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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

VERA-Grizzly Ex-Core Calculations: Watts Bar Unit 1 Cycles 1-2

The critical structures that comprise light-water reactor (LWR) nuclear power plants are subjected to operating environments that can challenge their integrity. Structures in close proximity to the reactor core, such as the reactor pressure vessel (RPV) and the biological shield wall, are subjected to high levels of radiation emanating from the core, as well as elevated temperatures. As the US fleet of operating LWRs ages, the effects of these operating environments on the integrity of these structures must be considered to ensure their continued safe operation. Extending the lifetime of commercial reactors and maintaining the aging reactor fleet require accurate prediction of the exposure of ex-core components to neutron and photon radiation. In particular, concrete degradation studies must be performed to evaluate the safety and long-term operation of reactors with lifetime extensions. The concrete reactor bioshield is important for providing radiological protection during operation and must last for the entire lifetime of the reactor. Recent interest in lifetime extensions furthers the need to accurately simulate concrete material degradation in the reactor bioshield. As a result of this need, the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has funded this study to couple its tools, Virtual Environment for Reactor Applications (VERA) and Grizzly. VERA allows users to set up models to calculate time-dependent and fully coupled solutions (with thermal feedback) for ex-core quantities of interest such as vessel and coupon fluence and detector responses for multiple statepoints and cycles. Grizzly is a finite-element application based on the Multiphysics Object Oriented Simulation Environment (MOOSE) framework that is used to enable aging materials calculations. This report highlights the work performed to calculate the fluence in the vessel and concrete for Watts Bar Nuclear Plant Unit 1 (WBN1) Cycles 1 and 2. The fluences obtained from VERA were successfully transferred to Grizzly using a Python script. Four simulations were run with Grizzly: (1) the Mazars model with the initial Young’s modulus being the instantaneous modulus, (2) the Mazars model with the initial Young’s modulus being the delayed modulus, (3) the Mazars model with the initial Young’s modulus being the delayed modulus with the addition of the effects of micro-damage caused by irradiation, and (4) the Mazars model with the initial Young’s modulus being the instantaneous modulus, and with the addition of micro-damage and creep. Details regarding the methods used to obtain the fluence and the statistical errors associated with the VERA Monte Carlo Shift calculations are discussed in greater detail in this report. The results obtained from the four Grizzly models are also presented in this report.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Efficient Bayesian inference with latent Hamiltonian neural networks in No-U-Turn Sampling

When sampling for Bayesian inference, one popular approach in the computational field is to use Hamiltonian Monte Carlo (HMC) and specifically the No-U-Turn Sampler (NUTS), which automatically decides the end time of the Hamiltonian trajectory. However, HMC and NUTS can require numerous numerical gradients of the target density and can prove slow in practice when relying on computationally expensive forward models. We propose Latent Hamiltonian neural networks (L-HNNs) with HMC and NUTS for solving Bayesian inference problems. Once trained, L-HNNs do not require numerical gradients of the target density during sampling, and hence numerous evaluations of the forward computational model. Moreover, L-HNNs satisfy important properties such as perfect time reversibility and Hamiltonian conservation, making them well-suited for use within HMC and NUTS because stationarity can be shown. We also propose the integration of L-HNNs in an online error monitoring scheme, in which numerical gradients of the target density are used for a few samples whenever the L-HNNs prediction errors are large. This online error monitor scheme prevents sample degeneracy in regions of low probability density and ensures robust uncertainty quantification. We demonstrate L-HNNs in NUTS with online error monitoring on several analytical examples involving complex, heavy-tailed, and high-local-curvature probability densities. We then demonstrate the applicability of L-HNNs in NUTS to two computational case studies, namely the Allen-Cahn stochastic partial differential equation and an elliptic partial differential equation with 25 and 50 inference parameters, respectively. Overall, the L-HNNs in NUTS with online error monitoring satisfactorily inferred these probability densities. In conclusion, compared to traditional NUTS, L-HNNs in NUTS with online error monitoring required 1–2 orders of magnitude fewer numerical gradients of the target density and improved the effective sample size (ESS) per gradient (which is a measure of both the sampling quality and the computational expense) by an order of magnitude.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

In situ X-ray imaging of pore formation mechanisms and dynamics in laser powder-blown directed energy deposition additive manufacturing

Directed energy deposition (DED) additive manufacturing (AM) is receiving growing attention in many applications, such as repair, remanufacturing, and fabrication of functionally graded structures. However, the laser-matter interactions and melt pool dynamics in laser DED with powder flow are still unclear, particularly in how pores form and flow inside the melt pool during the process. Understanding the porosity formation mechanisms is critical in the qualification, certification, and overall properties of a DED AM part. Porosity is a common phenomenon and can significantly hinder the quality of DED fabricated parts, as the pores can act as sites of crack nucleation and propagation. Here in this paper, we reveal four types of pore formation mechanisms through in-situ and operando high-speed high-resolution X-ray imaging in the DED AM process. Our results confirm that porosity within the feedstock powder induces pores in the process. We also observed pore formation mechanisms unique to the laser-based, powder-blown DED process as a result of powder delivery, keyhole dynamics, melt pool dynamics, and shield gas. High-speed X-ray images provide direct evidence for pore formation mechanisms and show that the pores related to the interaction between the delivered powder and melt pool are the largest in size in laser-based powder-blown DED AM. These results will guide porosity mitigation, elimination, and control in DED AM.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Structural Design Variations in Beetle Elytra

Abstract Beetles typically use their protective wing coverings or elytra to shield their membranous hindwings from the environment. Elytra in some terrestrial species have evolved a greater protective role capable of shielding the organism from powerful antagonistic predators. The structure–function relationships of these biological composites identify how architectural and chemical variations of the cuticle are tuned to create light‐weight, impact resistant composites. Specifically, the elytral structures of a tree dwelling beetle capable of flight, Trypoxylus dichotomus , and a terrestrial beetle incapable of flight, Phloeodes diabolicus , are compared to understand how their varied environmental needs forged the elytra to facilitate fight or resist fatal predator strikes. Mechanical and microstructural analysis reveals P . diabolicus has a harder, stiffer elytra that incorporate through‐thickness fibers to resist greater mechanical stresses imposed by bending and puncture. Conversely, the elytra of T . dichotomus have a compliant structure with large voids that facilitates localized deformation. Variations in flexural strength and puncture resistance remain attributed to P . diabolicus possessing a thicker cuticle with a greater degree of cross‐linking and an increased amount of endocuticular layers. These findings may provide useful insight into the design and manufacturing of composite materials for use in light‐weight or energy‐absorbing applications.

Rivera, Jesus↗
DOE OSTI2020

Improvements to the Modeling of the TREAT Reactor and Experiments

This paper summarizes the latest improvements and lessons learned from the modeling and simulation of the transient test reactor at Idaho National Laboratory using the MAM- MOTH reactor physics application. MAMMOTH is a MOOSE-based, Finite Element Method application that specializes in the analysis of the spatial dynamics behavior of nuclear reactors. Since early 2018 several transient tests have been conducted at TREAT, thus providing the opportunity to apply and benchmark modern modeling and simulation tools. MAMMOTH was used to provide predictions of the power coupling factor between the core and the experiment for various experiments. Even though the power coupling factor predictions agree very well with the experimental data, within the bounds of the experimental uncertainty, one shortcoming was the underprediction of the total energy deposited in the core and in the experiment. Determination of the sources for this discrepancy are ongoing, but several key problems have been identified and resolved, thus providing valuable insights for future research. This paper discusses several of these lessons learned. First, the heat capacity data for the TREAT fuel has some significant problems due to limitations of the measurement techniques used circa 1960s. The sensitivity of the peak power and the total energy deposition to various representations of the heat capacity is approximately 5%. Second, the effects of the biological shield and thermal column on the modeling of the core are non-negligible, since they affect the mean generation time and the effective reflection of neutrons back into the core, which is suspected to be important during the core heat up. Matching the reactor period resolves the fact that the reduced spatial domain used in the MAMMOTH model underpredicts the mean generation time. The neutron reflection from these regions is marginally improved with the use of an albedo boundary condition. Third, modeling of the control rod movement with a multi-scheme method is introduced and its current limitations are exposed. Fourth, we explore the effects of using a homogenized model with Superhomogenization equivalence and how that differs from fully heterogeneous simulations. Finally, the energy condensation effects for this graphite core are significant. Solutions with 10 and 26 energy groups show the benefits of using a finer coarse group structure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology

Atmospheric rivers (ARs) are now widely known for their association with high-impact weather events and long-term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR-related metrics based on 20+ different AR identification and tracking methods applied to Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria-based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all-method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR-related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider.

Rutz, Jonathan J.↗
DOE OSTI2022

Elucidating the Role of CO in the NO Storage Mechanism on Pd/SSZ-13 with in Situ DRIFTS

Pd ion exchanged zeolites emerged as promising materials for the adsorption and oxidation of air pollutants. For low-temperature vehicle exhaust, dispersed Pd ions are able to adsorb NO x even in H 2 O-rich exhaust in the presence of carbon monoxide. In order to understand this phenomenon, changes in Pd ligand environment have to be monitored in-situ. Herein, we directly observe the activation of hydrated Pd ion shielded by H 2 O into a carbonyl-nitrosyl complex Pd 2+ (NO)(CO) in SSZ-13 zeolite. The subsequent thermal desorption of ligands on Pd 2+ (NO)(CO) complex proceeds to nitrosyl Pd 2+ rather than to carbonyl Pd 2+ under various conditions. Thus, CO molecules act as additional ligands to provide alternative pathway through Pd 2+ (NO)(CO) complex with lower energy barrier for accelerating NO adsorption on hydrated Pd 2+ ion, which is kinetically limited in the absence of CO. We further demonstrate that hydration of Pd ions in the zeolite is a prerequisite for CO-induced reduction of Pd ions to metallic Pd. The reduction of Pd ions by CO is limited under dry conditions even at a high temperature of 500°C, while water makes it possible at near RT. However, the primary NO adsorption sites are Pd 2+ ions even in gases containing CO and water. These findings clarify additional mechanistic aspects of the passive NO x adsorption (PNA) process and will help extend the NOx adsorption chemistry in zeolite-based adsorbers to practical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Sulfur Pellets Responses to a Bare and Steel Reflected Pulse of the Oak Ridge National Laboratory Health Physics Research Reactor

The experiments analyzed in this report were conducted at the Health Physics Research Reactor (HPRR), also known as the $\textit{Fast Burst Reactor}$. The reactor was designed and built at Oak Ridge National Laboratory (ORNL) in 1961. The HPRR was an unmoderated, unshielded fast reactor that used highly enriched uranium and molybdenum alloy as fuel. The reactor was initially sent to the Nevada Test Site in 1962, where it was used to evaluate radiation doses received as a result of the Hiroshima and Nagasaki bombings during World War II. A few years later, the reactor was sent back to ORNL to be part of the Dosimetry Application Research (DOSAR) facility shown in Figure 1, which included a reactor building shown on the left (west) of the picture and a control and laboratory building in the upper right corner (northeast). The critical assembly was used for numerous technical studies, including systems calibration, dosimetry, radiobiology of plants and animals, testing of radiation alarms, as well as teaching and training in radiation dosimetry and nuclear engineering. Between 1963 and 1987, the HPRR was operated for thousands of hours, achieving criticality close to 10,000 times and motivating many publications. The HPRR was decommissioned in 1987. The goal of this effort was to use historical data from operation of the HPRR to create a criticality accident alarm system (CAAS) benchmark to be included in the $\textit{International Handbook of Evaluated Criticality Safety Benchmark Experiments}$ (ICSBEP Handbook). A thorough inspection was performed of all available documentation and information available. The most promising experiments that were selected for evaluation were those described in the 1987 ORNL report entitled $\textit{Health Physics Research Reactor Reference Dosimetry}$, ORNL-6240. The report includes reference dosimetry results of the shielded and unshielded configurations of the HPRR after burst operations. Because of changes to the reactor positioning and storage systems that were made in 1985, the previous dosimetry reports became obsolete, and the newly designed experiments were needed to create the HPRR’s adjusted dosimetry data. The various results reported in ORNL-6240 include reference doses and dose equivalents from different conventions at different distances and elevations as determined using the detected neutron fluence and conversion factors. The HPRR neutron fluence was obtained through different methods, including sulfur pellet analysis and threshold detector unit data. Information about the HPRR spectrum was also obtained through Bonner sphere measurements. This benchmark is focused on a part of the measured sulfur fluences reported in Appendix H of ORNL-6240. Standard commercial sulfur pellets were placed at different distances from the HPRR centerline during burst operation and were activated due to the 32 S(n,p) 32 P reaction. The resulting 32 P activity was then measured and the information about the corresponding sulfur fluence and/or neutron dose could be extracted. Many of those measurements have 7 been performed with the HPRR in its bare configuration or with different shields (combinations of Lucite, concrete, steel). All the necessary, precise information about material and/or dimensions of the different shields was not found, so it was decided to focus only on the unshielded and steel-shielded configurations to minimize the benchmark uncertainty. A total of 31 cases (24 unshielded and 7 shielded cases at different positions) of sulfur fluence were selected before evaluation to develop the benchmark.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

LA17-V-InverseRadTransportProblem-PD2Kb. Final Project Report

In the inverse radiation transport problem, a radiation signal emanating from an unknown object is used to reconstruct geometric and material parameters of the object , e.g., the mass of special nuclear material (SNM), the isotopic composition of the SNM (e.g., the enrichment of 235U), the thickness of shielding, etc. The inverse radiation transport problem is one of the most fundamental and important in the emergency response mission, and has been addressed in previous NA-22 and NA-42/84 investments by using derivative-free optimization methods. Our project was aimed at (i) consolidating previous work by applying polynomial chaos expansion (PCE) for reducing significantly the number of forward model simulations required by derivative-free optimization methods; (ii) developing a general-purpose framework and stand-alone software for solving the inverse radiation transport problem by applying a new “predictive modeling” methodology (predictive modeling of coupled multi-physics systems, PM_CMPS) which enables the fusing of experimental and computational information; and (iii) developing several benchmarks (for unscattered gamma rays) for demonstrating the quantification of non-Gaussian features (e.g., skewness and kurtosis, which characterize asymmetries and long tails) of response distributions by deterministically and efficiently propagating parameter uncertainties (covariances and higher-order moments, where available) using high-order sensitivities computed by the recently developed comprehensive adjoint sensitivity analysis methodology (C-ASAM). With regard to (i), application of PCE, the most significant accomplishment of this project was an implementation of PCE into INVERSE that significantly reduced computation time for the DiffeRential Evolution Adaptive Metropolis (DREAM) and Differential Evolution (DE) methods. For inverse transport problems with measurements of neutron leakage, neutron multiplication, and neutron-induced gamma-rays, the solution times for the DREAM and DE methods were reduced by factors of 30-300, reducing run times from hours to minutes while maintaining the same accuracy as the standard (non-PCE) DREAM and DE. Speedups were further improved by exploring sparse grid quadrature sets using the Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN (Tasmanian) toolset. An initial exploration of sparse grids indicated that further speedups factors of 5-10 are obtainable. The implementation of PCE in INVERSE is not in a form that users can easily access. With regard to (ii), solving the inverse problem using a predictive modeling paradigm, the most significant accomplishment of this project were the derivation and implementation of adjoint-based second derivatives in neutron and photon transport theory. There are two codes that compute these derivatives. SENSPG computes them for uncollided gamma rays and SENSMG computes them for neutron leakage. SENSMG is open-source code available on github. Other significant accomplishments included a stand-alone code, MULTI-PRED, that implements the predictive modeling paradigm, along with a detailed manual. The code was implemented in the existing INVERSE code for inverse problems, but a head-to-head comparison of inverse methods was not achieved. We also derived and implemented the capability to compute sensitivities for multiplicity measurements into the SENSMG code, and this capability was used in a data assimilation application, but it was not used for solving inverse problems. With regard to (iii), developing benchmarks to demonstrate the importance of high-order sensitivity and uncertainty analysis, the most significant accomplishment of this project were two test problems that showed how important second-order sensitivities are.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Neutral Silicon Vacancy Centers in Undoped Diamond via Surface Control

Neutral silicon vacancy centers (SiV 0 ) in diamond are promising candidates for quantum applications; however, stabilizing SiV 0 requires high-purity, boron-doped diamond, which is not a readily available material. Here, we demonstrate an alternative approach via chemical control of the diamond surface. We use low-damage chemical processing and annealing in a hydrogen environment to realize reversible and highly stable charge state tuning in undoped diamond. The resulting SiV 0 centers display optically detected magnetic resonance and bulklike optical properties. Controlling the charge state tuning via surface termination offers a route for scalable technologies based on SiV 0 centers, as well as charge state engineering of other defects.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Modeling, analysis, and optimization of complex nuclear processes and facilities via computational methods: The HALEU process case study

Improving and adapting industrial systems to timely meet changing programmatic and market demands is an important goal to achieve, including when operating and maintaining complex nuclear processes and facilities. However, changes to these complex systems are costly, particularly when they are already in place and bounded to stringent requirements and constraints such as when handling radioactive material and contaminated equipment. These conditions often exist when treating spent nuclear fuel remotely within shielded nuclear radiation chambers, commonly referred as hot cells, to condition nuclear material and/or fabricate products for utilization in other nuclear enterprises such as in the manufacture of advanced nuclear fuel. The illustrative case considered here is the production of high assay low enriched uranium (HALEU) products supporting the deployment of advanced nuclear reactors. For the HALEU program, resources invested were and are being systematically analyzed so that these investments are maximized in a facility that is nearly 60 years old. A methodology that has effectively enabled optimized and improvements in the Spent Fuel Treatment (SFT) program, and consequently the HALEU program, involves discrete event simulation as addressed in this article. Here, the quantification of multiple productivity metrics, including material processing rates, cycle times, bottlenecks, number of material transfers as well as equipment, workstation, and material handling utilization, has resulted in a myriad of diverse discoveries and data-informed decisions regarding process layout and constituent, labor levels and schedules, selection of new process units, storage needs, and other critical process configurations. This article describes such a computational capability being applied for decision-making, illustrates its application to an actual process and program, provides illustrative results, and argues how computational methods for the modeling, analysis, and optimization of complex processes and facilities does lead to informed decisions derived from data and not only from intuition.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

An Overview of ARTMIP's Tier 2 Reanalysis Intercomparison: Uncertainty in the Detection of Atmospheric Rivers and Their Associated Precipitation

Atmospheric rivers, or long but narrow regions of enhanced water vapor transport, are an important component of the hydrologic cycle as they are responsible for much of the poleward transport of water vapor and result in precipitation, sometimes extreme in intensity. Despite their importance, much uncertainty remains in the detection of atmospheric rivers in large datasets such as reanalyses and century scale climate simulations. To understand this uncertainty, the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) developed tiered experiments, including the Tier 2 Reanalysis Intercomparison that is presented here. Eleven detection algorithms submitted hourly tags--binary fields indicating the presence or absence of atmospheric rivers--of detected atmospheric rivers in the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts' Reanalysis Version 5 (ERA5) as well as six-hourly tags in the Japanese 55-year Reanalysis (JRA-55). Due to a higher climatological mean for integrated water vapor transport in MERRA-2, atmospheric rivers were detected more frequently relative to the other two reanalyses, particularly in algorithms that use a fixed threshold for water vapor transport. The finer horizontal resolution of ERA5 resulted in narrower atmospheric rivers and an ability to detect atmospheric rivers along resolved coastlines. We found that the fraction of hemispheric area covered by ARs varies throughout the year in all three reanalyses, with different atmospheric river detection tools having different seasonal cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Super Resolving Unrolled Neural Networks for Remote Sensing

In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

An Application of Faster R-CNNs in Object Detection Efforts Applied to the Critical Infrastructure Domain

Object detection and segmentation approaches have been successfully applied to multiple interdisciplinary domains ranging from medical imaging to autonomous vehicles. The following study details efforts to apply segmentation methods to the critical infrastructure domain. Specifically, the identification of critical sub-components of four facilities: airports, hospitals, hydroelectric dams, and water treatment plants. Data for this study was extracted from the U.S. Department of Agriculture’s National Agriculture Imagery Program data set. Each facility class was considered independently of the other facilities, producing four different segmentation models during the testing and training process. Ultimately, the study team concluded there are significant challenges associated with object detection modeling when applied to facility analysis within the critical infrastructure domain. The study team was able to identify possible sources of these challenges that could be potentially addressed in future work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Process-driven roadmap for depositing super duplex stainless steel via wire Arc additive manufacturing

Here, this study systematically investigates the effects of shielding gas, bead spacing, weld mode, and travel speed on the phase balance, porosity, and hardness of wire arc additively manufactured (WAAM) super duplex stainless steel (ER2594). Robotic WAAM was employed to fabricate multilayer walls under systematically varied process conditions, followed by phase transformation simulations, X-ray computed tomography (XCT), electron backscatter diffraction (EBSD), and microhardness evaluation. Thermodynamic simulations predicted rapid cooling of AM process can suppress the potential formation of deleterious precipitates which was later validated via cross-sectional microstructure analyses of printed samples. XCT revealed porosity levels below 0.2% for all deposits, with reduced travel speed significantly lowering defect volume. Microstructural analyses revealed the evolution of various austenite precipitates, including grain boundary austenite (GBA), Widmanstätten austenite (WA), and intergranular austenite (IGA), sequentially upon cooling of the ferrite phase. Among all process parameters, weld transfer mode exhibited the strongest influence on phase balance; pulsed mode promoted higher ferrite retention (~ 36%) compared to RapidX mode. No consistent relationship between stabilized phase fraction and captured microhardness was observed. This work provides critical insights for optimizing WAAM parameters to control phase balance and mechanical performance, which is essential for producing high-integrity super duplex stainless-steel components for nuclear and marine applications.

Grain orientation↗
DOE OSTI2022

MELCOR Accident Progression and Source Term Demonstration Calculations for a Heat Pipe Reactor

MELCOR is an integrated thermal hydraulics, accident progression, and source term code for reactor safety analysis that has been developed at Sandia National Laboratories for the United States Nuclear Regulatory Commission (NRC) since the early 1980s. Though MELCOR originated as a light water reactor (LWR) code, development and modernization efforts have expanded its application scope to includ e non-LWR reactor concepts. Current MELCOR development efforts include providing the NRC with the analytical capabilities to support regulatory readiness for licensing non-LWR techno logies under Strategy 2 of the NRC?s near- term Implementation Action Plans. Beginning with the Next Generation Nuclear Project (NGNP), MELCOR has undergone a range of enha ncements to provide analytical capabilities for modeling the spectrum of advanced non-LWR concepts. This report describes the generic plant model developed to demonstrate MELCOR capabilities to perform heat pipe reactor (HPR) safety evaluations. The generic plant mode l is based on a publicly-available Los Alamos National Laboratory (LANL) Megapower design as modified in the Idaho National Laboratory (INL) Design A description. For plant aspects (e.g., reactor building size and leak rate) that are not described in the LANL and INL references , the analysts made assumptions needed to construct a MELCOR full-plant model. The HP R uses high assay, low-enrichment uranium (HALEU) fuel with steel cladding that uses heat pipes to transfer heat to a secondary Brayton air cycle. The core region is surrounded by a stainless-steel shroud, alumina reflector, core barrel and boron carbide neutron shield. The reactor is secured inside a below-grade cavity, with the operating floor located above the cavity. Example calculations are performed to show the plant response and MELCOR capabilities to characterize a range of accident conditions. The accidents selected for evaluation consider a range of degraded and failed modes of operation for key safety functions providing re activity control, the primary and secondary system heat removal, and the effectiveness of th e confinement natural circulation flow into the reactor cavity (i.e., a flow blockage).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Neural operators for stochastic modeling of nonlinear structural system response to natural hazards

Traditionally, neural networks have been employed to learn the mapping between finite-dimensional Euclidean spaces. However, recent research has opened up new horizons, focusing on the utilization of deep neural networks to learn operators capable of mapping infinite-dimensional function spaces. Here, in this work, we employ two state-of-the-art neural operators, the deep operator network (DeepONet) and the Fourier neural operator (FNO) for the prediction of the nonlinear time history response of structural systems exposed to natural hazards, such as earthquakes and windstorms. Specifically, we propose two architectures, a self-adaptive FNO and a fast Fourier transform-based DeepONet (DeepFNOnet), where we employ a FNO beyond the DeepONet to learn the discrepancy between the ground truth and the solution predicted by the DeepONet. To demonstrate the efficiency and applicability of the architectures, two problems are considered. In the first, we use the proposed model to predict the seismic nonlinear dynamic response of a six-story shear building subject to stochastic ground motions. In the second problem, we employ the operators to predict the wind-induced nonlinear dynamic response of a high-rise building while explicitly accounting for the stochastic nature of the wind excitation. In both cases, the trained metamodels achieve high accuracy while being orders of magnitude faster than their corresponding high-fidelity models.

DeepONet↗
DOE OSTI2023

Implementation of Distributed Memory Computing in MOSAIC to Enable Large 3D Simulations of Irradiated Concrete

The concrete biological shield (CBS) of light-water reactors protects workers and the surrounding environment by absorbing neutron and gamma irradiation emitted from the reactor core. The radiation dose increases with the CBS’s operational time and, in the long term, becomes significant enough to raise the question of irradiation effects on concrete—and particularly on the structural integrity of the CBS. Irradiation-induced damage has been identified as one of the main degradation mechanisms in the CBS. Neutron radiation causes the swelling of aggregate-forming minerals at different rates and amplitudes depending on the mineral’s nature. Silicate-bearing minerals such as quartz are particularly sensitive to neutron radiation and experience up to 17.8% volumetric expansion. Aggregates comprise several minerals with different orientations and are, therefore, subject to cracking as a result of mismatch strains. Additionally, the swelling of aggregates creates significant stresses in the surrounding cement paste matrix, which also results in crack formation. In parallel with the collection of characterization and irradiation test data, development of modeling and simulation tools for irradiated concrete is ongoing with the support of the US Department of Energy Office of Nuclear Energy’s Light Water Reactor Sustainability (LWRS) program. This effort resulted in the development and application of the fast-Fourier transform (FFT)–based code Microstructure-Oriented Scientific Analysis of Irradiated Concrete (MOSAIC) at Oak Ridge National Laboratory.

61 RADIATION PROTECTION AND DOSIMETRY↗