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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Net-zero CO 2 by 2050 scenarios for the United States in the Energy Modeling Forum 37 study

The Energy Modeling Forum (EMF) 37 study on deep decarbonization and high electrification analyzed a set of scenarios that achieve economy-wide net-zero carbon dioxide (CO 2 ) emissions in North America by mid-century, exploring the implications of different technology evolutions, policies, and behavioral assumptions affecting energy supply and demand. Here, for this paper, 16 modeling teams reported resulting emissions projections, energy system evolution, and economic activity. This paper provides an overview of the study, documents the scenario design, provides a roadmap for complementary forthcoming papers from this study, and offers an initial summary and comparison of results for net-zero CO 2 by 2050 scenarios in the United States. We compare various outcomes across models and scenarios, such as emissions, energy use, fuel mix evolution, and technology adoption. Despite disparate model structure and sources for input assumptions, there is broad agreement in energy system trends across models towards deep decarbonization of the electricity sector coupled with increased end-use electrification of buildings, transportation, and to a lesser extent industry. All models deploy negative emissions technologies (e.g., direct air capture and bioenergy with carbon capture and storage) in addition to land sinks to achieve net-zero CO 2 emissions. Important differences emerged in the results, showing divergent pathways among end-use sectors with deep electrification and grid decarbonization as necessary but not sufficient conditions to achieve net zero. These differences will be explored in the papers complementing this study to inform efforts to reach net-zero emissions and future research needs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Virtual storage capability of residential buildings for sustainable smart city via model-based predictive control

This paper evaluates the virtual storage capability of a residential air-conditioning (AC) system by utilizing the building mass as a thermal storage to enable sustainable cities through model-based predictive control (MPC). The control-oriented building model was developed with a grey-box model structure based on the experimental data to predict the indoor air temperature. Based on the model, the operation cost-saving potential of the MPC was investigated in a single house with different comfort levels, electricity prices, and prediction horizons. The cost-saving of the MPC was approximately 10.6 % compared to the conventional control. The MPC was expanded to a residential building cluster considering the peak demand charge. In an hourly shedding and load-up scenario, the performances of the MPC and the heuristic prior-based control (PBC) are promising. When shedding was enforced all day, the peak demand saving of the MPC was 36~38 %, whereas that of the PBC was 25.7 % compared to the baseline. In addition, the monthly electricity bill, including the operation and peak demand cost, was investigated with different demand charge rates and weather conditions. The total saving decreased with a low demand charge rate and hot conditions, and the operation cost saving was compromised with more aggressive demand shedding.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Crystal Structure of the Siderophore Binding Protein BauB Bound to an Unusual 2:1 Complex Between Acinetobactin and Ferric Iron

The critical role that iron plays in many biochemical processes has led to an elaborate battle between bacterial pathogens and their hosts to acquire and withhold this critical nutrient. Exploitation of iron nutritional immunity is being increasingly appreciated as a potential antivirulence therapeutic strategy, especially against problematic multidrug resistant Gram-negative pathogens such as Acinetobacter baumannii. To facilitate iron uptake and promote growth, A. baumannii produces a nonribosomally synthesized peptide siderophore called acinetobactin. Acinetobactin is unusual in that it is first biosynthesized in an oxazoline form called preacinetobactin that spontaneously isomerizes to the final isoxazolidinone acinetobactin. Interestingly, both isomers can bind iron and both support growth of A. baumannii. To address how the two isomers chelate their ferric cargo and how the complexes are used by A. baumannii, structural studies were carried out with the ferric acinetobactin complex and its periplasmic siderophore binding protein BauB. Herein, we present the crystal structure of BauB bound to a bis-tridentate (Fe 3+ L 2 ) siderophore complex. Additionally, we present binding studies that show multiple variants of acinetobactin bind BauB with no apparent change in affinity. These results are consistent with the structural model that depicts few direct polar interactions between BauB and the acinetobactin backbone. Furthermore, this structural and functional characterization of acinetobactin and its requisite binding protein BauB provides insight that could be exploited to target this critical iron acquisition system and provide a novel approach to treat infections caused by this important multidrug resistant pathogen.

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

Fiber Diffraction of the Prion-Forming Domain HET-s(218-289) Shows Dehydration-Induced Deformation of a Complex Amyloid Structure

Amyloids are filamentous protein aggregates that can be formed by many different proteins and are associated with both disease and biological functions. The pathogenicities or biological functions of amyloids are determined by their particular molecular structures, making accurate structural models a requirement for understanding their biological effects. One potential factor that can affect amyloid structures is hydration. Previous studies of simple stacked β-sheet amyloids have suggested that dehydration does not impact structure, but other studies indicated dehydration-related structural changes of a putative water-filled nanotube. Our results show that dehydration significantly affects the molecular structure of the fungal prion-forming domain HET-s(218–289), which forms a β-solenoid with no internal solvent-accessible regions. The dehydration-related structural deformation of HET-s(218–289) indicates that water can play a significant role in complex amyloid structures, even when no obvious water-accessible cavities are present.

Wan, William↗
DOE OSTI2023

Accounting for herbaceous communities in process‐based models will advance our understanding of “grassy” ecosystems

Abstract Grassland and other herbaceous communities cover significant portions of Earth's terrestrial surface and provide many critical services, such as carbon sequestration, wildlife habitat, and food production. Forecasts of global change impacts on these services will require predictive tools, such as process‐based dynamic vegetation models. Yet, model representation of herbaceous communities and ecosystems lags substantially behind that of tree communities and forests. The limited representation of herbaceous communities within models arises from two important knowledge gaps: first, our empirical understanding of the principles governing herbaceous vegetation dynamics is either incomplete or does not provide mechanistic information necessary to drive herbaceous community processes with models; second, current model structure and parameterization of grass and other herbaceous plant functional types limits the ability of models to predict outcomes of competition and growth for herbaceous vegetation. In this review, we provide direction for addressing these gaps by: (1) presenting a brief history of how vegetation dynamics have been developed and incorporated into earth system models, (2) reporting on a model simulation activity to evaluate current model capability to represent herbaceous vegetation dynamics and ecosystem function, and (3) detailing several ecological properties and phenomena that should be a focus for both empiricists and modelers to improve representation of herbaceous vegetation in models. Together, empiricists and modelers can improve representation of herbaceous ecosystem processes within models. In so doing, we will greatly enhance our ability to forecast future states of the earth system, which is of high importance given the rapid rate of environmental change on our planet.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Geometric deep learning of RNA structure

RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures. The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges. By learning effectively even from a small amount of data, our approach overcomes a major limitation of standard deep neural networks. Because it uses only atomic coordinates as inputs and incorporates no RNA-specific information, this approach is applicable to diverse problems in structural biology, chemistry, materials science, and beyond.

Townshend, Raphael J. L.↗
DOE OSTI2026

Circularization of 23S rRNA but not 16S rRNA within archaeal ribosomes

Background Processing of archaeal 16S and 23S rRNAs is believed to involve excision of individual rRNAs from polycistronic precursors, circularization of excised rRNAs, and re-linearization before the incorporation into ribosomes. However, all the knowledge is derived from several isolated species, leaving open the possibility that different processes may occur in other archaeal groups. Results Here, we investigate rRNAs from diverse and mostly uncultivated archaea. Sequencing of total cellular RNA from eight phylum-level lineages indicates that archaeal circular 23S rRNA transcript abundances vastly exceed those of linear counterparts, and linear versions are often undetectable. As the majority of rRNAs derive from mature ribosomes, the data suggest that ribosomes contain circular 23S rRNAs. Thus, we directly sequence RNA extracted from isolated ribosomes of a model archaeon, Methanosarcina acetivorans, and confirm that the 23S rRNAs in the ribosomes are circular. Structural modeling places the 5′ and 3′ ends of the linear precursors of archaeal 23S rRNAs in close proximity to form a GNRA tetraloop (in which N is A, C, G, or U and R is A or G), consistent with their existence as circular molecules. We also confirm the existence of circular 16S rRNA intermediates in transcriptomes of most archaea, yet a circular form is not evident in some distinct archaeal groups, suggesting that certain archaea do not circularize 16S rRNA during processing. Conclusions Our findings uncover unexpected variations in the processing required to generate mature rRNAs and the conformation of functional molecules in archaeal ribosomes.

Archaea↗
DOE OSTI2023

Modeling Short-Range Order in Disordered Rocksalt Cathodes by Pair Distribution Function Analysis

Pair distribution function (PDF) analysis is a powerful technique for the characterization of short-range order (SRO) in disordered materials. Accurate interpretation of experimental PDF data is critically reliant on the development of structural models that can account for local variations in site occupancies and bond lengths. To this end, we outline an approach to model SRO using first-principles calculations based on the cluster-expansion formalism. These methods are validated on neutron scattering data from two disordered rocksalt oxyfluorides, Li 1.3 Mn 0.4 Ti 0.3 O 1.7 F 0.3 and Li 1.3 Mn 0.4 Nb 0.2 Ti 0.1 O 1.7 F 0.3 . For each composition, we demonstrate that an average structure without any SRO fails to reproduce several key features in the experimental PDF. To pinpoint the origin of the suspected SRO in these materials, configurational and displacive effects were separately investigated using two disparate models. Special quasi-random structures were relaxed using density functional theory to account for local changes in bond lengths while maintaining a near-random ionic configuration. This leads to slightly improved accuracy but still misrepresents asymmetry in the first few peaks of the PDF. Monte Carlo simulations were performed to model configurational SRO on a fixed lattice, which by itself is shown to have a minimal influence on the PDF. Instead, we find that it is the bond length relaxations within environments created by SRO which controls the details of the PDF, thereby highlighting the subtle but important coupling between configurational and displacive SRO in disordered materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening ∗

Abstract Interface structures in complex oxides remain an active area of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D STEM datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO 3 and SrTiO 3 , using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We test the DCNN on simulated data and show that it is possible (with >95% accuracy) to identify a physically rough from a chemically diffuse interface and create a DCNN regression model to predict step positions. We quantify the applicability of the model to different thicknesses and the transferability of the approach. The method shown here is general and can be applied for any inverse imaging problem where forward models are present.

42 ENGINEERING↗
DOE OSTI2013

ASSESSING MATERIAL DEGRADATION EFFECTS ON LONG TERM STORAGE STRUCTURAL ANALYSES

PNNL staff are currently working to conduct analyses, integrate experimental data, and develop the technical basis for extended long term storage and subsequent transportation of used nuclear fuel. Analyses will serve to augment the experimental work by addressing degradation phenomena not readily obtainable through experimentation, such as low temperature creep over very long periods of time. Analyses can also address issues such as concrete degradation that may not require a comprehensive R&D program to resolve, as well as evaluation of engineering design solutions that can be implemented to mitigate the amount and extent of experimental data needed. Most importantly, analyses are necessary to determine how much degradation any structure, system, or component can experience and still maintain its ability to meet its safety functions. This paper focuses on various structural modeling issues that are affected by material degradation in extended long term storage scenarios. Typical storage cask and transportation package designs have a safety basis that includes a number of structural static or dynamic finite element analysis (FEA) models. Some of the features of these standard models need to be reconsidered when the fuel is in an extended long term storage state. In this paper, a number of standard safety basis FEA models are discussed and evaluated for their applicability to long term storage scenarios. A vertical handling drop analysis is used to illustrate a case where additional modeling is needed to define the loads and loading history needed to properly evaluate material degradation in long term storage scenarios.

Klymyshyn, Nicholas A.↗
DOE OSTI2024

Implementation of a realistic artificial data generator for crash data generation

In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. In conclusion, the crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type - possibly establishing a universal benchmarking system for alternative model frameworks in safety literature.

42 ENGINEERING↗
DOE OSTI2015

Synthesis, structural and electrical properties of a new cobalt arsenate NaCo{sub 2}As{sub 3}O{sub 10}

The title compound sodium dicobalt triarsenate NaCo{sub 2}As{sub 3}O{sub 10} has been synthesized by solid state reaction. Crystal structure and electrical properties were studied by X-ray diffraction and complex impedance spectroscopy, respectively. The obtained structural model is supported by charge-distribution (CHARDI) analysis and bond-valence-sum (BVS) validations tools. The structure (triclinic, P−1, a=6.800 (8) Å, b=7.816 (9) Å, c=8.718 (3) Å, α=108.03 (2)°, β=108.48 (3)°, γ=100.11 (2)°) can be described as a three-dimensional framework resulted from corner-connection between cobalt metallic chains running along [−110] and As{sub 3}O{sub 10} groups; the negative charge is balanced by Na{sup +} ions which house the two tunnels parallel to a and b axes. Ball milling was used as mechanical means to reduce the particles sizes of the synthesized powder. At the optimal sintering temperature of 650 °C, 85% of the relative density was obtained. The conductivity measurements show that NaCo{sub 2}As{sub 3}O{sub 10} is a cationic conductor with an activation energy of 0.48 eV and a conductivity of σ=1.2×10{sup −5} S cm{sup −1} at 310 °C. The BVS model is extended to simulate the ionic migration pathways of alkali cations in the anionic framework. - Graphical abstract: 1D pathways link Na atoms along the a-axis with bond valence mismatch |ΔV(Na)|=0.64 v.u. - Highlights: • A new single crystal NaCo{sub 2}As{sub 3}O{sub 10} was grown by solid state reaction and its structure determined by single-crystal X-ray diffraction. • The purity of the powder sample was verified by Rietveld refinement. • The CIS measurements were optimized and the obtained spectra were fitted by electrical equivalent circuits. • The conduction pathways for Na{sup +} cations are simulated by means of the bond valence sum model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Evidence for the Band-Edge Exciton of CuInS 2 Nanocrystals Enables Record Efficient Large-Area Luminescent Solar Concentrators

Ternary I-III-VI 2 nanocrystals (NCs), such as CuInS 2 , are receiving attention as heavy-metals-free materials for solar cells, luminescent solar concentrators (LSCs), LEDs, and bio-imaging. The origin of the optical properties of CuInS 2 NCs are however not fully understood. A recent theoretical model suggests that their characteristic Stokes-shifted and long-lived luminescence arises from the structure of the valence band (VB) and predicts distinctive optical behaviours in defect-free NCs: the quadratic dependence of the radiative decay rate and the Stokes shift on the NC radius. If confirmed, this would have crucial implications for LSCs as the solar spectral coverage ensured by low-bandgap NCs would be accompanied by increased re-absorption losses. Here, by studying stoichiometric CuInS 2 NCs, it is revealed for the first time the spectroscopic signatures predicted for the free band-edge exciton, thus supporting the VB-structure model. At very low temperatures, the NCs also show dark-state emission likely originating from enhanced electron-hole spin interaction. In this study, the impact of the observed optical behaviours on LSCs is evaluated by Monte Carlo ray-tracing simulations. Based on the emerging device design guidelines, optical-grade large-area (30×30 cm 2 ) LSCs with optical power efficiency (OPE) as high as 6.8% are fabricated, corresponding to the highest value reported to date for large-area devices.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Crystal structure of WA352 provides insight into cytoplasmic male sterility in rice

Highlights: • Identification of a C-terminal structural and functional core domain of WA352. • The crystal structure of C-terminus of WA352 was determined at 1.3 Å resolution. • Molecular mechanism whereby WA352 perturbs COX11 function to cause CMS is proposed. Plant cytoplasmic male sterility (CMS) is an important phenomenon and is widely utilized in hybrid crop breeding. The Wild Abortive CMS (CMS-WA), a well-known CMS type, has been successfully applied in the commercial production of hybrid rice seeds for more than 40 years. The CMS-WA causal gene WA352 encodes a novel transmenbrane protein and the interacts with the mitochondrial copper chaperone COX11, triggering reactive oxygen species production and resulting in male sterility in CMS-WA lines. However, the structure of WA352 is currently unknown, and the structural mechanism whereby WA352 perturbs COX11 function to cause CMS remains largely unknown. Here, we report the crystal structure of the C-terminal functional domain of WA352 at 1.3 Å resolution. This functional domain, consisting of five α helices, is spindle-shaped with a length of 42 Å, and a diameter of 28 Å. Notably, the absence of any structural similarity to a known protein structure suggests that the WA352 functional domain is a novel fold. In addition, surface conservation analysis and structural modeling of the WA352-COX11 complex revealed details about the WA352-COX11 interaction. Further structural analysis suggested that the WA352-COX11 interaction blocks the copper ion transportation activity of COX11, which is essential for the assembly of cytochrome c oxidase, resulting in male sterility in CMS-WA lines. Our study paves the way toward structural determination of the WA352-COX11 complex and provides new insight into the mechanism of plant CMS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Numerical and experimental investigation of the flame kernel growth in a methane/air mixture near the lean flammability limit

Lean combustion has the potential to improve the thermal efficiency of spark-ignition engines, but it faces the significant challenge of increased cycle-to-cycle variation due to low mixture reactivity and unstable flame dynamics. Computational fluid dynamics (CFD) employing predictive models can guide engine design and optimize operating strategies for lean combustion. However, ignition and combustion models have rarely been validated at fuel-lean conditions, and a fundamental understanding of the early flame kernel growth process is also lacking for a successful sub-model development. Here, the present study develops a numerical simulation framework used to investigate early flame kernel growth in methane/air mixtures. A nanosecond-pulsed discharge (NPD) approach is employed to effectively decouple the flame kernel growth from the electrical discharge due to their difference in timescales, and equivalence ratios near the experimentally measured lean flammability limit (LFL) are selected to focus on challenging mixture conditions. Three numerical investigations, such as the choice of turbulence modeling, grid size, and grid control strategies, are examined to match both LFL and flame kernel structure measured from experiments. It is demonstrated that a quasi-direct numerical simulation (QDNS) with a fixed grid embedding of 10 μm can predict the LFL as φ CFD =0.61 and match the displacement speed of the kernel’s boundary marked in schlieren images. To predict the LFL and flame kernel shape, a fine grid (Δ≤12.5 μm) is needed to capture the consumption of formaldehyde (CH 2 O) in kernel’s reaction branches attached to the anode, and adaptive mesh refinement is replaced with the fixed embedding due to loss of simulation accuracy. Also, it is found that a large-eddy simulation (LES) using the Dynamic Structure model is not suitable for the NPD-induced flame kernel simulation because artificial sub-grid turbulent kinetic energy induced by shock dynamics alters the flow velocity calculation, resulting in divergence of LES from QDNS. Lastly, the simulation well matches the experimental data for the flame kernel evolution in three mixture conditions (φ = 0.7, 0.61, 0.55), showing toroidal flame kernel expansion and flame kernel growth/extinction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Impact of Lateral Flow on Surface Water and Energy Budgets over the Southern Great Plains – A Modeling Study

As we see the horizontal grid spacing decrease, treatment of hydrologic processes in land surface models, such as the lateral flow of surface and subsurface flow, need to be explicitly represented. Unlike previous studies that mainly focused on the mountainous regions, in this study the offline WRF-Hydro model is employed to study the impact of lateral flow on soil moisture and energy fluxes over the relatively flat southern Great Plains (SGP). The vast amount of measurements over the SGP provide an unique opportunity to assess the model behavior. In addition, newly developed land surface properties and input forcing are ingested into the model, in an attempt to reduce uncertainties associated with the initial and boundary forcing and help to identify model deficiencies. Our results show that the more realistic inputs (parameters, soil types, forcing) lead to larger underestimation of latent heat flux and dry bias, indicating the existence of model structural uncertainty (embedded errors) in WRF-Hydro that need to be characterized to inform future model development efforts. Including lateral flow processes partly mitigates the model deficiencies in representing hydrologic processes and alleviates the dry bias. In particular, both surface and subsurface lateral flow increase soil moisture mainly over the lower elevations, except that subsurface flow also affects soil moisture over steeper terrains. Additional simulations are performed to assess the effect of routing resolution on model results. When LSM resolution is high, noticeable differences in soil moisture are produced between different routing resolutions especially over steep terrain. Whereas when LSM resolution is coarse, differences between routing resolutions become negligible, especially over flat terrain.

54 ENVIRONMENTAL SCIENCES↗