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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Exploiting SMILEs and the CMIP5 Archive to Understand Arctic Climate Change Seasonality and Uncertainty

Arctic Amplification (AA) exhibits a distinct seasonal dependence; it is weakest in boreal summer and strongest in winter. Here, we analyze simulations from single-model initial-condition large ensembles and Coupled Model Intercomparison Project Phase 5 to decipher the seasonal evolution of Arctic climate change. Models agree that the annual maximum AA shifts from autumn into winter over the 21st century, accompanied by similar shifts in sea-ice loss and surface turbulent heat fluxes, whereas the maximum precipitation shifts only into late autumn. However, the exact seasonal timing and magnitude of these shifts are highly uncertain. Decomposing the uncertainty into model structural differences, emission scenarios, and internal variability reveals that model differences dominate the total uncertainty, which also undergo autumn-to-winter shifts. We also find that the scenario uncertainty is unimportant for projections of AA. These results highlight that understanding model differences is critical to reducing uncertainty in projected Arctic climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Investigating the Global Biogeophysical Impact of Area and Mass Based Wood Harvest in a Vegetation Demography Model

Wood harvesting alters land surface properties and energy redistribution, but there is a lack of studies estimating these changes on a global scale. We coupled a vegetation demographic model, the Functionally Assembled Terrestrial Ecosystem Simulator, with the E3SM land model to perform offline model simulation to investigate the land biogeophysical responses, including canopy coverage, leaf area index, albedo, surface roughness length, and energy fluxes, to historical wood harvest on the global scale. In this study, we found 50% less harvested carbon (C) when choosing the area-based harvest rate as driving data that has not been spatially harmonized, compared to reharmonized mass-based harvesting. By considering the uncertainty from reconstruction of historical wood harvest time series and the choice of wood harvest approach in the model, continuous wood harvest (1850–2015) results in 5%–10% of canopy coverage loss, contributing 0.5%–1% increase of albedo over disturbed land, which is much stronger than a non-demographic land surface model. Changes in energy flux from the wood harvest are negligible (<1%), but the responses of land surface properties vary (up to 30%) due to differences in model structure between the single canopy, sun-shade leaf model and vegetation demographic model.

Shu, Shijie [Lawrence Berkeley National Laboratory↗
DOE OSTI2023

NMPFamsDB: a database of novel protein families from microbial metagenomes and metatranscriptomes

Abstract The Novel Metagenome Protein Families Database (NMPFamsDB) is a database of metagenome- and metatranscriptome-derived protein families, whose members have no hits to proteins of reference genomes or Pfam domains. Each protein family is accompanied by multiple sequence alignments, Hidden Markov Models, taxonomic information, ecosystem and geolocation metadata, sequence and structure predictions, as well as 3D structure models predicted with AlphaFold2. In its current version, NMPFamsDB hosts over 100 000 protein families, each with at least 100 members. The reported protein families significantly expand (more than double) the number of known protein sequence clusters from reference genomes and reveal new insights into their habitat distribution, origins, functions and taxonomy. We expect NMPFamsDB to be a valuable resource for microbial proteome-wide analyses and for further discovery and characterization of novel functions. NMPFamsDB is publicly available in http://www.nmpfamsdb.org/ or https://bib.fleming.gr/NMPFamsDB.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

Abstract. The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the main set of future climate projections, based on concentration-driven simulations, within the Coupled Model Intercomparison Project phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century (2081–2100) encompassing the Tier 1 experiments based on the Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by close to 1.5 ∘C) reached at the upper end of the 5 %–95 % envelope of the highest scenario (SSP5-8.5). This is due to both the wider range of radiative forcing that the new scenarios cover and the higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensemble spreads, according to a set of initial condition ensemble simulations available under SSP3-7.0. These experiments suggest a tendency for internal variability to decrease along the course of the century in this scenario, a result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades around mid-century, as represented in SSP5-3.4OS, does not affect the end outcome of temperature and precipitation changes by 2100, which return to the same levels as those reached by the gradually increasing SSP4-3.4 (not erasing the possibility, however, that other aspects of the system may not be as easily reversible). Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level might be biased by the inclusion of models that have shown faster warming in the historical period than the observed. Those estimates show all scenarios reaching 1.5 ∘C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering between 20 and 27 years from present. The warming level of 2 ∘C of warming is reached as early as 2039 by the ensemble mean under SSP5-8.5 but as late as the mid-2060s under SSP1-2.6. The highest warming level considered (5 ∘C) is reached by the ensemble mean only under SSP5-8.5 and not until the mid-2090s.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Structured Neural Network Modeling for Developing Digital Twins Models of Hydropower Generation Units

Dynamic modeling is a key part in the development of digital twin (DT) for dynamic systems. This is true for hydropower systems, where whole system modeling including penstock, turbine and generators, etc is important in realizing actuate modeling for the real systems. On the other hand, in response to the large variations of the power demand due to increased penetration of renewables such as wind and solar, hydropower systems are now required to operate in a large power generation range. This situation triggers the nonlinear characteristics of the generation unit with respect to its models. As such, it is imperative to use data driven modeling such as neural networks to learn the nonlinear dynamics of the hydropower generation unit. To achieve this objective, this study constructs a modeling and learning algorithm integrated with multiple structured neural network models for the modeling of turbine shaft speed, penstock pressure, and generator power output based on the generator power control setpoint, field current, and field voltage. In addition, the study uses the hydropower data from Tacoma Public Utilities to train and validate the proposed neural network algorithm. The results have shown that this structured neural network modeling approach can learn the system dynamics effectively by using the real-time data collected from the hydropower system with the desired modeling results.

Wang, Hong↗
DOE OSTI2023

Convoluted filtering for process cycle modeling

Principles of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process-structure–property-performance relationships hidden in materials data. The process-structure modeling can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process-structure relations. In this work, a novel convoluted model-filtering technique was exploited to build and successfully train the Convoluted Filter (CoFi) artifacts for Fe-based alloy heat treatment cycles. The artifacts were pre-trained to filter out deep models that change the surrogate microstructure state after the heat treatment at ambient conditions. Direct representation of the thermal cycle features within knowledge Graph facilitated development of meaningful data models for microstructure evolution, which reduce overfitting to limited datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2024

NCAP: Noncanonical Amino Acid Parameterization Software for CHARMM Potentials

Noncanonical Amino Acids (NCAAs) provide numerous avenues for introduction of novel functionality to peptides and proteins. NCAAs can be incorporated through solid phase synthesis or genetic code expansion in conjugation with heterologous expression of the encoded protein modification. Due to the difficulty of synthesis, wide chemical space and lack of empirically resolved structures modeling the effects of NCAA mutation is critical for rational protein design. To evaluate the structural and functional perturbations NCAAs introduce we utilize molecular potentials that describe the forces in protein structure. Most potentials such as CHARMM are designed to model canonical residues but can be parameterized in include novel NCAAs. Here, in this work, we introduce NCAP a software package to generate CHARMM compatible parameters from quantum chemical calculation. Unlike currently available tools NCAP is designed to recognize NCAA structure and automatically bridge the gap between DFT calculations and potential parameters. For our software we discuss workflow, validation against canonical parameter sets and comparison to published NCAA-protein structures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The exo-β- N -acetylmuramidase NamZ from Bacillus subtilis is the founding member of a family of exo-lytic peptidoglycan hexosaminidases

Endo-β-N-acetylmuramidases, commonly known as lysozymes, are well-characterized antimicrobial enzymes that catalyze an endo-lytic cleavage of peptidoglycan; i.e., they hydrolyze the β-1,4-glycosidic bonds connecting N-acetylmuramic acid (MurNAc) and N-acetylglucosamine (GlcNAc). In contrast, little is known about exo-β-N-acetylmuramidases, which catalyze an exo-lytic cleavage of β-1,4-MurNAc entities from the non-reducing ends of peptidoglycan chains. Such an enzyme was identified earlier in the bacterium Bacillus subtilis, but the corresponding gene has remained unknown so far. We now report that ybbC of B. subtilis, renamed namZ, encodes the reported exo-β-N-acetylmuramidase. A ΔnamZ mutant accumulated specific cell wall fragments and showed growth defects under starvation conditions, indicating a role of NamZ in cell wall turnover and recycling. Recombinant NamZ protein specifically hydrolyzed the artificial substrate para-nitrophenyl β-MurNAc and the peptidoglycan-derived disaccharide MurNAc-β-1,4-GlcNAc. Together with the exo-β-N-acetylglucosaminidase NagZ and the exo-muramoyl-L-alanine amidase AmiE, NamZ degraded intact peptidoglycan by sequential hydrolysis from the non-reducing ends. A structure model of NamZ, built on the basis of two crystal structures of putative orthologs from Bacteroides fragilis, revealed a two-domain structure including a Rossmann-fold-like domain that constitutes a unique glycosidase fold. Thus, NamZ, a member of the DUF1343 protein family of unknown function, is now classified as the founding member of a new family of glycosidases (CAZy GH171). NamZ-like peptidoglycan hexosaminidases are mainly present in the phylum Bacteroidetes and less frequently found in individual genomes within Firmicutes (Bacilli, Clostridia), Actinobacteria, and γ-proteobacteria.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Expression of divergent methyl/alkyl coenzyme M reductases from uncultured archaea

Abstract Methanogens and anaerobic methane-oxidizing archaea (ANME) are important players in the global carbon cycle. Methyl-coenzyme M reductase (MCR) is a key enzyme in methane metabolism, catalyzing the last step in methanogenesis and the first step in anaerobic methane oxidation. Divergent mcr and mcr -like genes have recently been identified in uncultured archaeal lineages. However, the assembly and biochemistry of MCRs from uncultured archaea remain largely unknown. Here we present an approach to study MCRs from uncultured archaea by heterologous expression in a methanogen, Methanococcus maripaludis . Promoter, operon structure, and temperature were important determinants for MCR production. Both recombinant methanococcal and ANME-2 MCR assembled with the host MCR forming hybrid complexes, whereas tested ANME-1 MCR and ethyl-coenzyme M reductase only formed homogenous complexes. Together with structural modeling, this suggests that ANME-2 and methanogen MCRs are structurally similar and their reaction directions are likely regulated by thermodynamics rather than intrinsic structural differences.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Structural origin of recovered ferroelectricity in BaTi O 3 nanoparticles

Nanoscale BaTiO 3 particles (≈10 nm) prepared by ball-milling a mixture of oleic acid and heptane have been reported to have an electric polarization several times larger than that for bulk BaTiO 3 . In this work, detailed local, intermediate, and long-range structural studies are combined with spectroscopic measurements to develop a model structure of these materials. The X-ray spectroscopic measurements reveal large Ti off-centering as the key factor producing the large spontaneous polarization in the nanoparticles. Temperature-dependent lattice parameter changes reveal the sharpening of the structural phase transitions in these BaTiO 3 nanoparticles compared to the pure nanoparticle systems. In conclusion, sharp crystalline-type peaks in the barium oleate Raman spectra suggest that this component in the composite core-shell matrix, a product of mechanochemical synthesis, stabilizes an enhanced polar structural phase of the BaTiO 3 core nanoparticles.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Accurate prediction of protein structures and interactions using a three-track neural network

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo–electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. Here, we make the method available to the scientific community to speed biological research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Accelerating uncertainty quantification in incremental dynamic analysis using dimension reduction-based surrogate modeling

We propose a surrogate modeling framework based on dimension reduction to facilitate the quantification of seismic risk of structural systems in performance-based earthquake engineering. The framework adopts incremental dynamic analysis (IDA) for addressing hazard variability, and promotes significant computational efficiency improvement for propagating epistemic uncertainties associated with the structural models. It utilizes both linear and nonlinear dimension reduction approaches, equipped with inverse mappings, to learn a functional between the input parameter space (e.g., the epistemic uncertainties of the structure) to the high-dimensional output space created through the IDA implementation across different ground motions and seismic intensity levels. Polynomial chaos expansion is adopted as the surrogate model to learn this functional in the reduced space. A nine-story steel moment-resisting frame with uncertain structural properties is used as a testbed. Furthermore, we select the seismic fragility curves as a measure of the structure’s seismic performance, since it provides an estimate of the probability of entering specified damage states for given levels of ground shaking.

42 ENGINEERING↗
DOE OSTI2021

Low global warming potential (GWP) refrigerant supermarket refrigeration system modeling and its application

As an environmentally friendly low global warming potential (GWP) refrigerant, Carbon dioxide (CO 2 ) has continuously gained popularity and research attention as alternative refrigerant for supermarket refrigeration system. In this paper, to fulfill the increasing need of accurate Low-GWP supermarket refrigeration models for development of supervisory level control and optimization strategies, a high fidelity model is developed for CO 2 transcritical supermarket refrigeration system which includes compressor rack of low temperature (LT) compressors and medium temperature (MT) compressors, air-cooled gas cooler, evaporator, expansion valves and other auxiliary equipment. A resistance-capacity model structure is proposed to simulate the display cases. Semi-thermodynamic models are proposed to estimate reciprocating compressors volumetric efficiency and power consumption. Furthermore the zone modeling approach is used for evaporator simulation, and air-cooled gas cooler is modeled with distributed modeling method. The expansion valve simulation is based on orifice flow model. To calibrate these models, both manufacture performance data and experimental data are used. The experiments are conducted with a full instrumental CO 2 supermarket refrigeration system installed in Oak Ridge National Lab Flexible Research Platform (FRP). The simulation model can predict the system performance, including power consumption, cooling capacity, mass flow rate, temperature, and pressure, with high accuracy (within ±4%) compared to experimental data. In addition, this developed model has been used to create the system optimum high side pressure for high side expansion valve control, and to generate wide operating range simulation data for developing the batter-equivalent commercial refrigeration system model which can be used in grid interactive control development.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Atomic-Level Structure of Mesoporous Hexagonal Boron Nitride Determined by High-Resolution Solid-State Multinuclear Magnetic Resonance Spectroscopy and Density Functional Theory Calculations

Mesoporous hexagonal boron nitride (p-BN) has received significant attention over the last decade as a promising candidate for water cleaning/pollutant removal and hydrogen storage applications. In this work, high-resolution solid-state NMR spectroscopy and plane-wave density-functional theory (DFT) calculations are used to obtain an atomic-level description of p-BN. 1 H– 15 N or 1 H– 14 N heteronuclear (HETCOR) correlation experiments recorded with either conventional NMR at room temperature or dynamic nuclear polarization surface-enhanced spectroscopy (DNP-SENS) at ca. 100 K reveal NB 2 H, NBH 2 , NBH 3 + species residing on the edges of BN sheets. Ultra-high field 35.2 T 11 B NMR spectroscopy was used to resolve 11 B NMR signals from BN 3 , BN 2 O x (OH) 1–x (x = 0–1), BNO x (OH) 2–x (x = 0–2), BO x (OH) 3–x (x = 0–3), and BO x (OH) 4–x – (x = 0–4). Importantly, 2D 11 B dipolar double-quantum–single-quantum homonuclear correlation spectra reveal that many pore/defect sites are composed of boron oxide/hydroxide clusters connected to the BN framework through BN 2 O units. 1D and 2D 11 B{ 15 N} HETCOR NMR experiments, in addition to plane-wave DFT calculations of nine different structural models, further confirm the assignment of all NMR signals. The detailed structure determination of the pore and edge/defect sites within p-BN should further enable the rational design and development of next-generation p-BN-based materials. In addition, the techniques outlined here should be applicable to determine structure within other porous and/or boron-based materials.

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

BaCu 4/3 Si 2/3 P 2 and BaCu 2–( x + y ) Zn x Si y P 2 : Expanding the Semiconducting Landscape in the ThCr 2 Si 2 -Type Family

ThCr 2 Si 2 -type layered materials are a large family of compounds with applications ranging from thermoelectricity to magnetism, with the vast majority of the members exhibiting metallic behavior. Here, in this study, we synthesized a new group of materials with Cu-Si and Cu-Zn-Si square nets with the general formula BaCu 1.33 Si 0.67 P 2 and BaCu 2–(x+y) Zn x Si y P 2 (0 ≤ x ≤ 0.9; 0.3 ≤ y ≤ 0.7). Several synthesized compounds are charge-balanced semiconductors, which are rare in the ThCr 2 Si 2 family. All the reported compounds crystallize in the ThCr 2 Si 2 -type tetragonal I4/mmm space group, with Cu/Zn/Si jointly occupying the same 4d crystallographic site. In the Zn-free composition, BaCu 1.33 Si 0.67 P 2 , Ba, and P each occupy a single crystallographic site. The introduction of Zn results in the expansion of the unit cell and splitting the Ba atomic sites along the [001] direction. Such structural displacement of the Ba atoms was confirmed by the heat capacity measurements. Band structure and density-of-states calculations on ordered hypothetical structural models reveal either a small bandgap (∼0.2 eV) or semimetallic band structures. The compounds reported here exhibit high Seebeck coefficients and ultralow thermal conductivity, making them promising candidates for the development of thermoelectric materials.

crystal structure↗
DOE OSTI2022

A Process-based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Culex pipiens/restuans Mosquito Density

While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration, and validation is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a discrete, semi-stochastic, mechanistic process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens (Diptera, Culicidae) and Culex restuans (Diptera, Culicidae) mosquito populations. This model combines known models for development and survival into a fully connected age-structured model that can reproduce mosquito population dynamics. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. This approach accounts for the nonlinear interaction of temperature and aquatic habitat variability on the mosquito life stages. We demonstrate that the full model predicts the yearly variations in mosquito populations better than a statistical model using the same data sources. This improvement in modeling mosquito abundance can help guide interventions for reducing mosquito abundance in mitigating mosquito-borne diseases like West Nile virus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Massively Parallel Capability in Sierra/SD for Simulation Vibration with Piezoelectrics

Sierra/SD is an engineering structural dynamics code that provides Sandia and other customers a tool to model structural and acoustic physics on large complex physical systems using massively parallel processing. This report provides a detailed overview on Sierra/SD’s most recent physics package: coupled electro-mechanical physics. This capability uses the finite element method to model coupled electro-mechanical physics exhibited by piezoelectric materials. This report provides an applications overview, theory overview, and verification examples demonstrating the electro-mechanical physics modeling capabilities of Sierra/SD.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Fundamental problems in astroparticle physics and cosmology (Final Technical Report)

In the standard cosmological model, structure forms when gas cools and condenses to the center of dark matter halos. The onset of structure is governed by rapid star formation and subsequent evolution of the first massive stars. One of the endpoints of stellar evolution is the formation of a black hole. We showed that all black hole merger events must occur after the universe is about 85 million years old. This prediction is important because it means that if a future experiment makes a detection of a black hole merger event at an earlier time then our understanding of cosmology and the onset of structure formation is not fully developed. In addition, we explored dark matter models that have either self-interactions or experience decays. In the first case, self-interactions may lead to the formation of a thick disk that can be co-planar to the Galactic disc due to correlations in angular momentum between dark matter and baryons that are predicted by tidal torque theory. We showed that for heavy dark matter particles, either the self interaction may not be strong enough to solve the small scale structure motivation or a dark disc cannot be present in the Milky Way. In the second-case, we showed that if dark matter experiences decays into two particles, one of which behaves like radiation, then it is possible to relieve the tension that exists between measurements of the local expansion rate. Finally, we developed methods on how to distinguish gamma-ray excesses from promising dark matter targets as compared to conventional astrophysical sources. This is an important toolset that is applicable to any suspected dark matter source.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗