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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 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

The Role of Convective Gustiness in Reducing Seasonal Precipitation Biases in the Tropical West Pacific

Abstract Precipitation is an important climate quantity that is critically relevant to society. In spite of intense efforts, significant precipitation biases remain in most climate models. One pervasive and persistent bias found in many general circulation models occurs in the Tropical West Pacific where northern hemisphere summer‐time precipitation is often underestimated compared to observations. Using the DOE‐E3SM model, the inclusion of a missing process, convective gustiness, is shown to reduce those biases through a net increase in surface evaporation. Gustiness in surface wind fields is assumed to arise empirically in proportion to the intensity of convective precipitation. The increased evaporation can be treated as an increase in the moist static energy forcing into the atmosphere. A Normalized Gross Moist Stability (NGMS) framework (which characterizes the relationship between convective forcing and convective response) is used to explore the processes responsible for the precipitation bias, and the impact of the gustiness parameterization in reducing that bias. Because the NGMS of the Tropical West Pacific is less than unity in the E3SMv1 model, the increase in energy forcing amplifies the increase in precipitation to exceed that of the evaporative flux. Convective gustiness favors increased precipitation in regions where the resolved surface winds are weak and convection is present.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 2. Model Description, Sensitivity Studies, and Tuning Strategies

Here, in Part 2 of this two–part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part 1. Part 2 provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken to tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Optical Activity of Chiral Excitons

Recent activity in the area of chiroptical phenomena has been focused on the connection between structural asymmetry, electron spin configuration and light/matter interactions in chiral semiconductors. In these systems, spin‐splitting phenomena emerge due to inversion symmetry breaking and the presence of extended electronic states, yet the connection to chiroptical phenomena is lacking. Here, in this study, we develop an analytical effective mass model of chiral excitons, parameterized by density functional theory. The model accounts for parity mixing of the band edge Bloch functions resulting from polar distortions, resulting in allowed magnetic dipole transitions. Through the study of a prototypical chiral 2D hybrid perovskite semiconductor, we show that circular dichroism of the chiral exciton and its interband continuum emerges from spin‐splitting via cross‐coupling of Rashba‐like and chiral/helical spin‐texture components. To demonstrate the generality of our approach, and as a counterpoint, we apply our model to describe chiroptical properties of three‐dimensionally confined excitons in perovskite nanocrystals that occur without chiral lattice distortions.

14 SOLAR ENERGY↗
DOE OSTI2019

Interatomic Potential Model Development: Finite-Temperature Dynamics Machine Learning

Developing an accurate interatomic potential model is a prerequisite for achieving reliable results from classical molecular dynamics (CMD) simulations; however, most of the potentials are biased as specific simulation purposes or conditions are considered in the parameterization. Here, for developing an unbiased potential, a finite-temperature dynamics machine learning (FTD-ML) approach is proposed, and its processes and feasibility are demonstrated using the Buckingham potential model and aluminum (Al) as an example. Compared with conventional machine learning approaches, FTD-ML exhibits three distinguished features: 1) FTD-ML intrinsically incorporates more extensive configurational and conditional space for enhancing the transferability of developed potentials; 2) FTD-ML employs various properties calculated directly from CMD, for ML model training and prediction validation against experimental data instead of first-principles data; 3) FTD-ML is much more computationally cost effective than first-principles simulations, especially when the system size increases over 10 3 atoms as employed in this research for ensuring reliable training data. The Al Buckingham potential developed by the FTD-ML approach exhibits good performance for general simulation purposes. Thus, the FTD-ML approach is expected to contribute to a fast development of interatomic potential model suitable for various simulation purposes and conditions, without limitation of model type, while maintaining experimental-level accuracy.

42 ENGINEERING↗
DOE OSTI2017

The kinetics of polyurethane structural foam formation: Foaming and polymerization

We are developing kinetic models to understand the manufacturing of polymeric foams, which evolve from low viscosity Newtonian liquids, to bubbly liquids, finally producing solid foam. Closed-form kinetics are formulated and parameterized for PMDI-10, a fast curing polyurethane, including polymerization and foaming. PMDI- 10 is chemically blown, where water and isocyanate react to form carbon dioxide. The isocyanate reacts with polyol in a competing reaction, producing polymer. Our approach is unique, though it builds on our previous work and the polymerization literature. This kinetic model follows a simplified mathematical formalism that decouples foaming and curing, including an evolving glass transition temperature to represent vitrification. This approach is based on IR, DSC, and volume evolution data, where we observed that the isocyanate is always in excess and does not affect the kinetics. Finally, the kinetics are suitable for implementation into a computational fluid dynamics framework, which will be explored in subsequent papers.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Selection of solvents for integrated CO 2 absorption and electrochemical reduction systems

Abstract Solvent‐based electrochemical CO 2 reduction (CO 2 R) enables the production of chemicals or fuels using CO 2 from a preceding absorption process. Employing previously tested CO 2 capture solvents does not ensure their suitability for either CO 2 R or integrated CO 2 absorption‐reduction. We propose solvent selection criteria that include the CO 2 solubility, kinetic constant, ionic conductivity, concentration of the bicarbonate, carbamate, and solvent cation in the CO 2 ‐loaded solution, and sustainability indicators. They are implemented for solvent selection (a) from novel, aqueous mixtures of N ‐methylcyclohexylamine (MCA) with piperazine (PZ), 2‐amino‐2‐methyl‐1‐propanol (AMP), potassium hydroxide (KOH), and potassium chloride (KCl) and (b) from aqueous monoethanolamine (MEA), AMP, KOH, MCA, and PZ solutions. Versions of a modified Kent‐Eisenberg model for strong bases, carbamate, and non‐carbamate‐forming amine solutions are developed and parameterized through experimental equilibrium measurements. CO 2 R experimental results are presented for solutions of KOH and MCA + KOH, as these indicate desired trade‐offs for CO 2 absorption and reduction.

Amines↗
DOE OSTI2024

3D‐Printed Carbon Nanoneedle Electrodes for Dopamine Detection in Drosophila

In vivo electrochemistry in small brain regions or synapses requires nanoelectrodes with long straight tips for submicron scale measurements. Nanoelectrodes can be fabricated using a Nanoscribe two-photon printer, but annealed tips curl if they are long and thin. We propose a new pulling-force strategy to fabricate a straight carbon nanoneedle structure. A micron-width bridge is printed between two blocks. The annealed structure shrinks during pyrolysis, and the blocks create a pulling force to form a long, thin, and straight carbon bridge. Parameterization study and COMSOL modeling indicate changes in the block size, bridge size and length affect the pulling force and bridge shrinkage. Electrodes were printed on niobium wires, insulated with aluminum oxide, and the bridge cut with focused ion beam (FIB) to expose the nanoneedle tip. Annealed needle diameters ranged from 400 nm to 5.25 μm and length varied from 50.5 μm to 146 μm. The electrochemical properties are similar to glassy carbon, with good performance for dopamine detection with fast-scan cyclic voltammetry. Nanoelectrodes enable biological applications, such as dopamine detection in a specific Drosophila brain region. Long and thin nanoneedles are generally useful for other applications such as cellular sensing, drug delivery, or gas sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Fuel‐Driven π‐Conjugated Superstructures to Form Transient Conductive Hydrogels

Despite advances in creating dissipative materials with transient properties, such as hydrogels and active droplets, their application remains confined to temporal changes in structural properties. Developing out-of-equilibrium materials whose electronic functions are parameterized by a chemical reaction cycle is challenging. Yet, this class of materials is required to construct biomimetic materials. In contrast to traditional chemical reaction cycles that exploit molecularly dissolved building blocks at thermodynamic equilibrium, we show that fiber structures derived from reactive naphthalene diimide (NDI) building blocks can be used as resting states to form far-from-equilibrium conductive hydrogels after the addition of chemical fuels. Upon fueling the NDI-derived fibers, a dual-component activation and deactivation pathway is deduced by kinetic analysis and is absent when using a molecularly dissolved resting state. Investigating the solid-state morphologies of the structures formed throughout the fuel-driven reaction cycle using cryo-EM reveals that the resting thermodynamic fibers evolve to transient thicker fibrils and layered superstructures. We show that the transient redox-active hydrogels exhibit a nearly threefold increase in electrical conductivity upon fuel consumption before reverting to their original value over hours. In conclusion, these far-from-equilibrium materials are potential candidates in applications such as programmable biorobotics and chemical computing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

The underestimated magnitude and decline trend in near-surface wind over China

This study reports the magnitude, spatial pattern and temporal trend of near-surface wind speed (NWS) by comparing 20th century simulations in Coupled Model Intercomparison Project phase 5 (CMIP5) and 3 (CMIP3) and the climate reanalyses with measurements at 563 weather stations in China over 1961–2005. Both CMIP5 and CMIP3 agree quite well with observations in reproducing the spatial pattern of annual mean NWS. CMIP5 models are superior to CMIP3 models in hindcasting the magnitude and spatial pattern of seasonal mean NWS, the temporal trend in annual and seasonal mean NWS. Although both CMIP3 and CMIP5 reproduced the decline trend in the annual and seasonal mean NWS, the hindcasted decline rate is smaller than observed decline trend by the magnitude of one order. The ensemble of optimal models that are better correlated to observations in both NWS and temporal trend possesses advantages over individual model hindcast and reanalyses. The reanalyzed data are not able to represent the observed either the spatial pattern or the decline trend of NWS. The analyses presented here reveal the uncertainties in the current wind field products including reanalyses and model outputs and highlight the benefits of parameterization development and increased horizontal resolution in the new-generation CMIP models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

Model-Based Analysis of the Role of Biological, Hydrological and Geochemical Factors Affecting Uranium Bioremediation

Uranium contamination is a serious concern at several sites motivating the development of novel treatment strategies such as the Geobacter-mediated reductive immobilization of uranium. However, this bioremediation strategy has not yet been optimized for the sustained uranium removal. While several reactive-transport models have been developed to represent Geobacter-mediated bioremediation of uranium, these models often lack the detailed quantitative description of the microbial process (e.g., biomass build-up in both groundwater and sediments, electron transport system, etc.) and the interaction between biogeochemical and hydrological process. In this study, a novel multi-scale model was developed by integrating our recent model on electron capacitance of Geobacter (Zhao et al., 2010) with a comprehensive simulator of coupled fluid flow, hydrologic transport, heat transfer, and biogeochemical reactions. This mechanistic reactive-transport model accurately reproduces the experimental data for the bioremediation of uranium with acetate amendment. We subsequently performed global sensitivity analysis with the reactive-transport model in order to identify the main sources of prediction uncertainty caused by synergistic effects of biological, geochemical, and hydrological processes. The proposed approach successfully captured significant contributing factors across time and space, thereby improving the structure and parameterization of the comprehensive reactive-transport model. The global sensitivity analysis also provides a potentially useful tool to evaluate uranium bioremediation strategy. The simulations suggest that under difficult environments (e.g., highly contaminated with U(VI) at a high migration rate of solutes), the efficiency of uranium removal can be improved by adding Geobacter species to the contaminated site (bioaugmentation) in conjunction with the addition of electron donor (biostimulation). The simulations also highlight the interactive effect of initial cell concentration and flow rate on U(VI) reduction.

09 BIOMASS FUELS↗
DOE OSTI2022

Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning

Abstract Brønsted‐Evans‐Polanyi (BEP) relationships, i. e., a linear scaling between reaction and activation energies, lie at the core of computational design of heterogeneous catalysts. However, BEPs are not general and often require reparameterization for each class of reactions. Here we construct generalized BEPs (gBEPs), which can predict activation energies for a diverse dataset of reactions of C, O, N and H containing molecules on metal surfaces. In a first step we develop a set of descriptors based on scaling relationships that can capture the change in chemical identity of reactants during the reaction. Subsequently, we use the reaction energy, these descriptors and a single descriptor for the surface structure to parameterize machine learning based regression approaches for the prediction of activation energies. The best approach we developed shows a Mean Absolute Error (MAE) of 0.11 eV for the training set (80 % of the data set) and 0.23 eV for the test set (20 % of the data set). The methodology presented here allows to calculate activation energies within fractions of seconds on a typical personal computer and due to its generality, accuracy and simplicity in application it might prove to be useful in transition metal catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Quantum optimization algorithms: Energetic implications

Since the dawn of quantum computing (QC), theoretical developments like Shor's algorithm proved the conceptual superiority of QC over traditional computing. However, such quantum supremacy claims are difficult to achieve in practice because of the technical challenges of realizing noiseless qubits. In the near future, QC applications will need to rely on noisy quantum devices that offload part of their work to classical devices. One way to achieve this is by using parameterized quantum circuits in optimization or even in machine learning tasks. The energy requirements of quantum algorithms have not yet been studied extensively. Here in this article, we explore several optimization algorithms using both theoretical insights and numerical experiments to understand their impact on energy consumption. Specifically, we highlight why and how algorithms like quantum natural gradient descent, simultaneous perturbation stochastic approximations or circuit learning methods, are at least 2x to 4x more energy efficient than their classical counterparts; why feedback-based quantum optimization is energy-inefficient; and how techniques like Rosalin can improve the energy efficiency of other algorithms by a factor of ≥2 0 x. Finally, we use the NchooseK high-level programming model to run optimization problems on both gate-based quantum computers and quantum annealers. Empirical data indicate that these optimization problems run faster, have better success rates, and consume less energy on quantum annealers than on their gate-based counterparts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Landscape-scale consequences of differential tree mortality from catastrophic wind disturbance in the Amazon

Wind disturbance can create large forest blowdowns, which greatly reduces live biomass and adds uncertainty to the strength of the Amazon carbon sink. Observational studies from within the central Amazon have quantified blowdown size and estimated total mortality but have not determined which trees are most likely to die from a catastrophic wind disturbance. Also, the impact of spatial dependence upon tree mortality from wind disturbance has seldom been quantified, which is important because wind disturbance often kills clusters of trees due to large treefalls killing surrounding neighbors. We examine (1) the causes of differential mortality between adult trees from a 300-ha blowdown event in the Peruvian region of the northwestern Amazon, (2) how accounting for spatial dependence affects mortality predictions, and (3) how incorporating both differential mortality and spatial dependence affect the landscape level estimation of necromass produced from the blowdown. Standard regression and spatial regression models were used to estimate how stem diameter, wood density, elevation, and a satellite-derived disturbance metric influenced the probability of tree death from the blowdown event. The model parameters regarding tree characteristics, topography, and spatial autocorrelation of the field data were then used to determine the consequences of non-random mortality for landscape production of necromass through a simulation model. Tree mortality was highly non-random within the blowdown, where tree mortality rates were highest for trees that were large, had low wood density, and were located at high elevation. Of the differential mortality models, the non-spatial models overpredicted necromass, whereas the spatial model slightly underpredicted necromass. When parameterized from the same field data, the spatial regression model with differential mortality estimated only 7.5% more dead trees across the entire blowdown than the random mortality model, yet it estimated 51% greater necromass. As a result, we suggest that predictions of forest carbon loss from wind disturbance are sensitive to not only the underlying spatial dependence of observations, but also the biological differences between individuals that promote differential levels of mortality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Storm-driven particulate organic matter flux connects a tidal tributary floodplain wetland, mainstem river, and estuary

The transport of terrestrial plant matter into coastal waters is important to regional and global biogeochemical cycles, and methods for assessing and predicting fluxes in such dynamic environments are needed. We investigated the hypothesis that upon reconnection of a floodplain wetland to its mainstem river, organic matter produced in the wetland would reach other parts of the ecosystem. If so, we can infer that the organic matter would ultimately become a source for the food web in the mainstem river and estuary. To accomplish this, we adapted numerical hydrodynamic and transport modeling methods to estimate the mass of particulate organic matter (POM) derived from the annually senescent aboveground parts of herbaceous marsh plants (H–POM). The Finite–Volume Community Ocean Model (FVCOM), parameterized with flow, tide, and aboveground biomass data, simulated H–POM mobilization from fluid shear stress during tidal exchange, flooding, and variable river flow; entrainment into the water column; transport via channel and overland flow; and entrapment when wetted surfaces dry. We examined export from a recently reconnected, restoring tidal emergent marsh on the Grays River, a tributary to the Columbia River estuary. Modeling indicated that hydrologically reconnecting 65 ha at the site resulted in export of about 96 × 10 3 kg of H–POM, primarily during pulsed storm flooding events in autumn and early winter. This exported mass amounted to about 19% of the summer peak aboveground biomass measured at the site. Of that 19%, about 48% (47 × 10 3 kg) was deposited downstream in the Grays River and floodplain wetlands, and the remaining 52% (50 × 10 3 kg) passed the confluence of the Grays River and the mainstem estuary located about 7 km from the study site. The colonization of the restoring study site largely by nonnative Phalaris arundinacea (reed canarygrass) may have resulted in 18–28% lower H–POM mobilization than typical marsh plant communities on this floodplain, based on estimates from regional studies of marshes dominated by less recalcitrant species. Here, we concluded that restored floodplain wetlands can contribute significant amounts of organic matter to the estuarine ecosystem and thereby contribute to the restoration of historical trophic structure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Assessing dynamic vegetation model parameter uncertainty across Alaskan arctic tundra plant communities

Abstract As the Arctic region moves into uncharted territory under a warming climate, it is important to refine the terrestrial biosphere models (TBMs) that help us understand and predict change. One fundamental uncertainty in TBMs relates to model parameters, configuration variables internal to the model whose value can be estimated from data. We incorporate a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). We set up different parameterizations of TEM across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula. TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and the stomatal responses to ambient light conditions. Our analysis also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had about equal uncertainty, while heterotrophic respiration had higher uncertainty than the pool of soil C. Our study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Predicting future coexistence in a North American ant community

Global climate change will remodel ecological communities worldwide. However, as a consequence of biotic interactions, communities may respond to climate change in idiosyncratic ways. This makes predictive models that incorporate biotic interactions necessary. We show how such models can be constructed based on empirical studies in combination with predictions or assumptions regarding the abiotic consequences of climate change. Specifically, we consider a well-studied ant community in North America. First, we use historical data to parameterize a basic model for species coexistence. Using this model, we determine the importance of various factors, including thermal niches, food discovery rates, and food removal rates, to historical species coexistence. We then extend the model to predict how the community will restructure in response to several climate-related changes, such as increased temperature, shifts in species phenology, and altered resource availability. Interestingly, our mechanistic model suggests that increased temperature and shifts in species phenology can have contrasting effects. Nevertheless, for almost all scenarios considered, we find that the most subordinate ant species suffers most as a result of climate change. More generally, our analysis shows that community composition can respond to climate warming in nonintuitive ways. For example, in the context of a community, it is not necessarily the most heatsensitive species that are most at risk. Our results demonstrate how models that account for niche partitioning and interspecific trade-offs among species can be used to predict the likely idiosyncratic responses of local communities to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bringing function to structure: Root–soil interactions shaping phosphatase activity throughout a soil profile in Puerto Rico

Abstract Large areas of highly productive tropical forests occur on weathered soils with low concentrations of available phosphorus (P). In such forests, root and microbial production of acid phosphatase enzymes capable of mineralizing organic phosphorus is considered vital to increasing available P for plant uptake. We measured both root and soil phosphatase throughout depth and alongside a variety of root and soil factors to better understand the potential of roots and soil biota to increase P availability and to constrain estimates of the biochemical mineralization within ecosystem models. We measured soil phosphatase down to 1 m, root phosphatase to 30 cm, and collected data on fine‐root mass density, specific root length, soil P, bulk density, and soil texture using soil cores in four tropical forests within the Luquillo Experimental Forest in Puerto Rico. We found that soil phosphatase decreased with soil depth, but not root phosphatase. Furthermore, when both soil and root phosphatase were expressed per soil volume, soil phosphatase was 100‐fold higher that root phosphatase. Both root and soil factors influenced soil and root phosphatase. Soil phosphatase increased with fine‐root mass density and organic P, which together explained over 50% of the variation in soil phosphatase. Over 80% of the variation in root phosphatase per unit root mass was attributed to specific root length (positive correlation) and available (resin) P (negative correlation). Synthesis : Fine‐root traits and soil P data are necessary to understand and represent soil and root phosphatase activity throughout the soil column and across sites with different soil conditions and tree species. These findings can be used to parameterize or benchmark estimates of biochemical mineralization in ecosystem models that contain fine‐root biomass and soil P distributions throughout depth.

59 BASIC BIOLOGICAL SCIENCES↗