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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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DOE OSTI2021

VAINE: Visualization and AI for Natural Experiments

Natural experiments are observational studies where the assignment of treatment conditions to different populations occur by chance ``in the wild''. Researchers from fields such as economics, healthcare, and the social sciences leverage natural experiments to conduct hypothesis testing and causal effect estimation for treatment and outcome variables that would otherwise be costly, infeasible, or unethical. In this paper, we introduce VAINE (Visualization and AI for Natural Experiments), a visual analytics tool for identifying and understanding natural experiments from observational data. We then demonstrate how VAINE can be used to validate causal relationships, estimate average treatment effects, and identify statistical phenomena such as Simpson’s paradox through two use cases.

Guo, Grace↗

DOE OSTI2020

A proximity‐based image‐processing algorithm for colloid assignment in segmented multiphase flow datasets

Summary Colloidal transport and deposition are of both environmental and engineering importance. Easier access to x‐ray microtomography (XMT) coupled with improved imaging resolution has made XMT a unique and viable tool for visualizing and quantifying these processes. Currently, there is scant information in the literature addressing colloid segmentation and analysis in saturated and unsaturated porous media, in particular related to spatial partitioning of colloids. To support this need, an approach to assign segmented colloidal particles and aggregates to different partitioning classes based on their proximity to different phases is presented here. The method uses different markers for each attachment site (e.g. wetting‐nonwetting phase interfaces). An example XMT dataset from a drainage experiment is used to demonstrate the efficacy of the image processing algorithms. Flow conditions, and fluid and colloid properties, can thus be compared to the behaviour of colloids within the porous medium. This algorithm can help elucidate colloidal deposition mechanisms and the importance of different attachment sites, explore the importance of fluid properties, as well as the arrangement and shape of the colloids.

BRUECK, C. L.↗

DOE OSTI2013

Chemical sensing and imaging in microfluidic pore network structures relevant to natural carbon cycling and industrial carbon sequestration

Energy and climate change represent significant factors in global security. Atmospheric carbon dioxide levels, while global in scope, are influenced by pore-scale phenomena in the subsurface. We are developing tools to visualize and investigate processes in pore network microfluidic structures with transparent covers as representations of normally-opaque porous media. In situ fluorescent oxygen sensing methods and fluorescent cellulosic materials are being used to investigate processes related to terrestrial carbon cycling involving cellulytic respiring microorganisms. These structures also enable visualization of water displacement from pore spaces by hydrophobic fluids, including carbon dioxide, in studies related to carbon sequestration.

carbon sequestration↗

DOE OSTI2019

Macrophysical Properties of Continental Shallow Cumuli: Diurnal Evolution

We develop a new climatology of the macrophysical properties of single-layer shallow cumuli (ShCu), such as cloud amount and cloud base/top heights, observed during 19 summers (2000-2018) at the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) Central Facility in northern Oklahoma, USA. Similar to the established datasets, the climatology incorporates the well-known advantages of the narrow field-of-view (FOV) lidar-radar measurements to resolve the vertical structure of clouds along the wind direction. In contrast to these datasets, the climatology combines the well-known advantages of the wide-FOV sky images to describe the horizontal changes of cloud amount across the wind direction. The recent update includes (1) a new tool for visualization of these across-wind changes with user-selected spatial and temporal resolutions, (2) an additional macrophysical property, the so-called cloud equivalent diameter (CED), estimated over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30s) and (3) environmental parameters. Our development of the extended climatology is aimed to enhance understanding of the environmental impact on the diurnal evolution of the cloud macrophysical properties and thus to improve performance of ShCu parameterizations.

cloud area, Cloud cover, cloud fraction, continene↗

DOE OSTI2015

SU-E-T-622: Planning Technique for Passively-Scattered Involved-Node Proton Therapy of Mediastinal Lymphoma with Consideration of Cardiac Motion

Purpose: To develop a treatment planning method for passively-scattered involved-node proton therapy of mediastinal lymphoma robust to breathing and cardiac motions. Methods: Beam-specific planning treatment volumes (bsPTV) are calculated for each proton field to incorporate pertinent uncertainties. Geometric margins are added laterally to each beam while margins for range uncertainty due to setup errors, breathing, and calibration curve uncertainties are added along each beam. The calculation of breathing motion and deformation effects on proton range includes all 4DCT phases. The anisotropic water equivalent margins are translated to distances on average 4DCT. Treatment plans are designed so each beam adequately covers the corresponding bsPTV. For targets close to the heart, cardiac motion effects on dosemaps are estimated by using a library of anonymous ECG-gated cardiac CTs (cCT). The cCT, originally contrast-enhanced, are partially overridden to allow meaningful proton dose calculations. Targets similar to the treatment targets are drawn on one or more cCT sets matching the anatomy of the patient. Plans based on the average cCT are calculated on individual phases, then deformed to the average and accumulated. When clinically significant dose discrepancies occur between planned and accumulated doses, the patient plan is modified to reduce the cardiac motion effects. Results: We found that bsPTVs as planning targets create dose distributions similar to the conventional proton planning distributions, while they are a valuable tool for visualization of the uncertainties. For large targets with variability in motion and depth, integral dose was reduced because of the anisotropic margins. In most cases, heart motion has a clinically insignificant effect on target coverage. Conclusion: A treatment planning method was developed and used for proton therapy of mediastinal lymphoma. The technique incorporates bsPTVs compensating for all common sources of uncertainties and estimation of the effects of cardiac motion not commonly performed.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2015

TU-AB-303-02: A Novel Surrogate to Identify Anatomical Changes During Radiotherapy of Head and Neck Cancer Patients

Purpose: To develop a novel method to monitor external anatomical changes in head and neck cancer patients in order to help guide adaptive radiotherapy decisions. Methods: The method, developed in MATLAB, reveals internal anatomical changes based on variations observed in external anatomy. Weekly kV-CBCT scans from 11 Head and neck patients were retrospectively analyzed. The pre-processing step first corrects each CBCT for artifacts and removes pixels from the immobilization mask to produce an accurate external contour of the patient’s skin. After registering the CBCTs to the initial planning CT, the external contours from each CBCT (CBCTn) are transferred to the first week — reference — CBCT{sub 1}. Contour radii, defined as the distances between an external contour and the central pixel of each CBCT slice, are calculated for each scan at angular increments of 1 degree. The changes in external anatomy are then quantified by the difference in radial distance between the external contours of CBCT1 and CBCTn. The radial difference is finally displayed on a 2D intensity map (angle vs radial distance difference) in order to highlight regions of interests with significant changes. Results: The 2D radial difference maps provided qualitative and quantitative information, such as the location and the magnitude of external contour divergences and the rate at which these deviations occur. With this method, anatomical changes due to tumor volume shrinkage and patient weight loss were clearly identified and could be correlated with the under-dosage of targets or over-dosage of OARs. Conclusion: This novel method provides an efficient tool to visualize 3D external anatomical modification on a single 2D map. It quickly pinpoints the location of differences in anatomy during the course of radiotherapy, which can help determine if a treatment plan needs to be adapted.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Quantifying Uncertainty in HPC Job Queue Time Predictions

High Performance Computing (HPC) has developed at an unprecedented pace in recent decades. This growth has demanded corresponding development in the area of HPC Operational Data Analytics (ODA), which encompasses a wide range of data analysis techniques, ML/AI efforts, tools, and visualizations. Published studies in ODA offer a variety of practical ways to inform HPC users, administrators, procurement managers, and other stakeholders. Uncertainty analysis, however, is rare in the related published literature. For instance, we identify only 1 out of 14 existing studies focused on job queue time prediction that investigates the uncertainty aspect of their proposed predictions. We recognize the utmost importance uncertainty quantification can have in such predictive analytics solutions, with consequences in how users interpret information they receive, and attempt to bridge this gap. With the goal of improving access to such insights, we develop a process for determining upper and lower bounds of the predicted queue times of a regression model at a specified confidence level. Our current research is focused on the uncertainty in predicting job queue times, yet our approach may be employed in predicting other metrics.

HPC↗

DOE OSTI2020

Three-Dimensional Single-Molecule Localization Microscopy in Whole-Cell and Tissue Specimens

Super-resolution microscopy techniques are versatile and powerful tools for visualizing organelle structures, interactions, and protein functions in biomedical research. However, whole-cell and tissue specimens challenge the achievable resolution and depth of nanoscopy methods. We focus on three-dimensional single-molecule localization microscopy and review some of the major roadblocks and developing solutions to resolving thick volumes of cells and tissues at the nanoscale in three dimensions. These challenges include background fluorescence, system- and sample-induced aberrations, and information carried by photons, as well as drift correction, volume reconstruction, and photobleaching mitigation. We also highlight examples of innovations that have demonstrated significant breakthroughs in addressing the abovementioned challenges together with their core concepts as well as their trade-offs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

becquerel (bq) v0.4.0

Becquerel is a Python package for analyzing nuclear spectroscopic measurements. The core functionalities are reading and writing different spectrum file types, fitting spectral features, rebinning spectrum counts to different bin edges, performing detector calibrations and interpreting measurement results. It also includes tools for visualizing radiation spectra and fits of different spectral features, as well as convenient access to tabulated nuclear data both from remote servers and local caches. It relies heavily on the standard scientific Python stack of numpy, scipy, matplotlib, pandas, and numba. It is intended to be general-purpose enough that it can be useful to anyone from an undergraduate taking a laboratory course to the advanced

Bandstra, Mark↗

DOE OSTI2022

cisagov/RedEye

RedEye is a visual analytic tool for supporting Red & Blue Team operations

Golding, Austin↗

DOE OSTI2021

Battery Lifecycle Framework

The Battery Lifecycle (BLC) Framework is an open-source platform that provides tools to visualize and share battery data from material characterization, cell testing, manufacturing, and field testing through the technology development cycle. BLC has three components: data importers, a front-end for querying the data and creating visualizations, and an application programming interface to provide access to the data from Python. BLC has been deployed for tracking the development of a battery from the lab to a manufacturing line and systems installed in the field and for comparing studies of multiple cells of the same battery chemistry and configuration. The code was developed around Redash, a robust open-source extract-transform-load engine. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-4546 O

De Angelis, Valerio↗

DOE OSTI2025

T3CO-Go: A web-based dashboard for the Transportation Technology Total Cost of Ownership tool [SWR-25-38]

T3CO-Go is a web-based dashboard with a user interface to modify input assumptions, run the T3CO tool, and visualize results. The dashboard, built using Python, can be run on a local server when installed from PyPI or hosted on the cloud and embedded in a webpage. T3CO-Go allows even non-Python-proficient users to customize their T3CO experience and gain insights from results customized for their analysis purpose. See also, PyPI Package: https://pypi.org/project/t3co-go/

Panneer Selvam, Harish [National Renewable Energy ↗

DOE OSTI2025

Electric Load Planning Tool (ELPT) v0.9

The Electric Load Planning Tool (ELPT) helps facilities understand the economic and environmental impacts of their electricity consumption. Using a user-provided Excel input, ELPT analyzes electricity use, costs, and grid CO2e emissions to identify savings opportunities through load management strategies such as load shifting, shedding, and planning. It accounts for Time-of-Use (TOU) tariffs and hourly emissions factors, varying by location and time of day. Users input details about their facility's load profile, location, year of analysis, and electricity billing tariff to receive customized insights. The tool provides visual representations of cost and GHG impacts, helping users understand the benefits of adjusting electricity usage to align with periods of cheaper and cleaner electricity, thereby achieving cost savings and reducing Scope 2 CO2e emissions

Karki, Unique [Lawrence Berkeley National Laborato↗

DOE OSTI2025

becquerel (bq) v0.7.0

Bandstra, Mark [Lawrence Berkeley National Laborat↗

DOE OSTI2022

Toward Human-Centric Transportation and Energy Metrics: Influence of Mode, Vehicle Occupancy, Trip Distance, and Fuel Economy

Traditional metrics measuring transportation and energy outcomes can be augmented to better represent impacts on people's lives and systems-level performance. In this context, this study introduces two novel metrics: road capacity (as number of people traveling and accessing services) and energy intensity (as energy use for people traveling and accessing services). Current national-level distributions of available data in the United States for factors contributing to the two new integrated metrics are used as context to evaluate potential outcomes. These factors include vehicle occupancy, mode share, fuel economy, and trip distance. Variations in input values provide insights on how these factors shape efficiencies in road capacity and energy intensity. Parametric sensitivity analysis indicates that the impact of each input depends upon the metric being evaluated. For the human-centered road capacity mobility metric, increasing vehicle occupancy has the largest effect – twice that of increasing mode share for bike, walk, and transit. For the energy intensity mobility metric, the effect of improving fuel economy is the largest. However, when the focus is on accessibility (instead of mobility), for both metrics the effect of lowering average trip distance is the largest. Additionally, a novel interactive tool to visualize the results for various parameter combinations makes the metrics practitioner ready. The findings suggest that the diffusion of new human-centric metrics that benchmark outcomes associated with road capacity and energy may be significant in motivating new sustainable transportation investments and efficient utilization of infrastructure, mobility assets, and services.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2019

Preliminary Designs for Criticality Safety Benchmarks—Iron/Steel/Chromium Series

The nuclear criticality safety community uses critical benchmarks extensively when analyzing nuclear systems. Having benchmarks sensitive to important materials of interest helps to ensure safety and constrain results. Critical benchmarks are also invaluable to the nuclear data community, which uses these benchmarks as a check and measure of quality of nuclear data libraries. Additionally, integral benchmarks are often used to validate the performance of computational tools for simulating nuclear systems. Many integral benchmarks have been documented in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) handbook, the International Reactor Physics Experiment Evaluation Project (IRPhEP) handbook, and the Shielding Integral Benchmark Archive Database (SINBAD). The National Criticality Experiments Research Center (NCERC), operated by Los Alamos National Laboratory, is the only general purpose critical experiments facility in the United States of America. NCERC regularly designs and executes critical experiments and other measurements useful to a wide variety of fields including nuclear criticality safety, commercial nuclear energy, nonproliferation, and nuclear data. NCERC has recently developed tools to visualize gaps and deficiencies in existing criticality benchmarks – this work is still ongoing. Through this work and other reviews, some high priority deficiencies have been identified. This paper aims to highlight three areas where additional critical benchmarks would be useful to the nuclear criticality safety community and various other groups. Additionally, NCERC is seeking feedback in regard to these proposed measurements to make sure they are as useful to as many communities as possible.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

EXCESS workshop: Descriptions of rising low-energy spectra

Many low-threshold experiments observe sharply rising event rates of yet unknown origins below a few hundred eV, and larger than expected from known backgrounds. Due to the significant impact of this excess on the dark matter or neutrino sensitivity of these experiments, a collective effort has been started to share the knowledge about the individual observations. For this, the EXCESS Workshop was initiated. In its first iteration in June 2021, ten rare event search collaborations contributed to this initiative via talks and discussions. The contributing collaborations were CONNIE, CRESST, DAMIC, EDELWEISS, MINER, NEWS-G, NUCLEUS, RICOCHET, SENSEI and SuperCDMS. They presented data about their observed energy spectra and known backgrounds together with details about the respective measurements. In this paper, we summarize the presented information and give a comprehensive overview of the similarities and differences between the distinct measurements. The provided data is furthermore publicly available on the workshop's data repository together with a plotting tool for visualization.

Adari, Prakruth↗

DOE OSTI2012

Methods for Data-based Delineation of Spatial Regions

In data analysis, it is often useful to delineate or segregate areas of interest from the general population of data in order to concentrate further analysis efforts on smaller areas. Three methods are presented here for automatically generating polygons around spatial data of interest. Each method addresses a distinct data type. These methods were developed for and implemented in the sample planning tool called Visual Sample Plan (VSP). Method A is used to delineate areas of elevated values in a rectangular grid of data (raster). The data used for this method are spatially related. Although VSP uses data from a kriging process for this method, it will work for any type of data that is spatially coherent and appears on a regular grid. Method B is used to surround areas of interest characterized by individual data points that are congregated within a certain distance of each other. Areas where data are “clumped” together spatially will be delineated. Method C is used to recreate the original boundary in a raster of data that separated data values from non-values. This is useful when a rectangular raster of data contains non-values (missing data) that indicate they were outside of some original boundary. If the original boundary is not delivered with the raster, this method will approximate the original boundary.

Algorithm↗