and gas (14%). State-level analyses reveal higher residential combustion contributions (35%-39%) in states (Delhi, Uttar-Pradesh, Haryana) with high ambient PM 2.5 (>95 µg/m 3 ). The combined mortality burden associated with residential combustion (ambient) and household air pollution (HAP) in India is 0.72 million (95% CI:0.54-0.89) (68% attributable to HAP, 32% attributable to residential combustion). Our results illustrate the potential to reduce PM 2.5 mass and improve population health by reducing emissions from traditional energy sources across multiple sources in South Asia.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

Efficient Topology Assessment for Integrated Transmission and Distribution Network with 10,000+ Inverter-based Resources

The renewable energy proliferation calls upon the grid operators and planners to systematically evaluate the potential impacts of distributed energy resources (DERs). Considering the significant differences between various inverter-based resources (IBRs), especially the different capabilities between grid-forming inverters and grid-following inverters, it is crucial to develop an efficient and effective assessment procedure besides available co-simulation framework with high computation burdens. This paper presents a streamlined graph-based topology assessment for the integrated power system transmission and distribution networks. Graph analyses were performed based on the integrated graph of modified miniWECC grid model and IEEE 8500-node test feeder model, high performance computing platform with 40 nodes and total 2400 CPUs has been utilized to process this integrated graph, which has 100,000+ nodes and 10,000+ IBRs. The node ranking results not only verified the applicability of the proposed method, but also revealed the potential of distributed grid forming (GFM) and grid following (GFL) inverters interacting with the centralized power plants.

Graph Analysis, Topology evaluation, Infrastructur↗
DOE OSTI2024

Validating a microphysical prognostic stratospheric aerosol implementation in E3SMv2 using observations after the Mount Pinatubo eruption

Abstract. This paper describes the addition of a stratospheric prognostic aerosol (SPA) capability – developed with the goal of accurately simulating sulfate aerosol formation and evolution in the stratosphere – in the Department of Energy (DOE) Energy Exascale Earth System Model, version 2 (E3SMv2). The implementation includes changes to the four-mode Modal Aerosol Module microphysics in the stratosphere to allow for larger particle growth and more accurate stratospheric aerosol lifetime following the Pinatubo eruption. E3SMv2-SPA reasonably reproduces stratospheric aerosol lifetime, burden, aerosol optical depth, and top-of-atmosphere flux when compared to remote sensing observations. E3SMv2-SPA also has close agreement with the interactive chemistry–climate model CESM2-WACCM (Community Earth System Model version 2–Whole Atmosphere Community Climate Model) – which has a more complete chemical treatment – and the observationally constrained, prescribed volcanic aerosol treatment in E3SMv2. Global stratospheric aerosol size distributions identify the nucleation and growth of sulfate aerosol from volcanically injected SO2 from both major and minor volcanic eruptions from 1991 to 1993. The modeled aerosol effective radius is consistently lower than satellite and in situ measurements (max differences of ∼ 30 %). Comparisons with in situ size distribution samples indicate that this simulated underestimation in both E3SMv2-SPA and CESM2-WACCM is due to overly small accumulation and coarse-mode aerosols 6–18 months post-eruption, with E3SMv2-SPA simulating ∼ 50 % of the coarse-mode geometric mean diameters of observations 11 months post-eruption. Effective radii from the models and observations are used to calculate offline scattering and absorption efficiencies to explore the implications of smaller simulated aerosol size for the Pinatubo climate impacts. Scattering efficiencies at wavelengths of peak solar irradiance (∼ 0.5 µm) are 10 %–80 % higher for daily samples in models relative to observations through 1993, suggesting higher diffuse radiation at the surface and a larger cooling effect in the models due to the smaller simulated aerosol; absorption efficiencies at the peak wavelengths of outgoing terrestrial radiation (∼ 10 µm) are 15 %–40 % lower for daily samples in models relative to observations, suggesting an underestimation in stratospheric heating in the models due to the smaller simulated aerosol. These potential biases are based on aerosol size alone and do not take into account differences in the aerosol number. The overall agreement of E3SMv2-SPA with observations and its similar performance to the well-validated CESM2-WACCM makes E3SMv2-SPA a viable alternative to simulating climate impacts from stratospheric sulfate aerosols.

Brown, Hunter York (ORCID:0000000218147874)↗
NASA NTRS2021

San Diego Urban Development: Utilizing NASA Earth Observations to Identify Drivers of Extreme Urban Heat and Generate a High-Resolution Vulnerability Index for Urban Planning and Climate Resiliency in San Diego, California

Exposure to heat exacerbated by an increase in urbanization as well as increasing global temperatures has become a growing concern for cities and their residents. Excess heat can cause increased heat-related morbidity, mortality, and energy costs. Vulnerability to heat-related illnesses is oftentimes correlated to demographics, socioeconomic status, and pre-existing health conditions. The City of San Diego, California boasts 1.4 million residents and, like many other major cities, has experienced increases in heat-related hospitalizations and mortality. The burden of urban heat is also not equal amongst communities; areas with lower income and communities of color bear a disproportionate burden. In partnership with the City of San Diego, and American Geophysical Union’s (AGU) Thriving Earth Exchange, the DEVELOP team used Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) imagery to identify areas of highest heat based on land surface temperature from 2015-2020. Our analyses showed that health demographics such as obesity and cardiovascular health were the strongest indicators for heat vulnerability. In addition, various inputs (land use/land cover, tree canopy, and building intensity derived from the City of San Diego data along with albedo from Landsat 8) were used in the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model to investigate changes in cooling rates in current and future scenarios for the city. The model results showed that cooling is expected to occur due to a 5% increase in tree canopy. The City of San Diego can use these results to inform the development of the Climate Resilient San Diego plan and prioritize at-risk communities for cooling interventions.

John Dialesandro↗
DOE OSTI2023

A systematic literature review on residential demand response with a focus on opportunities for low-income communities

Demand Response improves efficiency and grid stability, reducing peak load and total system costs. It can potentially contribute to other societal aspects, including mitigating climate change and improving air quality, health, stress levels, and comfort. This paper reviewed the literature on residential demand response programs in the United States, through the lens of the five energy justice principles, identifying and categorizing a set of 165 papers from 1960 to 2024. As a result, we observe significant progress in distributive justice, however, mostly focused on increasing participation rather than attending specific needs of historically marginalized communities. Concordantly, recognition and procedural justice dimensions are lagging, while cosmopolitan justice studies are scarce, underscoring a need to improve the measurement of impacts resulting from the implementation of demand response programs. Future research opportunities include the consolidation of standardized definitions and metrics, to better understand burdens and needs of diverse residential users. In terms of policy, collection and transparent access to standardized data is required as a condition to the design of flexible, customizable, transparent, and easy to navigate demand response programs. As a result, a better understanding of and engagement with customers has the potential to increase adoption rates, persistence in the program, and overall impacts for the system, households, and society.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Bioaccumulation of Mercury and Radiocesium in Waterfowl Introduced to a Site with Legacy Contamination

Despite the propensity of waterfowl species to readily accumulate anthropogenic contaminants within polluted environments, few studies have examined bioaccumulation rates over time when entering such a contaminated site. We examined mercury (Hg) and radiocesium ( 137 Cs) bioaccumulation over time in two waterfowl species released into a wetland system containing legacy contamination on the US Department of Energy's Savannah River Site in South Carolina. Released birds were collected at select time intervals over an exposure period of 94 days. We quantified total Hg concentrations in blood, muscle, and liver tissues, and 137 Cs activity in whole-body and muscle tissues. The relationship between the contaminant burdens of different body tissue types was examined over time. Likely a result of microhabitat selection, mallards in our study readily accumulated both Hg and 137 Cs at consistent rates over time within our study system, while ring-neck ducks did not. The findings demonstrated that whole blood can be used as a robust, nondestructive sampling alternative to estimate Hg burdens within muscle and liver, and whole-body 137 Cs activity is a good predictor of muscle burdens. Understanding such bioaccumulation information in waterfowl is useful for the assessment of the potential health risk in wildlife, as well as being important for human risk assessment toward the consumption of popular game species. Environ Toxicol Chem 2022;41:2479–2487. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1994

Effects of isotope selection on solution convergence in HZE transport

A fragmenting iron ion produces hundreds of isotopes during nuclear reactions. These isotopes are represented in the solution of the transport problem. A reduced set of isotopes is selected to minimize the computational burden but introduces error in the final result. A minimum list of 122 isotopes is required for adequate representation of the mass and charge distributions of the secondary radiation fields. A reduced set of 80 isotopes is sufficient to represent the charge distribution alone and represents reasonably well the linear energy transfer properties of the iron beam. Because iron fragmentation produces nearly every isotope lighter than iron, the resulting 122-isotope list should be adequate for ion beams with charges equal to or less than 26.

Kim, Myung-Hee Y.↗