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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 OSTI2020

Adapting Existing Energy Planning, Simulation, and Operational Models for Resilience Analysis

Resilience is a topic receiving much attention in relation to energy systems, with particular attention being paid to the supply of electricity. As a result, research communities have proposed a plethora of candidate indicators and metrics for resilience, most of which remain immature at different scales and segments within the energy system. Given the complexity of resilience analyses and mitigation strategies, there is limited value in attempting to identify a single resilience metric, as no one metric can quantify resilience or its associated value for all stakeholders. Instead, a necessary focus of the research community should lie in implementing, testing, and validating resilience metrics and analysis approaches in energy sector models, which will be invaluable for informing resilience planning and investment decisions. Recognizing that implementing, testing, and validating resilience metrics are challenges that need to be addressed, the National Renewable Energy Laboratory (NREL) dedicated staff and time to researching how to effectively integrate resilience considerations into energy sector models and tools, as part of the Laboratory Directed Research and Development (LDRD) program. The overarching goal of the effort was to evaluate the data needs, methodologies, and outcomes - including consequences and/or changes in investment or operational decisions due to avoided consequences - based on resilience analysis in a range of existing tools. In particular, we selected five models originally built at NREL to explore non-resilience energy research questions to implement and exercise resilience metrics and analysis approaches.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Advancing Sea Ice Predictability in E3SM with Machine Learning

Focal area(s): To improve predictions of sea ice in E3SM we propose to develop a hierarchy of data-driven models using observational and simulation data to investigate the most important Earth system drivers of sea ice variability and loss, develop surrogates that build on the reduced parameter space of important drivers, and, where appropriate, couple machine learning models with standard PDE models to capture important physical behavior at different scales. This work falls under Focal Area 2. Predictive modeling through the use of AI techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An Integrated Multiscale Modeling Framework for Unconventional Stimulation and Production (Final Report)

The production of oil and gas from unconventional reservoirs largely depends upon two main features operating at different scales: (1) the establishment of a reservoir scale stimulated fracture network that effectively communicates with the rock volume, enhancing permeability and transport to the wellbore and (2) the coupled multi-phase flow, chemical and mechanical processes affecting the migration of hydrocarbons from the low permeability country rock adjacent to the stimulated fracture network. To date, there has been no simulation framework that allows seamless and integrated prediction of these features across spatial scales extending from the pore structure of the reservoir rock to the volume of the reservoir. In addition, there has been a lack of suitable field measurements to test such models, as stimulation and production data are often proprietary and not freely available to national laboratories and academic institutions. New multi-scale simulation capabilities are needed that are validated against suitable field-based research experiments on hydraulic fracturing and shale production.

02 PETROLEUM↗
DOE OSTI2022

Multi-modal Approach to Modeling Creep Deformation in Nickel-base Superalloy

This study developed the multi-modal modeling approaches to elucidate the physical and overall mechanical properties of two complex alloy systems of Ni-based Superalloys, namely Haynes 282 and Inconel 740. The overall strategies employed systematically in different scales of modeling are based on the common structural framework of the multi phases that made up the Ni-based Superalloys, namely the matrix phase of the Gamma ($\gamma$) phase which is embedded with ordered Gamma prime ($\gamma$’) precipitates and decorated with the dispersions of mostly carbide precipitates. With this in mind, we had set up the modeling platforms to incorporate a wide range of scales, starting from electronic structures to the continuum level via the crystal plasticity model.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Grid Capacity – What is it, what determines it, does one number work, and how does it relate to electric vehicles?

Grid capacity is effectively how much power the system can reliably deliver, whether that is to serve loads (load service capacity) or accept generation (hosting capacity). Grid capacity can also mean different things at different scales. On the whole power system, grid capacity may be the maximum amount of power generation available. For a specific region, grid capacity may be limited by how much power the transmission and distribution lines can safely carry to that region. At the feeder level, it may be how much photovoltaic generation can be included before reliability or operations are impacted. At the end-use or residential level, grid capacity may be the size of the service breaker for that house.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Visualizing Uranium Crystallization from Melt: Experiment-Informed Phase Field Modeling and Machine Learning

The focus of this project was to observe and simulate the solidification of uranium metal at the crystallographic level from its molten state. Melting experiments were conducted at two different scales to observe microstructural evolution using either a laboratory-scale induction furnace (hundreds of grams of metal) or a microscope heating stage (hundreds of milligrams of metal), respectively. Experimental parameters and characterization data were then used to inform a phase field model of gamma-U crystal growth as dendrites with or without secondary phase impurities in the form of uranium carbide particles. Finally, training datasets were generated by the phase field model as inputs to a neural network, developed with the aim of providing a faster, cheaper surrogate model for microstructural simulations within a given parameter space. Progress is reported herein for each of these task areas. Ultimately, 1) an optical microscope heating stage capability has been stood-up for uranium metal solidification studies, 2) a phase field model was advanced to simulate multiple uranium grains growing in the presence of carbide impurity particles and 3) a neural network was constructed and optimized to predict the microstructure features of individually growing uranium crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

InterGraph-CPS: A Graph-Theoretic Approach to Characterize Cross-Domain Cyber-Physical Interdependencies and Uncertainties in Electric Grid Systems for Improved Decision-Making in Operation and Response

Critical infrastructure systems such as the electric grid are increasingly cyber-physical; yet, despite the cyber-physical characteristics of critical infrastructure systems, the physical process system and communication/control network system are traditionally analyzed in siloes. As these systems become more cyber-physical, it is crucial that models and methods are available to assess the cyber physical system (CPS) interdependencies, characteristics, and event propagation for improved planning, operation, and response. Thus, we proposed an integrated structural and temporal CPS interdependency analysis framework, InterGraph-CPS, that provides insight into the CPS function during normal operation as well as disturbances. This integrated structural and temporal interdependency framework is uniquely designed for assessing CPSs by account for the challenges of analyzing cyber and physical data streams together due to data availability, data type, and time scale differences. By leveraging both structural (e.g., graph analysis) and temporal (e.g., data analytics) techniques, different CPS behaviors and configurations can be accounted for.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Fast GPU-Based Generation of Large Graph Networks From Degree Distributions

Synthetically generated, large graph networks serve as useful proxies to real-world networks for many graph-based applications. The ability to generate such networks helps overcome several limitations of real-world networks regarding their number, availability, and access. Here, we present the design, implementation, and performance study of a novel network generator that can produce very large graph networks conforming to any desired degree distribution. The generator is designed and implemented for efficient execution on modern graphics processing units (GPUs). Given an array of desired vertex degrees and number of vertices for each desired degree, our algorithm generates the edges of a random graph that satisfies the input degree distribution. Multiple runtime variants are implemented and tested: 1) a uniform static work assignment using a fixed thread launch scheme, 2) a load-balanced static work assignment also with fixed thread launch but with cost-aware task-to-thread mapping, and 3) a dynamic scheme with multiple GPU kernels asynchronously launched from the CPU. The generation is tested on a range of popular networks such as Twitter and Facebook, representing different scales and skews in degree distributions. Results show that, using our algorithm on a single modern GPU (NVIDIA Volta V100), it is possible to generate large-scale graph networks at rates exceeding 50 billion edges per second for a 69 billion-edge network. GPU profiling confirms high utilization and low branching divergence of our implementation from small to large network sizes. For networks with scattered distributions, we provide a coarsening method that further increases the GPU-based generation speed by up to a factor of 4 on tested input networks with over 45 billion edges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Carbon neutrality in Malaysia and Kuala Lumpur: Insights from stakeholder-driven integrated assessment modeling

Several cities in Malaysia have established plans to reduce their CO2 emissions, in addition to Malaysia submitting a Nationally Determined Contribution to reduce its carbon intensity (against GDP) by 45% in 2030 compared to 2005. Meeting these emissions reduction goals will require a joint effort between governments, industries, and corporations at different scales and across sectors. In collaboration with national and sub-national stakeholders, we developed and used a global integrated assessment model to explore emissions mitigation pathways in Malaysia and Kuala Lumpur. Guided by current climate action plans, we created a suite of scenarios to reflect uncertainties in policy ambition, level of adoption, and implementation for reaching carbon neutrality. Through iterative engagement with all parties, we refined the scenarios and focus of the analysis to best meet the stakeholders’ needs. We found that Malaysia can reduce its carbon intensity and reach carbon neutrality by 2050, and that action in Kuala Lumpur can play a significant role. Decarbonization of the power sector paired with extensive electrification, energy efficiency improvements in buildings, transportation, and industry, and the use of advanced technologies such as hydrogen and carbon capture and storage will be major drivers to mitigate emissions, with carbon dioxide removal strategies being key to eliminate residual emissions. This study highlights the participatory process in which stakeholders contributed to the development of the model and guided the analysis, as well as insights into Malaysia’s decarbonization potential and the role of multilevel governance.

cities↗
DOE OSTI2021

Shock Propagation and Associated Particle Acceleration in the Presence of Ambient Solar-Wind Turbulence

The topic of this review paper is on the influence of solar wind turbulence on shock propagation and its consequence on the acceleration and transport of energetic particles at shocks. As the interplanetary shocks sweep through the turbulent solar wind, the shock surfaces fluctuate and ripple in a range of different scales. We discuss particle acceleration at rippled shocks in the presence of ambient solar-wind turbulence. This strongly affects particle acceleration and transport of energetic particles (both ions and electrons) at shock fronts. In particular, we point out that the effects of upstream turbulence is critical for understanding the variability of energetic particles at shocks. Moreover, the presence of pre-existing upstream turbulence significantly enhances the trapping near the shock of low-energy charged particles, including those near the thermal energy of the incident plasma, even when the shock propagates normal to the average magnetic field. Pre-existing turbulence, always present in space plasmas, provides a means for the efficient acceleration of low-energy particles and overcoming the well known injection problem at shocks.

, solar wind↗
DOE OSTI2020

A First Investigation of Agriculture Sector Perspectives on the Opportunities and Barriers for Agrivoltaics

Agrivoltaic systems are a strategic and innovative approach to combine solar photovoltaic (PV)-based renewable energy generation with agricultural production. Recognizing the fundamental importance of farmer adoption in the successful diffusion of the agrivoltaic innovation, this study investigates agriculture sector experts’ perceptions on the opportunities and barriers to dual land-use systems. Using in-depth, semistructured interviews, this study conducts a first study to identify challenges to farmer adoption of agrivoltaics and address them by responding to societal concerns. Results indicate that participants see potential benefits for themselves in combined solar and agriculture technology. The identified barriers to adoption of agrivoltaics, however, include: (i) desired certainty of long-term land productivity, (ii) market potential, (iii) just compensation and (iv) a need for predesigned system flexibility to accommodate different scales, types of operations, and changing farming practices. The identified concerns in this study can be used to refine the technology to increase adoption among farmers and to translate the potential of agrivoltaics to address the competition for land between solar PV and agriculture into changes in solar siting, farming practice, and land-use decision-making.

14 SOLAR ENERGY↗
DOE OSTI2023

Evolution of Robustness in Growing Random Networks

Networks are widely used to model the interaction between individual dynamic systems. In many instances, the total number of units and interaction coupling are not fixed in time, and instead constantly evolve. In networks, this means that the number of nodes and edges both change over time. Various properties of coupled dynamic systems, such as their robustness against noise, essentially depend on the structure of the interaction network. Therefore, it is of considerable interest to predict how these properties are affected when the network grows as well as their relationship to the growth mechanism. Here, we focus on the time evolution of a network’s Kirchhoff index. We derive closed-form expressions for its variation in various scenarios, including the addition of both edges and nodes. For the latter case, we investigate the evolution where single nodes with one or two edges connecting to existing nodes are added recursively to a network. In both cases, we derive the relations between the properties of the nodes to which the new node connects along with the global evolution of network robustness. In particular, we show how different scalings of the Kirchhoff index can be obtained as a function of the number of nodes. We illustrate and confirm this theory via numerical simulations of randomly growing networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Resource and Load Compatibility Assessment of Wind Energy Offshore of Humboldt County, California

Floating offshore wind is being considered in northern California as indicated by the Bureau of Ocean Energy Management’s issuance of a lease consideration in the Humboldt Call Area. Humboldt County offers access to this enormous resource, but local electric load and transmission are limited. The potential impacts of offshore wind generators at three different scales were studied using a regional grid model of Humboldt County. Offshore wind generation was calculated using modeled wind speed data and 12-MW turbine specifications and integrated with projected load and historical generation. Offshore wind farms deployed in the Humboldt Call Area achieve annual capacity factors between 45% and 54% after losses and maintenance. Power output is variable between and within seasons, with full power output 30% of the time and no output approximately 20% of the time. Electricity from a 48-MW wind farm provides 22% of regional load with limited exports. A 144-MW wind farm serves 38% of local load, exporting 40% of its electricity with the extant 70-MW transmission capacity. A full build-out of 1836 MW would result in 88% curtailment with existing transmission. Across scenarios, offshore wind variability necessitates reliance on existing power plants to meet local demand in periods of low wind.

17 WIND ENERGY↗
DOE OSTI2022

Designing Harvesting and Hauling Cost Models for Energy Cane Production for Biorefineries

The harvesting and hauling operations of bioenergy feedstock is an important area in biofuel production. Production costs can be minimized by maintaining optimal machinery units for these operations. The objective of this study is to design an optimal harvesting unit for bioenergy refinery and estimate harvesting and hauling costs of energy cane. A biorefinery with the annual capacity of processing twenty-five million imp. gallons of ethanol were considered. Given the efficiency of harvesting, a two-row soldier system was considered. Considering the year-round supply of energy cane to the refinery, the optimal machinery unit was designed, and the combined operation costs were derived. The average estimated ownership, repair, labor and fuel and lubricant costs of biomass harvest unit were calculated to be $\$$0.50, $\$$0.54, $\$$1.78 and $\$$1.51/mt, respectively. The costs distribution generated showed harvesting and hauling costs could range between $\$$5.47–$\$$9.23/mt of energy cane. The methodology and the research output will provide guidelines for investors in designing harvesting and hauling units and estimating costs for different scales of operation.

09 BIOMASS FUELS↗
DOE OSTI2024

Surface Wettability Analysis from Adsorption Energy and Surface Electrical Charge

Surface wettability is determined by the attraction of a liquid phase to a solid surface. It is typically quantified by using contact angle measurements at mineral surfaces in the case of the flotation of mineral particles. Contact angle research to describe wettability has been investigated at different scales by sessile drop measurements, molecular dynamic simulation, and atomic force microscopy. In this study, the density functional theory (DFT) was employed for predicting the surface free energy and contact angles of a well-known hydrophobic phyllosilicate mineral talc and a well-known hydrophilic phyllosilicate mineral muscovite based on the calculated interfacial energy and surface charge. The results revealed that the predicted contact angle at the atomic scale was larger than the experimental value, and identified two interactions: electrostatic interaction and hydrogen bonding, between the hydrophilic muscovite surface and the water layer, while a water-exclusion zone of 3.346 Å was found between the hydrophobic talc surface and the first water layer. This investigation gives a new perspective for wettability determination at the atomic scale.

Geochemistry & Geophysics↗
DOE OSTI2021

The Spiral Galaxies Flat Rotational Velocity Curve Explained by the Constant Group Velocity of a Nonlinear Density Wave

The rotation velocity curves of stars in galaxies, the motions of pairs of galaxies, and the behavior of galaxies in clusters and super-clusters all indicate that there is a lack of mass on different scales in the universe. In this paper, we derive the expression for rotational velocity using the nonlinear density wave theory considering only stellar components and we show that such theory can support the observed flat rotational velocity curve due to the main property of the soliton wave, which is a constant group velocity of the wave. The surface mass density (SMD) function, used to derive gravitational potential gradient and rotational velocity, is not assumed but rather derived as a solution of the nonlinear Srödinger equation, on the contrary to the widely used, in the literature, exponential disk approximation. Three parameters relevant to the curve shape are the intensities of equilibrium SMD, the amplitude of the wave, and total angular velocity or differential rotation, equivalently. Since the shape of the rotational velocity is highly sensitive to the mentioned parameters, this theory eventually provides a method for a very accurate estimation of galaxy mass and angular velocity as well.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Velocity Dispersions of Massive Quiescent Galaxies from Weak Lensing and Spectroscopy

We use MMT spectroscopy and deep Subaru Hyper Suprime-Cam (HSC) imaging to compare the spectroscopic central stellar velocity dispersion of quiescent galaxies with the effective dispersion of the dark matter halo derived from the stacked lensing signal. The spectroscopic survey (the Smithsonian Hectospec Lensing Survey) provides a sample of 4585 quiescent galaxy lenses with measured line-of-sight central stellar velocity dispersion ($\sigma_{\rm SHELS}$) that is more than 85\% complete for $R < 20.6$, $D_{n}4000> 1.5$ and $M_{\star} > 10^{9.5}{\rm M}_{\odot}$.The median redshift of the sample of lenses is 0.32. We measure the stacked lensing signal from the HSC deep imaging. The central stellar velocity dispersion is directly proportional to the velocity dispersion derived from the lensing $\sigma_{\rm Lens}$, $\sigma_{\rm Lens} = (1.05\pm0.15)\sigma_{\rm SHELS}+(-21.17\pm35.19)$. The independent spectroscopic and weak lensing velocity dispersions probe different scales, $\sim3$kpc and $\gtrsim$ 100 kpc, respectively, and strongly indicate that the observable central stellar velocity dispersion for quiescent galaxies is a good proxy for the velocity dispersion of the dark matter halo. We thus demonstrate the power of combining high-quality imaging and spectroscopy to shed light on the connection between galaxies and their dark matter halos.

79 ASTRONOMY AND ASTROPHYSICS↗