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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Identification of Important Phenomena for Light Water Reactors During Heat Transport System Failure Events in Integrated Energy Systems

This work adapts historical literature and existing phenomena identification and ranking tables (PIRT) to be applicable to a novel nuclear power plant (NPP) and chemical or thermal process integrated energy system (IES), particularly focusing on the process heat and heat transport system failure events that are not a concern during normal NPP operation but become vital when an IES is considered. Nuclear energy has been suggested to go beyond base-load applications and be used for hydrogen co-generation systems, amongst other IESs. Prior to the implementation of nuclear IESs, sufficient analysis must be performed on accident events to ensure public safety. The events considered were deemed important because of their potential to damage systems, structures, and components (SSCs). Process thermal events of concern include loss of heat load and temperature transient events. Loss of heat load events were characterized as having high importance and being well understood. Temperature transient events may be further categorized by the cyclic loading and harmonics phenomena. Cyclic loading issues were classified as medium to high importance with knowledge gaps existing regarding fatigue and low power operation, while harmonics phenomena were classified as low importance and are well understood. Heat transport system failure events of concern include intermediate and process heat exchanger failures, mass addition to reactor coolant, ingress of material from thermal manifold/energy storage, and loss of intermediate fluid. Furthermore, these events tended to be of high or medium importance, with some knowledge gaps needing to be filled for individual reactor systems due to unique designs.

Integrated Energy System (IES)↗

DOE OSTI2021

Resiliency in numerical algorithm design for extreme scale simulations

Here this work is based on the seminar titled ‘Resiliency in Numerical Algorithm Design for Extreme Scale Simulations’ held March 1–6, 2020, at Schloss Dagstuhl, that was attended by all the authors. Advanced supercomputing is characterized by very high computation speeds at the cost of involving an enormous amount of resources and costs. A typical large-scale computation running for 48 h on a system consuming 20 MW, as predicted for exascale systems, would consume a million kWh, corresponding to about 100k Euro in energy cost for executing 10 23 floating-point operations. It is clearly unacceptable to lose the whole computation if any of the several million parallel processes fails during the execution. Moreover, if a single operation suffers from a bit-flip error, should the whole computation be declared invalid? What about the notion of reproducibility itself: should this core paradigm of science be revised and refined for results that are obtained by large-scale simulation? Naive versions of conventional resilience techniques will not scale to the exascale regime: with a main memory footprint of tens of Petabytes, synchronously writing checkpoint data all the way to background storage at frequent intervals will create intolerable overheads in runtime and energy consumption. Forecasts show that the mean time between failures could be lower than the time to recover from such a checkpoint, so that large calculations at scale might not make any progress if robust alternatives are not investigated. More advanced resilience techniques must be devised. The key may lie in exploiting both advanced system features as well as specific application knowledge. Research will face two essential questions: (1) what are the reliability requirements for a particular computation and (2) how do we best design the algorithms and software to meet these requirements? While the analysis of use cases can help understand the particular reliability requirements, the construction of remedies is currently wide open. One avenue would be to refine and improve on system- or application-level checkpointing and rollback strategies in the case an error is detected. Developers might use fault notification interfaces and flexible runtime systems to respond to node failures in an application-dependent fashion. Novel numerical algorithms or more stochastic computational approaches may be required to meet accuracy requirements in the face of undetectable soft errors. These ideas constituted an essential topic of the seminar. The goal of this Dagstuhl Seminar was to bring together a diverse group of scientists with expertise in exascale computing to discuss novel ways to make applications resilient against detected and undetected faults. In particular, participants explored the role that algorithms and applications play in the holistic approach needed to tackle this challenge. This article gathers a broad range of perspectives on the role of algorithms, applications and systems in achieving resilience for extreme scale simulations. The ultimate goal is to spark novel ideas and encourage the development of concrete solutions for achieving such resilience holistically.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Compositional effects on cure kinetics, mechanical properties and printability of dual-cure epoxy/acrylate resins for DIW additive manufacturing

Interest in 3D printing of thermoset resins has increased significantly in recent years. One approach to additive manufacturing of thermoset resins is printing dual-cure resins with direct ink write (DIW). Dual-cure resins are multi-component resins which employ an in situ curable constituent to enable net-shape fabrication while a second constituent and cure mechanism contribute to the final mechanical properties of the printed materials. In this work, the cure kinetics, green strength, printability, and print fidelity of dual-cure epoxy/acrylate thermoset resins are investigated. Resin properties are evaluated as a function of acrylate concentration and in situ UV exposure conditions. The acrylate cure kinetics are probed using photo-differential scanning calorimetry and the impacts of resin composition and UV cure profile on the acrylate extent of conversion are presented. Continuous and pulsed UV cure profiles are shown to affect total conversion due to variances in radical efficiency at different UV intensities and acrylate concentrations. The effects of acrylate concentration on the kinetics of the epoxy thermal cure and the final mechanical properties are also investigated using dynamic mechanical analysis and three-point bend measurements. The glass transition temperature is dependent on formulation, with increasing acrylate content decreasing the T g . However, the room temperature shear moduli, flexural moduli, strength, strain-to-failure, and toughness values are relatively independent of resin composition. The similarity of the final properties allows for greater flexibility in resin formulation and in situ cure parameters, which can enable the printing of complex parts that require high green strength. We found that the in situ UV print intensities and exposure profiles that are necessary to achieve the best print quality are not, in most cases, the conditions that maximize conversion of the acrylate network. Lastly, this highlights the importance of developing optimized resin compositions which enable complete cure of the acrylate network by promoting acrylate dark cure or thermal cure.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Machine learning for single-ended event reconstruction in PROSPECT experiment

The Precision Reactor Oscillation and Spectrum Experiment, PROSPECT, was a segmented antineutrino detector that successfully operated at the High Flux Isotope Reactor in Oak Ridge, TN, during its 2018 run. Despite challenges with photomultiplier tube base failures affecting some segments, innovative machine learning approaches were employed to perform position and energy reconstruction, and particle classification. This work highlights the effectiveness of convolutional neural networks and graph convolutional networks in enhancing data analysis. By leveraging these techniques, a 3.3% increase in effective statistics was achieved compared to traditional methods, showcasing their potential to improve analysis performance. Furthermore, these machine learning methodologies offer promising applications for other segmented particle detectors, underscoring their versatility and impact.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Component level modeling of materials degradation for insights into operational flexibility of Existing Coal Power Plants

Increasingly, coal-fired power plants are required to balance power grids by compensating for the variable electricity supply from renewable energy sources. Fossil-fueled power plants, originally designed to be base loaded, will increasingly need to operate on a load following or cyclic basis. This demanding requirement for operational flexibility needs insights into accelerated material degradation arising due to the harsh operating conditions (e.g., fatigue, early oxide exfoliation due to stresses) along with current damage mechanisms (fireside corrosion, creep and erosion) observed in service. Our research objective is to develop component level modeling toolkit for materials-based degradation for two key mechanisms that can accelerate with cyclic operations. In more detail, this includes the fireside corrosion/steam oxidation/erosion/creep/fatigue of superheaters/reheaters and steam pipework and also the water droplet erosion/ fatigue of last stage steam turbine blades degradation mechanisms, that demand routine and sometimes unplanned maintenance and repair. The innovation is in developing a computational fluid dynamics/finite element (CFD/FE) modeling toolkit for the component level models of the boilers and low-pressure steam turbines in coal power plants that can tackle multidisciplinary failure mechanisms occurring concurrently for extreme environment materials. Lifetime assessment in such environments also needs to account for the unit-specific analyses, operational history and fuel feedstock; this can only be obtained by destructive analysis of components. This, in turn, enables validation of the model toolkits utilizing service feedback data, improving the probability of time/temperature dependent life prediction.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2026

A Methodology to Evaluate the Grid Reliability Impact of Oscillations Induced by Large Loads

The rapid growth of hyperscale AI data centers is bringing renewed attention to the reliability risk that sustained forced oscillations pose to bulk power systems, with cyclic computational workloads emerging as a new forcing source. Unlike the broadband, stochastic disturbances from traditional industrial loads such as arc furnaces, AI training and inference facilities can inject large active power swings concentrated at specific frequencies over extended durations - characteristics that existing grid planning practices do not account for. While the North American Electric Reliability Corporation (NERC) has recognized this gap and called for system-level studies of large load interconnections, no standardized methodology exists to screen, simulate, and quantify these risks at the planning stage. This report presents the Risk Assessment Tool for Large Load-induced Events (RATLLE), a Python-based, publicly available script suite developed at the Pacific Northwest National Laboratory to evaluate bulk power system reliability risks from data center-induced oscillations. RATLLE implements a three-module workflow: a screening module that identifies vulnerable interconnection locations and excitable system modes; a simulation module that models cyclic data center load behavior using a commercial positive sequence simulation platform; and an analysis module that computes risk metrics and generates interactive visualization dashboards. The risk metrics, formulated around simulation observables, map oscillation impacts to a three-stage severity scale spanning latent equipment fatigue through imminent cascading failure. The methodology is demonstrated on two Western Electricity Coordinating Council (WECC) system models: a publicly available 240-bus reduced representation and a detailed 2031 Heavy Winter planning case. Case studies illustrate that even modest 50 MW forced oscillations at resonant frequencies can produce wide-area power swings, N-1 security constraint violations, and cascading generator trips through protection actions - outcomes that would not occur under normal operating conditions without oscillations present. The results underscore the need for standardized oscillation impact assessment in large load interconnection studies and provide a reproducible, extensible framework for utilities to adopt or customize within their existing planning workflows.

Biswas, Shuchismita↗

DOE OSTI2023

Large-Scale Inference of Multivariate Regression for Heavy-Tailed and Asymmetric Data

Large-scale multivariate regression is a fundamental statistical tool with a wide range of applications. Here, this study considers the problem of simultaneously testing a large number of general linear hypotheses, encompassing covariate-effect analysis, analysis of variance, and model comparisons. The challenge that accompanies a large number of tests is the ubiquitous presence of heavy-tailed and/or highly skewed measurement noise, which is the main reason for the failure of conventional least squares-based methods. For large-scale multivariate regression, we develop a set of robust inference methods to explore data features such as heavy tailedness and skewness, which are not visible to least squares methods. The new testing procedure is based on the data-adaptive Huber regression and a new covariance estimator of regression estimates. Under mild conditions, we show that our methods produce consistent estimates of the false discovery proportion. Extensive numerical experiments and an empirical study on quantitative linguistics demonstrate the advantage of the proposed method over many state-of-the-art methods when the data are generated from heavy-tailed and/or skewed distributions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Passive Safety System Reliability Analysis: Methodology, Application, and Results

Many advanced reactor designs rely on passive systems to provide assurance that certain safety functions, such as reactivity control and reactor heat removal, are maintained during transient scenarios. The performance of these systems is sensitive to boundary and environmental conditions and can lead to functional failure, which is the inability of the system to perform as intended even without any physical failures. Additionally, conditions can cause the passive systems to operate in an intermediate or degraded state. These operational modes of passive systems and their overall reliability can be difficult to capture in traditional probabilistic frameworks where discrete operating modes are assumed or time-dependent boundary conditions are not accounted for. Novel strategies and methods are needed to be able to capture the reliability of these passive systems and their impact on facility safety. The current abstract provides a summary of such a methodology and examples of its use in a case study, the GE Hitachi (GEH) PRISM probabilistic safety assessment (PSA), and the U.S. Department of Energy (USDOE) Versatile Test Reactor (VTR) PSA.

Bucknor, M.↗

DOE OSTI2025

srlife : A software tool for estimating the life of high temperature concentrating solar receivers. Part II – Ceramic receivers

As Concentrating Solar Power (CSP) technologies aim for higher operating temperatures to enhance efficiency and meet industrial process heat demands, high-temperature metallic materials, including nickel-based superalloys, face challenges in maintaining structural integrity. Advanced ceramics offer a promising alternative due to their superior high-temperature strength. However, accurately assessing the performance of ceramic components requires a fundamentally different approach from that used for metallic components. This Part II of a two-part paper describes the integration of ceramic statistical failure models within srlife – an open-source tool for predicting the life of high-temperature CSP receivers. These models account for the inherent variability in ceramic strength, as well as the effects of subcritical crack growth (SCG) under high temperature cyclic loads. Here, the paper includes an example problem that demonstrate the process of evaluating ceramic receivers using srlife. Part I details the life estimation process for metallic receivers (i.e. creep-fatigue life) along with input and output data structure, thermohydraulic analysis, and structural analysis. The complete tool is available as open-source software at https://github.com/srlife-project/srlife and can be installed via the PyPi package manager (https://pypi.org). By supporting both ceramic and metallic receiver analyses, srlife facilitates fair comparisons between competing metallic and ceramic designs, enabling accurate evaluations of plant efficiency and the economic benefits of ceramic solar receivers and other components.

High temperature ceramic receivers↗

DOE OSTI2020

Seismic response to paleo-sand dunes in the Nugget Sandstone Formation, southwestern Wyoming

We have analyzed a 3D seismic survey acquired for a carbon sequestration project on top of the Moxa Arch in southwestern Wyoming. We observed a zone of discontinuous reflectors on vertical slices of seismic amplitude volume, whereas, the northwest–southeast lineations were observed on the time slices. We performed a seismic to well tie that suggested that the lineations occur within the Nugget Sandstone. The Nugget Sandstone is an eolian sandstone deposit of Early Jurassic age, deposited as a subtropical dune field, and equivalent to the Navajo Sandstone of southwestern Utah. Petrophysical analysis indicates that the Nugget Sandstone is dominated by clean sandstone (70%–80%), whereas evaporites, including halite and anhydrite, are present in certain zones. Previous outcrop studies on the Navajo Sandstone indicate the wind direction to be predominantly northeast–southwest. Seismic attributes, including coherence and curvature, displayed on stratal slices within the Nugget Sandstone interval indicate the presence of lineations in the northwest–southeast direction with irregular spacing. These lineations are approximately perpendicular to the inferred dominant wind direction. We computed the dominant wind direction from the average azimuth of the lineations as seen on the curvature attribute in the Nugget Sandstone interval. Geological feature: Eolian sand dunes with interdunal evaporites Seismic appearance: Parallel lineations with irregular spacing on seismic attribute horizon slices Alternative interpretations: Canyons at continental slopes; slope failures Features with a similar appearance: Marine bars; contourites Formation: The Nugget Sandstone — equivalent to the Navajo Sandstone Age: Early Jurassic Location: Moxa Arch, Wyoming Seismic data: Obtained by the University of Wyoming with U.S. DOE funding Contributors: Dhruv Agrawal, Brady Lujan, Sumit Verma, Shuvajit Bhattacharya, and Subhashis Mallick Analysis tools: Coherence and curvature attributes; seismic inversion; petrophysical inversion

Geochemistry & Geophysics↗

DOE OSTI2026

A Cautionary Tale: Failure of the Valence CASSCF to Describe the Hallmark of Hydrogen Bonding

Valence CASSCF (vCAS) calculations for the hydrogen-bonded (H2O)2, (HF)2, and HF–H2O dimers fail to predict the lengthening of the hydrogen donor bond and the corresponding red shift in the donor bond vibrational stretching frequency. Analysis of the active orbitals in the vCAS calculation reveals that one of the weakly occupied orbitals in each dimer is used to correlate the electrons in the acceptor lone pairs rather than those in the donor bonding orbitals. This has significant implications for subsequent treatments of the dynamical correlation that are based on the vCAS wavefunction.

Nguyen, Long H.↗

DOE OSTI2023

Analysis of Real-World Preignition Data Using Neural Networks

Increasing adoption of downsized, boosted, spark-ignition engines has improved vehicle fuel economy, and continued improvement is desirable to reduce carbon emissions in the near-term. However, this strategy is limited by damaging preignition events which can cause hardware failure. Research to date has shed light on various contributing factors related to fuel and lubricant properties as well as calibration strategies, but the causal factors behind an individual preignition cycle remain elusive. If actionable precursors could be identified, mitigation through active control strategies would be possible. This paper uses artificial neural networks to search for identifiable precursors in the cylinder pressure data from a large real-world data set containing many preignition cycles. It is found that while follow-up preignition cycles in clusters can be readily predicted, the initial preignition cycle is not predictable based on features of the cylinder pressure. Further, this indicates that the alternating pattern of preignition cycles within clusters is influenced by the thermodynamic state as reflected in the pressure, but that the trigger for the initial preignition cycle is not thermodynamic in nature, but more likely tied to a critical threshold in the chemistry of the fuel/lubricant mixture in the upper crevice or other factors related to the presence of an ignition source.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

One dimensional wormhole corrosion in metals

Corrosion is a ubiquitous failure mode of materials. Often, the progression of localized corrosion is accompanied by the evolution of porosity in materials previously reported to be either three-dimensional or two-dimensional. However, using new tools and analysis techniques, we have realized that a more localized form of corrosion, which we call 1D wormhole corrosion, has previously been miscategorized in some situations. Using electron tomography, we show multiple examples of this 1D and percolating morphology. To understand the origin of this mechanism in a Ni-Cr alloy corroded by molten salt, we combined energy-filtered four-dimensional scanning transmission electron microscopy and ab initio density functional theory calculations to develop a vacancy mapping method with nanometer-resolution, identifying a remarkably high vacancy concentration in the diffusion-induced grain boundary migration zone, up to 100 times the equilibrium value at the melting point. Deciphering the origins of 1D corrosion is an important step towards designing structural materials with enhanced corrosion resistance.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Test Plan to Investigate the Optimal Cure Schedule of an Epoxy Resin for Its Application as an Adhesive in Optocouplers

Epoxy resins are polymeric materials that are known for their versatility, low cost, and strong thermal and mechanical properties. They can be used as coatings, adhesives, automotive primers, and have become staples in electronics, aerospace composites and semiconductor encapsulates. Recently, LANL has experienced a decline in the quality of a specialized electronic device, called an optocoupler, which is normally acquired from commercial suppliers. Because of this, a group at LANL is exploring the feasibility of in-house manufacturing of an optocoupler that is better suited for their application. The current prototype has resulted in adhesive failure after being exposed to several thermal cycles. There is a need to investigate the possible reasons for this failure, particularly how the cure schedule of the epoxy might affect its adhesive strength over time and in cycled temperatures. To assist in this investigation, a test plan has been developed involving multiple experiments using thermomechanical analysis (TMA), dynamic mechanical analysis (DMA) and differential scanning calorimetry (DSC).

36 MATERIALS SCIENCE↗

DOE OSTI2020

Stochastic nonlinear analysis of unidirectional fiber composites using image-based microstructural uncertainty quantification

Here, we present a data-driven nonlinear uncertainty quantification and propagation framework to study the microstructure-induced stochastic performance of unidirectional (UD) carbon fiber reinforced polymer (CFRP) composites. The proposed approach integrates (1) microscopic image characterization, (2) stochastic microstructure reconstruction, and (3) efficient multiscale finite element simulations enabled by self-consistent clustering (SCA) analysis. To model the complex microstructural variability, the proposed UQ methods take the non-Gaussian uncertainty sources into account through a distribution-free sampling approach leveraging nonparametric and asymptotic statistical tools. A hierarchical conditional sampling strategy enables the simultaneous sampling of multiple sources of uncertainties. Our approach provides insights into the impact of microstructural variabilities, which are shown to have an increasing impact on the nonlinear responses of UD CFRP parts under progressive compression loading and ultimately on the failure rate over time. We discover that before CFRP parts start to fail, a characteristic time period emerges with distinctive uncertainty distributions specific to the microstructure variability and the probability of failure. Identifying the failure time period is crucial to the reliability prediction, which is an essential component of CFRP design.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Software Quality Assurance Plan ANSYS LSDYNA Version 2023R1

ANSYS Inc. develops and markets engineering simulation software and services used in the aerospace, automotive, manufacturing, electronics, biomedical, energy, defense, and many other industries. ANSYS is dedicated to engineering simulation and is the world’s leading software provider. ANSYS was founded in 1970 and is headquartered in Canonsburg, Pennsylvania. ANSYS provides an engineering analysis tool combining structural, thermal, computational fluid dynamics, acoustic and electromagnetic simulation capabilities. ANSYS LS-DYNA is the most used explicit simulation program capable of simulating the response of materials to short periods of severe loading. Its many elements, contact formulations, material models, and other controls can be used to simulate complex models with control over all the details of the problem. ANSYS LS-DYNA has a vast array of capabilities to simulate extreme deformation problems using its explicit solver. Engineers can tackle simulations involving material failure and look at how the failure progresses through a part or through a system. Models with large amounts of parts or surfaces interacting with each other are also easily handled, and the interactions and load passing between complex behaviors are modeled accurately. Using computers with higher numbers of CPU cores can drastically reduce solution times. In addition, many consulting firms and hundreds of universities use ANSYS for analysis, research, and educational purposes. ANSYS is recognized worldwide as one of the most widely used and capable programs of its type. ANSYS has successfully passed over 100 customer quality system audits against American Society of Mechanical Engineers (ASME) NQA-1 and 10 CFR Part 50, Appendix B, since the company was founded, over 60 of which have been since 1997. ANSYS has successfully passed over 100 International Organization for Standardization (ISO) 9001 assessments. ANSYS design analysis software is the first created within a quality system with ISO 9001 certification, which is the internationally accepted quality standard. Product development, testing, maintenance, and support processes also meet the US Nuclear Regulatory Commission’s (NRC’s) quality requirements, as they have for nearly four decades. ANSYS staff perform more than 60,000 software verification tests before releasing each new product. ASME NQA-1-2012 (Subpart 2.7 is specific to software) is the industry- and NRC-accepted approach (consensus standard) for meeting 10 CFR Part 50, Appendix B, requirements.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Automatically adaptive stabilized finite elements and continuation analysis for compaction banding in geomaterials

Under compressive creep, viscoplastic solids experiencing internal mass transfer processes can accommodate singular cnoidal wave solutions as material instabilities at the stationary wave limit. These instabilities appear when the loading rate is significantly faster than the material's capacity to diffusive internal perturbations, leading to localized failure features (e.g., cracks and compaction bands). These cnoidal waves, generally found in fluids, have strong nonlinearities that produce periodic patterns. Due to the singular nature of the solutions, the applicability of the theory is currently limited. Additionally, practical simulation tools require proper regularization to overcome the challenges that singularity induces. We focus on the numerical treatment of the governing equation using a nonlinear approach building on a recent adaptive stabilized finite element method. This automatic refinement method provides an error estimate that drives mesh adaptivity, a crucial feature for the problem at hand. We compare the performance of this adaptive strategy against analytical and standard finite element solutions. We then investigate the sensitivity of the diffusivity ratio, the parameter controlling the process, and identify multiple possible solutions with several stress peaks. We also show the evolution of the spacing between peaks for all solutions as a function of that parameter.

42 ENGINEERING↗

DOE OSTI2022

Qualitative and quantitative analysis of neutron irradiation effects in SiC/SiC composites using X-ray computed tomography

Silicon carbide fiber-reinforced silicon carbide matrix (SiC/SiC) composites are candidate materials for cladding of light water reactor (LWR) fuels. Loss of fission product gas retention due to the formation of microcrack networks is considered a potential failure mechanism for SiC/SiC-cladded fuels. In this work, a variety of SiC/SiC composite tubes were irradiated with and without an LWR-relevant radial heat flux in the High Flux Isotope Reactor, followed by detailed characterization with X-ray computed tomography (XCT). This first set of XCT data for neutron-irradiated samples confirmed that the internal stresses arising from a combination of temperature gradients and irradiation-induced swelling act as the primary driver for cracking. Consequently, while the observed cracking patterns varied depending on the tube architectures, the sharp edges of relatively large pores were found to be the common stress concentrator. These findings are useful to help improve the design and manufacturing of SiC/SiC fuel claddings for reduced failure probability.

42 ENGINEERING↗