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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Convective-Permitting Hindcast Simulations during the North American Monsoon GPS Transect Experiment 2013: Establishing Baseline Model Performance without Data Assimilation

During the North American monsoon global positioning system (GPS) Transect Experiment 2013, daily convective-permitting WRF simulations are performed in northwestern Mexico and the southern Arizona border region using the operational Global Forecast System (GFS) and North American Mesoscale Forecast System (NAM) models as lateral boundary forcing and initial conditions. Compared to GPS precipitable water vapor (PWV), the WRF simulations display a consistent moist bias in the initial specification of PWV leading to convection beginning 3–6 h early. Given appreciable observed rainfall, days are classified as strongly and weakly forced based only on the presence of an inverted trough (IV); gulf surges did not noticeably impact the development of mesoscale convective systems (MCSs) and related convection in northwestern Mexico. Strongly forced days display higher modeled precipitation forecast skill than weakly forced days in the slopes of the northern Sierra Madre Occidental (SMO) away from the crest, especially toward the west where MCSs account for the greatest proportion of all monsoon-related precipitation. A case study spanning 8–10 July 2013 illustrates two consecutive days when nearly identical MCSs evolved over northern Sonora. Although a salient MCS is simulated on the strongly forced day (9–10 July 2013) when an IV is approaching the core monsoon region, a simulated MCS is basically nonexistent on the weakly forced day (8–9 July 2013) when the IV is farther away. Finally, the greater sensitivity to the initial specification of PWV in the weakly forced day suggests that assimilation of GPS-derived PWV for these types of days may be of greatest value in improving model precipitation forecasts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

SUMO Maximum Assimilation

The Los Alamos Survival–Mortality experiment (SUMO) is located on Frijoles Mesa near Los Alamos, New Mexico, USA, at an elevation of 2150 m. This was a tree manipulation study that investigated the relative impacts of drought and warming on plant function and reveals how trees adapt to drought and heat in semi-arid regions. The study factored the role of tree hydraulic acclimation to both precipitation and temperature and separated their effects.The experiment is located in a pinon-juniper woodland near the ponderosa pine (Pinus ponderosa) forest ecotone. Maximum assimilation rate measured monthly for each target tree. See SUMO Target Tree Information data package (doi:10.15485/1440544) for additional information. Data released by Los Alamos National Lab for public use under LA-UR-18-23656.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

"Sintering" Models and In-Situ Experiments: Data Assimilation for Microstructure Prediction in SLS Additive Manufacturing of Nylon Components

Selective laser sintering methods are workhorses for additively manufacturing polymer-based components. The ease of rapid prototyping also means it is easy to produce illicit components. It is necessary to have a data-calibrated in-situ physical model of the build process in order to predict expected and defective microstructure characteristics that inform component provenance. Toward this end, sintering models are calibrated and characteristics such as component defects are explored. This is accomplished by assimilating multiple data streams, imaging analysis, and computational model predictions in an adaptive Bayesian parameter estimation algorithm. From these data sources, along with a phase-field model, bulk porosity distributions are inferred. Model parameters are constrained to physically-relevant search directions by sensitivity analysis, and then matched to predictions using adaptive sampling. Using this feedback loop, data-constrained estimates of sintering model parameters along with uncertainty bounds are obtained.

3D printing, Additive Manufacturing, polymer, sint↗
DOE OSTI2021

Oxygen isotope ratios in zircon and garnet: A record of assimilation and fractional crystallization in the Dinkey Dome peraluminous granite, Sierra Nevada, California

Abstract The 119 Ma Dinkey Dome pluton in the central Sierra Nevada Batholith is a peraluminous granite and contains magmatic garnet and zircon that are complexly zoned with respect to oxygen isotope ratios. Intracrystalline SIMS analysis tests the relative importance of magmatic differentiation processes vs. partial melting of metasedimentary rocks. Whereas δ18O values of bulk zircon concentrates are uniform across the entire pluton (7.7‰ VSMOW), zircon crystals are zoned in δ18O by up to 1.8‰, and when compared to late garnet, show evidence of changing magma chemistry during multiple interactions of the magma with wall rock during crustal transit. The evolution from an early high-δ18O magma [δ18O(WR) = 9.8‰] toward lower values is shown by high-δ18O zircon cores (7.8‰) and lower δ 18O rims (6.8‰). Garnets from the northwest side of the pluton show a final increase in δ18O with rims reaching 8.1‰. In situ REE measurements show zircon is magmatic and grew before garnets. Additionally, δ18O in garnets from the western side of the pluton are consistently higher (avg = 7.3‰) relative to the west (avg = 5.9‰). These δ18O variations in zircon and garnet record different stages of assimilation and fractional crystallization whereby an initially high-δ18O magma partially melted low-δ18O wallrock and was subsequently contaminated near the current level of emplacement by higher δ18O melts. Collectively, the comparison of δ18O zoning in garnet and zircon shows how a peraluminous pluton can be constructed from multiple batches of variably contaminated melts, especially in early stages of arc magmatism where magmas encounter significant heterogeneity of wall-rock assemblages. Collectively, peraluminous magmas in the Sierran arc are limited to small <100 km2 plutons that are intimately associated with metasedimentary wall rocks and often surrounded by later and larger metaluminous tonalite and granodiorite plutons. The general associations suggest that early-stage arc magmas sample crustal heterogeneities in small melt batches, but that with progressive invigoration of the arc, such compositions are more effectively blended with mantle melts in source regions. Thus, peraluminous magmas provide important details of the nascent Sierran arc and pre-batholithic crustal structure.

Geochemistry & Geophysics↗
DOE OSTI2018

Pressure-Based Inversion and Data Assimilation System (PIDAS) for CO 2 Leakage Detection (Final Report)

This project aims to develop a Pressure-based Inversion and Data Assimilation System (PIDAS) for detecting CO 2 leakage from storage formations. Carbon capture, utilization, and sequestration (CCUS) has the potential to enable deep reductions in global carbon emissions if high storage efficiency can be achieved. A major hurdle to industrial-scale implementation of geological carbon sequestration (GCS) projects is the potential migration of fluids (either brine or injected CO 2 ) from the storage formations and the resulting legal and financial liabilities. The capability to accurately identify leakage pathways by which stored CO 2 could leak, has leaked, or is leaking from the targeted storage zone is thus of paramount importance to site licensees and regulators. Although many MVA techniques have been devised, pressure-based monitoring technology remains the most sensitive and reliable technique for early detection. Pressure-based monitoring has consistently received the highest score in terms of benefit/cost ratio and it provides the greatest potential for leakage detection with broad areal coverage. Under this project we have (a) demonstrated the utility of the proposed well testing technique for leakage detection through integrated theoretical and numerical analysis, laboratory experiments, and field tests; (b) developed effective data analysis and inversion algorithms for identifying leakage pathways by fusing data generated during well testing; (c) provided a designing tool for maximizing the utility of the developed PIDAS tool for early leakage detection.

02 PETROLEUM↗
DOE OSTI2021

Probabilistic Machine Learning and Data Assimilation

This white paper responds to Focal Area 1. The associated portfolio of research activities is well-suited to DOE’s asset mix of HPC platforms, climate expertise, climate simulation codes, and AI expertise, which creates an opportunity to use manifold-finding probabilistic AI methods to create more powerful data assimilation techniques that increase the fidelity and forecasting skill of Earth System Prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Process Discovery through Assimilation of Complex Biogeochemical Datasets

This white paper addresses two of three focal areas identified in the white paper call: 1) Biogeochemical data acquisition and assimilation enabled by machine learning and 3) Insight gleaned from complex data using AI. We focus on AI application to complex biogeochemistry (BGC) data (e.g. laboratory experimental data, field manipulation data, literature data), which is an untapped source of information for improving Earth System Predictability (ESP).

58 GEOSCIENCES↗
DOE OSTI2021

AI4ESP: Process Discovery through Assimilation of Complex Geochemical Datasets

This white paper addresses two of three focal areas identified in the white paper call: 1) Biogeochemical data acquisition and assimilation enabled by machine learning and 3) Insight gleaned from complex data using AI. We focus on AI application to complex biogeochemistry (BGC) data (e.g. laboratory experimental data, field manipulation data, literature data), which is an untapped source of information for improving Earth System Predictability (ESP).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Data Assessment and Assimilation for Atmospheric Radiation Measurement Data Using Dynamic Bayesian Networks

A data-driven machine learning approach to scientific sensor data assessment may automatically learn directly from the structural and temporal patterns in abundant observations. The project has developed data assessment and assimilation models to screen datasets using dynamic Bayesian network (DBN) and deep learning (DL) methods, which rely on correlations between variables, across time, and between spatial locations, to detect poor or invalid data with a high degree of confidence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

CO2 Plume Imaging with Accelerated Deep Learning-based Data Assimilation Considering Multiple Realizations: Application to the Illinois Basin-Decatur Carbon Sequestration Project

We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site. Unlike the previous work, this study incorporates the impact of spatial heterogeneity using multiple realizations. In the proposed workflow, a neural network model utilizes available monitoring data such as downhole pressure measurements as input and predicts the propagating pressure ‘front’ using the diffusive time of flight (DTOF) map which is considered as representative reservoir image of the flow field. The DTOF is the arrival time of pressure front propagation, which can be computed by the Fast Marching Method rapidly without flow simulations. Reservoir model calibration can be implemented by selecting the training data samples that describe the predicted DTOF map based on observed data. The power and efficacy of our workflow is demonstrated by application to the Illinois Basin-Decatur Project.

CO2 plume imaging↗
DOE OSTI2022

Post-flowering Soil Waterlogging Curtails Grain Yield Formation by Restricting Assimilates Supplies to Developing Grains

Soil waterlogging is among the major factors limiting the grain yield of winter wheat crops in many parts of the world, including the middle and lower reaches of the Yangtze River China. In a field study, we investigated the relationship between leaf physiology and grain development under a varying duration of post-flowering waterlogging. A winter wheat cultivar Ningmai 13 was exposed to soil waterlogging for 0 (W0), 3 (W3), 6 (W6), and 9 d (W9) at anthesis. Increasing waterlogging duration significantly reduced flag leaf SPAD (soil plant analysis development) values and net photosynthetic rate (Pn). There was a linear reduction in flag leaf Pn and SPAD as plant growth progressed under all treatments; however, the speed of damage was greater in the waterlogged leaves. For example, compared with their respective control (W0), flag leaves of W9 treatment have experienced 46% more reduction in Pn at 21 d after anthesis (DAA) than at 7 DAA. Increasing waterlogging duration also induced oxidative damage in flag leaves, measured as malondialdehyde (MDA) contents. The capacity to overcome this oxidative damage was limited by the poor performance of antioxidant enzymes in wheat leaves. Inhibited leaf Pn and capacity to sustain assimilate synthesis under waterlogged environments reduced grain development. Compared with W0, W6 and W9 plants experienced a 20 and 22% reduction in thousand grain weight (TGW) in response to W6 and W9, respectively at 7 DAA and 11 and 19%, respectively at 28 DAA. Sustained waterlogging also significantly reduced grain number per spike and final grain yield. Averaged across two years of study, W9 plants produced 28% lesser final grain yield than W0 plants. Our study suggested that wheat crops are highly sensitive to soil waterlogging during reproductive and grain filling phases due to their poor capacity to recover from oxidative injury to photosynthesis. Management strategies such as planting time, fertilization and genotype selection should be considered for the areas experiencing frequent waterlogging problems.

Ma, Shangyu↗
DOE OSTI2019

Application and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in Poorly Gauged Regions in Western China

The temporal and spatial differentiation of the underlying surface in East Asia is complex. Due to a lack of meteorological observation data, human cognition and understanding of the surface processes (runoff, snowmelt, soil moisture, water production, etc.) in the area have been greatly limited. With the Heihe River Basin, a poorly gauged region in the cold region of Western China, selected as the study area, three meteorological datasets are evaluated for their suitability to drive the Soil and Water Assessment Tool (SWAT): China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), Climate Forecast System Reanalysis (CFSR), and Traditional Weather Station (TWS). Resultingly, (1) the runoff output of CMADS + SWAT mode is generally better than that of the other two modes (CFSR + SWAT and TWS + SWAT) and the monthly and daily Nash–Sutcliffe efficiency ranges of the CMADS + SWAT mode are 0.75–0.95 and 0.58–0.77, respectively; (2) the CMADS + SWAT and TWS + SWAT results were fairly similar to the actual data (especially for precipitation and evaporation), with the results produced by CMADS + SWAT lower than those produced by TWS + SWAT; (3) the CMADS + SWAT mode has a greater ability to reproduce water balance than the other two modes. Overestimation of CFSR precipitation results in greater error impact on the uncertainty output of the model, whereas the performances of CMADS and TWS are more similar. This study addresses the gap in the study of surface processes by CMADS users in Western China and provides an important scientific basis for analyzing poorly gauged regions in East Asia.

CMADS, SWAT, poorly gauged regions↗
DOE OSTI2016

INFERRING THE MAGNETOHYDRODYNAMIC STRUCTURE OF SOLAR FLARE SUPRA-ARCADE PLASMAS FROM A DATA-ASSIMILATED FIELD TRANSPORT MODEL

Supra-arcade fans are highly dynamic structures that form in the region above post-reconnection flare arcades. In these features the plasma density and temperature evolve on the scale of a few seconds, despite the much slower dynamics of the underlying arcade. Further, the motion of supra-arcade plasma plumes appears to be inconsistent with the low-beta conditions that are often assumed to exist in the solar corona. In order to understand the nature of these highly debated structures, it is, therefore, important to investigate the interplay of the magnetic field with the plasma. Here we present a technique for inferring the underlying magnetohydrodynamic processes that might lead to the types of motions seen in supra-arcade structures. Taking as a case study the 2011 October 22 event, we begin with extreme-ultraviolet observations and develop a time-dependent velocity field that is consistent with both continuity and local correlation tracking. We then assimilate this velocity field into a simplified magnetohydrodynamic simulation, which deals simultaneously with regions of high and low signal-to-noise ratio, thereby allowing the magnetic field to evolve self-consistently with the fluid. Ultimately, we extract the missing contributions from the momentum equation in order to estimate the relative strength of the various forcing terms. In this way we are able to make estimates of the plasma beta, as well as predict the spectral character and total power of Alfvén waves radiated from the supra-arcade region.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0

Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate–carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. This paper describes adJULES in a data assimilation framework and demonstrates its ability to improve the model–data fit using eddy-covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85 % of the sites used in the study, at both the calibration and evaluation stages. Furthermore, the new improved parameters for JULES are presented along with the associated uncertainties for each parameter.

58 GEOSCIENCES↗
DOE OSTI2019

Leaf Photosynthetic Parameters: Quantum Yield, Convexity, Respiration, Gross CO2 Assimilation Rate and Raw Gas Exchange Data, Utqiagvik (Barrow), Alaska, 2016

Leaf photosynthetic parameters including quantum yield, convexity, respiration and gross CO2 assimilation rate determined from the response of photosynthesis to irradiance and supporting raw gas exchange data. Measured in Barrow, Alaska in 2016 on six plant species: Arctophila fulva, Arctagrostis latifolia, Carex aquatilis, Eriophorum angustifolium, Petasites frigidus, and Salix pulchra. Version 2 (V2) note (01 Feb 2021): An earlier version of this data contained an incorrectly labeled data column in NGA175_AQfittedParams_Barrow_2016.csv. Mean_Ci was incorrectly labeled as Mean_Ci_LowLight. "_v2" contains the correct data for both of these variables. In December 2020, these data were updated to comply with the leaf-level gas exchange data and metadata reporting format (Ely et al 2020, doi:10.15485/1659484). These updates involved editing of some variable names, change of file formats to csv, addition of AQ curves data (subset of complete instrument output included in the original data package) and inclusion of additional methods and instrument details metadata files. No changes were made to the data. The package includes six *.csv files and one *.pdf. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗