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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

The National Carbon Capture Center at the Power Systems Development Facility

The Power Systems Development Facility (PSDF) is a state-of-the-art test center sponsored by the U.S. Department of Energy (DOE) and dedicated to the advancement of clean coal technology. In addition to the development of high efficiency coal gasification processes, the PSDF features the National Carbon Capture Center (NCCC) to promote new technologies for CO2 capture from coal-derived flue gas and syngas. The NCCC includes multiple, adaptable test skids that allow technology development of CO2 capture concepts using coal-derived flue gas and syngas in industrial settings. Because of the ability to operate under a wide range of flow rates and process conditions, research at the NCCC can effectively evaluate technologies at various levels of maturity and accelerate their development paths to commercialization. During the calendar year 2013 portion of the Budget Period Four reporting period, efforts at the NCCC focused on post-combustion CO2 capture, gasification, and pre-combustion CO2 capture technology testing. Preparations for future testing were on-going as well, and involved facility upgrades and collaboration with numerous technology developers. In the area of post-combustion, testing was conducted on an enzyme-based technology, advanced solvents from two major developers, and a gas separation membrane. During the year, the gasification process was operated for three test runs, supporting development of water-gas shift and COS hydrolysis catalysts, a mercury sorbent, and several gasification support technologies. Syngas produced during gasification operation was also used for pre-combustion capture technologies, including gas separation membranes from three different technology developers, a CO2 sorbent, and CO2 solvents.

None, None↗
DOE OSTI2016

SU-G-201-01: An Automated Treatment Plan Quality Assurance Program for High-Dose Rate (HDR) Brachytherapy with a VaginalCylinder Applicator

Purpose: Plan specific quality assurance (QA) is an important step in high dose rate (HDR) brachytherapy to ensure the integrity of a treatment plan. The conventional approach is to assemble a set of plan screen-captures in a document and have an independent plan-checker to verify it. Not only is this approach cumbersome and time-consuming, using a document also limits the items that can be verified, hindering plan quality and patient safety. We have initiated efforts to develop a web-based HDR brachytherapy QA system called AutoBrachy QA, for comprehensive and efficient QA. This abstract reports a new plugin in this system for the QA of a cylinder HDR brachytherapy treatment. Methods: A cylinder plan QA module was developed using Python. It was plugged into our AutoBrachy QA system. This module extracted information from CT images and treatment plan. Image processing techniques were employed to obtain geometric parameters, e.g. cylinder diameter. A comprehensive set of eight geometrical and eight dosimetric features of the plan were validated against user specified planning parameter, such as prescription value, treatment depth and length, etc. A PDF document was generated, consisting of a summary QA sheet with all the QA results, as well as images showing plan details. Results: The cylinder QA program has been implemented in our clinic. To date, it has been used in 11 patient cases and was able to successfully perform QA tests in all of them. The QA program reduced the average plan QA time from 7 min using conventional manual approach to 0.5 min. Conclusion: Being a new module in our AutoBrachy QA system, an automated treatment plan QA module for cylinder HDR brachytherapy has been successfully developed and clinically implemented. This module improved clinical workflow and plan integrity compared to the conventional manual approach.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

NCAR-RAL Surface Hydrometeorological Observation Network Data for LASSO-CACTI Overview Paper

This data set contains the 15 minute resolution surface meteorology and soils data from the 15 NCAR/RAL weather stations that were operated around central Argentina during the RELAMPAGO (Remote sensing of Electrification, Lightning, And Meso-scale/micro-scale Processes with Adaptive Ground Observations) Extended Observing Period (EOP). Data providence, citation, and acknowledgement This ARM data set is a copy of v1.0 of the NCAR data set obtained in June 2024 from https://doi.org/10.26023/KW8Z-F2WX-H0Y. The citation for the original data source is: Gochis, D., et al. 2019. NCAR-RAL Surface Hydrometeorological Observation Network Data. Version 1.0. UCAR/NCAR - Earth Observing Laboratory. https://doi.org/10.26023/KW8Z-F2WX-H0Y Accessed June 2024. In addition to the citation reference and any other acknowledgements, please acknowledge NCAR/EOL in your publications with text such as: “Data provided by NCAR/EOL under the sponsorship of the National Science Foundation. https://data.eol.ucar.edu/”

air temperature↗
DOE OSTI2014

Diffusion Processes Satisfying a Conservation Law Constraint

We investigate coupled stochastic differential equations governing N non-negative continuous random variables that satisfy a conservation principle. In various fields a conservation law requires that a set of fluctuating variables be non-negative and (if appropriately normalized) sum to one. As a result, any stochastic differential equation model to be realizable must not produce events outside of the allowed sample space. We develop a set of constraints on the drift and diffusion terms of such stochastic models to ensure that both the non-negativity and the unit-sum conservation law constraint are satisfied as the variables evolve in time. We investigate the consequences of the developed constraints on the Fokker-Planck equation, the associated system of stochastic differential equations, and the evolution equations of the first four moments of the probability density function. We show that random variables, satisfying a conservation law constraint, represented by stochastic diffusion processes, must have diffusion terms that are coupled and nonlinear. The set of constraints developed enables the development of statistical representations of fluctuating variables satisfying a conservation law. We exemplify the results with the bivariate beta process and the multivariate Wright-Fisher, Dirichlet, and Lochner’s generalized Dirichlet processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Biogenic manganese oxides as reservoirs of organic carbon and proteins in terrestrial and marine environments

Manganese (Mn) oxides participate in a range of interactions with organic carbon (OC) that can lead to either carbon degradation or preservation. In this work, we examine the abundance and composition of OC associated with biogenic and environmental Mn oxides to elucidate the role of Mn oxides as a reservoir for carbon and their potential for selective partitioning of particular carbon species. Mn oxides precipitated in natural brackish waters and by Mn(II)-oxidizing marine bacteria and terrestrial fungi harbor considerable levels of organic carbon (4.1-17.0 mol OC per kg mineral) compared to ferromanganese cave deposits which contain 1-2 orders of magnitude lower OC. Spectroscopic analyses indicate that the chemical composition of Mn oxide-associated OC from microbial cultures is homogeneous with bacterial Mn oxides hosting primarily proteinaceous carbon and fungal Mn oxides containing both protein- and lipopolysaccharide-like carbon. The bacterial Mn oxide-hosted proteins are involved in both Mn(II) oxidation and metal binding by these bacterial species and could be involved in the mineral nucleation process as well. By comparison, the composition of OC associated with Mn oxides formed in natural settings (brackish waters and particularly in cave ferromanganese rock coatings) is more spatially and chemically heterogeneous. Cave Mn oxide-associated organic material is enriched in aliphatic C, which together with the lower carbon concentrations, points to more extensive microbial or mineral processing of carbon in this system relative to the other systems examined in this study, and as would be expected in oligotrophic cave environments. This study highlights Mn oxides as a reservoir for carbon in varied environments. Lastly, the presence and in some cases dominance of proteinaceous carbon within the biogenic and natural Mn oxides may contribute to preferential preservation of proteins in sediments and dominance of protein-dependent metabolisms in the subsurface biosphere.

58 GEOSCIENCES↗
DOE OSTI2021

Efficient Active Learning for Gaussian Process Classification by Error Reduction

Active learning sequentially selects the best instance for labeling by optimizing an acquisition function to enhance data/label efficiency. The selection can be either from a discrete instance set (pool-based scenario) or a continuous instance space (query synthesis scenario). In this work, we study both active learning scenarios for Gaussian Process Classification (GPC). The existing active learning strategies that maximize the Estimated Error Reduction (EER) aim at reducing the classification error after training with the new acquired instance in a onestep-look-ahead manner. The computation of EER-based acquisition functions is typically prohibitive as it requires retraining the GPC with every new query. Moreover, as the EER is not smooth, it can not be combined with gradient-based optimization techniques to efficiently explore the continuous instance space for query synthesis. To overcome these critical limitations, we develop computationally efficient algorithms for EER-based active learning with GPC. Further, we derive the joint predictive distribution of label pairs as a one-dimensional integral, as a result of which the computation of the acquisition function avoids retraining the GPC for each query, remarkably reducing the computational overhead. We also derive the gradient chain rule to efficiently calculate the gradient of the acquisition function, which leads to the first query synthesis active learning algorithm implementing EER-based strategies. Our experiments clearly demonstrate the computational efficiency of the proposed algorithms. We also benchmark our algorithms on both synthetic and real-world datasets, which show superior performance in terms of sampling efficiency compared to the existing state-of-the-art algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Learning the solution operator of parametric partial differential equations with physics-informed DeepONets

Partial differential equations (PDEs) play a central role in the mathematical analysis and modeling of complex dynamic processes across all corners of science and engineering. Their solution often requires laborious analytical or computational tools, associated with a cost that is markedly amplified when different scenarios need to be investigated, for example, corresponding to different initial or boundary conditions, different inputs, etc. In this work, we introduce physics-informed DeepONets, a deep learning framework for learning the solution operator of arbitrary PDEs, even in the absence of any paired input-output training data. We illustrate the effectiveness of the proposed framework in rapidly predicting the solution of various types of parametric PDEs up to three orders of magnitude faster compared to conventional PDE solvers, setting a previously unexplored paradigm for modeling and simulation of nonlinear and nonequilibrium processes in science and engineering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

High Gradient Accelerator Cavities Using Atomic Layer Deposition

In the Phase I program, Calabazas Creek Research, Inc. (CCR), in collaboration with North Carolina State University (NCSU), fabricated copper accelerator cavities and used Atomic Layer Deposition (ALD) to apply thin metal coatings of tungsten and platinum. It was hypothesized that a tungsten coating would provide a robust surface more resistant to arcing and arc damage. The platinum coating was predicted to reduce processing time by inhibiting oxides that form on copper surfaces soon after machining. Two sets of cavity parts were fabricated. One was coated with 35 nm of tungsten, and the other with approximately 10 nm of platinum. Only the platinum cavity parts could be high power tested during the Phase I program due to schedule and funding constraints. The platinum coated cavity exhibit poor performance when compared with pure copper cavities. Not only did arcing occur at lower power levels, but the processing time was actually longer. There were several issues that contributed to the poor performance. First, machining of the base copper cavity parts failed to achieve the quality and cleanliness standards specified to SLAC National Accelerator Center. Secondly, the ALD facilities were not configured to provide the high levels of cleanliness required. Finally, the nanometer coating applied was likely far too thin to provide the performance required. The coating was ablated or peeled from the surface in regions of high fields. It was concluded that the current ALD process could not provide improved performance over cavities produced at national laboratories using dedicated facilities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Use of Machine Learning for Signature Development in a Multi-Sensor Environment for Safeguard Applications of Solvent Extraction Processes

Idaho National Laboratory is constructing the Special Nuclear Material test bed (Beartooth) for research and development pertaining to nuclear fuel processing operations using centrifugal contactors. Beartooth will enable give researchers the opportunity to study the dissolution, separation, and conversion of special nuclear materials (e.g., plutonium, enriched uranium, and thorium). As a lead-in to Beartooth, this project constructed a test bed to study the process and equipment behavior with real time series data collection using a variety of non-traditional sensors, in order to identify process failures and label them as either accidental or purposeful, thus enhancing nuclear nonproliferation stewardship. The measurement sources in this test bed include vibration, acoustic, current, flow, colorimetric, and thermal data. These measurements vary in terms of magnitude, frequency, and location. This information will be combined and used within data-driven machine-learning methods to detect operation condition changes that are either intentional (i.e., changes in operating set points) or malicious (i.e., flow diversion) in nature. This presentation covers the data processing and signature development activities necessary for utilizing this heterogeneous dataset to classify process anomalies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Qualification of Processing Batches for Tank Closure Cesium Removal Through In-Tank Batch Contact Testing - 20470

The Tank Closure Cesium Removal (TCCR) process at the Savannah River Site (SRS) is currently processing waste from Tank 10H, generating decontaminated salt solution that is sent to Tank 11H. Transfers out of Tank 11H are then disposed of on site as a grout wasteform in the SRS Saltstone Processing Facility. This is a full-scale demonstration of the use of Crystalline Silicotitanate (CST) for the decontamination of aqueous nuclear waste supernate. CST primarily removes cesium from the high sodium alkaline feed, but also adsorbs strontium, actinides, and some other trace metals. The TCCR unit is an at-tank set of four ion exchange columns, with two typically being operated in series during waste processing. Tank 10H is serving a dual function as both the salt dissolution tank as well as the feed tank for the TCCR system. Prior to operation of TCCR, Tank 10H must undergo dissolution campaigns, dissolving the salt cake to form an aqueous salt solution (supernate). After each dissolution campaign, the supernate created must be qualified prior to processing through the TCCR system. Qualification includes detailed characterization of the supernate as well as in-tank batch contact tests to determine the equilibrium loading of Cs-137 on the CST ion exchange media (IONSIV{sup TM} R9120-Ba). In support of the in-tank batch contact testing, Savannah River National Laboratory (SRNL) developed a CST sample holder, also referred to as a 'tea-bag', to hold a measured amount (∼0.1 grams) of pretreated CST between stainless steel screens. The tea-bag fits within a standard stainless steel sample vial that has been modified to allow the flow of supernate to the CST. This sample holder is then immersed in the waste tank allowing free contact between the CST beads and the surrounding liquid, at an effectively infinite liquid-to-solid phase ratio. A pair of tea-bags are deployed in the tank for a period of 10 days for each batch, after which the CST is recovered, digested, and analyzed to determine the loading of cesium isotopes on the CST. This result then supports the TCCR column thermal loading limits for processing as part of the TCCR safety basis. This paper will discuss the design and testing of the CST sample holder, as well as results from the first few batches of waste qualified and subsequently processed through the TCCR system. In addition, results from analyses of Tank 11H samples, showing decontamination of the product will be included. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

3D Gel-Printing of TiC-Reinforced 316L Stainless Steel: Influence of the Printing Parameters

In this paper, the composite composed of TiC (35 wt.%) reinforced 316L stainless steel matrix was successfully prepared using a 3D gel-printing (3DGP) process. The “step effect” of 3D printing can be reduced using reasonable parameter settings. The properties of TiC/316L slurry for 3DGP and the influence of the different printing parameters on the surface quality of the samples, such as the extrusion overlaps and printing speed, were researched. The microstructure and fracture morphology of the sintered sample were revealed using scanning electron microscopy. The surface roughness, density, and mechanical properties were also measured. When the internal diameter of the nozzle was 0.50 mm, the result showed that the proper extrusion overlap parameter was 0.08 mm and the maximum printing speed was 20 mm/s. Furthermore, the minimum surface roughness value was 10 μm. After sintering, the mechanical performances were similar to those obtained using a conventional powder metallurgy process.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Mega-Gauss Plasma Jet Creation Using a Ring of Laser Beams

Using 20 OMEGA laser beams at the Laboratory for Laser Energetics, University of Rochester, to irradiate a flat plastic target in a hollow ring configuration, we created supersonic cylindrical stable plasma jets with self-generated megagauss magnetic fields extending out to >4 mm. These well-collimated magnetized jets possess a number of distinct and novel properties that will allow us to study the dynamics, physical processes, and scaling properties of astrophysical jets with a dynamic range exceeding those of previous laboratory settings. The dimensionless parameters of these laboratory jets fall in the same regime as those of young stellar object jets. As a result, these jets will also provide new versatile laser-based platforms to study magnetized shocks, shear flows, and other plasma processes under controllable conditions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Policy Reforms to Unleash Domestic Critical Minerals Mining and Processing

The United States faces growing strategic and economic risks due to its limited ability to mine, process, and refine the minerals required for national defense, energy systems, advanced manufacturing, and emerging technologies. Although the country possesses significant geological resources, development has been slowed by long and unpredictable permitting timelines, fragmented regulatory responsibilities, limited midstream processing capacity, and a shrinking technical workforce. These structural barriers have created supply chain vulnerabilities that constrain industrial growth and reduce national resilience. This report presents a comprehensive set of reforms intended to modernize the nation’s approach to critical minerals. The recommendations address federal permitting, environmental review processes, the legal framework governing mining activities, interagency coordination, domestic processing and refining capacity, and the education and workforce systems needed to support long term industry development. The analysis emphasizes practical steps to shorten project timelines, improve regulatory clarity, expand processing infrastructure, enable recovery from both conventional and nontraditional sources, and update outdated requirements that hinder the development of essential materials. Taken together, the recommended reforms would strengthen domestic supply chains, improve investment certainty, and reduce dependence on external minerals and processing infrastructure. By aligning policy, regulatory frameworks, and workforce capabilities with national needs, the United States can build a more resilient and secure critical minerals ecosystem that supports long term economic competitiveness and technological leadership.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2021

Conditioned Simulation of Ground-Motion Time Series at Uninstrumented Sites Using Gaussian Process Regression

Ground-motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free-field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground-motion recording instrumentation. In this study, given a set of observed motions, ground-motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics-based simulated ground motions of the 1906 Mw 7.9 San Francisco earthquake and Mw 7.0 Hayward fault scenario earthquake in northern California. Additionally, the method’s performance is further evaluated using the 2019 Mw 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground-motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long-period content of the ground motions as well as directivity pulses.

58 GEOSCIENCES↗
DOE OSTI2014

RAVEN. Dynamic Event Tree Approach Level III Milestone

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics are not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed to perform two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, the control logic infrastructure is used to model stochastic events, such as components failures, and perform uncertainty propagation. Such stochastic modeling is deployed using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This report focuses on the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, a DPRA analysis, using DET, of a simplified pressurized water reactor for a Station Black-Out (SBO) scenario is presented.

42 ENGINEERING↗
DOE OSTI2022

Characterization of the Fusion Zone in Laser Beam Welds on SS304L

Designing a welded joint requires consideration of the welding process, component/assembly structure and weld geometry, material composition, properties, and desired joint performance. The designer must also specify a set of acceptance criteria used to evaluate the welds. This study was conducted to support the design team responsible for specifying the spot weld profile by investigating the relationship between weld process parameters and the weld geometry and properties to further inform design optimization. The subjects of this study are two thin stainless steel 304L (SS304L) components joined by Laser Beam Welding (LBW). The components are the Housing, which contains flexible circuitry, and the Cover that protects the circuitry during handling, installation, and under service loads. Figure 1 illustrates coupon versions of the Cover and Housing used to simulate the interface between these components for weld testing. These coupon representations of assembly components were designed to match the material and relevant geometry related to the welded joint while other features not relevant to the welded joint are excluded.The objective of this study was to characterize the sensitivity of the weld geometry and mechanical properties to the input weld parameters to inform further Finite Element Analysis (FEA) efforts in balancing joint strength against assembly deformation caused by the welding process. Characterization was accomplished by varying laser welding parameters around the current design values and measuring weld geometry on the cross-sections. Nanoindentation with a spherical and flat-ended tip was used on polished section cuts of the weld to obtain direct estimates of the stress-strain response of the Fusion Zone (FZ) for comparison to the Base Metal (BM).

42 ENGINEERING↗
DOE OSTI2019

Modeling of the Heat-Affected and Thermomechanically Affected Zones in a Ti-6Al-4V Inertia Friction Weld

Inertia friction welding has been used across the aerospace, automotive, and power-generation industries for the fabrication of complex axisymmetric components for over forty years. The process involves one axisymmetric piece being held stationary and another piece being brought into contact set to rotate about its axis of symmetry by a flywheel with the system under an applied load across the joint. Plasticization at the joint interface through the frictional heating sees the two pieces bond together. The titanium alloy Ti-6Al-4V has been widely studied for inertia welding applications. A successful selection of processing parameters (flywheel energy and mass, applied load) allows an inertia welding process which produces a very high-integrity weld, with a minimal heat-affected zone (HAZ) and thermomechanically affected zone (TMAZ), formed as a narrow band at the interface and extending further into the material. The width of this narrow band of heated material is dependent upon the process parameters used. A series of experimental inertia friction welds were performed using Ti-6Al-4V, and a finite element (FE) modeling framework was developed using the FE code Deform in order to predict the widths of the HAZ and TMAZ at the weld interface. The experimentally observed HAZ boundaries were correlated with the thermal fields from the FE model, while TMAZ boundaries were correlated with the Von Mises plastic strain fields.

42 ENGINEERING↗
DOE OSTI2020

Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification

This report summarizes a joint effort between Argonne National Laboratory, Idaho National Laboratory, and Los Alamos National Laboratory to develop and deploy constitutive models targeted at predicting the life of Grade 91 alloy components subjected to high temperature environments typical of those that structural components in advanced nuclear reactors would experience. Two distinct, but complementary constitutive modeling approaches have been taken here. The first employs a phenomenological viscoplastic model for which parameters have been calibrated based on experimental data for a wide range of Grade 91 alloy that has undergone a variety of processing. A Bayesian approach was used to derive distributions of uncertain parameters for this model based on this data set. The second approach is a reduced order model suitable for engineering-scale analysis that is based on the results of a large set of mesoscale simulations. Mesoscale models allow for the microstructure and composition of a particular alloy to be directly taken into account in the computation of the viscoplastic response, but are computationally expensive, which makes it impractical to directly call those models for the material constitutive response in an engineering-scale simulation. The reduced-order representation of the response of the underlying model used here allows for an engineering-scale model to take into account the characteristics of the underlying microstructure, while only incurring a reasonable computational expense. Both of these approaches have different strengths, and are applicable for different parts of the design/analysis process. The phenomenological models can be readily parameterized based on a set of experimental data for a given class of materials and used for scoping calculations. Once a specific material is chosen and adequately characterized, the reduced order models can accurately predict the response of that specific alloy, and because the models are based on predictive models of the underlying microstructure, they can be used to more confidently predict the response under conditions in regions where there is limited experimental data. Both of these models have been integrated in the Grizzly code, which is used here to perform proof-of-concept uncertainty quantification analyses of a simple component under prototypical conditions. The built- in stochastic analysis capabilities in the MOOSE framework that Grizzly is built on are used here to run large sets of simulations for this uncertainty quantification analysis. As would be expected, because the reduced order models are developed for a much more tightly defined alloy, they predict tighter distributions of the time to failure than the phenomenological models, which are calibrated to a broader set of data. Also important is that these simulations demonstrate that a reduced order modeling approach can be successfully deployed to propagate uncertainties from the material scale to practical engineering-scale component simulations.

42 ENGINEERING↗