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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Radiation Damage Studies on Titanium Alloys as High Intensity Proton Accelerator Beam Window Materials

A high-strength dual a+ß phase titanium alloy Ti–6Al–4V is utilized as a material for beam windows in several accelerator target facilities. However, relatively little is known about how material properties of this alloy are affected by high-intensity proton beam irradiation. With plans to upgrade neutrino facilities at J-PARC and Fermilab to over 1 MW beam power, the radiation damage in the window material will reach a few displacements per atom (dpa) per year, significantly above the ~0.3 dpa level of existing data. The international RaDIATE collaboration, Radiation Damage In Accelerator Target Environments, has conducted a high intensity proton beam irradiation of various target and window material specimens at Brookhaven Linac Isotope Producer (BLIP) facility, including a variety of titanium alloys. Post-Irradiation Examination (PIE) of the specimens in the 1st capsule, irradiated at up to 0.25 dpa, is in progress. Tensile tests in a hot cell at Pacific Northwest National Laboratory (PNNL) exhibited a clear signature of radiation hardening and loss of ductility for Ti–6Al–4V, while Ti–3Al–2.5V, with less ß phase, exhibited less severe hardening. Microstructural investigations will follow to study the cause of the difference in tensile behavior between these alloys. High-cycle fatigue (HCF) performance is critical to the lifetime estimation of beam windows exposed to a periodic thermal stress from a pulsed proton beam. The first HCF data on irradiated titanium alloys are to be obtained by a conventional bend fatigue test at Fermilab and by an ultrasonic mesoscale fatigue test at Culham Laboratory. Specimens in the 2nd capsule, irradiated at up to ~1 dpa, cover typical titanium alloy grades, including possible radiation-resistant candidates. These systematic studies on the effects of radiation damage of titanium alloy materials are intended to enable us not only to predict realistic lifetimes of current beam windows made of Ti–6Al–4V, but also to extend the lifetime by choosing a more radiation and thermal shock tolerant alloy with a preferable heat treatment, or even by developing new materials.

Titanium Alloy, Beam Window, Proton Beam, Radiatio↗
DOE OSTI2022

Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-Learning Models

Operations and maintenance (O&M) cost of wind plant accounts up to 30% of total energy cost, which can be reduced through continuous monitoring and successfully detecting incipient wind turbine failures. To accomplish this, condition monitoring and predictive maintenance systems are being implemented in wind industry to support O&M decision making. A wide range of approaches for condition monitoring and fault prediction have been developed. These approaches generally use historical data of wind turbines collected by Supervisory Control and Data Acquisition (SCADA) system to identify patterns that lead to failure. These SCADA data show the overall condition of a wind turbine and can be leveraged to detect when the turbine's performance is degrading and to identify if a fault is developing. However, it becomes challenging to predict the failure of a specific wind turbine gearbox bearing, because the SCADA data are often not directly linked to the component. To bridge the gap, we have investigated features calculated from SCADA data using physics-based models and the gearbox design over the years. The damaged metric we used in the physics domain is frictional energy. Combining these physics domain variables with SCADA data as inputs to various machine learning models for gearbox bearing fault prediction, we have demonstrated the benefits of leveraging both physics and data domain models. It was an attempt to improve frictional-energy-based damage metric by adding data domain inputs, as we had learned that the frictional-energy-based damage metric alone is not sufficient to single out failed bearings from healthy. As condition monitoring data (either vibration or oil debris data) has become available at more and more wind plants, we would like to evaluate whether by adding the condition monitoring data can help further improve the performance of frictional-energy-based damage metric for gearbox bearing fault prediction. Both cases by modeling through various machine learning algorithms are discussed in this study along with some observations.

fault prediction↗
DOE OSTI2020

Lattice modeling of excavation damage in argillaceous clay formations: Influence of deformation and strength anisotropy

This paper presents modeling of mechanical anisotropy in argillaceous rocks using an irregular lattice modeling approach, namely the rigid-body-spring network. To represent the mechanical anisotropy, new schemes are implemented in the modeling framework. The directionality of elastic deformation is resolved by modifying the element formulation with anisotropic elastic properties. The anisotropy of strength and failure characteristics is facilitated by adopting orientation-dependent failure criteria into the failure model. The verification of the improved modeling procedures is performed against theoretical model predictions for unconfined compression tests with various bedding orientations. Furthermore, excavation damage and fracturing processes in rock formations are simulated for different geomechanical configurations, such as rock anisotropy and tectonic heterogeneity. The simulated excavation damage characteristics are realistic and comparable with the actual field observation at a tunnel located in an argillaceous clay formation. The simulation results provide insights into the excavation damage zone phenomena with an explicit representation of fracturing processes.

58 GEOSCIENCES↗
DOE OSTI2022

AI-Enabled Robots for Automated Nondestructive Evaluation and Repair of Power Plant Boilers. Final Report

Boiler failure could cause loss of life and safety issues, cost hundreds of thousands of dollars in equipment repairs, property damage and production losses, and drive up the cost of electric power. Boiler maintenance is challenging and risky for inspectors working on scaffolding in confined hazardous spaces inside of a boiler and sometimes the space is hard to access. The operation is also time-consuming due to the large area of vertical structures for inspection and the tremendous effort needed for scaffolding. Recently, the use of robotics (e.g., drones and crawlers) in power plants for maintenance is growing rapidly. However, the existing robotics solutions show two notable technological gaps: no live repair capability, and no Artificial Intelligence (AI) for smart autonomy. The objective of this project is to develop an integrated autonomous robotic platform that is equipped with compact non-destructive evaluation (NDE) sensors to perform live inspection, operates onboard repair devices to perform live repair, and uses AI for intelligent data fusion and predictive analysis for automated and smart spatiotemporal inspection, analysis and repair of the furnace walls in coal-fired boilers. The approach to achieve the objective includes developing NDE sensors with signal processing techniques, designing and evaluating repair devices for robots based on fusion and solid-state technologies, and an autonomous robotic platform that can attach to and navigate on boiler furnace walls using magnetic drive tracks. The robot is also powered by AI to automate data gathering (e.g., 3D mapping and damage localization) and predictive analysis. This project has advanced the state-of-the-art by providing technological breakthroughs including compact NDE and repair tools for robots, AI capabilities for smart autonomy, and a robotic platform for automated boiler maintenance. This project has great potential to result in significant benefits including limiting or eliminating the need to send operators to assess difficult-to-access or hazardous areas, enabling automated live inspection and repair, avoiding time consuming scaffolding (especially for partial maintenance during unplanned outage), collecting comprehensive and well-organized data smartly, and avoiding or limiting the need for onsite or remote piloting technicians. The impacts can be tremendous in terms of the time and cost savings, reducing the risk for human operators, and increasing boiler reliability, usability, and efficiency. In addition, by developing the new technologies on the autonomous inspection and repair robot, by involving multiple undergraduate and graduate students working together with the faculty members on this project, and by generating knowledge and building up collaborations with industrial partners, this effort will significantly update the education capabilities, support long-term fundamental research, and maintain the leadership of Colorado School of Mines and Michigan State University in energy fields.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Guidelines for predicting stress in cemented doublets undergoing temperature change

This work explores quick predictive methods for calculating potentially risky stresses and deflections in cemented doublets experiencing temperature change that agree well with finite element analysis. There are three failure modes of interest: cohesive failure of the adhesive, delamination (surface bond failure or debonding), and glass fracture. Adhesive theory, confirmed by finite element analysis, predicts stress singularities that complicate interpretation of the stress calculations. The presence of a stress singularity indicates the breakdown of linear elastic assumptions, but damage initiation and stress singularities are related. The authors find that geometry details near a bond edge can exacerbate or minimize damage initiation and stress concentrations. Because the interpretation of the stress results is complicated, the authors investigated predicted stresses in doublets that have been successfully tested between -40 and 85 °C. This study found that the product (ΔT ∙ Δα) should be less than 189 ppm, where ΔT is the temperature excursion and Δα is the difference in glass coefficient of thermal expansion. If the product (ΔT ∙ Δα) is equal to or greater than 189 ppm, further analysis and testing is warranted. But the authors also show that the fabrication process can significantly influence stress failure, particularly with large diameter doublets.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Guidelines for predicting stress in cemented doublets undergoing temperature change

This work explores quick predictive methods for calculating potentially risky stresses and deflections in cemented doublets experiencing temperature change that agree well with finite element analysis. There are three failure modes of interest: cohesive failure of the adhesive, delamination (surface bond failure or debonding), and glass fracture. Adhesive theory, confirmed by finite element analysis, predicts stress singularities that complicate interpretation of the stress calculations. The presence of a stress singularity indicates the breakdown of linear elastic assumptions, but damage initiation and stress singularities are related. The authors find that geometry details near a bond edge can exacerbate or minimize damage initiation and stress concentrations. Because the interpretation of the stress results is complicated, the authors investigated predicted stresses in doublets that have been successfully tested between -40 and 85 °C. This study found that the product (∆T ∙ ∆α) should be less than 189 ppm, where ∆T is the temperature excursion and ∆α is the difference in glass coefficient of thermal expansion. If the product (∆T ∙ ∆α) is equal to or greater than 189 ppm, further analysis and testing is warranted. But the authors also show that the fabrication process can significantly influence stress failure, particularly with large diameter doublets

42 ENGINEERING↗
DOE OSTI2023

Hybrid Particle Method for Computational Shock Physics

A long-standing area of research for Eulerian shock wave physics codes has been the treatment of strength and damage for materials. Here we present a method that will aid in the analysis of strength and failure in shock physics applications where excessive diffusion of critical variables can occur and control the solution outcome. Eulerian methods excel for large deformation simulations in general but are inaccurate in capturing structural behavior. Lagrangian methods provide better structural response, but finite element meshes can become tangled. Therefore, a technique for merging Lagrangian and Eulerian treatments of material response, within a single numerical framework, was implemented in the Multiple Component computational shock physics hydrocode. The capability is a Lagrangian/Eulerian Particle Method (LEPM) that uses particles to interface a Lagrangian treatment of material strength with a more traditional Eulerian treatment of the Equation of State (EOS). Lagrangian numerical methods avoid the advection diffusion found in Eulerian methods, which typically strongly affects strength constitutive law internal variables, such as equivalent plastic strain, porosity and/or damage. The Lagrangian capability enhances existing capabilities and permits accurate predictions of high rate, large deformation and/or shock of mechanical structures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Interpretation of Ion Irradiation and Neutron Irradiation Damage in Additively Manufactured 316 Stainless Steel using Multiscale Modeling

The accelerated adoption of nuclear energy necessitates advanced manufacturing technologies, such as additive manufacturing, to meet heightened supply chain requirements and support innovative reactor technologies. Due to the unique microstructural characteristics of additively manufactured materials under distinct solidification conditions, comprehensive evaluation of their performance in reactor environments is essential. The Advanced Materials and Manufacturing Technologies program under the Department of Energy's Office of Nuclear Energy focuses on understanding the irradiation performance and damage evolution of laser powder bed fusion 316 stainless steel, with an emphasis on integrating ion and neutron irradiation data to accelerate the development and qualification of materials for advanced nuclear reactor applications. While ion irradiation is a cost- and time-effective method, modeling and simulation are required to interpret the data for the broader range of irradiation conditions encountered in advanced reactors. In fiscal year 2025, integrated multiscale modeling and simulations were conducted to assess irradiation damage in additively manufactured 316 stainless steel. Key outcomes include predictions of chromium enrichment at grain boundaries, nickel enrichment at dislocation cell walls and void surfaces, and heterogeneous void evolution under ion and neutron irradiation conditions. Cluster dynamics simulations revealed the coarsening of voids at high irradiation temperatures and the suppression of void growth by high network dislocation density, while also demonstrating significant growth and coarsening of voids and self-interstitial atom loops at low dose rates. Machine learning-accelerated atomistic simulations highlighted the impact of the local environment and chromium concentration on vacancy diffusivity, providing key insights on the influence of composition on void swelling and radiation-induced segregation. Additionally, molecular dynamics simulations demonstrated the presence of defect production bias and a significant effect of carbon content on defect cluster behavior. These combined efforts aim to predict the performance of additively manufactured materials under various reactor conditions, supporting their qualification for nuclear reactor applications by interpreting ion irradiation data. This report underscores the potential of integrated multiscale modeling to analyze ion irradiation data in the effort to accelerate the qualification of additively manufactured materials for nuclear reactor components.

316 stainless steel↗
DOE OSTI2024

Using GANs to predict milling stability from limited data

Milling is a key manufacturing process that requires the selection of operating parameters that provide efficient performance. However, the presence of chatter, a self-excited vibration causing poor surface finish and potential damage to the machine and cutting tool, makes it challenging to select the appropriate parameters. To predict chatter, stability maps are commonly used, but their generation requires expensive data, making it difficult to employ these maps in industry. Therefore, there is a pressing need for an approach that can accurately predict stability maps using limited experimental data. This study introduces the new Encoder GAN (EGAN) approach based on Generative Adversarial Networks (GANs) that predicts stability maps using limited experimental data. The approach consists of the encoder, generator, and discriminator subnetworks and uses the trained encoder and generator to predict the target stability map. This versatile method can be applied to various tool setups and can accurately predict stability maps with limited experimental data (five to 10 cutting tests) even when there is little information available for unknown parameters. In conclusion, the study evaluates the proposed approach using both numerical data and experiments and demonstrates its superior performance compared to state-of-the-art benchmarks.

42 ENGINEERING↗
DOE OSTI2025

A review of displacement cascade simulations using molecular dynamics emphasizing interatomic potentials for TPBAR components

This review explores molecular dynamics simulations for studying radiation damage in Tritium Producing Burnable Absorber Rod (TPBAR) materials, emphasizing the role of interatomic potentials in displacement cascades. Recent machine learning potentials (MLPs), trained on quantum data, enhance prediction accuracy over traditional models like EAM. We highlight temperature, PKA energy, and composition effects on damage evolution in TPBAR components, recommending suitable potentials and discussing advancements for materials in extreme radiation environments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Physics-Informed Machine Learning Model for Ceramic Matrix Composite Creep

A physics-informed recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear, time-dependent constitutive behavior of ceramic matrix composites (CMCs) driven by matrix damage and constituent creep at the microscale. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the high-fidelity generalized method of cells (HFGMC) approach which calls appropriate creep and damage models for each of the constituents. This coupling permits simulating the nonlinear behavior of CMCs based on constituent response at the microscale along with microstructural features such as fiber and porosity volume fraction and fiber radius. The microscale repeating unit cell is loaded under creep fatigue conditions to replicate the material loading experienced in a turbine engine. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input stress sequence, temperature, and microstructural features, the resulting strain history response while satisfying physical constraints related to creep rate, isochoric inelastic deformation, and strain energy density. The trained surrogate model is shown to effectively match the strain history over quantified distributions of microstructural features and relevant loading regimes and temperatures. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore, the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex microstructures, nonlinear time-dependent material response, and under non-monotonic loading conditions.

ceramic matrix composites↗
DOE OSTI2022

FFT-based model for irradiated aggregate microstructures in concrete

The concrete biological shield of light water reactors is exposed to neutron and gamma irradiation throughout its lifetime, which results in the long-term degradation of the concrete’s mechanical properties. Under neutron irradiation, the concrete’s aggregates are subjected to radiation-induced volumetric expansion (RIVE), which strongly depends on the mineral content of the aggregate and exhibits the largest expansion in silicate-bearing minerals. In this work, the authors used the fast Fourier transform-based code Microstructure-Oriented Scientific Analysis of Irradiated Concrete (MOSAIC) in 2D to model the expansion of five different aggregates provided by the Japan Concrete Aging Management Program (JCAMP). Comparable rock specimens were irradiated at the JEEP-II test reactor. The model uses realistic aggregate microstructure reconstruction based on high-resolution characterization images. The model accounts for anisotropic RIVE, thermal expansion, and the associated initiation and propagation of damage. The RIVE models are calibrated based on expansion data in the literature. The authors assume that damage occurs exclusively at interfaces between the particles that compose an aggregate and that these interfaces also exhibit swelling. Using a micromechanical model, the evolution of Young’s modulus with RIVE is calculated for each aggregate and compared with Russian irradiation data. The modeled linear expansion agrees well with the experimentally measured expansion. Furthermore, the model also predicts that anisotropic RIVE and thermal expansion result in an earlier onset of damage with neutron fluence than in the isotropic case.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Mechanisms of Mobility of Grain Boundaries in Tungsten

The objective of this study is to assess the mechanical robustness of tungsten in first-wall applications for tokamaks. These applications unavoidably involve the challenges of high temperature and radiation damage. Here we report progress on determining the mechanisms governing the mobility of tungsten grain boundaries at high temperature, a key property in the process of recrystallization. The determination of mechanisms enables predictive analytic models of grain boundary response to driving forces like the energy stored in the damaged metal lattice that is released as a grain boundary sweeps through it. In prior work we studied tungsten grain boundary properties such as the energies of boundaries with various orientations and misorientations, including energies of grain boundaries whose high-temperature structure differs from the structure at ambient conditions. We have calculated properties associated with the grain boundary mobility including the response of grain boundaries to applied shear stress. We have also developed methods for direct calculation of the motion of curved grain boundaries at high temperature using molecular dynamics. Here we again utilize molecular dynamics simulation of grain boundary motion and go further to analyze how the defect content in a grain boundary evolves as the boundary moves under the action of a driving force.

36 MATERIALS SCIENCE↗
DOE OSTI2026

In situ TEM annealing of neutron-irradiated Ti reveals a two-stage mechanism for elevated temperature radiation damage recovery

Understanding how irradiation-induced defects evolve at elevated temperatures is of critical importance to predicting materials' behavior under steady-state and accident scenarios. However, such mechanistic insight into microstructural evolution is limited by the nature of ex situ annealing and subsequent imaging. Here we show direct observation and quantification of defect recovery in neutron-irradiated Ti using in situ transmission electron microscopy (TEM) annealing experiments. In agreement with our prior work, and at temperatures below the irradiation temperature (T irr = 300 °C), dislocation loops are observed to glide. At elevated temperatures (>500 °C), dislocation lines become mobile and promote significant recovery of the microstructure. These mechanisms challenge the established electron irradiation-based model for radiation damage recovery, which originally suggests dissolution of static defect clusters, and demonstrates the importance of in situ characterization in understanding defect evolution in irradiated materials.

Hirst, Charles A. [Univ. of Michigan, Ann Arbor, M↗
DOE OSTI2023

A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia

Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and can lead to personal and property damage. While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational complexity renders them unsuitable for real-time applications. Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The comparison of deep learning to the random forest algorithm is motivated by the desire to utilize a machine learning architecture that allows for the future inclusion of common uncertainty quantification techniques and the effective integration of relevant, multi-modal features.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Short–Term Forecasting of Wind Gusts at Airports Across CONUS Using Machine Learning

Short–term forecasting of wind gusts, particularly those of higher intensity, is of great societal importance but is challenging due to the presence of multiple gust generation mechanisms. Wind gust observations from eight high–passenger–volume airports across the continental United States (CONUS) are summarized and used to develop predictive models of wind gust occurrence and magnitude. These short–term (same hour) forecast models are built using multiple logistic and linear regression, as well as artificial neural networks (ANNs) of varying complexity. A suite of 19 upper–air predictors drawn from the ERA5 reanalysis and an autoregressive (AR) term are used. Stepwise procedures instruct predictor selection, and resampling is used to quantify model stability. All models are developed separately for the warm (April–September) and cold (October–March) seasons. Results show that ANNs of 3–5 hidden layers (HLs) generally exhibit higher hit rates than logistic regression models and also improve skill with respect to wind gust magnitudes. However, deeper networks with more HLs increase false alarm rates in occurrence models and mean absolute error in magnitude models due to model overfitting. For model skill, inclusion of the AR term is critical while the majority of the remaining skill derives from wind speeds and lapse rates. A predictive ceiling is also clearly demonstrated, particularly for the strong and damaging gust magnitudes, which appears to be partially due to ERA5 predictor characteristics and the presence of mixed wind climates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Acceleration mechanisms of energetic ion debris in laser-driven tin plasma EUV sources

Laser-driven tin plasmas are driving new-generation nanolithography as sources of extreme ultraviolet (EUV) radiation centered at 13.5 nm. A major challenge facing industrial EUV source development is predicting energetic ion debris produced during the plasma expansion that may damage the sensitive EUV channeling multilayer optics. Gaining a detailed understanding of the plasma dynamics and ion acceleration mechanisms in these sources could provide critical insights for designing debris mitigation strategies in future high-power EUV sources. Here, we develop a fully kinetic model of tin-EUV sources using one-dimensional particle-in-cell simulations to study ion debris acceleration, which will be valuable for cross-validation of radiation-hydrodynamic simulations. An inverse-bremsstrahlung heating operator is used to model the interaction of a tin target with an Nd:YAG laser, and thermal conduction is included through a Monte Carlo Coulomb collision operator. While the large-scale evolution is in reasonable agreement with analogous hydrodynamic simulations, the significant timescale for collisional equilibration between electrons and ions allows for the development of prominent two-temperature features. A collimated flow of energetic ions is produced with a spectrum that is significantly enhanced at high energies compared to fluid simulations. The dominant acceleration mechanism is found to be a large-scale electric field supported mainly by the electron pressure gradient, which is enhanced in the kinetic simulations due to the increased electron temperature. We discuss the implications of these results for future modeling of tin-EUV sources and the development of debris mitigation schemes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

An Engineering Approach for Weld Creep Lifetime Assessment Based on Local Property Measurement

The localized creep failure in the heat-affected zone (HAZ) of Grade 91 steel weldments has been identified as one of the most important factors causing significantly shortened service lifetime and structural integrity issues of welded components in advanced fossil and nuclear power plants. To conduct a reliable creep lifetime assessment, a new engineering assessment approach has been developed by incorporating the experimentally determined local properties of the heterogeneous HAZ. By creep testing a purposely simulated HAZ specimen with in situ digital image correlation (DIC) technique, the highly gradient creep properties across the HAZ of Grade 91 steel was quantitatively measured. A physical creep cavitation constitutive model was proposed to investigate the local creep deformation and damage accumulation within the heterogeneous HAZ, which takes into account the nucleation of creep cavities and their growth by both grain boundary diffusion and creep deformation. The relationship among the local material property, creep strain accumulation, and evolution characteristic of creep cavities was established. The approach was then utilized to investigate the creep response and subsequent life for an ex-service 9% Cr steel weldment by incorporating the effects of pre-existing damages which developed and accumulated during long-term services. The predicted results exhibited quantitative agreement with the DIC measurement in terms of both nominal/local creep deformation as well as the subsequent life under the test conditions at 650 and 80 MPa.

Zhang, Wei↗