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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Spark plasma sintering of structure-tailored ultrahigh-temperature components: First step to complex net shaping

The net-shaping capability in spark plasma sintering (SPS) of ultrahigh-temperature ceramics has been explored in this study. The annular or ring-like shape is considered here as the first step to a more complex geometry compared to a solid cylinder or disk. ZrC powders have been SPS processed in specially designed graphite tooling to achieve the annular shape geometry. Experimental runs have been carried out to determine the optimal processing parameters for producing highly dense ZrC specimens in this geometry. Finite-element modeling framework has been constructed to determine the internal stress evolution, as well as densification, during the SPS of the annular-shaped ZrC. The formulated processing schemes for ZrC have been adapted to process SiC/ZrB2 composite powder with the purpose of making tooling components for SPS applications. The applicability of the obtained composite SPS tooling has been evaluated at high temperature and high pressure associated with SPS regimes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Net Zero Energy Model for Wastewater Treatment Plants

The primary objective of this study is to achieve net-zero energy (NZE) wastewater treatment plants (WWTPs) by utilizing energy efficiency opportunities (EEO), combined heat and power (CHP) systems, and other renewable energy (RE) sources, e.g., solar, water, and wind powers. Herein, this study discusses an innovative energy solution for WWTPs in the United States, and one of the WWTPs with a flow capacity of 1.5 million gallons per day (MGD) was selected as a case study. An optimization tool, Hybrid Optimization of Multiple Energy Resources (HOMER) software, is used in this study to find the best energy system configuration to run the system. An energy audit for one WWTP was conducted in early 2020 and the report is used to do this study. The proposed EEOs were able to reduce WWTP energy consumption by about 11%. The excess anaerobic digester gas was utilized in a CHP system to cover about 42% of the facility’s consumption. Also, 3% of the utility energy consumption can be claimed by microturbines in the aeration tanks. Another two renewable energy systems, solar photovoltaic (PV) with 29% and water turbines with 15%, contribute to covering 100% of the WWTP energy consumption and achieving an NZE WWTP.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

Variation and Trends of Landscape Dynamics, Land Surface Phenology and Net Primary Production of the Appalachian Mountains

The gradients of the Appalachian Mountains in elevations and latitudes provide a unique regional perspective of landscape variations in the eastern United States and a section of the southeastern Canada. This study reveals patterns and trends of landscape dynamics, land surface phenology and ecosystem production along the Appalachian Mountains using time series data from Global Inventory Modeling and Mapping Studies (GIMMS) and AVHRR Global Production Efficiency Model (GloPEM) datasets. We analyzed the spatial and temporal patterns of Normalized Difference Vegetation Index (NDVI), length of growing season (LOS) and net primary production (NPP) of selected ecoregions along the Appalachian Mountains regions. We compared the results out of the Appalachian Mountains regions in different spatial contexts including the North America and the Appalachian Trail corridor area. To reveal latitudinal variations we analyzed data and compared the results between 30°N-40°N and 40°N-50°N latitudes. The result revealed significant decreases in annual peak NDVI in the Appalachian Mountains regions. The trend for the Appalachian Mountains regions was -0.0018 (R2=0.55, P<0.0001) NDVI unit decrease per year during 25 years between 1982 and 2006. The LOS had prolonged 0.3 day yr-1 during 25 years over the Appalachian Mountains regions. The NPP increased by 2.68 gC m-2yr-2 in Appalachian Mountains regions from 1981 to 2000. The comparison with the North America reveals the effects of topography and ecosystem compositions of the Appalachian Mountains. The comparison with the Appalachian Trail corridor area provides a regional mega-transect view of the measured variables.

Wang, Yeqiao↗
DOE OSTI2020

The Mertens Unrolled Network (MU-Net): A High Dynamic Range Fusion Neural Network for Through the Windshield Driver Recognition

Face recognition of vehicle occupants through windshields in unconstrained environments poses a number of unique challenges ranging from glare, poor illumination, driver pose and motion blur. In this paper, we further develop the hardware and software components of a custom vehicle imaging system to better overcome these challenges. After the build out of a physical prototype system that performs High Dynamic Range (HDR) imaging, we collect a small dataset of through-windshield image captures of known drivers. We then reformulate the classical Mertens-Kautz-Van Reeth HDR fusion algorithm as a pre-initialized neural network, which we name the Mertens Unrolled Network (MU-Net), for the purpose of fine-tuning the HDR output of through-windshield images. Reconstructed faces from this novel HDR method are then evaluated and compared against other traditional and experimental HDR methods in a pre-trained state-of-the-art (SOTA) facial recognition pipeline, verifying the efficacy of our approach.

Ruby, Max↗
DOE OSTI2014

SU-F-BRD-11: Prediction of Dosimetric Endpoints From Patient Geometry Using Neural Nets

Purpose: The previously-published overlap volume histogram (OVH) technique lends itself naturally to prediction of the dose received by a given volume of tissue (e.g. D90) in intensity-modulated radiotherapy (IMRT) treatment plans. Here we extend the OVH technique using artificial neural networks in order to predict the volume of tissue receiving a given dose (e.g. V90) in both prostate IMRT and conventional breast radiotherapy. Methods: Twenty-nine prostate treatment plans and forty-three breast treatment plans were analyzed. The spatial relationships between the prostate and rectum and between the breast and ipsilateral lung were characterized using OVHs. The OVH is a cumulative histogram representing the fractional volume of the risk organ overlapped by a series of isotropic expansions of the planning target volume (PTV). Seven cases were identified as outliers and replanned. OVH points were used as inputs to a one hidden layer feed forward artificial neural network with quality parameters of the corresponding plan, such as the rectum V50, as targets. A 3-fold cross-validation was used to estimate the prediction error. Results: The root mean square (RMS) error between the predicted rectum V50s and the planned values was 2.3, which was 35% of the standard deviation of V50 for the twenty-nine plans. The RMS error of prediction of V20 of the ipsilateral lung in breast cases was 3.9, which was 90% of the standard deviation of the V20 values in the breast plan database. Conclusion: This study demonstrates that artificial neural nets can be used to extend the OVH technique to predict dosimetric endpoints taking the form of a volume receiving a given dose, rather than the minimum dose received by a given volume. Prediction of ipsilateral lung dose in breast radiotherapy using the OVH technique remains a work in progress.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-D-BRD-06: Creating a Safety Net for a Fully Automated, Script Driven Electronic Medical Record

Purpose: Demonstrate the effectiveness of in-house software in ensuring EMR workflow efficiency and safety. Methods: A web-based dashboard system (WBDS) was developed to monitor clinical workflow in real time using web technology (WAMP) through ODBC (Open Database Connectivity). Within Mosaiq (Elekta Inc), operational workflow is driven and indicated by Quality Check Lists (QCLs), which is triggered by automation software IQ Scripts (Elekta Inc); QCLs rely on user completion to propagate. The WBDS retrieves data directly from the Mosaig SQL database and tracks clinical events in real time. For example, the necessity of a physics initial chart check can be determined by screening all patients on treatment who have received their first fraction and who have not yet had their first chart check. Monitoring similar “real” events with our in-house software creates a safety net as its propagation does not rely on individual users input. Results: The WBDS monitors the following: patient care workflow (initial consult to end of treatment), daily treatment consistency (scheduling, technique, charges), physics chart checks (initial, EOT, weekly), new starts, missing treatments (>3 warning/>5 fractions, action required), and machine overrides. The WBDS can be launched from any web browser which allows the end user complete transparency and timely information. Since the creation of the dashboards, workflow interruptions due to accidental deletion or completion of QCLs were eliminated. Additionally, all physics chart checks were completed timely. Prompt notifications of treatment record inconsistency and machine overrides have decreased the amount of time between occurrence and execution of corrective action. Conclusion: Our clinical workflow relies primarily on QCLs and IQ Scripts; however, this functionality is not the panacea of safety and efficiency. The WBDS creates a more thorough system of checks to provide a safer and near error-less working environment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Construction of fractal nanostructures based on Kepler-Shubnikov nets

A system of information codes for deterministic fractal lattices and sets of multifractal curves is proposed. An iterative modular design was used to obtain a series of deterministic fractal lattices with generators in the form of fragments of 2D structures and a series of multifractal curves (based on some Kepler-Shubnikov nets) having Cantor set properties. The main characteristics of fractal structures and their lacunar spectra are determined. A hierarchical principle is formulated for modules of regular fractal structures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2012

Artificial nets from superconducting nanogranules

We show that a large transport current can flow through superconducting nets composed of nano-clusters. Although thermal and quantum fluctuations lead to a finite value of dissipation, this value can be very small in one- and two-dimensional systems for realistic parameters of the nanoclusters and distances between them. The value of the action for vortex tunneling at zero temperature can be made sufficiently large to make the dissipation negligibly small. We estimate the temperature T{sub 0} of the transition from the thermal activation to quantum tunneling.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Measurements of higher-order cumulants of multiplicity and net-electric charge distributions in inelastic proton-proton interactions by NA61/SHINE

This paper presents the energy dependence of multiplicity and net-electric charge fluctuations in p+p interactions at beam momenta 20, 31, 40, 80, and 158 \(\text{ Ge }\hspace{-1.00006pt}\text{ V }\!/\!c\). Results are corrected for the experimental biases and quantified with the use of cumulants and factorial cumulants. Cumulant ratios are an essential tool in the search for the critical point of strongly interacting matter in heavy ion collisions. Measurements performed in p+p interactions provide a vital baseline estimation in these studies. The measured signals are compared with the string hadronic models Epos1.99 and FTFP-BERT.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Graphene U-Net v1

Graphene U-Net is a library that provides a simplified platform for training deep neural networks for the task of microscopy image segmentation. It contains functions and classes that make the process of training a neural network simple such that non-ML experts can train and evaluate models on their own datasets. It uses Pytorch as a backend and can run on both CUDA-enable GPUs and CPUs. It contains the main library file Microscopy_Unet.py as well as unet.py which contains the UNET model used for this software. This can be replaced with any other fully convolutional deep learning architecture with relative ease.

Sadre, Robbie↗
DOE OSTI2022

U-Net Decoder CRF v0.1.0

We introduce a new encoder-decoder system that overcomes adaptability and scalability issues. We adapt multiple CNNs as encoders, allowing for the definition of multiple function parameter arguments to structure the models according to the targeted datasets and scientific problem. We leverage the flexibility of the U-Net architecture to act as a scalable decoder. The CRF-RNN layer is integrated into the decoder as an optional final layer, keeping the entire system fully compatible with back-propagation.

Avaylon, Matthew↗
DOE OSTI2015

The Rainfall Sensitivity of Tropical Net Primary Production in CMIP5 Twentieth- and Twenty-First-Century Simulations

In this study, the authors used the relationship between mean annual rainfall (MAR) and net primary production (NPP) (MAR–NPP) observed in tropical forests to evaluate the performance (twentieth century) and predictions (twenty-first century) of tropical NPP from 10 earth system models (ESMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Over the tropical forest domain most of the CMIP5 models showed a positive correlation between NPP and MAR similar to observations. The GFDL, CESM1, CCSM4, and Beijing Normal University (BNU) models better represented the observed MAR–NPP relationship. Compared with observations, the models were able to reproduce the seasonality of rainfall over areas with long dry seasons, but NPP seasonality was difficult to evaluate given the limited observations. From 2006 to 2100, for representative concentration pathway 8.5 (RCP8.5) (and most RCP4.5 simulations) all models projected increases in NPP, but these increases occurred at different rates. By the end of the twenty-first century the models with better performance against observed NPP–MAR projected increases in NPP between ~2% (RCP4.5) and ~19% (RCP8.5) relative to contemporary observations, representing increases of ~9% and ~25% relative to their historical simulations. When climate and CO 2 fertilization are considered as separate controls on plant physiology, the current climate yields maximum productivity. Yet, as future climate changes become detrimental to productivity, CO 2 fertilization becomes the dominant response, ending in an overall increase in NPP toward the end of the twenty-first century. Thus, the way in which models represent CO 2 fertilization affects their performance. Additional studies addressing the individual and simultaneous effect of other climate variables on NPP are needed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Neural net detection of seismic features related to gas hydrates and free gas accumulations on the northern U.S. Atlantic margin

Bottom-simulating reflections (BSRs) that sometimes mark the base of the gas hydrate stability zone in marine sediments are often identified based on the reverse polarity reflections that cut across stratigraphic layering in seismic amplitude data. On the northern U.S. Atlantic margin (USAM) between Cape Hatteras and Hudson Canyon, legacy seismic data have revealed pronounced BSRs south of the deepwater extension of Hudson Canyon and more subtle ones from offshore Delaware south to Cape Hatteras, where the reflections sometimes follow stratigraphic layering. Using high-resolution seismic data acquired during the 2018 Mid-Atlantic Resource Imaging Experiment and a supervised neural net, we identify seismic features associated with gas hydrates and/or the top of gas between Hudson Canyon and Cape Hatteras. Using seismic attributes especially sensitive to the presence of gas, we train a neural network algorithm on seismic data from an area with strong BSRs and then apply the model to the rest of the data set. The results indicate that gas hydrate and/or shallow free gas are significantly more widespread on the northern part of the USAM than previously known. Seismic indicators of gas extend landward from the 2000 m isobath to the upper continental slope in sectors with (offshore Virginia) and, to a lesser extent, without (offshore New Jersey) pervasive upper slope methane seeps. Higher sand content and intermediate sediment thickness, factors related to the container size and gas charge in a petroleum systems framework, are associated with more robust gas indicators.

Geochemistry & Geophysics↗
DOE OSTI2024

UNNT: A novel Utility for comparing Neural Net and Tree-based models

The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep learning in various domains of cancer research such as drug response problems. In our study, we explored tree-based models to improve the accuracy of a single drug response model and demonstrate that tree-based models such as XGBoost (eXtreme Gradient Boosting) have advantages over deep learning models, such as a convolutional neural network (CNN), for single drug response problems. However, comparing models is not a trivial task. To make training and comparing CNNs and XGBoost more accessible to users, we developed an open-source library called UNNT (A novel Utility for comparing Neural Net and Tree-based models). The case studies, in this manuscript, focus on cancer drug response datasets however the application can be used on datasets from other domains, such as chemistry.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.

97 MATHEMATICS AND COMPUTING↗