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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Characterization of the Finite Element Computational Fluid Dynamics Capabilities in the Multiphysics Object Oriented Simulation Environment

We report the multiphysics object-oriented simulation environment (moose) is a code package that couples a variety of physics modules, allowing for highly accessible multiphysics simulations. The physics modules include a finite element Navier–Stokes (N–S) module that is designed to solve laminar fluid dynamics problems. The usage of this module in multiple recent studies coupled with the growing interest in moose for usage in nonlight water reactor safety studies by the Nuclear Regulatory Commission (NRC) prompted the authors to investigate the computational fluid dynamics capabilities of moose. A two-dimensional laminar flow past a circular cylinder scenario is simulated in the moose framework to investigate the effectiveness of the N–S module. Simulations assumed an unsteady laminar flow with a Reynolds number of 200. To verify the results from moose, similar simulations were conducted using the well-utilized simulation of turbulent flow in arbitrary regions—computational continuum mechanics C++ (star-ccm + ) finite volume code. Results from both codes are also compared to some results from literature. Velocity and pressure profiles of both transient simulations were compared. The numerical and input errors in moose are also visualized with contour plots to qualitatively understand the evolution of the errors across time and space. The comparisons between moose and star-ccm + showed nearly perfect agreement between the codes for velocity and pressure, especially after the development of the vortex street in later time-steps. The force coefficients showed excellent agreement after the development of the vortex street, but demonstrated notable discrepancies prior to the vortex street development, which is likely due to how each code simulated the approach to the vortex street in earlier time-steps.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Grid Operator Analytics and Assessment Tools for Inverter- Based Resources Dominated Grid (GOAAT-IBR) Project Update

This presentation provides an update on the OPTIMA GOAAT project, with emphasis on the cloud-native data platform developed in-house to ingest, manage, and operationalize high-resolution power system data. Since our last NASPI presentation, accessible via OSTI ID #2671437, the project team advanced the design and deployment of a scalable architecture capable of handling both synchronized and non-synchronized streams, including PMU, point-on-wave (POW), COMTRADE, and SCADA data. These materials review the project status, recent progress, and key lessons learned. The core of the presentation examines the architecture and engineering of our cloud-native ingestion and data management platform. We then explain how pipelines were designed to collect, normalize, time-align, store, and serve heterogeneous data at scale. We will discuss design choices such as data models, streaming versus batch ingestion, storage tiers, and interoperability with analytics applications. Practical experiences with cloud-native technologies were shared during the event, including benefits, limitations, and integration challenges in a utility environment, along with methods used to improve performance, reduce latency, and optimize resource usage. The presentation also showcases user interface designs and visualization tools that convert raw measurements and analytics results into intuitive, actionable insights for operators and engineers. During the presentation examples were provided demonstrating how visualization, event views, and summarized analytics enhance situational awareness and support operational decision-making. These use cases illustrate how a well-designed data infrastructure can bridge the gap between high-volume measurements and practical grid operations.

Aminifar, Farrokh↗

DOE OSTI2022

Deep-learning-based workflow for boundary and small target segmentation in digital rock images using UNet++ and IK-EBM

We report three-dimensional (3D) X-ray micro-computed tomography (μCT) has been widely used in petroleum engineering because it can provide detailed pore structural information for a reservoir rock, which can be imported into a pore-scale numerical model to simulate the transport and distribution of multiple fluids in the pore space. The partial volume blurring (PVB) problem is a major challenge in segmenting raw μCT images of rock samples, which impacts boundaries and small targets near the resolution limit. We developed a deep-learning (DL)-based workflow for accurate and fast partial volume segmentation. The DL model's performance depends primarily on the training data quality and model architecture. This study employed the entropy-based-masking indicator kriging (IK-EBM) to segment 3D Berea sandstone images as training datasets. The comparison between IK-EBM and manual segmentation using a 3D synthetic sphere pack, which had a known ground truth, showed that IK-EBM had higher accuracy on partial volume segmentation. We then trained and tested the UNet++ model, a state-of-the-art supervised encoder-decoder model, for binary (i.e., void and solid) and four-class segmentation. We compared the UNet++ with the commonly used U-Net and wide U-Net models and showed that the UNet++ had the best performance in terms of pixel-wise and physics-based evaluation metrics. Specifically, boundary-scaled accuracy demonstrated that the UNet++ architecture outperformed the regular U-Net architecture in the segmentation of pixels near boundaries and small targets, which were subjected to the PVB effect. Feature map visualization illustrated that the UNet++ bridged the semantic gaps between the feature maps extracted at different depths of the network, thereby enabling faster convergence and more accurate extraction of fine-scale features. The developed workflow significantly enhances the performance of supervised encoder-decoder models in partial volume segmentation, which has extensive applications in fundamental studies of subsurface energy, water, and environmental systems.

02 PETROLEUM↗

DOE OSTI2024

DaYu: Optimizing Distributed Scientific Workflows by Decoding Dataflow Semantics and Dynamics

The combination of ever-growing scientific datasets and distributed workflow complexity creates I/O performance bottlenecks due to data volume, velocity, and variety. Although the increasing use of descriptive data formats (e.g., HDF5, netCDF) helps organize these datasets, it also creates obscure bottlenecks due to the need to translate high level operations into file addresses and then into low-level I/O operations. To address this challenge, we introduce DaYu, a method and toolset for analyzing (a) semantic relationships between logical datasets and file addresses, (b) how dataset operations translate into I/O, and (c) the combination across entire workflows. DaYu's analysis and visualization enables identification of critical bottlenecks and reasoning about remediation. We describe our methodology and propose optimization guidelines. Evaluation on scientific workflows demonstrates up to 3.7x performance improvements in I/O time for obscure bottlenecks. The time and storage overhead for DaYu's time-ordered data is typically under 0.2% of runtime and 0.25% of data volume, respectively.

Tang, Meng↗

DOE OSTI2024

In situ tumor model for longitudinal in silico imaging trials

Abstract Objective.In this article, we introduce a computational model for simulating the growth of breast cancer lesions accounting for the stiffness of surrounding anatomical structures.Approach.In our model, ligaments are classified as the most rigid structures while the softer parts of the breast are occupied by fat and glandular tissues As a result of these variations in tissue elasticity, the rapidly proliferating tumor cells are met with differential resistance. It is found that these cells are likely to circumvent stiffer terrains such as ligaments, instead electing to proliferate preferentially within the more yielding confines of the breast’s soft topography. By manipulating the interstitial tumor pressure in direct proportion to the elastic constants of the tissues surrounding the tumor, this model thus creates the potential for realizing a database of unique lesion morphology sculpted by the distinctive topography of each local anatomical infrastructure. We modeled the growth of simulated lesions within volumes extracted from fatty breast models, developed by Graffet alwith a resolution of 50μm generated with the open-source and readily available Virtual Imaging Clinical Trials for Regulatory Evaluation (VICTRE) imaging pipeline. To visualize and validate the realism of the lesion models, we leveraged the imaging component of the VICTRE pipeline, which replicates the siemens mammomat inspiration mammography system in a digital format. This system was instrumental in generating digital mammogram (DM) images for each breast model containing the simulated lesions.Results.By utilizing the DM images, we were able to effectively illustrate the imaging characteristics of the lesions as they integrated with the anatomical backgrounds. Our research also involved a reader study that compared 25 simulated DM regions of interest (ROIs) with inserted lesions from our models with DM ROIs from the DDSM dataset containing real manifestations of breast cancer. In general the simulation time for the lesions was approximately 2.5 hours, but it varied depending on the lesion’s local environment.Significance.The lesion growth model will facilitate and enhance longitudinal in silico trials investigating the progression of breast cancer.

Engineering↗

DOE OSTI2024

AMPP-4 Tank Tracking Tools

The Material Recovery & Recycle group (AMPP-4) Tank Tracking Tools are Microsoft Excel-based tools used to visualize the liquid levels in the tanks used by teams within AMPP-4. The tools rely on reports generated by the Tracking Hydrogen Accumulation in Aqueous Operations spreadsheet to provide daily data of the volume occupied by radioactive liquid waste (RLW). This set-up allows for rapid-response in viewing historical (from October 2024) and current data in thermometer-type charts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Development of a silicon carbide ceramic based counter-flow heat exchanger by binder jetting and liquid silicon infiltration for concentrating solar power

A silicon carbide ceramic counter-flow heat exchanger with integrated headers was printed by binder jetting additive manufacturing process. Multiple phenolic binder infiltration cycles (3 or 5) followed by pyrolysis were conducted to increase the net carbon content of the printed SiC specimens. Subsequently, to attain full densification, silicon melt infiltration was used. The microstructure and mechanical properties were comprehensively characterized on the densified material. The chemical compositions and visual distribution of the various regions in the specimens were determined via scanning electron microscopy, while X-ray diffraction and synchrotron µ-computed tomography were used to provide a quantitative assessment of the volume fractions of the identified phase regions. Microhardness measurements showed dependence on the local microstructure. The fracture strength of the material was correlated with the specimen density and agreed with the reported values in the literature. High-temperature exposure at 750 °C for up to 200h did not degrade the strength for the specimens with three phenolic-binder infiltrations; however, the strengths degraded for ones with five phenolic-binder infiltrations. The associated fracture toughnesses of the specimens were ~3.4 MPam 1/2 at room temperature and 750°C, and the thermal conductivities varied from >150 W/mK at room temperature to ~45 W/mK at 750°C. Hence, this study validated the use of the binder-jetting printed SiC ceramic materials for high-temperature heat exchanges. Lastly, we also present in this work the first successful fabrication of a binder-jetting printed one-piece dense SiC ceramic heat exchanger body with unblocked channels that can be used for the flow of heat transfer fluids.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Flame stabilization in DME spray flames under engine-relevant conditions characterized by OH* chemiluminescence and formaldehyde laser-induced fluorescence

The transient and quasi-steady flame structures of Dimethyl Ether (DME) fuel sprays, produced by a single-hole injector (Spray D), were investigated using Planar Laser-Induced Fluorescence (PLIF) and chemiluminescence imaging in a constant-volume chamber under Engine Combustion Network (ECN) Spray A conditions (900 K ambient temperature, 60 bar ambient pressure, 1500 bar injection pressure, and 22.8 kg/m 3 ambient density). Low-temperature chemical reaction zones were visualized using formaldehyde (CH 2 O) PLIF with 355 nm excitation, while high-temperature flame regions were captured via chemiluminescence imaging of excited-state hydroxyl radicals (OH*). Both transient and quasi-steady flame structures clearly show the transition from CH 2 O to OH*, highlighting the progression from low- to high-temperature combustion, while the position of the flame is displaced for DME compared to reference hydrocarbon n-dodecane. Homogeneous reactor calculations with detailed chemistry and using adiabatic mixing for initial temperature show that CH 2 O peaks are significantly higher for DME at the same equivalence ratio, with a higher heat-release during the cool-flame regime with respect to the fuel heating value. Thus, the cool-flame dynamic as a precursor to high-temperature combustion and flame stabilization exhibit distinct behavior for DME relative to conventional hydrocarbons, and these phenomena are effectively resolved through the soot-free nature of DME and the high-speed, time-resolved diagnostics.

CH2O laser-induced fluorescence↗

DOE OSTI2023

Anatomy of an agricultural antagonist: Feeding complex structure and function of three xylem sap‐feeding insects illuminated with synchrotron‐based 3D imaging

Abstract Many insects feed on xylem or phloem sap of vascular plants. Although physical damage to the plant is minimal, the process of insect feeding can transmit lethal viruses and bacterial pathogens. Disparities between insect‐mediated pathogen transmission efficiency have been identified among xylem sap‐feeding insects; however, the mechanistic drivers of these trends are unclear. Identifying and understanding the structural factors and associated integrated functional components that may ultimately determine these disparities are critical for managing plant diseases. Here, we applied synchrotron‐based X‐ray microcomputed tomography to digitally reconstruct the morphology of three xylem sap‐feeding insect vectors of plant pathogens: Graphocephala atropunctata (blue‐green sharpshooter; Hemiptera, Cicadellidae) and Homalodisca vitripennis (glassy‐winged sharpshooter; Hemiptera, Cicadellidae), and the spittlebug Philaenus spumarius (meadow spittlebug; Hemiptera, Aphrophoridae). The application of this technique revealed previously undescribed anatomical features of these organisms, such as key components of the salivary complex. The visualization of the 3D structure of the precibarial valve led to new insights into the mechanism of how this structure functions. Morphological disparities with functional implications between taxa were highlighted as well, including the morphology and volume of the cibarial dilator musculature responsible for extracting xylem sap, which has implications for force application capabilities. These morphological insights will be used to target analyses illuminating functional differences in feeding behavior.

3D imaging↗

DOE OSTI2024

YOLO2U-Net: Detection-guided 3D instance segmentation for microscopy

Microscopy imaging techniques are instrumental for characterization and analysis of biological structures. As these techniques typically render 3D visualization of cells by stacking 2D projections, issues such as out-of-plane excitation and low resolution in the z-axis may pose challenges (even for human experts) to detect individual cells in 3D volumes as these non-overlapping cells may appear as overlapping. In this paper a comprehensive method for accurate 3D instance segmentation of cells in the brain tissue is introduced. The proposed method combines the 2D YOLO detection method with a multi-view fusion algorithm to construct a 3D localization of the cells. Next, the 3D bounding boxes along with the data volume are input to a 3D U-Net network that is designed to segment the primary cell in each 3D bounding box, and in turn, to carry out instance segmentation of cells in the entire volume. The promising performance of the proposed method is shown in comparison with current deep learning-based 3D instance segmentation methods.

3D instance segmentation↗

DOE OSTI2022

MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.

Tabassum, Anika↗

DOE OSTI2022

Li-ion Battery Material phase prediction through Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are widely adopted in many industrial applications. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Therefore, it can eventually help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. However, these images often suffer from low image contrast between multiple material constituents , making it difficult for humans to distinguish and characterize these constituents through visualization. A minor error in detecting distributions among the material constituents can lead to a high error in the calculated parameters of material properties.We present a novel hierarchical curriculum learning framework to address the complex task of estimating material constituent distribution in battery electrodes. To provide spatially smooth prediction, our framework comprises three modules: (i) an uncertainty-aware model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a technique to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator to appropriately fuse the local and global effects towards obtaining the final distribution.

Tabassum, Anika↗

DOE OSTI2022

Influence of Entrainment on Centimeter-Scale Cloud Microphysics in Marine Stratocumulus Clouds Observed during CSET

Abstract Cloud microphysical relationships observed during the Cloud System Evolution in the Trades (CSET) campaign held between Northern California and Hawaii were analyzed to study the effects of entrainment and subsequent mixing of free-tropospheric and cloudy air on cloud microphysical properties of marine stratocumulus clouds. The data measured by Holographic Detector for Clouds (HOLODEC) were extensively used because they could provide the 3D positions and sizes of droplets within sample volume on the centimeter scale, making it possible to explore the 3D spatial distribution of droplets, which has not been possible for conventional cloud probes. This study focused on analyzing the 3D spatial distribution of droplets and visual traits of inhomogeneous mixing and on quantifying the relationship between 3D spatial distributions and traits of inhomogeneous mixing. Two types of spatial distributions are compared. The first is measured droplet spatial distribution and the second type is generated randomly distributed droplets using the Monte Carlo approach, that is, to analyze whether or not clustering is strong enough to classify as a clustered distribution for a hologram. The difference between the two types of spatial distributions depends on whether they are affected by entrainment and mixing. The holograms observed near the cloud top, where the effects of entrainment and mixing would be immediate, showed relatively high confidence in the significance test for spatially clustered populations of droplets. Moreover, spatially clustered holograms appeared to exhibit stronger visual traits of inhomogeneous mixing than perfectly randomly distributed holograms only when observed near the cloud top. On the other hand, these characteristics did not appear for holograms observed deeper into the cloud where the effects of entrainment and mixing would be reduced. Such 3D structural characteristics of droplet distributions seem to be consistent with vertical circulation mixing.

Meteorology & Atmospheric Sciences↗

DOE OSTI2023

Design and Testing of the Vortex Ring Facility

This document summarizes the efforts of building an experimental test facility to study the evolution of the cloud following a nuclear detonation above ground level and support the development of numerical models to describe it. The experimental facility allows nonintrusive flow visualization and measurements of buoyant vortex ring formation and evolution using background-oriented Schlieren and particle image velocimetry techniques. In addition, 3D unsteady computational fluid dynamics simulations using unsteady Reynolds-averaged Navier–Stokes and volume-of-fluid numerical methods were performed to support the experimental design as well as to provide insight about the formation and evolution of the vortex ring.

42 ENGINEERING↗

DOE OSTI2022

SDSS IV MaNGA: visual morphological and statistical characterization of the DR15 sample

ABSTRACT We present a detailed visual morphological classification for the 4614 MaNGA galaxies in SDSS Data Release 15, using image mosaics generated from a combination of r band (SDSS and deeper DESI Legacy Surveys) images and their digital post-processing. We distinguish 13 Hubble types and identify the presence of bars and bright tidal debris. After correcting the MaNGA sample for volume completeness, we calculate the morphological fractions, the bi-variate distribution of type and stellar mass M* – where we recognize a morphological transition ‘valley’ around S0a-Sa types – and the variations of the g − i colour and luminosity-weighted age over this distribution. We identified bars in 46.8 per cent of galaxies, present in all Hubble types later than S0. This fraction amounts to a factor ∼2 larger when compared with other works for samples in common. We detected 14 per cent of galaxies with tidal features, with the fraction changing with M* and morphology. For 355 galaxies, the classification was uncertain; they are visually faint, mostly of low/intermediate masses, low concentrations, and discy in nature. Our morphological classification agrees well with other works for samples in common, though some particular differences emerge, showing that our image procedures allow us to identify a wealth of added value information as compared to SDSS-based previous estimates. Based on our classification, we also propose an alternative criteria for the E–S0 separation, in the structural semimajor to semiminor axis versus bulge to total light ratio (b/a − B/T) and concentration versus semimajor to semiminor axis (C − b/a) space.

Vázquez-Mata, J. A. (ORCID:0000000186941204)↗

DOE OSTI2022

Stochastic Neutronics Primer (Volume I)

The purpose of this document is to introduce the elementary concepts and to build a concrete understanding of the stochastic theory of neutron transport to the motivated undergraduate student, the Ph.D. engineer/physicist/mathematician, and the staff scientist or professor learning yet another new skill. The authors understand that there are many learning types- from visual to analytical to repetitive to word-based to analogy-based (and combinations thereof)- and so we have tasked ourselves with providing as many representations as possible within to ensure every reader a fruitful endeavor. This document is written as a set of chapters that continually build upon the previous chapter. In this chapter, we provide the discussion, motivation, and background topics for the remainder of the text.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Cost and Performance Baseline for Fossil Energy Plants – Volume 1: Bituminous Coal and Natural Gas to Electricity: May 2025

This presentation at EPRI's annual Generation Sector Meeting during the Advanced Generation and Carbon Capture and Storage session provides an overview of the recently published Cost and Performance Baseline for Fossil Energy Plants – Volume 1: Bituminous Coal and Natural Gas to Electricity dated May 2025. It further connects viewers with the recently released tool/dashboard, an interactive tool that allows users to manipulate six key study parameters simultaneously for each case and visualize the impact on three metrics: 1) levelized cost of electricity (LCOE), 2) cost of CO2 captured (CCC), and 3) cost of CO2 avoided (CCA).

baseline study↗

DOE OSTI2023

Analysis of Pitting Corrosion on Wrought and Additively Manufactured 316L Stainless Steel in Atmospheric Environments

Additive manufacturing of metal components enables rapid fabrication of complex geometries. However, metal additive manufacturing also introduces new morphological and microstructural characteristics which might be detrimental to component performance. Here we report the pitting corrosion properties of wrought and additively manufactured 316L stainless steel after atmospheric exposure to coastal environments and laboratory-created environments. Qualitative visualization in combination with quantitative analysis of resulting pits provided an in-depth understanding of pitting differences between wrought and additively manufactured 316L stainless steel and between coastal and laboratory-based exposure. Optical and scanning electron microscopy were utilized for visualization, while white light interferometry measured pits across approximately 5mm x 5mm areas on each sample. Post-processing of the interferometry data enables quantification of pitting attack for each sample in terms of both pit depth and pit volume. The pitting analysis introduced herein offers a new technique to compare pitting attack between different manufacturing processes and materials.

36 MATERIALS SCIENCE↗