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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A study of adhesive bonding in metal–metal, metal–CFRP, and CFRP–CFRP material combinations under shear deformation: Fracture morphologies and damage mechanisms

Safe design of adhesive joining in multi-materials in engineered structures requires the accumulation of numerous experimental data on the failure behavior of various adhesively-bonded material combinations under different loading conditions. The deep understanding of mechanical performance, fracturing morphologies, and main damage mechanisms is also quintessential for accelerating the development of proper physics-based and multi-scale models for assisting the design. Towards this goal, this work presents a comprehensive characterization of the failure behavior of adhesively-bonded metal–metal, metal–CFRP, and CFRP–CFRP material combinations under global shear deformation via single lap shear testing. Thanks to a synergistic combination of measurement methods by using Digital Imaging Correlation (DIC) and 3D optical profilometry, adhesive features on the adherend after failure were quantified and the main progressive damage mechanisms were identified. The characterization performed in this work provides quantitative data that contributes to a better understanding of shear failure in adhesive bonding across different bi-material combinations. Finally, the obtained results have practical implications, including the potential to enhance adhesive bonding design, identify failure causes in adhesive joints, and develop or validate computational models capable of capturing the observed behavior in various adhesively-bonded materials under global shear deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Improved proton computed tomography by dual modality image reconstruction

Purpose: Proton computed tomography (CT) is a promising image modality for improving the stopping power estimates and dose calculations for particle therapy. However, the finite range of about 33 cm of water of most commercial proton therapy systems limits the sites that can be scanned from a full 360° rotation. In this paper the authors propose a method to overcome the problem using a dual modality reconstruction (DMR) combining the proton data with a cone-beam x-ray prior. Methods: A Catphan 600 phantom was scanned using a cone beam x-ray CT scanner. A digital replica of the phantom was created in the Monte Carlo code Geant4 and a 360° proton CT scan was simulated, storing the entrance and exit position and momentum vector of every proton. Proton CT images were reconstructed using a varying number of angles from the scan. The proton CT images were reconstructed using a constrained nonlinear conjugate gradient algorithm, minimizing total variation and the x-ray CT prior while remaining consistent with the proton projection data. The proton histories were reconstructed along curved cubic-spline paths. Results: The spatial resolution of the cone beam CT prior was retained for the fully sampled case and the 90° interval case, with the MTF = 0.5 (modulation transfer function) ranging from 5.22 to 5.65 linepairs/cm. In the 45° interval case, the MTF = 0.5 dropped to 3.91 linepairs/cm For the fully sampled DMR, the maximal root mean square (RMS) error was 0.006 in units of relative stopping power. For the limited angle cases the maximal RMS error was 0.18, an almost five-fold improvement over the cone beam CT estimate. Conclusions: Dual modality reconstruction yields the high spatial resolution of cone beam x-ray CT while maintaining the improved stopping power estimation of proton CT. In the case of limited angles, the use of prior image proton CT greatly improves the resolution and stopping power estimate, but does not fully achieve the quality of a 360° proton CT scan.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

A HPC Theory-Guided Machine Learning Cyberinfrastructure for Communicating Hydrometeorological Data Across Scales

High-resolution predictions of hydrometeorological variables are critical for supporting hydropower generation decisions and flood control at hydroelectric power plants. Traditional climate and hydrologic models rely on the numerical simulation of detailed physical processes. Therefore, running these simulations is time-, labor-, and computation-intensive. Improving the spatial and temporal resolution in these modeling outputs could lead to cubic increases in both the simulation time and computational demands, rendering high-resolution hydrometeorological predictions expensive and impractical. Many past studies apply the super resolution (SR) technique to downscale climate models using deep learners. However, deep learners are deemed “black-boxes,” as their derivation processes from low-resolution outputs to high-resolution outputs are often hidden. Their results are difficult for domain scientists to interpret and validate. Thus, there is a need for an exploratory machine learning approach that can partially integrate domain-specific theory and knowledge into the data-driven mapping process between simulation outputs of different spatial scales. The domain-specific theory and knowledge can be incorporated into the data model through an inductive approach in which process-related environmental variables are used and analyzed as key drivers (i.e., environmental surrogates) to reflect the complex physical processes. Many of these variables, such as land use land cover, soil types, topography, digital elevation, air temperature, and various watershed characteristics, can be directly measured through sensors or remote sensing techniques. Additionally, SR applications that can downscale hydrological and hydrodynamics models to efficiently produce high-resolution (1 m) flood depth grids are still rare. Since the flood depth grid can be used to support critical decisions for flood control operation at hydroelectric power plants, it is crucial to enable an SR-based capability for interpolating high-resolution flood inundation maps.

13 HYDRO ENERGY↗
DOE OSTI2019

Comparison of maximal Rubidium-82 activities for myocardial blood flow quantification between digital and conventional PET systems

Background: PET-based myocardial blood flow (MBF) quantification can be inaccurate when using high tracer activities. Our aim was to derive the maximal Rubidium-82 activity for MBF assessment using a new digital PET system and compare the results with conventional analog systems. Methods: 1.8 GBq Rubidium-82 was injected into the cardiac insert of an anthropomorphic torso phantom. Data were acquired for 10 min using an Ingenuity TF (Philips Healthcare), Discovery 690 (D690, GE Healthcare), and digital PET prototype system (Philips Healthcare). The dynamic ranges, defined as the maximal measured activity in the reconstructed images deviating < 10% from the true present activity, were determined in all scans. Results: The dynamic ranges were 312 MBq for Ingenuity TF, 650 MBq for D690, and 654 MBq for digital PET prototype. Conclusions: The maximal Rb-82 activity for MBF assessment using digital PET prototype is higher than that for its analog counterpart (Ingenuity TF), but seems comparable to the D690.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

Extraction of Hydrological Proximity Measures from DEMs using Parallel Processing

Land surface topography is one of the most important terrain properties which impact hydrological, geomorphological, and ecological processes active on a landscape. In our previous efforts to develop a soil depth model based upon topographic and land cover variables, we extracted a set of hydrological proximity measures (HPMs) from a Digital Elevation Model (DEM) as potential explanatory variables for soil depth. These HPMs may also have other, more general modeling applicability in hydrology, geomorphology and ecology, and so are described here from a general perspective. The HPMs we derived are variations of the distance up to ridge points (cells with no incoming flow) and variations of the distance down to stream points (cells with a contributing area greater than a threshold), following the flow path. These HPMs were computed using the D-infinity flow model that apportions flow between adjacent neighbors based on the direction of steepest downward slope on the eight triangular facets constructed in a 3 x 3 grid cell window using the center cell and each pair of adjacent neighboring grid cells in turn. The D-infinity model typically results in multiple flow paths between 2 points on the topography, with the result that distances may be computed as the minimum, maximum or average of the individual flow paths. In addition, each of the HPMs, are calculated vertically, horizontally, and along the land surface. Previously, these HPMs were calculated using recursive serial algorithms which suffered from stack overflow problems when used to process large datasets, limiting the size of DEMs that could be analyzed using that method to approximately 7000 x 7000 cells. To overcome this limitation, we developed a message passing interface (MPI) parallel approach for calculating these HPMs. The parallel algorithms of the HPMs spatially partition the input grid into stripes which are each assigned to separate processes for computation. Each of those processes then uses a queue data structure to order the processing of cells so that each cell is visited only once and the cross-process communications that are a standard part of MPI are handled in an efficient manner. This parallel approach allows analysis of much larger DEMs as compared to the serial recursive algorithms. In this paper, we present the definitions of the HPMs, the serial and parallel algorithms used in their extraction and their potential applications in hydrology, geomorphology and ecology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

X-ray computed tomography-based porosity analysis: Algorithms and application for porous woody biomass

The properties of lignocellulosic biomass, such as bulk physical, thermal, and mechanical properties, as well as the mobility of enzymes or catalysts, are largely affected by porosity, pore structures, and pore size distribution. While X-ray computed tomography (CT) has been introduced to effectively produce 3D volumetric images of material microstructure, a quantitative porosity analysis from the 3D images has never been done. This work introduces a first-of-its-kind X-ray CT-based quantitative porosity analysis method and applies the developed toolkit to characterize the internal porosity distribution of loblolly pine. A sample loblolly pine chip was scanned by a nano-resolution X-ray CT system and digitally reconstructed after a sequence of image processing operations. Here, a comprehensive porosity analysis was then performed to quantify the envelope porosity, the spatial distribution of local porosity, and the directional porosity. Solutions to the various challenges in image processing, porosity calculation, and large data handling are provided.

42 ENGINEERING↗
DOE OSTI2013

PHOTOMETRIC REDSHIFTS FOR QUASARS IN MULTI-BAND SURVEYS

The Multi Layer Perceptron with Quasi Newton Algorithm (MLPQNA) is a machine learning method that can be used to cope with regression and classification problems on complex and massive data sets. In this paper, we give a formal description of the method and present the results of its application to the evaluation of photometric redshifts for quasars. The data set used for the experiment was obtained by merging four different surveys (Sloan Digital Sky Survey, GALEX, UKIDSS, and WISE), thus covering a wide range of wavelengths from the UV to the mid-infrared. The method is able (1) to achieve a very high accuracy, (2) to drastically reduce the number of outliers and catastrophic objects, and (3) to discriminate among parameters (or features) on the basis of their significance, so that the number of features used for training and analysis can be optimized in order to reduce both the computational demands and the effects of degeneracy. The best experiment, which makes use of a selected combination of parameters drawn from the four surveys, leads, in terms of {Delta}z{sub norm} (i.e., (z{sub spec} - z{sub phot})/(1 + z{sub spec})), to an average of {Delta}z{sub norm} = 0.004, a standard deviation of {sigma} = 0.069, and a median absolute deviation, MAD = 0.02, over the whole redshift range (i.e., z{sub spec} {<=} 3.6), defined by the four-survey cross-matched spectroscopic sample. The fraction of catastrophic outliers, i.e., of objects with photo-z deviating more than 2{sigma} from the spectroscopic value, is <3%, leading to {sigma} = 0.035 after their removal, over the same redshift range. The method is made available to the community through the DAMEWARE Web application.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Deep Learning for Spectral Filling in Radio Frequency Applications

Due to the Internet of Things (IoT) proliferation, Radio Frequency (RF) channels are increasingly congested with new kinds of devices, which carry unique and diverse communication needs. This poses complex challenges in modern digital communications, and calls for the development of technological innovations that (i) optimize capacity (bitrate) in limited bandwidth environments, (ii) integrate cooperatively with already-deployed RF protocols, and (iii) are adaptive to the ever-changing demands in modern digital communications. In this paper we present methods for applying deep neural networks for spectral filling. Given an RF channel transmitting digital messages with a pre-established modulation scheme, we automatically learn novel modulation schemes for sending extra information, in the form of additional messages, “around” the fixed-modulation signals (i.e., without interfering with them). In so doing, we effectively increase channel capacity without increasing bandwidth. We further demonstrate the ability to generate signals that closely resemble the original modulations, such that the presence of extra messages is undetectable to third-party listeners. We present three computational experiments demonstrating the efficacy of our methods, and conclude by discussing the implications of our results for modern RF applications.

Setzler, Matthew D.↗
DOE OSTI2020

Athena - Digital Twin and Infrastructure Model [SWR 20-106]

The software provides database access and analysis code for project ATHENA data. In particular, it provides (1) access to the AWS RDS database and (2) supporting code for analysis. Additionally, the software contains all the code related to modeling the Dallas-Fort Worth International Airport (DFW) curbside (CTA modeling) using SUMO simulator. It demonstrates how to install, run and derive useful data from SUMO simulations. We will describe the basic files needed to run a simulation and how those files are generated. The software also has code for generating simulations representing different traffic management policies for DFW and simulating those policies on personal computer or on NREL's Eagle supercomputer. The examples provided are a representation of the DFW airport for curbside dropoff and and pickup.

Severino, Joseph↗
DOE OSTI2017

X-ray tomography of feed-to-glass transition of simulated borosilicate waste glasses

The feed composition of a high level nuclear waste (HLW) glass melter affects the overall melting rate by influencing the chemical, thermophysical, and morphological properties of a relatively insulating cold cap layer over the molten phase where the primary feed vitrification reactions occur. Data from X ray computed tomography imaging of melting pellets comprised of a simulated high-aluminum HLW feed heated at a rate of 10°C/min reveal the distribution and morphology of bubbles, collectively known as primary foam, within this layer for various SiO 2 /(Li 2 CO 3 +H 3 BO 3 +Na 2 CO 3 ) mass fractions at temperatures between 600°C and 1040°C. To track melting dynamics, cross-sections obtained through the central profile of the pellet were digitally segmented into primary foam and a condensed phase. Pellet dimensions were extracted using Photoshop CS6 tools while the DREAM.3D software package was used to calculate pellet profile area, average and maximum bubble areas, and two-dimensional void fraction. The measured linear increase in the pellet area expansion rates – and therefore the increase in batch gas evolution rates – with SiO 2 /(Li 2 CO 3 +H 3 BO 3 +Na 2 CO 3 ) mass fraction despite an exponential increase in viscosity of the final waste glass at 1050°C and a lower total amount of gas-evolving species suggest that the retention of primary foam with large average bubble size at higher temperatures results from faster reaction kinetics rather than increased viscosity. However, viscosity does affect the initial foam collapse temperature by supporting the growth of larger bubbles. Because the maximum bubble size is limited by the pellet dimensions, larger scale studies are needed to understand primary foam morphology at high temperatures. This temperature-dependent morphological data can be used in future investigations to synthetically generate cold cap structures for use in models of heat transfer within a HLW glass melter.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Search for extremely metal-poor galaxies in the Sloan Digital Sky Survey. II. High electron temperature objects

Extremely metal-poor (XMP) galaxies are defined to have a gas-phase metallicity smaller than a tenth of the solar value (12+log[O/H]<7.69). They are uncommon, chemically and possibly dynamically primitive, with physical conditions characteristic of earlier phases of the universe. We search for new XMPs in the Sloan Digital Sky Survey (SDSS) in a work that complements Paper I. This time, high electron temperature objects are selected; metals are a main coolant of the gas, so metal-poor objects contain high-temperature gas. Using the algorithm k-means, we classify 788,677 spectra to select 1281 galaxies that have particularly intense [O iii]λ4363 with respect to [O iii]λ5007, which is a proxy for high electron temperature. The metallicity of these candidates was computed using a hybrid technique consistent with the direct method, rendering 196 XMPs. A less restrictive noise constraint provides a larger set with 332 candidates. Both lists are provided in electronic format. The selected XMP sample has a mean stellar mass around 10{sup 8} M{sub ⊙}, with the dust mass ∼10{sup 3}M{sub ⊙} for typical star-forming regions. In agreement with previous findings, XMPs show a tendency to be tadpole-like or cometary. Their underlying stellar continuum corresponds to a fairly young stellar population (<1 Gyr), although young and aged stellar populations coexist at the low-metallicity starbursts. About 10% of the XMPs show large N/O. Based on their location in constrained cosmological numerical simulations, XMPs have a strong tendency to appear in voids and to avoid galaxy clusters. The puzzling 2%-solar low-metallicity threshold exhibited by XMPs remains.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations

This article presents an application of the Kalman filtering technique to estimate loads on a wind turbine. The approach combines a mechanical model and a set of measurements to estimate signals that are not available in the measurements, such as wind speed, thrust, tower position, and tower loads. The model is severalfold faster than real time and is intended to be run online, for instance, to evaluate real-time fatigue life consumption of a field turbine using a digital twin, perform condition monitoring, or assess loads for dedicated control strategies. The mechanical model is built using a Rayleigh–Ritz approach and a set of joint coordinates. We present a general method and illustrate it using a 2-degrees-of-freedom (DOF) model of a wind turbine and using rotor speed, generator torque, pitch, and tower-top acceleration as measurement signals. The different components of the model are tested individually. The overall method is evaluated by computing the errors in estimated tower-bottom-equivalent moment from a set of simulations. From this preliminary study, it appears that the tower-bottom-equivalent moment is obtained with about 10 % accuracy. The limitation of the model and the required steps forward are discussed.

17 WIND ENERGY↗
DOE OSTI2012

Sloan Digital Sky Survey Photometric Calibration Revisited

The Sloan Digital Sky Survey calibration is revisited to obtain the most accurate photometric calibration. A small but significant error is found in the flat-fielding of the Photometric telescope used for calibration. Two SDSS star catalogs are compared and the average difference in magnitude as a function of right ascension and declination exhibits small systematic errors in relative calibration. The photometric transformation from the SDSS Photometric Telescope to the 2.5 m telescope is recomputed and compared to synthetic magnitudes computed from measured filter bandpasses.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Development of Digital Twin Predictive Model for PWR Components: Updates on Multi Times Series Temperature Prediction Using Recurrent Neural Network, DMW Fatigue Tests, System Level Thermal-Mechanical-Stress Analysis

The long-term operation (LTO) of nuclear power plant (NPP) beyond their original design life of 40 years, can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Frequent NDE requirement may lead to frequent shutdown of the NPP. This in turn can lead to power outage and additional NDE-inspection-cost related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing more digital approach such as by using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. Towards this goal Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program is working on the development of a DT framework that can be used for real time environmental fatigue prediction of reactor components. The DT framework is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics such as finite element (FE) based modeling tools. Towards this overall goal, following are some of the major contributions made during the FY21: 1) Multiple 82/182 dissimilar metal weld (DMW) specimens (both solid-weld and joint-weld representing the actual reactor multi-metal nozzles) were fatigue tested. The resulting fatigue lives were compared to the NUREG-6909 based best-fit and design fatigue curves. Additionally, the results of 52/152 DMW fatigue specimens (which were recently tested at Republic of Korea under the sponsorship of International Nuclear Energy Research Initiative - INERI program) were compared to the NUREG-6909 based best-fit and design fatigue curves. From the comparison of 82/182 and 52/152 DMW test data with NUREG-6909 best-fit curve, most of the reported test data fall way away from the NUREG-6909 suggested best-fit or mean curve. The NUREG-6909 suggested best-fit curve is the best-fit curve of austenitic stainless steel and due to lack of enough data on Nickel-based welds, this is currently being used for predicting the life of Nickel-alloy-based welded components. However, the above observation may require higher scaling factor (e.g., ASME suggested factor of 20 on cycles rather than the current NUREG-6909 suggested factor of 12 on cycles) for scaling the austenitic-stainless-steel best-fit-curve for estimating the design or safe-life of a welded component. Accordingly, for example, if a DMW component experience a strain amplitude of 0.6% the PWR-water life of the component would be 52 cycles instead of 85 cycles. However, more DMW tests are required to further ascertain the above-mentioned observations. 2) A system level CAD and finite element model were developed which consists of reactor pressure vessel (RPV), part of steam generator (SG), part of pressurizer (PRZ), hot leg (HL), and surge line (SL). This is with detailed nozzle geometry and thermal-mechanical material properties of different metals to simulate realistic thermal-mechanical stress under connected system global thermal-mechanical boundary conditions. 3) Different system level heat transfer analyses were performed with estimation of relevant heat transfer coefficients. The resulting data were used in subsequent system level thermal-mechanical stress analysis and for generating spatial-temporal training and validation data for a system level digital-twin based temperature predictor. Transient heat transfer analyses were performed considering thermal boundary condition under design-basis (DB) loading and EDF (Électricité de France) data-based grid-load-following (EDF-GLF) loading cycles. 4) System level thermal-mechanical stress analysis was performed for identifying damage-prone hotspots and for future extension of the model for cyclic state prediction. From the system-level model simulation under DB loading cycle it is found that HL and the SL nozzle that connect to the HL can experience significant stress and strain and could be one of the weakest links in the overall reactor coolant system (RCS). 5) An AI/ML based DT model was developed for multi-time-series temperature prediction at any inside/outside thickness locations of PWR pressure boundary components. This is by using Recurrent-neural-network (RNN) and keras machine learning libraries. The RNN model was validated against two laboratory test-based data sets with one obtained through ANL’s in-air fatigue test system and other through PWR-water test loop. The experimentally validated DT model further validated against FE model results to predict thermal scarification related spatialtemporal temperatures at random locations of a component. The well validated DT model was then used for demonstrating spatial-temporal temperature prediction under 100+ years of reactor operation subjected to combined DB, EDF-GLF and randomized grid-load-following (RANDOMGLF) loading Cycles. The expert-elicitation DT model framework was developed assuming field/input/process measurements can be available from a few existing plant sensors and can readily be used by the NPP operators. The above temperature prediction model will feed to the next-step stress analysis model based on which the life of a component can be predicted in realtime, which is one of our future works.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

SU-E-J-92: Validating Dose Uncertainty Estimates Produced by AUTODIRECT, An Automated Program to Evaluate Deformable Image Registration Accuracy

Purpose: Deformable image registration (DIR) is a powerful tool with the potential to deformably map dose from one computed-tomography (CT) image to another. Errors in the DIR, however, will produce errors in the transferred dose distribution. We have proposed a software tool, called AUTODIRECT (automated DIR evaluation of confidence tool), which predicts voxel-specific dose mapping errors on a patient-by-patient basis. This work validates the effectiveness of AUTODIRECT to predict dose mapping errors with virtual and physical phantom datasets. Methods: AUTODIRECT requires 4 inputs: moving and fixed CT images and two noise scans of a water phantom (for noise characterization). Then, AUTODIRECT uses algorithms to generate test deformations and applies them to the moving and fixed images (along with processing) to digitally create sets of test images, with known ground-truth deformations that are similar to the actual one. The clinical DIR algorithm is then applied to these test image sets (currently 4) . From these tests, AUTODIRECT generates spatial and dose uncertainty estimates for each image voxel based on a Student’s t distribution. This work compares these uncertainty estimates to the actual errors made by the Velocity Deformable Multi Pass algorithm on 11 virtual and 1 physical phantom datasets. Results: For 11 of the 12 tests, the predicted dose error distributions from AUTODIRECT are well matched to the actual error distributions within 1–6% for 10 virtual phantoms, and 9% for the physical phantom. For one of the cases though, the predictions underestimated the errors in the tail of the distribution. Conclusion: Overall, the AUTODIRECT algorithm performed well on the 12 phantom cases for Velocity and was shown to generate accurate estimates of dose warping uncertainty. AUTODIRECT is able to automatically generate patient-, organ- , and voxel-specific DIR uncertainty estimates. This ability would be useful for patient-specific DIR quality assurance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Simulating the Fracture of Notched Mortar Beams through Extended Finite-Element Method and Peridynamics

This paper simulates fracture in notched mortar beams under three-point bending using extended finite element method (XFEM) and peridynamics. A three-phase microstructure (i.e., cement paste, aggregates, and paste-aggregate interface) is used for constitutive modeling of the mortar to obtain the elastic properties for simulation. In the XFEM approach, the simulated homogenized elastic modulus is used along with the total fracture energy of the cement mortar in a damage model to predict the fracture response of the mortar including crack propagation and its fracture parameters (Mode I stress intensity factor, K IC and critical crack tip opening displacement, CTOD C ). The damage model incorporates a maximum principal stress-based damage initiation criteria and a traction-separation law for damage evolution. In the peridynamics approach, a bond-based model involving a prototype microelastic brittle (PMB) material model is used. The elastic properties and fracture energy release rates are used as inputs in the PMB model, along with the choice of peridynamic horizon size. Comparison with experimental fracture properties ( K IC , CTOD C ) as well as crack propagation paths from digital image correlation show that both the approaches yield satisfactory results, particularly for K IC and crack extension. Furthermore, both these methods can be adopted for fracture simulation of cement-based materials.

42 ENGINEERING↗
DOE OSTI2021

Titan on the Red (advanced AI / ML system) [Slides]

Born out of a need to make LANL Weapons Program archival material available to scientists and engineers. Material is the end result of decades of consolidation of mini-libraries and mini archives at LANL. Latest consolidation brought together LANL’s digital archives and physical archives. This houses over 75 years of nuclear weapons research, designs, procedures, videos, photos, and other reports. For the Titan on the Red machine learning project, the system must be able to automatically extract metadata from digitized documents, perform natural-language search, enforce security classification and NTK protocols, be expandable to ingest various data stores (Online Vault, PDMLink, shared drives, SharePoint, etc.), and utilize commercial, public domain, and LANL ontologies.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

ARIES Annual Report FY25

Advanced Research on Integrated Energy Systems (ARIES) at the National Laboratory of the Rockies (NLR) is the U.S. Department of Energy's (DOE's) test bed for energy system demonstration and de-risking. ARIES comprises the largest collection of physical and digital assets in the DOE laboratory complex, supporting flexible configuration across a broad range of energy scenarios. In Fiscal Year 2025, ARIES provided a platform for system-level research to anticipate and address future energy needs in energy security, system reliability, and technology deployment.

24 POWER TRANSMISSION AND DISTRIBUTION↗