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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Modulating the dynamics of Brønsted acid sites on PtWOx inverse catalyst

Here metal-metal oxide (M-MO) inverse catalysts are broadly applied. Brønsted acid sites on the oxide overlayers are often hypothesized to drive selective C-O bond activation. However, the Brønsted acid site nature and dynamics under working conditions remain poorly understood due to multiple materials functionalities. Here, we investigate the formation and the dynamics of Brønsted acid and redox sites on PtWO x /C under working conditions. DFT-based thermodynamic calculations and microkinetic modeling reveal a complex interplay between Brønsted acid and redox sites and potentially fast catalyst dynamics at comparable time scales to the chemistry. Combining in situ characterization and probe chemistry, we demonstrate that the density of Brønsted acid sites on the PtWO x /C inverse catalyst could be modulated by up to two orders of magnitude by altering the reaction parameters and by the chemistry itself. We elicit an order of magnitude increase in the acid-catalyzed dehydration average reaction rate by periodic hydrogen pulsing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Physics-Based Analysis of Cell Imbalances and Aging in Lithium-Ion Battery Modules and Packs

Lithium-ion battery (LIB) packs are a key solution for grid-scale energy storage, enabling grid resilience and supporting critical infrastructure. LIB modules and packs experience current imbalances and uneven cell aging due to various design and operational factors, and require a battery management system (BMS) to continuously monitor and control. In this context, a physics-based modeling framework for LIB modules and packs (liionpack) was enhanced to identify design and control strategies that minimize current imbalance and improve module/pack operation. Simulations of an 8-cell parallel-connected module demonstrate that reducing current imbalance leads to more uniform cell aging and improved module/pack-level degradation predictions. The analysis shows that current imbalance are affected by the electrical resistances. Terminal location significantly affects imbalance, with opposite-end terminal connections at intermediate branches minimizing the imbalance, and the pack circuit construction influences the accuracy of physics-based analysis at the pack scale. This framework enables design optimization of modules and packs through a fast and easy evaluation of pack performance and aging, and supports the development of aging-informed balancing strategies compatible with BMS implementation. Thereby, offering practical pathways to improve reliability and cycle life predictions in large-scale battery energy storage systems.

Ayalasomayajula, Surya Mitra [Oak Ridge National L↗
DOE OSTI2020

Dynamic System Scaling Applied to Zr-4 Cladding under RIA Conditions

The research and development lifecycle for new fuel designs currently has an ideal 20-25 year timeline. Efforts are currrently underway to accelerate fuel design and development using high performance fuel modeling codes and reduced scale experiments with new instrumentation techniques. However current tools available require a full scale prototype to be designed and tested for fuel qualification. The thermal-hydraulics community has leveraged scaling methodologies to use reduced scale integral effect test facilities (Westinghouse AP600/1000, NuScale Power Module) in lieu of building full scale prototypes for reactor design certification. Dynamic System Scaling (DSS) is a recent development to scaling methodologies that enable the tracking and quantification of dynamic scaling distortions experienced throughout a transient. Recent TREAT experiments performed reactivity initiated accident (RIA) pulses on UO2 fuel rodlets in Zr-4 cladding and were accompanied by BISON predictions for the cladding temperature. DSS is applied to pyrometer data of outer cladding temperatures and two BISON predictions after peak cladding temperature has been reached. One BISON case uses a constant gap conductance value between the fuel and cladding and the other using a conventional gap conductance model. Results from the DSS analysis show that there is good agreement between both BISON predictions and the experimental data and that there is not a significant distortion seen between using either gap conductance model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

An oceanic pathway for Madden–Julian Oscillation influence on Maritime Continent Tropical Cyclones

While the Madden–Julian Oscillation (MJO) has been shown to affect tropical cyclones (TCs) worldwide through its modulation of large-scale circulation in the atmosphere, little or no role for the ocean has been identified to date in this influence of MJO on TCs. Using observations and numerical model simulations, we demonstrate that MJO events substantially impact TCs over the Maritime Continent (MC) region through an oceanic pathway. While propagating across the MC region, MJO events cause significant sea surface cooling with an area-averaged value of about 0.35 ± 0.12 °C. Hence, TCs over the MC region immediately following the passage of MJO events encounter considerably cooler sea surface temperatures. Consequently, the enthalpy fluxes under the storms are reduced and the intensification rates decrease by more than 50% on average. These results highlight an important role played by the ocean in facilitating MJO-induced sub-seasonal variability in TC activity over the MC region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

The International Supply Chain and Manufacturing Costs for Photovoltaic Modules, and Project Economics of Systems Including Storage

This talk will highlight the most recent efforts from the National Renewable Energy Laboratory's (NREL) Strategic Energy Analysis Center (SEAC) to track solar PV manufacturing capacities and trade flows, bottom-up manufacturing costs, and project capital costs including storage. We will begin with an overview of the global PV supply chain and the 2019 benchmark input data used to inform NREL's crystalline silicon (c-Si) and thin film module manufacturing cost models. The accounting framework that we follow and that will be reviewed during this talk provides a methodology to prepare cost models in accordance with the Generally Accepted Accounting Principles (GAAP) and the International Financial Reporting Standards (IFRS). For the polysilicon, wafer, cell conversion, and module assembly steps of the c-Si supply chain, and for thin film modules, we will review input data and methods useful for calculating the costs of goods sold (COGS); research and development (R&D) expenses; and sales, general, and business administration (S, G, &A) expenses. This 2019 benchmark analysis is compiled for state-of-the-art polysilicon-to-module manufacturing across the globe. We will also provide an overview of opportunities for solar plus storage and show our 2018 benchmark bottom-up capital cost model results for utility-scale PV systems with battery storage.

14 SOLAR ENERGY↗
DOE OSTI2021

A cosmic microscope for the preheating era

Light fields with spatially varying backgrounds can modulate cosmic preheating, and imprint the nonlinear effects of preheating dynamics at tiny scales on large scale fluctuations. This provides us a unique probe into the preheating era which we dub the “cosmic microscope”. We identify a distinctive effect of preheating on scalar perturbations that turns the Gaussian primordial fluctuations of a light scalar field into square waves, like a diode. The effect manifests itself as local non-Gaussianity. We present a model, “modulated partial preheating”, where this nonlinear effect is consistent with current observations and can be reached by near future cosmic probes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Listening to the Forest: An Artificial Neural Network‐Based Model of Carbon Uptake at Harvard Forest

Abstract The terrestrial biosphere strongly modulates atmospheric CO 2 mixing ratios, whose inexorable rise propels anthropogenic climate change. Modeling and mechanistically understanding C uptake by the terrestrial biosphere are thus of broad societal concerns. Yet despite considerable progress, scaling up point observations to landscape and larger scales continues to frustrate analyses of the anthropogenically perturbed global C cycle. While that up‐scaling is our overarching motivation, here we focus on one of its elements, modeling C uptake at a given site. We devise a novel artificial neural network (ANN)‐based model of C uptake at Harvard Forest that combines locally observed and remotely sensed variables. Most of our model predictors are those used by an established ecosystem C uptake model, the Vegetation Photosynthesis and Respiration Model (VPRM), easing comparisons. To those, we add observed cumulative antecedent precipitation and soil temperature. We find that model errors are much larger in winter, indicating that better understanding and modeling of respiration will likely discernibly improve model performance. Comparing the ANN and VPRM results reveals errors attributed to unrealistic treatment of temperature in the VPRM formulation, indicating that better representation of temperature dependencies is also likely to enhance model skill. By judiciously comparing VPRM and ANN errors we thus overcome ANNs' notoriety for concealing the mechanisms underlying their predictive skills. We demonstrate their ability to identify outstanding ecosystem science knowledge gaps and particularly fruitful corresponding model development directions, improving site specific and up‐scaling flux modeling and understanding of the climate impacts of the northern forest.

Eshel, Gidon↗
DOE OSTI2020

Hydrogen Mitigation Process Installation at Nevada Solar One

The National Renewable Energy Laboratory (NREL) and Acciona Solar Power (ASP) have developed and are implementing a process that addresses the issue of hydrogen buildup in parabolic trough power plants. Our method selectively removes hydrogen from the expansion tanks of the power plant to control hydrogen levels in the circulating heat-transfer fluid (HTF). During previous work, we developed a sensor that measures hydrogen partial pressure in the expansion-tank headspace gas. We demonstrated that our sensor measures hydrogen levels over a wide range of partial pressure—from 1.33 mbar down to 0.003 mbar. More recently, we conceived and developed an integrated process module that performs both hydrogen sensing and separating functions. The sensor/separator measures hydrogen partial pressure in the headspace gas in the same way as our original sensor design. Additionally, the integrated module separates hydrogen from the headspace gas to reduce hydrogen to the level needed to maintain the performance of receivers in the collector field. We demonstrated the performance of a laboratory-scale version of this module. Testing showed that the module performed as expected: the accuracy of the sensing function was ±7%, and the hydrogen extraction rate for the separating mode was consistent with our modeling predictions. The primary benefit of this module is its simple design, both in terms of function and incorporation into the HTF subsystem of the power plant. Most recently, NREL and ASP planned, specified, and designed a mitigation process that is based on the integrated module. We are currently completing installation of this process at ASP's Nevada Solar One power plant in Boulder City, Nevada, USA. The mitigation process is being installed at ground level below the HTF expansion tanks, where it draws headspace gas from the tanks, removes hydrogen, and returns the treated gas back to the tanks. In this paper, we report progress on the installation and describe some of the many design details and challenges that we addressed during the past year. We will generate initial performance data from the Nevada Solar One mitigation process in early 2020.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

On the Development of Compact HTS Coil Modules for Large Bore High Field Superconducting Magnets

Lower cost, high current density superconducting coil modules producing higher magnetic fields and cooled affordably are crucial for obtaining cost-effective, compact commercial fusion reactors. Accessibility to low cost, higher field magnets (>30 T) is also critical for the discovery of new quantum phenomena in materials, cosmic frontier and other topics in basic science research. The Princeton Plasma Physics Laboratory (PPPL) is working with Princeton University to develop unique large bore, compact superconducting magnets to support science experiments including the development of new instrumentation for condensed matter physics and Axion dark matter search in the cosmic frontier. Core elements of these experiments are unique for access to lower cost, simple fabrication of compact superconducting magnets that can be cooled affordably, while integrated with dedicated science instruments. Conductor qualification and coil design concepts are discussed in support of needs for these experiments. PPPL has the unique expertise and experimental facilities to design, construct and test subscale coil modules for these projects. Compact high field coil modules were fabricated and tested to validate coil design concepts and coil performance. Finally, the design and model coil integration challenges are discussed to identify performance risks and demonstrate feasibility for deploying full scale large bore compact superconducting magnets for cost effective operations of multiple laboratory experiments.

axion dark matter↗
DOE OSTI2022

Microphysical-macrophysical interactions in low cloud systems over the Eastern North Atlantic

How low clouds over the oceans respond to changes in atmospheric greenhouse gases and aerosol particles is poorly understood. Climate model disagreement in these responses limits the accuracy of predictions of future climate. Processes governing the behavior of marine low cloud need to be improved in models and comprehensive long-term observations provide important constraints. Cloud structure and impacts on climate are shaped by processes operating on a wide range of time and space scales, which interact to modulate cloud dynamics, boundary layer structure, and precipitation. The Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) site at Graciosa Island (Azores, 28°W 39°N) began taking measurements in late 2013 to address the need for comprehensive cloud, aerosol, and meteorological observations in a remote marine low cloud environment. The site straddles the boundary between the subtropical and midlatitude climate zones in the eastern North Atlantic and experiences a great diversity of meteorological and cloud conditions. In addition, the ENA site is downwind of the North American continent and is periodically impacted by continental aerosol particles transported there on the prevailing winds. In response to a 2014 proposal call, our team (ENA Site Science Team) laid out a strategy to accelerate research focused on understanding the multi-scale interactions that shape cloud properties by maximizing the utility of the observational capabilities of the ENA site. The proposed work aligned with the scientific directions of the ASR Working Groups. The team, together with collaborators, conducted a program of research focused upon multi-scale cloud processes. The team worked with scientists both within and outside the Atmospheric System Research (ASR) program, and with the ARM infrastructure to ensure that the full potential of the ENA site is realized. The suite of instruments at the ENA site has been increasing since the site’s inception and is now essentially complete. In 2019, we laid out a two-year extension of the research, and were granted an extension of the Site Science award.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data

Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Modeling Flow and Mass Transfer within Hollow Fiber Packaging for Gas Separation

Hollow fiber membrane modules are used for gas purification by their selective permeation properties. Intensification of the process to minimize the retentate loss and gas pressure involves optimization at various scales. In this work, we outline a numerical investigation of the gas separation performance at the scale of fiber bundles and its impact on module performance. Flow channeling and anisotropy govern the mass-transfer coefficient in axial and cross-flow configurations. These effects are quantified in terms of a permeability tensor or an anisotropy ratio and the effective mass-transfer coefficient or the Sherwood number. The results show a trade-off between purification and recovery. While smaller fibers offer a large specific surface area to enable high purification, it comes at a huge penalty on the separation performance due to reduced penetration within bundles. Optimum performance indicators are emphasized.

Fibers↗
DOE OSTI2022

Cloud adjustments from large-scale smoke–circulation interactions strongly modulate the southeastern Atlantic stratocumulus-to-cumulus transition

Smoke from southern Africa blankets the southeastern Atlantic Ocean from June to October, producing strong and competing aerosol radiative effects. Smoke effects on the transition between overcast stratocumulus and scattered cumulus clouds are investigated along a Lagrangian (air-mass-following) trajectory in regional climate and large eddy simulation models. Results are compared with observations from three recent field campaigns that took place in August 2017: ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES), CLouds and Aerosol Radiative Impacts and Forcing: Year 2017 (CLARIFY), and Layered Atlantic Smoke Interactions with Clouds (LASIC). The case study is set up around the joint ORACLES–CLARIFY flight that took place near Ascension Island on 18 August 2017. Smoke sampled upstream on an ORACLES flight on 15 August 2017 likely entrained into the marine boundary layer later sampled during the joint flight. The case is first simulated with the WRF-CAM5 regional climate model in three distinct setups: (1) FireOn, in which smoke emissions and any resulting smoke–cloud–radiation interactions are included; (2) FireOff, in which no smoke emissions are included; (3) RadOff, in which smoke emissions and their microphysical effects are included but aerosol does not interact directly with radiation. Over the course of the Lagrangian trajectory, differences in free tropospheric thermodynamic properties between FireOn and FireOff are nearly identical to those between FireOn and RadOff, showing that aerosol–radiation interactions are primarily responsible for the free tropospheric effects. These effects are non-intuitive: in addition to the expected heating within the core of the smoke plume, there is also a “banding” effect of cooler temperature (~1–2 K) and greatly enhanced moisture (>2 g kg -1 ) at the plume top. This banding effect is caused by a vertical displacement of the former continental boundary layer in the free troposphere in the FireOn simulation resulting from anomalous diabatic heating due to smoke absorption of sunlight that manifests primarily as a few hundred meters per day reduction in large-scale subsidence over the ocean. A large eddy simulation (LES) is then forced with free tropospheric fields taken from the outputs for the WRF-CAM5 FireOn and FireOff runs. Cases are run by selectively perturbing one variable (e.g., aerosol number concentration, temperature, moisture, vertical velocity) at a time to better understand the contributions from different indirect (microphysical), “large-scale” semi-direct (above-cloud thermodynamic and subsidence changes), and “local” semi-direct (below-cloud smoke absorption) effects. Despite a more than 5-fold increase in cloud droplet number concentration when including smoke aerosol concentrations, minimal differences in cloud fraction evolution are simulated by the LES when comparing the base case with a perturbed aerosol case with identical thermodynamic and dynamic forcings. A factor of 2 decrease in background free tropospheric aerosol concentrations from the FireOff simulation shifts the cloud evolution from a classical entrainment-driven “deepening–warming” transition to trade cumulus to a precipitation-driven “drizzle-depletion” transition to open cells, however. The thermodynamic and dynamic changes caused by the WRF-simulated large-scale adjustments to smoke diabatic heating strongly influence cloud evolution in terms of both the rate of deepening (especially for changes in the inversion temperature jump and in subsidence) and in cloud fraction on the final day of the simulation (especially for the moisture “banding” effect). Such large-scale semi-direct effects would not have been possible to simulate using a small-domain LES model alone.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Atmospheric Rivers Impacting Northern California Exhibit a Quasi-Decadal Frequency

Periods of water surplus and deficit in Northern California follow a pronounced quasi-decadal cycle. This cycle is largely driven by the frequency of atmospheric rivers (ARs), affecting the region’s wet and dry periods. Our analyses demonstrate that the quasi-decadal cycle of AR frequency relies on moisture transport associated with the position and intensity of the Aleutian Low. In observations, the Aleutian Low is shown to covary with tropical Pacific sea surface temperature anomalies. A modeling experiment, which incorporates ocean observations from the equatorial Pacific into the fully coupled climate model, provides support that the quasi-decadal cycle of the Aleutian Low is forced by the tropical Pacific. Subsequently, the tropical Pacific modulates the wet season moisture transport toward California on decadal time scales, affecting AR frequency. These results provide metrics for improving interannual-to-decadal prediction of AR activity, which drives hydrological cycles in Northern California.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Business Models for Scaling Demand Flexibility Volume III – Stakeholder ecosystem management challenges, and lessons learned from U.S. programs

Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility program stakeholder ecosystem management strategies, which provide information on value delivery and describe how program implementers leverage partner capabilities and collaborate to deliver customer and grid benefits. This report discusses the role of stakeholder ecosystem management in demand flexibility programs, identifies existing challenges to effective stakeholder management, and describes lessons learned. This report is part of a series that includes reports on value propositions, customer relationship management strategies, and program life cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Business Models for Scaling Demand Flexibility Volume I – Value proposition characteristics, challenges, and lessons learned from U.S. programs

Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility value propositions, which provide information on value creation and describe how programs deliver clear benefits that address customer and grid needs. This report discusses the role of value propositions in demand flexibility programs, provides an overview of value propositions for a range of demand flexibility stakeholders, identifies existing challenges to establishing an effective value proposition, and describes lessons learned. This report is part of a series that includes reports on customer relationship management strategies, stakeholder ecosystem management, and program life cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Business Models for Scaling Demand Flexibility Volume IV – Program life cycle challenges and lessons learned from U.S. programs

Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on the life cycle of demand flexibility programs, which provides information on value creation and describes the various deployment phases program implementers navigate from initial program conceptualization through to program expansion and replication to new customer segments and regions. This report characterizes the key phases of the demand flexibility program life cycle, identifies existing challenges across the program deployment phases, and describes lessons learned. This report is part of a series that includes reports on customer relationship management strategies, stakeholder ecosystem management, and program life cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗