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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

The fast and the furious: Ultrafast hot electrons in plasmonic metastructures. Size and structure matter

This review focuses on the generation of energetic (hot) electrons in plasmonic metastructures and nanomaterials, and their characterization through time-resolved spectroscopy. Excitation of hot electrons under illumination occurs in any metal or conductor, but their number will vary for each type of nanostructure. While plasmonic resonances are well described classically, the excitation of hot electrons (HEs) is a quantum process and its description requires further elaboration. Some potential applications for HEs lie in the fields of photo-catalysis and optoelectronics, and their study constitutes a very active interdisciplinary research direction that involves chemistry, physics and device engineering. Here we focus on some particular developments enabling the use of hot carriers for these applications. Particularly, we discuss the approaches and structures required to create hot carriers, the temporal dynamics of hot carrier formation and relaxation, and relevant theoretical methods used to compute the HE dynamics. The observations presented here support the conclusion that the shape of the nanostructure matters. Although metastructures with infrared gap plasmons can exhibit spatially extended hot spots and anomalously large numbers of non-thermalized HEs, most excited carriers in a plasmonic nanostructure typically have small excitation energies. Here in this paper, we discuss ways to strongly increase the number of high-energy electrons, highlighting the role of hot spots, system size, geometry and resonant frequencies. To generate HEs efficiently, we can take advantage of special geometries with hot spots, such as metamaterial absorbers with ultra-narrow gaps or nanostars. Furthermore, we discuss the applications in ultrafast electronics based on plasmon-enhanced photoemission and tunneling in the nonlinear regime. Considering the longer timescale phenomena, we also present studies on the coherent dynamics in a nanostructure after electron thermalization, showing acoustic breathing modes. In this paper, we review some key developments in the field of ultra-fast plasmonic dynamics and provide a perspective for its possible next steps.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Pressure Drop Correlation Improvement for the Near-Wall Region of Pebble-Bed Reactors

Packed beds play an important role in several engineering fields, with their applications in nuclear energy being driven by the development of next-generation reactors utilizing pebble fuel. The random nature of a packed pebble bed creates a flow field that is complex and difficult to predict. Porous media models are an attractive option for modeling pebble-bed reactors (PBRs), as they provide intermediate fidelity results and are computationally efficient. Porous media models, however, rely on the use of correlations to estimate the effect of complicated flow features on the pressure drop and heat transfer in the system. Existing correlations were developed to predict the average behavior of the bed, but they are inaccurate in the near-wall region where the presence of the wall affects the pebble packing. This work aims to investigate the accuracy of a porous media model using the Kerntechnischer Ausschuss (KTA) correlation, the most common pressure drop correlation for PBRs compared to the high-fidelity large eddy simulation (LES). A bed of 1568 pebbles is investigated at Reynolds numbers from 625 to 10 000. The bed is divided into five concentric subdomains to compare the average velocity, friction losses, and form losses between the porous media and LES codes. The comparison between the LES simulation and the KTA correlation revealed that the KTA correlation largely underpredicts the form losses in the near-wall region, leading to an overprediction of the velocity near the wall by nearly 30%. An investigation of the form losses across the range of Reynolds numbers in the LES results provided additional insight into how the KTA correlation may be improved to better predict these spatial effects in a pebble bed. These data suggest that the form coefficient near the wall must be increased by 48% while decreasing the form coefficient of the inner bulk region of the bed by 15%. The implementation of these improvements to the KTA correlation in a porous media model produced a radial velocity profile that saw significantly improved agreement with the LES results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Meteoric 10Be Flux Calibration Data for the East River Watershed, Colorado, USA

This data package contains tabular and geospatial data used to quantify and model meteoric beryllium-10 fluxes in the East River watershed, Colorado, USA. The tabular component includes calibration-site data from five glacial moraine sites and includes environmental variables used to evaluate spatial controls on meteoric 10Be delivery, including elevation, mean annual precipitation (MAP), mean snow depth, and mean snow water equivalent (SWE). These site-level data were used to compare observed fluxes with environmental gradients across the watershed and to evaluate the effects of erosion correction on flux estimates. The package also includes supporting slope and curvature values used to assess topographic inputs to the erosion analysis. A second component of the data package contains updated manuscript tables and regression outputs used to summarize the relationships between meteoric 10Be flux and environmental predictors. These tables include meteoric 10Be sample information and AMS results, site-level environmental values, site-level meteoric 10Be inventory and flux values, watershed-averaged predicted fluxes, soil bulk density measurements, fine-fraction values, soil pH measurements, and regression statistics including slope, intercept, coefficient of determination, and p-value. The regression products include both standard linear regressions and regressions in which the intercept is constrained to pass through zero, and they support the analyses presented in the companion manuscript. Together, these tabular files provide the numerical basis for the manuscript tables and the regression-based interpretation of meteoric 10Be flux variability in a snow-dominated mountain watershed. The geospatial component of the package consists of GeoTIFF raster files used to generate the map products presented in Figures 2 and 6 of the companion manuscript. These rasters represent watershed-scale spatial layers for environmental variables and regression-based predictions of meteoric 10Be flux. This dataset contains comma-separated values files (.csv), Microsoft Excel files (.xlsx), GeoTIFF raster files (.tif), and upporting metadata files, including CSV data dictionaries and readme text files (.csv, .txt). The tabular files can be opened with standard spreadsheet software, and the raster files can be viewed and analyzed in GIS software such as ArcGIS Pro or QGIS. Together, these files document the numerical and spatial datasets used to calibrate and predict meteoric 10Be delivery in the East River watershed.

East River↗
DOE OSTI2023

Spatiotemporal analysis of 3D human iPSC-derived neural networks using a 3D multi-electrode array

While there is a growing appreciation of three-dimensional (3D) neural tissues (i.e., hydrogel-based, organoids, and spheroids), shown to improve cellular health and network activity to mirror brain-like activity in vivo , functional assessment using current electrophysiology techniques (e.g., planar multi-electrode arrays or patch clamp) has been technically challenging and limited to surface measurements at the bottom or top of the 3D tissue. As next-generation MEAs, specifically 3D MEAs, are being developed to increase the spatial precision across all three dimensions (X, Y, Z), development of improved computational analytical tools to discern region-specific changes within the Z dimension of the 3D tissue is needed. In the present study, we introduce a novel computational analytical pipeline to analyze 3D neural network activity recorded from a “bottom-up” 3D MEA integrated with a 3D hydrogel-based tissue containing human iPSC-derived neurons and primary astrocytes. Over a period of ~6.5 weeks, we describe the development and maturation of 3D neural activity (i.e., features of spiking and bursting activity) within cross sections of the 3D tissue, based on the vertical position of the electrode on the 3D MEA probe, in addition to network activity (identified using synchrony analysis) within and between cross sections. Then, using the sequential addition of postsynaptic receptor antagonists, bicuculline (BIC), 2-amino-5-phosphonovaleric acid (AP-5), and 6-cyano-5-nitroquinoxaline-2,3-dione (CNQX), we demonstrate that networks within and between cross sections of the 3D hydrogel-based tissue show a preference for GABA and/or glutamate synaptic transmission, suggesting differences in the network composition throughout the neural tissue. The ability to monitor the functional dynamics of the entire 3D reconstructed neural tissue is a critical bottleneck; here we demonstrate a computational pipeline that can be implemented in studies to better interpret network activity within an engineered 3D neural tissue and have a better understanding of the modeled organ tissue.

3D culture↗
DOE OSTI2021

Temporally and spatially resolved X-ray densitometry in a shock tube

Gas densities were measured by x-ray absorption spectroscopy to examine spatial inhomogeneity in density behind incident and reflected shock waves in a miniature (12.7 mm bore) high repetition rate shock tube. Shock waves were generated in argon. The pressure and temperature behind the incident shock were P 2 ~ 1.72 bar and T 2 ~ 800 K, while those behind the reflected shock were P 5 ~ 6.5 bar and T 5 ~ 1480 K. Time-resolved line-of-sight transmission measurements using x-rays at 9 keV photon energy were made along various axial and transverse locations covering the entire shock tube cross section. The x-ray transmission at each location was converted to pathlength-integrated densities, which in some conditions could be further converted to pathlength-averaged densities. The measured gas densities were compared with those calculated by the normal shock wave relations, and good agreement was found in the preshock and immediately behind the incident shock regions. Similar agreement was found for reflected shock conditions very near the endwall. However, increasingly large differences between the measured and calculated gas densities were found as distance from the endwall increased. Furthermore, reconstruction by Abel inversion allowed time and radially resolved densities to be obtained from the transverse measurements. While the profiles measured in this experiment are of insufficient signal-to noise level and resolution to definitively assign boundary layer thicknesses, future improvements may be able to do this. The radially resolved densities reveal growing sidewall thermal boundary layers in gases behind the reflected shock. These reconstructions were compared with a model of the thermal boundary layer and are consistent with values of the thermal boundary layer thickness being between 0.25 mm and 1 mm at 0.7 ms after shock reflection.

42 ENGINEERING↗
DOE OSTI2025

Assessing methods in fusion and fitting for time series construction in remote sensing-based earth observations

This study evaluates the comparative performance of spatiotemporal fusion and time-series fitting methods for constructing high-spatiotemporal-resolution remote sensing time-series data. Due to in-class similarity of fusion methods and fitting methods, we employ the Fit-FC (Fitting, spatial Filtering, and residual Compensation) model as a representative fusion method and the linear harmonic fitting model as a representative fitting method. Both Fit-FC and the linear harmonic fitting are widely used for high-spatiotemporal-resolution time-series data construction, and we modify the original Fit-FC model to enable automatic time-series fusion. To ensure data representativeness, we use 3 years (2019–2021) of Harmonized Landsat and Sentinel-2 surface reflectance datasets and Terra MCD43A4 products. Eight experimental regions are selected worldwide to guarantee generalization of the comparative performance between fusion and fitting methods, covering diverse land-use types (cropland, developed land, forest, and grassland) and varying climatological conditions. Time-series of NDVI and surface reflectance are analyzed under both actual observations and simulated data-missing scenarios. The constructed time-series data reveals that (1) the modified Fit-FC and linear harmonic fitting model achieve excellent performance in constructing high-resolution time-series images; (2) the fusion method outperforms the fitting method in constructing time-series of NDVI and surface reflectance images in cropland-, forest-, and grassland-dominated regions; (3) both methods achieve comparable performance in developed-dominated regions; (4) the fusion method is more robust to missing data, and better captures abrupt phenological transitions under conditions of continuous missing data; (5) the fitting method is computationally more efficient, making it suitable for large-scale time-series image reconstruction. This study provides valuable insights for selecting optimal strategies to generate high-resolution time-series images across diverse application scenarios and lays a foundation for extensions to other vegetation indices or land surface variables.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Intelligent Process Visualization through Nuclear Operation Process Modeling, Reasoning, and Object Detection from Field Videos (Final Report)

This report is a deliverable for the “Final Report” task of DOE NEET Project 19-16790, "Context-Aware Safety Information Display for Nuclear Field Workers." This project's overall goal is to test the hypothesis that integrating computer vision and process reasoning methods will enable proactive visualization of the safe operation and maintenance processes of Nuclear Power Plants (NPP) for field workers. Augmented Reality (AR) glasses adopting such proactive safety information visualization techniques can significantly increase personnel safety and reduce the NPP’s operating costs. The current practice of monitoring NPPs requires workers to switch between digital models, data, and physical workspaces in identifying relevant but potentially occluded objects and in assessing the risks of operation and maintenance processes. On the other hand, frequently changed field conditions require field workers to report to supervisors for real-time guidance. Such guidance is essential to ensure that changing conditions will not invalidate or endanger the work order and other ongoing processes that may jeopardize NPP operations. Additionally, incorrect recognition of equipment objects can result in communication errors and safety problems. AR techniques can assist engineers in viewing the physical workspaces with objects labeled with detailed operation procedures and safety reminders during field operations. The project team developed an “Intelligent Context-Aware Safety Information Display” (ICAD) for supporting Nuclear Power Plant (NPP) field workers in achieving safe and efficient execution of a series of operational tasks in uncertain and changing workspaces of an NPP. Before designing the ICAD-AR prototype, the project team synthesized NPP operational knowledge models through literature review studies, surveys, interviews with domain experts, and knowledge modeling. The project team conducted an extensive study of the operational procedures of various NPPs, and digital technologies that can support the safe and efficient execution of those procedures in different NPP operational contexts. This literature review helped the project team conduct surveys and interviews with nuclear engineers and field workers to identify three categories of information. The NPP knowledge modeling efforts reveal that the three categories of information identified have different levels of importance in a typical procedure of carrying out a series of tasks to achieve a specific NPP operation goal (e.g., shutdown, mode changes). These three categories of information include 1) Workspace dynamics – the changing spatial arrangements of workspaces, tools, protection equipment, and supporting materials, 2) Workflow prognostics – the dynamic dependencies between different parts of an NPP that functionally support and influence each other in terms of safety and efficiency, and 3) Hazards – objects and spaces that contain hazardous materials or physical conditions that can pose risks to workers or mechanical systems. The project team has profiled the importance levels of these categories of information into a knowledge model. This knowledge model specifies what types of information are more critical for a given task in a given workspace so that computers can automatically identify critical objects and sensors in a scene for delivering context-ware safety information to field workers through AR devices. Significant research development of this project results in technical research outcomes and a prototyping system that illustrates the technical feasibility of establishing an ICAD-AR system supporting the proactive safety information display for nuclear field workers. This final report summarizes the project team’s technological achievements in the past three years. Overall, the project team completed the development and integration of five techniques into a prototype ICAD Augmented Reality (ICAD-AR) system and demonstrated the developed system’s real-time execution in a mechanical room. The project team completed the analysis of using this prototype in other types of workspaces based on 3D image data and digital design models collected from two additional workspaces (a water treatment plant and a flow loop training facility). The integrated techniques include 1) Natural Language Processing (NLP) algorithms supporting the generation and updates of nuclear fieldwork process models based on text analysis of work packages and operation manuals; 2) sensor log analysis for predicting control actions in given sensor reading contexts; 3) computer vision algorithms for automatic localization and navigation of workers; 4) object detection algorithms for identifying task-related objects and correlated sensors for safety checking; 5) AR technique as a platform for supporting the integration. The testing results of these five techniques have shown that 1) the sensor log analysis model can predict the next control action with an accuracy of 0.883; 2) the trained natural language processing model can extract more than 80% of the critical information from paper-based procedures (PBPs); 3) the navigation algorithm with the integration of Visual Inertial Odometry (VIO) and Non-Recursive Bayesian Filter methods make operator’s trajectory estimation resilient to drift error; 4) the computer vision algorithm can detect task-specific and safety-critical objects with an average accuracy of 95.3%. The project team used work procedures collected from a flow loop training facility and two datasets collected from two mechanical rooms simulating the workspaces of NPPs to demonstrate the technical capabilities of the developed ICAD-AR prototype. The demonstration validated the technical feasibility of establishing the ICAD-AR system for nuclear field workers and identified the challenges in 1) automatic text analysis of work packages; 2) use of limited samples of sensor logs for predicting the proper timings of control actions; 3) reliably tracking workers and their task progress in mechanical rooms with many similar objects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

A ModEx Framework for Watershed Subsurface Investigation With Limited Geophysical Data Using Machine Learning and Hydrologic Modeling

Abstract Subsurface heterogeneity influences watershed hydrology strongly but remains difficult to characterize at catchment scales with sparse and costly field data. Geophysical surveys such as electromagnetic induction (EMI) provide local spatial subsurface images yet scaling them to watershed scales and converting EMI‐derived resistivity into hydraulic properties remains a challenge. We present a Model–Experiment (ModEx) framework that integrates limited EMI data with machine learning (ML) and hydrologic modeling to improve process representation and guide field investigations. Sparse EMI surveys were scaled to the catchment scale using a Random Forest model, and the resulting resistivity fields were combined with nearby borehole constraints to parameterize a hydrologic model. The EMI‐informed hydrological simulations improved predictions of streamflow sustained by subsurface flow and shallow saturation patterns. By combining EMI data and ML with hydrologic modeling, the ModEx framework guides future subsurface surveys, providing a transferable and efficient strategy for data–model integration across diverse watersheds. Plain Language Summary Mapping the underground network of soil and rock that controls water is essential for predicting floods and droughts, but seeing underground is difficult and expensive. We cannot drill everywhere, so scientists use geophysical tools to scan broad areas. There are two key challenges: these geophysical scans are often sparse across the whole watershed, and the geophysical data is hard to translate into water‐related properties. We used artificial intelligence to solve these problems. We taught a computer to find patterns linking the limited geophysical data to the land surface properties. This allowed it to fill in the gaps and create a complete, useful subsurface map for the entire watershed. This new map improves hydrologic simulations, leading to more accurate predictions of water movement in the watershed. It also helps scientists build better models with less data and generates a priority map showing where to measure next, making future investigations more efficient. Key Points Limited EMI scaled with ML improves catchment‐scale subsurface parameterization for hydrologic models The framework integrates hydrologic modeling with limited geophysical data to support subsurface investigation design ModEx framework offers a transferable data–model integration strategy that quantifies and reduces uncertainty guiding watershed studies

Chen, Hang↗
DOE OSTI2018

Complementary Probes of Dark Energy using Galaxy Clusters

Here, we have built a framework to constrain Dark Energy (DE) using the spatial distributions, number counts, and escape velocities of galaxy clusters in the context of next generation spectroscopic surveys. In the ΛCDM paradigm, galaxy clusters trace the expansion of the Universe in three different ways. (1.) Spatial distribution:The baryon acoustic oscillations (BAO) formed during recombination are imprinted into the spatial distribution of clusters. The locations of the BAO are standard-rulers which map the length-scale of the sound horizon. Statistical measures like the two-point correlation function and the power spectrum can be used to trace the scale of the BAO and thus the expansion history of the Universe.(2.) Number counts: Galaxy clusters are identified as peaks above a threshold in the underlying matter density field. Galaxies attract onto these peaks over time until gravity loses the battle against the accelerating expansion of space. The number and mass of galaxy clusters over time is sensitive to the strength of the DE and the amount of matter in the Universe.(3.) Escape velocity:The radius-velocity phase-space of galaxies within clusters has a sharp edge which traces the escape velocity of galaxies. This escape velocity is a combination of the local Newtonian gravitational potential and the acceleration of the expansion of the Universe. Weak lensing mass profiles of clusters can be used to predict the escape velocity profile from just the gravitational Newtonian component. In an expanding or contracting Universe, the observed phase-space escape velocity surface would be modified from this prediction.We built the large-scale structure covariances from mock catalogs which we can now use in the likelihood analyses to constrain Dark Energy models. We have developed the analytical framework to conduct the joint likelihood analysis of the bias function and the abundance function in order to constrain DE through their redshift evolution.We have investigated a new Dark Energy test that uses the weak lensing prediction of the grav-itational potential compared to the observed escape velocity profile from galaxy clusters. We have made predictions on the improvement in the Dark Energy figure-of-merit from this new test. We have also investigated the predicted enhancements of the cluster escape velocity profiles from modifi-cations to gravity. This effort can help rule out model classes of modified gravity as alternatives to Dark Energy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X

Mapping in situ eddy covariance measurements of terrestrial land–atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented upscaling framework, FLUXCOM-X, representing an advancement from the previous generation of FLUXCOM products in terms of flexibility and technical capabilities. The X-BASE products are comprised of estimates of CO 2 net ecosystem exchange (NEE), gross primary productivity (GPP), evapotranspiration (ET), and for the first time a novel, fully data-driven global transpiration product (ETT), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE estimates the global NEE at −5.75 ± 0.33 Pg C yr −1 for the period 2001–2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site years resulting in a wider coverage of bioclimatic conditions. However, X-BASE global net ecosystem exchange shows a very low interannual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 Pg C yr −1 for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7×10 3 ± 0.9×10 3 km 3 globally for the years 2001–2020 but exceeds precipitation in many dry areas, likely indicating overestimation in these regions. On average 57 % of evapotranspiration is estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous upscaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

Nelson, Jacob A.↗
DOE OSTI2019

Sub-cloud moist entropy curvature as a predictor for changes in the seasonal cycle of tropical precipitation

Convective Quasi-Equilibrium (CQE) may be a useful framework for understanding the precipitation minus evaporation (P-E) response to CO2-induced warming. To explore this proposition, a suite of aquaplanet simulations with a slab ocean from TRACMIP (the Tropical Rain belts with an Annual cycle and a Continent Model Intercomparison Project) is analyzed. A linear relationship between P-E and the curvature of sub-cloud moist entropy, a marker for the spatial distribution of sub-cloud moist energy and onset of a tropical direct overturning circulation under CQE conditions, is shown to exist across many of the TRACMIP simulations. Furthermore, this linear relationship is a skillful predictor of changes in P-E in response to CO2-induced warming. The curvature metric also shows improvement in predicting P-E changes compared to the simpler method of relating P-E directly to the sub-cloud moist entropy field or a simple ‘wet-get-wetter’ type null hypothesis, especially on seasonal and shorter timescales. Using fixed relative humidity in the curvature metric and sub-cloud moist entropy degrades their ability to predict P-E changes, implying that both temperature and relative humidity changes in the boundary layer are important for characterizing future precipitation changes. To understand why the curvature metric is a skillful predictor of hydrological changes, a moist static energy (MSE) budget analysis is performed, which shows that MSE divergence on sub-daily timescales is well parameterized as a downgradient diffusive process. Additionally, this transient MSE divergence has a similar structure to the curvature term. Assuming that the transient MSE divergence is related to convective activity, these findings suggest that the theoretical underpinning of the linear relationship between the curvature term and P-E resides in cloud processes that both remove spatial gradients of sub-cloud moist entropy and generate precipitation at sub-seasonal and shorter timescales.

aquaplanet simulations↗
DOE OSTI2022

Monte Carlo Simulation and Reconstruction: Assessment of Myocardial Perfusion Imaging of Tracer Dynamics With Cardiac Motion Due to Deformation and Respiration Using Gamma Camera With Continuous Acquisition

Purpose: Myocardial perfusion imaging (MPI) with single photon emission computed tomography (SPECT) is routinely used for stress testing in nuclear medicine. Recently, our group extended its potential going from 3D visual qualitative image analysis to 4D spatiotemporal reconstruction of dynamically acquired data to capture the time variation of the radiotracer concentration and the estimated myocardial blood flow (MBF) and coronary flow reserve (CFR). However, the quality of reconstructed image is compromised due to cardiac deformation and respiration. The work presented here develops an algorithm that reconstructs the dynamic sequence of separate respiratory and cardiac phases and evaluates the algorithm with data simulated with a Monte Carlo simulation for the continuous image acquisition and processing with a slowly rotating SPECT camera. Methods: A clinically realistic Monte Carlo (MC) simulation is developed using the 4D Extended Cardiac Torso (XCAT) digital phantom with respiratory and cardiac motion to model continuous data acquisition of dynamic cardiac SPECT with slowly rotating gamma cameras by incorporating deformation and displacement of the myocardium due to cardiac and respiratory motion. We extended our previously developed 4D maximum-likelihood expectation-maximization (MLEM) reconstruction algorithm for a data set binned from a continuous list mode (LM) simulation with cardiac and respiratory information. Our spatiotemporal image reconstruction uses splines to explicitly model the temporal change of the tracer for each cardiac and respiratory gate that delineates the myocardial spatial position as the tracer washes in and out. Unlike in a fully list-mode data acquisition and reconstruction the accumulated photons are binned over a specific but very short time interval corresponding to each cardiac and respiratory gate. Reconstruction results are presented showing the dynamics of the tracer in the myocardium as it continuously deforms. These results are then compared with the conventional 4D spatiotemporal reconstruction method that models only the temporal changes of the tracer activity. Mean Stabilized Activity (MSA), signal to noise ratio (SNR) and Bias for the myocardium activities for three different target-to-background ratios (TBRs) are evaluated. Dynamic quantitative indices such as wash-in (K1) and wash-out (k2) rates at each gate were also estimated. Results: The MSA and SNR are higher with higher TBRs while biases were improved with higher TBRs to less than 10%. The correlation between exhalation-inhalation sequence with the ground truth during respiratory cycle was excellent. Our reconstruction method showed better resolved myocardial walls during diastole to systole as compared to the ungated 4D image. Estimated values of K1 and k2 were also consistent with the ground truth. Conclusion: The continuous image acquisition for dynamic scan using conventional two-head gamma cameras can provide valuable information for MPI. Our study demonstrated the viability of using a continuous image acquisition method on a widely used clinical two-head SPECT system. Our reconstruction method showed better resolved myocardial walls during diastole to systole as compared to the ungated 4D image. Precise implementation of reconstruction algorithms, better segmentation techniques by generating images of different tissue types and background activity would improve the feasibility of the method in real clinical environment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Evaluation of Global Fire Simulations in CMIP6 Earth System Models

Fire is the primary form of terrestrial ecosystem disturbance on a global scale and an important Earth system process. Most Earth system models (ESMs) have incorporated fire modeling, with 19 of them submitting model outputs of fire-related variables to the Coupled Model Intercomparison Project Phase 6 (CMIP6). This study provides the first comprehensive evaluation of CMIP6 historical fire simulations by comparing them with multiple satellite-based products and charcoal-based historical reconstructions. Our results show that most CMIP6 models simulate the present-day global burned area and fire carbon emissions within the range of satellite-based products. They also capture the major features of observed spatial patterns and seasonal cycles, the relationship of fires with precipitation and population density, and the influence of the El Niño–Southern Oscillation (ENSO) on the interannual variability of tropical fires. Regional fire carbon emissions simulated by the CMIP6 models from 1850 to 2010 generally align with the charcoal-based reconstructions, although there are regional mismatches, such as in southern South America and eastern temperate North America prior to the 1910s and in temperate North America, eastern boreal North America, Europe, and boreal Asia since the 1980s. The CMIP6 simulations have addressed three critical issues identified in CMIP5: (1) the simulated global burned area being less than half of that of the observations, (2) the failure to reproduce the high burned area fraction observed in Africa, and (3) the weak fire seasonal variability. Furthermore, the CMIP6 models exhibit improved accuracy in capturing the observed relationship between fires and both climatic and socioeconomic drivers and better align with the historical long-term trends indicated by charcoal-based reconstructions in most regions worldwide. However, the CMIP6 models still fail to reproduce the decline in global burned area and fire carbon emissions observed over the past 2 decades, mainly attributed to an underestimation of anthropogenic fire suppression, and the spring peak in fires in the Northern Hemisphere midlatitudes, mainly due to an underestimation of crop fires. In addition, the model underestimates the fire sensitivity to wet–dry conditions, indicating the need to improve fuel wet-ness estimation. Based on these findings, we present specific guidance for fire scheme development and suggest a postprocessing methodology for using CMIP6 multi-model outputs to generate reliable fire projection products.

Wildfire, Earth system models↗
DOE OSTI2025

Arctic shrub expansion generates regional variation in litter decomposition by altering litter quality and the decomposition environment

Abstract The expansion of deciduous shrubs into the graminoid‐dominated arctic tundra is expected to alter litter decomposition by changing litter quality and local abiotic and biotic conditions. However, it remains unclear how shrub expansion will affect litter decomposition at regional scales, where macroclimate is expected to be the dominant regulator of decomposition. To determine the relative influence of macroclimate and local controls on regional patterns of litter mass loss and nitrogen release, we conducted two hierarchical litter decomposition experiments across spatial scales. We decomposed leaf and root litter from a prominent graminoid ( Eriophorum vaginatum ) and three genera of deciduous shrubs ( Betula , Alnus and Salix ) for 1 year within replicated plots at five sites spanning a 160 km latitudinal gradient in northern Alaska. Using Eriophorum litter as a substrate, we found that macroclimate was the primary regulator of mass loss but had opposing effects on leaf and root litter. As summer temperature increased along the latitudinal gradient (11.9 to 13.9°C), leaf litter mass loss increased by 20% whereas root litter mass loss decreased by 33%. Leaf nitrogen release also increased with summer temperature. Conversely, root nitrogen release was controlled by the vegetation type of the decomposition environment. Using different shrub litters as substrates, we found that litter quality and its interaction with soil microclimate and macroclimate controlled decomposition. Overall, shrub root litters decomposed faster than Eriophorum root litter, losing 53% more mass and 190% more nitrogen across all sites and decomposition environments. For leaf litter, however, patterns varied by litter genus, with Salix losing more mass and Betula and Alnus losing less mass than Eriophorum . Our findings demonstrate that shrub expansion in the Arctic can regulate leaf and root litter decomposition at the regional scale through its effects on local controls, primarily litter quality. Ongoing increases in shrub cover are likely to accelerate the turnover of root litter carbon and nitrogen pools in tundra ecosystems. Therefore, including shrub‐related processes in Earth system models will improve our ability to predict regional‐scale litter decomposition and its effects on carbon and nutrient cycling in a warming Arctic. Read the free Plain Language Summary for this article on the Journal blog.

Vozzo, Justin T. [Department of Natural Resources ↗
DOE OSTI2021

Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam replacement structures

Additive Manufacturing (AM) of porous polymeric materials, such as foams, recently became a topic of intensive research due their unique combination of low density, impressive mechanical properties, and stress dissipation capabilities. Conventional methods for fabricating foams rely on complex and stochastic processes, making it challenging to achieve precise architectural control of structured porosity. In contrast, AM provides access to a wide range of printable materials, where precise spatial control over structured porosity can be modulated during the fabrication process enabling the production of foam replacement structures (FRS). Current approaches for designing FRS are based on intuitive understanding of their properties or an extensive number of finite element method (FEM) simulations. These approaches, however, are computationally expensive and time consuming. As such, in this work, we present a novel methodology for determining the mechanical compression response of direct ink write (DIW) 3D printed FRS using a simple cross-sectional image. By obtaining measurement data for a relatively small number of samples, an artificial neural network (ANN) was trained, and a computer vision algorithm was used to make inferences about foam compression characteristics from a single cross-sectional image. Finally, a genetic algorithm (GA) was used to solve the inverse design problem, generating the AM printing parameters that an engineer should use to achieve a desired compression response from a DIW printed FRS. The methods developed herein present an avenue for entirely autonomous design and analysis of additively manufactured structures using artificial intelligence.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A mass-transfer particle-tracking method for simulating transport with discontinuous diffusion coefficients

The problem of a spatially discontinuous diffusion coefficient (D(x)) is one that may be encountered in hydrogeologic systems due to natural geological features or as a consequence of numerical discretization of flow properties. To date, mass-transfer particle-tracking (MTPT) methods, a family of Lagrangian methods in which diffusion is jointly simulated by random walk and diffusive mass transfers, have been unable to solve this problem. This manuscript presents a new mass-transfer (MT) algorithm that enables MTPT methods to accurately solve the problem of discontinuous D(x). To achieve this, we derive a semi-analytical solution to the discontinuous D(x) problem by employing a predictor-corrector approach, and we use this semi-analytical solution as the weighting function in a reformulated MT algorithm. Furthermore, this semi-analytical solution is generalized for cases with multiple 1D interfaces as well as for 2D cases, including a 2 × 2 tiling of 4 subdomains that corresponds to a numerically-generated diffusion field. The solutions generated by this new mass-transfer algorithm closely agree with an analytical 1D solution or, in more complicated cases, trusted numerical results, demonstrating the success of our proposed approach.

42 ENGINEERING↗
DOE OSTI2015

Comparison of the PHISICS/RELAP5-3D ring and block model results for phase I of the OECD/NEA MHTGR-350 benchmark

The PHISICS code system has been under development at INL since 2010. It consists of several modules providing improved coupled core simulation capability: INSTANT (3D nodal transport core calculations), MRTAU (depletion and decay heat generation) and modules performing criticality searches, fuel shuffling and generalized perturbation. Coupling of the PHISICS code suite to the thermal hydraulics system code RELAP5-3D was finalized in 2013, and as part of the verification and validation effort the first phase of the OECD/NEA MHTGR-350 Benchmark has now been completed. The theoretical basis and latest development status of the coupled PHISICS/RELAP5-3D tool are described in more detail in a concurrent paper. This paper provides an overview of the OECD/NEA MHTGR-350 Benchmark and presents the results of Exercises 2 and 3 defined for Phase I. Exercise 2 required the modelling of a stand-alone thermal fluids solution at End of Equilibrium Cycle for the Modular High Temperature Reactor (MHTGR). The RELAP5-3D results of four sub-cases are discussed, consisting of various combinations of coolant bypass flows and material thermophysical properties. Exercise 3 required a coupled neutronics and thermal fluids solution, and the PHISICS/RELAP5-3D code suite was used to calculate the results of two sub-cases. The main focus of the paper is a comparison of results obtained with the traditional RELAP5-3D “ring” model approach against a much more detailed model that include kinetics feedback on individual block level and thermal feedbacks on a triangular sub-mesh. The higher fidelity that can be obtained by this “block” model is illustrated with comparison results on the temperature, power density and flux distributions. Furthermore, it is shown that the ring model leads to significantly lower fuel temperatures (up to 10%) when compared with the higher fidelity block model, and that the additional model development and run-time efforts are worth the gains obtained in the improved spatial temperature and flux distributions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

A cost-benefit analysis of a pellet boiler with electrostatic precipitator versus conventional biomass technology: A case study of an institutional boiler in Syracuse, New York

Background: Biomass facilities have received increasing attention as a strategy to increase the use of renewable fuels and decrease greenhouse gas emissions from the electric generation and heating sectors, but these facilities can potentially increase local air pollution and associated health effects. Comparing the economic costs and public health benefits of alternative biomass fuel, heating technology, and pollution control technology options provides decision-makers with the necessary information to make optimal choices in a given location. Methods: For a case study of a combined heat and power biomass facility in Syracuse, New York, we used stack testing to estimate emissions of fine particulate matter (PM{sub 2.5}) for both the deployed technology (staged combustion pellet boiler with an electrostatic precipitator) and a conventional alternative (wood chip stoker boiler with a multicyclone). We used the atmospheric dispersion model AERMOD to calculate the contribution of either fuel-technology configuration to ambient primary PM{sub 2.5} in a 10 km×10 km region surrounding the facility, and we quantified the incremental contribution to population mortality and morbidity. We assigned economic values to health outcomes and compared the health benefits of the lower-emitting technology with the incremental costs. Results: In total, the incremental annualized cost of the lower-emitting pellet boiler was $190,000 greater, driven by a greater cost of the pellet fuel and pollution control technology, offset in part by reduced fuel storage costs. PM{sub 2.5} emissions were a factor of 23 lower with the pellet boiler with electrostatic precipitator, with corresponding differences in contributions to ambient primary PM{sub 2.5} concentrations. The monetary value of the public health benefits of selecting the pellet-fired boiler technology with electrostatic precipitator was $1.7 million annually, greatly exceeding the differential costs even when accounting for uncertainties. Our analyses also showed complex spatial patterns of health benefits given non-uniform age distributions and air pollution levels. Conclusions: The incremental investment in a lower-emitting staged combustion pellet boiler with an electrostatic precipitator was well justified by the population health improvements over the conventional wood chip technology with a multicyclone, even given the focus on only primary PM{sub 2.5} within a small spatial domain. Our analytical framework could be generalized to other settings to inform optimal strategies for proposed new facilities or populations. - Highlights: • PM{sub 2.5} emissions vary greatly across alternative biomass fuels and technologies. • A pellet boiler had a factor of 23 lower emissions than a conventional alternative. • The incremental annualized cost of the lower-emitting technology was $190,000. • The monetary value of annual health benefits of reduced emissions was $1.7 million. • Our analytical framework provides insight about optimal biomass technologies.

09 BIOMASS FUELS↗