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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Evaluation of Hybrid FPOG Applications in Regulated and Deregulated Markets Using HERON

Recent changes in the U.S. energy market, such as low natural gas prices and increased electricity production for variable renewable energy (VRE) sources, have led to an economic crisis for existing light-water reactor (LWR) nuclear power plants (NPP). Many owners and operators of LWRs have elected to decommission these plants rather than continue using them as consistent sources of clean baseload power. This has led to exploration of various possibilities to increase the economic viability of these units, including market restructuring to monetize benefits LWRs already provide to the grid through ancillary markets, load following and economic dispatch, and possible integration of secondary systems directly to the NPP for production of additional products through technologies such as hydrogen electrolysis or water desalination. Previous studies have considered the technologies associated with these Integrated Energy Systems (IES) activities, and the analysis of markets for these secondary products. To analyze the economic viability of various system configurations including IES, especially given the uncertainty surrounding load demand, electricity prices, and the availability of VRE resources, the stochastic technoeconomic analysis package HERON (Heuristic Energy Resource Optimization Network) was released earlier this year as an extension of the risk analysis framework RAVEN (Risk Analysis Virtual Environment). HERON focuses foremost on making the complex uncertainty quantification analysis tools approachable for energy systems analysts, also providing general dispatch optimization algorithms for those workflows. HERON continues to be improved and tested as a significant part of the IES viability analyses performed in this work. HERON is not a capacity expansion model. To consider market and grid energy system development in a variety of scenarios, HERON is best used in coupling with modelling tools such as US-REGEN, which sacrifice some of the uncertainty analysis and resolution of HERON's modelling for the ability to efficiently predict the change in the grid energy system's profile due to economic drivers over decades. HERON can then use this information to explore the economic viability of introducing changes to the predicted outcomes, such as the introduction of an IES. In this work, experts at EPRI using US-REGEN provide six projection scenarios for use in HERON stochastic technoeconomic analysis (STEA) in considering the options available for increasing LWR economic viability through introduction of a hydrogen-centric IES using a high-temperature steam electrolysis plant (HTSE), hydrogen storage, and a constant-rate contracted hydrogen consumer. The results obtained are differential in nature; they do not report expected profits for any configuration, but rather report on the possible increase in the NPV of a configuration with respect to a baseline no-IES configuration. Due to the uncertainty captured in the variable net load of the systems, there is likewise uncertainty in the mean values reported. We consider this viability both in terms of a regulated market, where the energy producers and IES are owned and operated by single entity, as well as a deregulated market, where the IES chooses its bid for electricity generation and is then dispatched by the grid system operator. Results indicate that for deregulated markets, the inclusion of the IES is often statistically beneficial. This is especially true in policies that are not favorable towards nuclear, as nuclear is less often dispatched and is forced to deal with frequent idle capacity. In the nominal case as well as the case of carbon tax policies, inclusion of the IES clearly benefited the economic performance of the NPP. In the regulated case, however, there was a trend towards minimizing the IES, likely due to the optimal sizing performed by US-REGEN of the NPP within the system as well as the lack of penalty for idle capacity at the NPP in the regulated market analyses.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

How fishing intensity affects the spatial and trophic ecology of two gull species breeding in sympatry

Abstract Fisheries produce large quantities of discards, an important resource for scavenging seabirds. However, a policy reform banning discards, which is soon to be implemented within the EU, will impose a food shortage upon scavengers, and it is still largely unknown how scavengers will behave. We studied the diet (hard remains), trophic (stable isotope analysis), and foraging (individual tracking) ecology of two gull species breeding in sympatry: Audouin’s gull Larus audouinii (AG) and yellow-legged gull Larus michahellis (YLG), in South Portugal, under normal fishery activity (NFA; work days) and low fishery activity (LFA; weekends), over two consecutive years. We established a pattern of dietary, spatial, and temporal segregation between the two gull species. Under LFA, yellow-legged gulls reduced their time spent at-sea, thus foraging more in alternative habitats (e.g. refuse dumps) and widening their isotopic niche (i.e. generalist behaviour). Contrastingly, Audouin’s gull had a narrower trophic niche (i.e. specialist behaviour), foraging exclusively at-sea, reducing the amount of demersal fish and increasing the amount of pelagic fish in their diet. Under NFA, both species foraged mostly at-sea, feeding almost exclusively on fish, with increased consumption of demersal species (i.e. fishery discards). In general, yellow-legged gull had a broader trophic niche (i.e. generalist behaviour) when compared with the narrower isotopic niche of Audouin’s gull (i.e. specialist behaviour). Overall, both gull species relied heavily on fishery discards. However, there was visible dietary, spatial, and temporal segregation between the two species, associated with their dietary and habitat preferences that could be attributed to the availability of anthropogenic resources, such as fishery discards.

Matos, Diana M.↗
DOE OSTI2024

Plant responses to elevated CO 2 under competing hypotheses of nitrogen and phosphorus limitations

Abstract The future ecosystem carbon cycle has important implications for biosphere‐climate feedback. The magnitude of future plant growth and carbon accumulation depends on plant strategies for nutrient uptake under the stresses of nitrogen (N) versus phosphorus (P) limitations. Two archetypal theories have been widely acknowledged in the literature to represent N and P limitations on ecosystem processes: Liebig's Law of the Minimum (LLM) and the Multiple Element Limitation (MEL) approach. LLM states that the more limiting nutrient controls plant growth, and commonly leads to predictions of dramatically dampened ecosystem carbon accumulation over the 21st century. Conversely, the MEL approach recognizes that plants possess multiple pathways to coordinate N and P availability and invest resources to alleviate N or P limitation. We implemented these two contrasting approaches in the E3SM model, and compiled 98 in situ forest N or P fertilization experiments to evaluate how terrestrial ecosystems will respond to N and P limitations. We find that MEL better captured the observed plant responses to nutrient perturbations globally, compared with LLM. Furthermore, LLM and MEL diverged dramatically in responses to elevated CO 2 concentrations, leading to a two‐fold difference in CO 2 fertilization effects on Net Primary Productivity by the end of the 21st century. The larger CO 2 fertilization effects indicated by MEL mainly resulted from plant mediation on N and P resource supplies through N 2 fixation and phosphatase activities. This analysis provides quantitative evidence of how different N and P limitation strategies can diversely affect future carbon and nutrient dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Ethanol–Ethyl Acetate System as a Biogenic Hydrogen Carrier

Liquid organic hydrogen carriers will likely be a key element of a future hydrogen economy by enabling the storage and transport of large quantities of hydrogen. Ethanol is a liquid organic hydrogen carrier that is readily available from biological resources, which undergoes a reversible reaction to yield hydrogen and ethyl acetate. The objective of the present study is to obtain a better understanding of the thermodynamic and environmental suitability of the ethanol–ethyl acetate cycle for hydrogen storage applications. The analysis covers three aspects: thermodynamics of the chemical reaction, energy balance of the process, and a first‐order assessment of greenhouse gas emissions. Thermodynamics of the reaction are characterized by a standard Gibbs energy of reaction close to zero which allows the reaction to be shifted between hydrogenation and dehydrogenation within a moderate window of temperature and pressure conditions. The energy demand for dehydrogenation is comparatively small, resulting in an overall system efficiency of 88%. A life cycle greenhouse gas analysis over a 20‐year storage system lifetime gives a carbon intensity of 7.0 kg‐CO 2eq /kg‐H 2 delivered. These results indicate that the ethanol–ethyl acetate system has considerable promise as a hydrogen carrier and should be the subject of further research.

08 HYDROGEN↗
DOE OSTI2026

Roadmap and Benchmarking: Privacy in Federated Load Forecasting

Data-driven techniques for energy demand forecasting continue to emerge with promising impacts on distribution grid planning. However, the development of robust and generalizable machine learning models requires that representative high quality training data are available. Distributed energy resources have begun to embed intelligence, gathering large amounts of data on customer demand, behavior, and household devices that are connected to the grid. Though utilities aggregate meter-level demand data for load shaping, demand response, outage management, reliability planning, and billing applications, there lies an inherent privacy concern in sharing consumption data that may identify individual consumer behavioral patterns. Hence, while sharing the data is crucial, the private sensitive customer data must be safeguarded from being exposed or manipulated. In this study, we propose a roadmap for implementing a based privacy preserving framework to support the advancement of data-driven analytics in data-sensitive distributed energy resources environments. The roadmap incorporates federated learning–a distributed training framework, differential privacy–a statistical framework that provides guarantees to safeguard the leakage of sensitive data, secure multiparty computation and homomorphic encryption– techniques for encrypting model gradients and applying secure aggregation on the server. Moreover, we perform baseline experiments on the federated short-term load forecasting (STLF) task using open-source residential load profile datasets, offering insights into the challenges of integrating differential privacy into federated learning.

Abebe, Waqwoya [Oak Ridge National Laboratory (ORN↗
DOE OSTI2023

Recent advances in integrated hydrologic models: Integration of new domains

Over the past several decades, hydrologic models have advanced from independent models of the surface and subsurface to integrated models that can capture the terrestrial hydrologic cycle within one framework. In recent years, these coupled frameworks have seen the inclusion of biogeochemical processes, ecohydrology, sedimentation and erosion, cold region hydrology, anthropogenic activities, and atmospheric processes. This expansion is the result of increased computational, data, and modeling capabilities and capacities, as well as improved understanding of the processes that drive these integrated systems. Here, in this study, we review these recent advances to integrate new processes and systems into existing terrestrial hydrologic models and highlight the significant challenges and opportunities that remain. We identify that with so many models currently available and in development, selecting the most appropriate model is difficult, and we suggest a path for new or novice modelers to find the most appropriate code based on their needs. In addition, data required to parameterize and calibrate these models can often constrain their applicability and usefulness. However, advances in environmental sensors and measurement technology, in addition to data assimilation of non-traditional data (e.g. remote sensing, qualitative data) are providing new ways of addressing this issue. As we expand hydrologic models to integrate more processes and systems, our computational demands also increase. Recent and emerging advances in computational platforms, including cloud and quantum computing, in addition to the use of machine learning to capture some processes, will continue to support the use of increasingly larger and more complex, process-based models. Finally, we highlight that it is critical to develop state-of-the-science models that are accessible to all model users, not just those applied for research and development. We encourage continued development of diverse modeling platforms, considering the user needs, data availability, and computational resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Combustion machine learning: Principles, progress and prospects

Progress in combustion science and engineering has led to the generation of large amounts of data from large-scale simulations, high-resolution experiments, and sensors. This corpus of data offers enormous opportunities for extracting new knowledge and insights—if harnessed effectively. Machine learning (ML) techniques have demonstrated remarkable success in data analytics, thus offering a new paradigm for data-intense analyses and scientific investigations through combustion machine learning (CombML). While data-driven methods are utilized in various combustion areas, recent advances in algorithmic developments, the accessibility of open-source software libraries, the availability of computational resources, and the abundance of data have together rendered ML techniques ubiquitous in scientific analysis and engineering. This article examines ML techniques for applications in combustion science and engineering. Starting with a review of sources of data, data-driven techniques, and concepts, we examine supervised, unsupervised, and semi-supervised ML methods. Various combustion examples are considered to illustrate and to evaluate these methods. Next, we review past and recent applications of ML approaches to problems in combustion, spanning fundamental combustion investigations, propulsion and energy-conversion systems, and fire and explosion hazards. Challenges unique to CombML are discussed and further opportunities are identified, focusing on interpretability, uncertainty quantification, robustness, consistency, creation and curation of benchmark data, and the augmentation of ML methods with prior combustion-domain knowledge.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Encapsulation of Metallic Na in an Electrically Conductive Host with Porous Channels as a Highly Stable Na Metal Anode

Room-temperature Na ion batteries (NIBs) have attracted great attention because of the widely available, abundant sodium resources and potentially low cost. Currently, the challenge of the NIB development is due primarily to the lack of a high-performance anode, while the Na metal anode holds great promise considering its highest specific capacity of 1165 mA h/g and lowest anodic potential. However, an uneven deposit, relatively infinite volume change, and dendritic growth upon plating/stripping cycles cause a low Coulombic efficiency, poor cycling performance, and severe safety concerns. In this study, a stable Na carbonized wood (Na–wood) composite anode was fabricated via a rapid melt infusion (about 5 s) into channels of carbonized wood by capillary action. The channels function as a high-surface-area, conductive, mechanically stable skeleton, which lowers the effective current density, ensures a uniform Na nucleation, and restricts the volume change over cycles. As a result, the Na–wood composite anode exhibited flat plating/stripping profiles with smaller overpotentials and stable cycling performance over 500 h at 1.0 mA/cm 2 in a common carbonate electrolyte system. Finally, in sharp comparison, the planar Na metal electrode showed a much shorter cycle life of 100 h under the same test conditions.

09 BIOMASS FUELS↗
DOE OSTI2022

Analysis of Brackish Water Desalination for Municipal Uses: Case Studies on Challenges and Opportunities

Brackish water is a widely available, nontraditional water resource that can augment limited freshwater supplies. Although brackish water desalination has been continuously implemented in the United States and worldwide, it is necessary to reduce further its energy consumption, costs, and environmental impacts. This study conducted technoeconomic analyses to evaluate the current desalination and brine management technologies, focusing on the key factors and opportunities for sustainable brackish water desalination for municipal uses. Three case studies were selected as baseline representative of different geographic and operational conditions, including water quality, plant size, pretreatment, desalination, and concentrate management. Here, the technoeconomic analyses and model simulations identified challenges, opportunities, and research priorities to achieve specific pathways for enhanced brackish water desalination regarding levelized costs of water, electricity intensity, water recovery, zero liquid discharge, and brine valorization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Microstructural Response of Variably Hydrated Ca-Rich Montmorillonite to Supercritical CO2

We report on ab initio molecular dynamics simulations of Ca-rich montmorillonite systems, in different hydration states in the presence of supercritical CO2. Analysis of the molecular trajectories provides estimates of the relative H2O:CO2 ratio per interspatial cation. The vibrational density of states in direct comparison with dipole moment derived IR spectra for these systems provide unique signatures that can used to follow molecular transformation. In a co-sequestration scenario, these signatures could be used to identify the chemical state and fate of Sulfur compounds. Interpretation of CO2 asymmetric stretch shift is given based on a detailed analysis of scCO2 structure and intermolecular interactions of the intercalated species. Based on our simulations, smectites with higher charge interlayer cations at sub-single to single hydration states should be more efficient in capturing CO2, while maintaining caprock integrity. This research would not have been possible without the support of the office of Fossil Energy, Department of Energy. The computational resources were made available through a user proposal of the EMSL User facility, a national scientific user facility sponsored by the Department of Energy's Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory.

AIMD↗
DOE OSTI2025

The Impacts of Rotational Mixing on the Precipitation Simulated by a Convection Permitting Model

With increased availability of computational resources, regional and global scale convection-permitting model (CPM, Δx ~ 1–10 km) simulations are becoming more common. CPMs have improved accuracy in their representation of deep convection and mesoscale convective systems (MCSs) compared to coarser resolution models. However, CPMs still exhibit convective cloud and precipitation biases relative to observations, notably a lesser frequency of light precipitation rates and greater frequency of heavy precipitation rates. In this work we hypothesize that these CPM biases are related to under-resolved mixing between convective updrafts and their surrounding environment. To test this hypothesis, we introduce a parameterization to the Weather Research and Forecasting model (WRF) that adds a small angular rotation of the grid-scale flow about the axis perpendicular to the plane of convective drafts. This rotated flow is then allowed to alter advection of moisture and hydrometeors. The effects of such mixing on precipitation characteristics are evaluated in month-long 4-km grid spacing simulations over the Amazon. The enhanced mixing transports moisture and condensate from convective cores to other areas including downdrafts. This increases the frequency of low-precipitable water and light precipitation. It also decreases the frequency of intense precipitation from isolated deep convection and MCSs, increases cloud top temperatures, reduces radar echo-top heights, and increases overall precipitation by altering the relationship of precipitation with precipitable water, in better agreement with observations. The results suggest when optimized using multiple observations, such an approach may provide a path toward more accurate representation of convection and precipitation statistics in convection-permitting simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Solution techniques for transient stability‐constrained optimal power flow – Part I

This series of studies present the state‐of‐the‐art for the solution of the transient stability constrained optimal power flow problem (TSC‐OPF). Three different classes of solution techniques: dynamic optimisation‐based, SIME method, and computational intelligence, are discussed in detail. Moreover, discussed are issues to consider while solving such problems, various application areas, and future directions in this research area. A comprehensive resource of the available literature, publicly available test systems, and relevant numerical libraries is also provided. This study presents the TSC‐OPF formulation and discusses various dynamic optimisation‐based approaches. Two optimisation techniques, full‐space and reduced‐space method, are presented for solving the resulting non‐linear optimisation problem.

Abhyankar, Shrirang↗
DOE OSTI2017

Solution techniques for transient stability-constrained optimal power flow – Part II

Transient stability-constrained optimal power flow is an important emerging problem with power systems pushed to the limits for economic benefits, dense and larger interconnected systems, and reduced inertia due to expected proliferation of renewable energy resources. In this study, two more approaches: single machine equivalent and computational intelligence are presented. Also discussed are various application areas, and future directions in this research area. In conclusion, a comprehensive resource for the available literature, publicly available test systems, and relevant numerical libraries is also provided.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2011

The Mu2e Experiment at Fermilab

The Mu2e collaboration has proposed an experiment to search for the coherent, neutrino-less conversion of a muon into an electron in the Coulomb field of a nucleus with an expected sensitivity of $R_{\mu e}<6.0\times 10^{-17}$, at the 90% confidence level. Mu2e has received CD-0 approval from the US Department of Energy. If all resources are made available as required, the experiment could begin taking data as early as 2018.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Accurate and precise determination of critical properties from Gibbs ensemble Monte Carlo simulations

Since the seminal paper by Panagiotopoulos [Mol. Phys. 61, 813 (1997)], the Gibbs ensemble Monte Carlo (GEMC) method has been the most popular particle-based simulation approach for the computation of vapor–liquid phase equilibria. However, the validity of GEMC simulations in the near-critical region has been questioned because rigorous finite-size scaling approaches cannot be applied to simulations with fluctuating volume. Valleau [Mol. Simul. 29, 627 (2003)] has argued that GEMC simulations would lead to a spurious overestimation of the critical temperature. More recently, Patel et al. [J. Chem. Phys. 134, 024101 (2011)] opined that the use of analytical tail corrections would be problematic in the near-critical region. To address these issues, we perform extensive GEMC simulations for Lennard-Jones particles in the near-critical region varying the system size, the overall system density, and the cutoff distance. For a system with N = 5500 particles, potential truncation at 8σ and analytical tail corrections, an extrapolation of GEMC simulation data at temperatures in the range from 1.27 to 1.305 yields T{sub c} = 1.3128 ± 0.0016, ρ{sub c} = 0.316 ± 0.004, and p{sub c} = 0.1274 ± 0.0013 in excellent agreement with the thermodynamic limit determined by Potoff and Panagiotopoulos [J. Chem. Phys. 109, 10914 (1998)] using grand canonical Monte Carlo simulations and finite-size scaling. Critical properties estimated using GEMC simulations with different overall system densities (0.296 ≤ ρ{sub t} ≤ 0.336) agree to within the statistical uncertainties. For simulations with tail corrections, data obtained using r{sub cut} = 3.5σ yield T{sub c} and p{sub c} that are higher by 0.2% and 1.4% than simulations with r{sub cut} = 5 and 8σ but still with overlapping 95% confidence intervals. In contrast, GEMC simulations with a truncated and shifted potential show that r{sub cut} = 8σ is insufficient to obtain accurate results. Additional GEMC simulations for hard-core square-well particles with various ranges of the attractive well and for n-decane molecules represented by the TraPPE force field yield data that support the trends observed for Lennard-Jones particles. The finite-size dependence of the critical properties obtained from GEMC simulations is significantly smaller than those from grand-canonical ensemble simulations. Thus, when resources are not available for a rigorous finite-size scaling study, GEMC simulations provide a straightforward route to determine fairly accurate critical properties using relatively small system sizes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Elucid—exploring the local universe with the reconstructed initial density field. I. Hamiltonian Markov chain Monte Carlo method with particle mesh dynamics

Simulating the evolution of the local universe is important for studying galaxies and the intergalactic medium in a way free of cosmic variance. Here we present a method to reconstruct the initial linear density field from an input nonlinear density field, employing the Hamiltonian Markov Chain Monte Carlo (HMC) algorithm combined with Particle-mesh (PM) dynamics. The HMC+PM method is applied to cosmological simulations, and the reconstructed linear density fields are then evolved to the present day with N-body simulations. These constrained simulations accurately reproduce both the amplitudes and phases of the input simulations at various z. Using a PM model with a grid cell size of 0.75 h {sup –1} Mpc and 40 time steps in the HMC can recover more than half of the phase information down to a scale k ∼ 0.85 h Mpc{sup –1} at high z and to k ∼ 3.4 h Mpc{sup –1} at z = 0, which represents a significant improvement over similar reconstruction models in the literature, and indicates that our model can reconstruct the formation histories of cosmic structures over a large dynamical range. Adopting PM models with higher spatial and temporal resolutions yields even better reconstructions, suggesting that our method is limited more by the availability of computer resource than by principle. Dynamic models of structure evolution adopted in many earlier investigations can induce non-Gaussianity in the reconstructed linear density field, which in turn can cause large systematic deviations in the predicted halo mass function. Such deviations are greatly reduced or absent in our reconstruction.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Optical potentials for the rare-isotope beam era

Abstract We review recent progress and motivate the need for further developments in nuclear optical potentials that are widely used in the theoretical analysis of nucleon elastic scattering and reaction cross sections. In regions of the nuclear chart away from stability, which represent a frontier in nuclear science over the coming decade and which will be probed at new rare-isotope beam facilities worldwide, there is a targeted need to quantify and reduce theoretical reaction model uncertainties, especially with respect to nuclear optical potentials. We first describe the primary physics motivations for an improved description of nuclear reactions involving short-lived isotopes, focusing on its benefits for fundamental science discoveries and applications to medicine, energy, and security. We then outline the various methods in use today to build optical potentials starting from phenomenological, microscopic, and ab initio methods, highlighting in particular, the strengths and weaknesses of each approach. We then discuss publicly-available tools and resources facilitating the propagation of recent progresses in the field to practitioners. Finally, we provide a set of open challenges and recommendations for the field to advance the fundamental science goals of nuclear reaction studies in the rare-isotope beam era. This paper is the outcome of the Facility for Rare Isotope Beams Theory Alliance (FRIB-TA) topical program ‘Optical Potentials in Nuclear Physics’ held in March 2022 at FRIB. Its content is non-exhaustive, was chosen by the participants and reflects their efforts related to optical potentials.

42 ENGINEERING↗