High Temperature Operable, Harsh Environment Tolerant Flow Sensors for Nuclear Reactor Applications
Explore the source record for details and available documents.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
SEARCH · Search NASA
Results for “Nuclear applications”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Influence of Powder Characteristics and Processing Methods on Creep Performance of Powder Metallurgy Hot Isostatic Pressed SS316
The U.S. nuclear energy expansion goals are driving the demand for manufacturing routes that can rapidly produce large, complex, near net shape components. Powder metallurgy hot isostatic pressing (PM HIP) is an advanced manufacturing technique that can be economically scaled-up, while alleviating the supply chain challenges that forging and casting face in terms of cost and lead time constraints. This makes PM-HIP a viable technology to aid and accelerate large-scale part manufacturing for nuclear applications. However, large-scale qualification and deployment of this technology require a thorough understanding of the influence of powder feedstock quality, powder handling history, hot isostatic pressing (HIP) parameters, and subsequent heat treatment on microstructural evolution and elevated temperature mechanical performance. The present work focusses on 316 austenitic stainless steel (SS316) which is most commonly used in high temperature environments for nuclear applications Results from this study show that PM HIPed SS316 meets ASME tensile requirements at room temperature and at elevated temperature. However, creep performance of PM-HIPed SS316 remains inferior to its wrought counterpart, demonstrating that tensile performance alone is not a reliable metric for long duration high temperature integrity. Further, this report delineates powder derived microstructural features that govern creep damage, with key evidences pointing to “microstructural inheritance” from gas atomized powder feedstocks. Commercial SS316 powders of varying chemical compositions and recycling histories were studies, and the results showed large differences in elemental segregation, oxide surface layers and secondary phase distributions. Multi-scale characterization revealed segregation of chromium, molybdenum, manganese and silicon at the boundaries and the precipitation of manganese-, silicon-, and molybdenum-oxides. During HIP consolidation, these surface oxides transform into decorated prior particle boundaries (PPBs) and grain boundary inclusions that persist through conventional post-HIP solution annealing treatment. The retained oxides in post-HIP microstructures were found to influence grain growth, precipitation behavior, and ultimately creep cavitation and fracture. Such post-HIP heat treatments are therefore limited by a complex trade-off between grain growth, and oxide coarsening which aggravate creep damage by acting as nucleation sites for cavities. The objective of this work is to establish an integrated processing–structure–property framework for PM-HIP 316 stainless steel by investigating the influence of powder feedstock characteristics in pre- and post-HIP processing as well as to understand the significance of post-HIP heat treatment on microstructural evolution and creep properties. The results from this report emphasize the significance of powder feedstock integrity in improving creep performance of PM-HIPed SS316, by highlight the effect of rapid solidification, elemental segregation, oxide formation and powder recycling on microstructural defect inheritance following HIP consolidation. Rather than considering HIP processing, solution annealing, and mechanical performance independently, this report treats powder production, HIP consolidation, post-HIP thermal processing, and creep deformation as interconnected stages within a continuous metallurgical process. The resulting framework will provide a scientific basis for developing feedstock engineering strategies capable of improving long-term reliability of PM-HIP stainless steels and accelerating their qualification for advanced nuclear applications.
Ajjarapu, Pavan [Oak Ridge National Laboratory (OR↗
Modeling and Simulation of Radioactive Nanomaterials of Pb-U, Pb-Th, and Pb-Co in Water-Filled Containers for Nuclear Security Applications
Not provided.
Nuclear Science & Technology↗
Recent MCNP® Code Developments and Improvements for Nuclear Energy Applications [Slides]
The MCNP® code is a general-purpose radiation transport code developed at LANL over the past 46+ years. This code is capable of modeling the fundamental physics of particles as they move through and interact with materials. It is used in many LANL and worldwide radiation transport applications (see next slide) for example in the Fundamental nuclear physics and data experiments at LANSCE.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Investigation of interfacial strength in nacre-mimicking tungsten heavy alloys for nuclear fusion applications
Abstract Tungsten heavy alloys have been proposed as plasma facing material components in nuclear fusion reactors and require experimental investigation in their confirmation. For this purpose, a 90W–7Ni–3Fe alloy has been selected and microstructurally manipulated to present a multiphase brick-and-mortar structure of W-phase ‘bricks’ surrounded by a ductile ‘mortar’. This work draws inspiration from nature to artificially imitate the extraordinary combination of strength and stiffness exhibited by mollusks and produce a nacre-mimicking metal matrix composite capable of withstanding the extremely hostile environment of the reactor interior and maintaining structural integrity. The underlying mechanisms behind this integrity have been probed through high-resolution structural and chemical characterization techniques and have revealed chemically diffuse phase boundaries exhibiting unexpected lattice coherency. These features have been attributed to an increase in the energy required for interfacial decohesion in these systems and the simultaneous expression of high strength and toughness in tungsten heavy alloys.
36 MATERIALS SCIENCE↗
ThO 2 and Th 1– x U x O 2 Nanoscale Materials and Thin Films for Nuclear Science Applications
This study investigates the dynamics and mechanisms of solution combustion synthesis (SCS) for the preparation of nanoscale ThO 2 and Th 1–x U x O 2 materials, utilizing metal nitrates (Th(NO 3 ) 4 and UO 2 (NO 3 ) 2 ) and acetylacetone (C 5 H 8 O 2 ) as reactants dissolved in a 2-methoxyethanol (C 3 H 8 O 2 ) solvent. By combining thermodynamic calculations, dynamic time–temperature profile measurements with differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), this research reveals how variations in acetylacetone concentration and uranium content influence the structural parameters of the synthesized oxides. The time–temperature measurements show that the heating rate and maximum combustion temperatures are sensitive to acetylacetone concentration. DSC-TGA results indicate shifts in exothermic peak temperatures as the uranium content changes. The complexation between thorium and acetylacetone emerges as a critical factor, impacting combustion parameters and the structural characteristics of the final products. The uniform distribution of Th and U in the Th 1–x U x O 2 solid solution and the formation of nanoscale particles with strained crystallites are considered essential for the low-temperature densification of these materials for nuclear fuel pellet applications. Additionally, high-quality ThO 2 and Th 1–x U x O 2 thin (100–150 nm) films are successfully synthesized via electrospray deposition of combustible solutions followed by a brief period of heat treatment. Furthermore, these films exhibit excellent structural and morphological uniformity, making them ideal candidates for nuclear measurements, irradiation damage studies, and investigations into the physical properties of both pure and mixed oxides.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Accelerating Thermochemical Equilibrium Calculations for Nuclear Reactor Applications
Thermochemical properties play a key role in modeling and simulation of several key phenomena in nuclear reactors. There has been an increasing interest in incorporating CALPHAD-based formulations in multiphysics simulations including for Molten Salt Reactors where knowledge of phase evolution of the salt and the chemical potentials of various elements are of utmost importance in source term analyses and redox control. However, the size of such simulations is often limited by the high computational cost of full thermodynamic equilibrium calculations. This work discusses the current efforts aimed at accelerating thermochemical equilibrium calculations for multiphysics simulations performed using the open-source finite element / finite volume code Multiphysics Object Oriented Simulation Environment (MOOSE) [1]. While several methods have been proposed for accelerating phase equilibrium calculations [2], most focus on relatively small systems and often rely on a- priori knowledge of the state-space of the system. Nuclear materials, however, are often multi-component systems owing to the evolution of composition under irradiation and an approach based on a-priori mapping of phase diagram is often not enough. This work is aimed at demonstrating an on-the-fly surrogate modeling framework that uses active learning to reduce the number of full equilibrium calculations that must be performed. By combining with efficient coupling approaches, the surrogate framework helps in reducing the computational cost of thermodynamic equilibrium informed multiphysics simulations of nuclear materials. The performance is benchmarked against full coupling with the thermochemistry library Thermochimica [3]. This work uses a machine learning based approach for constructing surrogate models to predict the stable phases in a multicomponent system. The surrogates were constructed using neural networks and Gaussian process classification. In this work, we compare the relative performance of the two methods. We also demonstrate the use of caching previous calculations by interpolating the values from nearest neighbors. References [1] Lindsay, A.D., et al. "2.0 – MOOSE: Enabling massively parallel multiphysics simulation", SoftwareX, 20 (2022): 101202. [2] Roos, W.A. and Zietsman J.H. "Accelerating complex chemical equilibrium calculations – A Review", Calphad, 77 (2022): 102380. [3] Piro, M.H.A., et al. "The thermochemistry library Thermochimica", Computational Materials Science, 67 (2013): 266-272.
36 MATERIALS SCIENCE↗
Additively Manufactured Strain Sensing for Nuclear Reactor Applications
Real-time monitoring of materials in harsh environments is a crucial technique towards reducing innovation time in nuclear systems. The successful measurement of real time, in-situ strain measurements during nuclear reactor operation requires innovative sensing solutions, including novel sensing strategies as well as advanced sensor design, manufacturing, and materials selection. In this paper, we will discuss two primary strategies for in-situ strain measurement strategies: additively manufactured (AM) capacitive strain gauges (CSGs) and digital image correlation (DIC). Current commercial strain gauges have limited applications in reactors due to the harsh operating conditions and non-trivial attachment strategies (i.e., welding, epoxy-adhesive) that can affect both the sensing performance and the underlying substrate under testing. AM CSGs are a viable solution as they have a low profile, low hysteresis, and wireless sensing integration capabilities that will enhance nuclear sensing technologies. In this work, the mechanical and thermal performance of the AM CSGs were tested up to 300 °C using ASTM standardized testing procedures to simulate the temperatures found in existing light water reactors. The AM CSGs had a similar performance across multiple samples which correlates to analytical models. This work leads towards the development of CSGs designed for higher temperatures and additional environmental factors found in Generation-IV reactors. Non-contact sensors, such as DIC, offer a less destructive way to measure deformation of materials when compared to alternative methods of in-situ strain determination, such as weldable strain gauges. However, DIC requires high contrast surfaces, which often relies on the implementation of artificial patterns. Using traditional splatter techniques to fabricate these patterns have limitations, including poor surface adhesion and reproducibility. In this work, AM fabrication techniques were implemented to avoid such limitations. Accordingly, aerosol jet printing (AJP) was used to print small scale periodic patterns of silver on stainless steel and aluminum tensile specimens. DIC was employed to monitor strain (up to 1100 µe) during temperature cycling from 23-600 °C. The results validated the use of AJP to better control pattern parameters for small fields of view applications at high temperatures.
36 MATERIALS SCIENCE↗
Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?
Machine learning (ML) has been leveraged to tackle a diverse range of tasks in almost all branches of nuclear engineering. Many of the successes in ML applications can be attributed to the recent performance breakthroughs in deep learning, the growing availability of computational power, data, and easy-to-use ML libraries. However, these empirical successes have often outpaced our formal understanding of the ML algorithms. An important but under-rated area is uncertainty quantification (UQ) of ML. ML-based models are subject to approximation uncertainty when they are used to make predictions, due to sources including but not limited to, data noise, data coverage, extrapolation, imperfect model architecture and the stochastic training process. The goal of this paper is to clearly explain and illustrate the importance of UQ of ML. We will elucidate the differences in the basic concepts of UQ of physics-based models and data-driven ML models. Various sources of uncertainties in physical modeling and data-driven modeling will be discussed, demonstrated, and compared. We will also present and demonstrate a few techniques to quantify the ML prediction uncertainties, including Monte Carlo dropout, deep ensemble, Bayesian neural networks, Gaussian Processes and conformal prediction. Lastly, we will discuss the need for building a verification, validation and UQ framework to establish ML credibility.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
A high-strength precipitation hardened cobalt-free high-entropy alloy
Recent studies on precipitation-hardened high-entropy alloys (HEAs) demonstrate their high strength and thermal stability, making them promising materials for high-temperature structural applications such as nuclear reactors. However, many existing HEAs contain cobalt (Co), which is unsuitable for nuclear applications because of the long-term activation issue of Co. Co is also expensive and considered a critical material for other applications. Therefore, it is desired to exclude Co from the composition. A Co-free (Fe 0.3 Ni 0.3 Mn 0.3 Cr 0.1 ) 88 Ti 4 Al 8 HEA was developed and studied in this work. In contrast to previous Co-free HEAs, this alloy is close to equiatomic in its composition and promises a more pronounced high-entropy effect. Scanning electron microscopy, transmission electron microscopy, atom probe tomography, and synchrotron-based, high-energy X-ray diffraction were used to characterize this alloy and revealed a complex four-phase structure, with an FCC matrix, γ’ precipitates, and a network of B2 and χ phase particles. This structure granted 2151 MPa compressive strength and good thermal stability, but with limited ductility and slow precipitation kinetics. A strengthening analysis of the alloy shows that the B2 and χ provided the most significant strengthening contribution, adding 312 MPa and 788 MPa respectively. Furthermore the strengthening effect from the nanoscale γ' is also considerable, adding 608 MPa in total. This study lays the foundation for the continued development of high-strength Co-free HEAs with improved and satisfactory ductility.
36 MATERIALS SCIENCE↗
Demonstrating Advanced Sensors for In-Situ Monitoring Towards Qualification of Nuclear Relevant Components
The U.S. Department of Energy’s Office of Nuclear Energy Advanced Materials and Manufacturing Technologies (AMMT) program is pursuing qualification of laser powder bed fusion (LPBF) components for nuclear applications. A major focus of this effort is the use of in situ process monitoring and machine learning–based tools to establish real-time quality assurance. The primary objective of this report is to identify and evaluate the most relevant in situ sensor systems for LPBF, and to document the deployment of these systems across platforms critical to the AMMT program. This work demonstrates how in situ monitoring can detect process anomalies, track geometry-dependent flaws, and identify limiting combinations of processing parameters—particularly those related to energy density and complex geometries (e.g., overhanging structures). To support this goal, a diverse suite of sensor modalities was evaluated across LPBF platforms, including visible and near-infrared (NIR) imaging, fringe projection profilometry, long-wavelength infrared (LWIR) thermography, and high-speed photodiode/pyrometry systems. These sensor streams were integrated with Peregrine, a machine-agnostic software platform that, among other capabilities, can generate real-time process anomaly classification. This report documents sensor deployments on multiple AMMT flagship platforms, including the Concept Laser M2 and Renishaw AM400/AM250 systems. Calibration builds with complex, flaw-prone geometries such as unsupported overhangs, stepped features, and thin walls, were used to evaluate how well Peregrine and its associated sensors could detect process anomalies and other instabilities under varied energy densities. It will be shown how Peregrine reliably identifies common process anomalies such as recoater streaking, superelevation, etc., and can be used in post-build analysis for anomaly spatial distributions throughout the build height to better understand the impact of geometry and processing parameter choice on the build. This work demonstrates measurable progress toward the vision that components can be born-qualified by establishing a real-time monitoring framework, identifying limiting process conditions, and laying the foundation for sensor fusion–enabled prediction pipelines that are scalable across platforms and applicable to nuclear-relevant components.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Measurement of ( n, γ ) cross section of 90 Zr [Abstract]
The isotopes of Zr with A = [90, 91, 92, 94] make up more than 97% of naturally occurring Zr and are important to many nuclear applications such as nuclear reactors. One of the attractive qualities of naturally occurring Zr isotopes is that they have a low σγ/σt ratio at most neutron energies, such that they improve the neutron economy in reactors by preferentially scattering neutrons rather than absorbing them. This same quality also presents a challenge to measuring the capture cross section, σγ, of Zr isotopes. The ENDF/B-VIII.0 library has a relative uncertainty of approximately 10-20% for incident neutron energies < 0.1 MeV, and uncertainty greater than 20% for energies > 0.1 MeV for the majority of natural Zr isotopes. This motivated the Nuclear Criticality Safety Program (NCSP) to embark on a campaign to accurately measure and evaluate these isotopes of Zr. Here we will show energy-dependent neutron capture cross section measurements for the first enriched sample to be measured: 90 Zr. The measurements of isotopically enriched samples are being carried out at the Geel Electron Linear Accelerator (GELINA) facility of the Joint Research Center - Geel (JRC-Geel) of the European Union. As isotopic enrichment is a costly process we are careful not to activate any of the samples, as this may hinder future radiation-sensitive measurements. The activation analysis is presented in a report by Brown et al.. Once we were satisfied that the Zr samples would not be activated by the measurements, the 90 Zr sample was fabricated at Oak Ridge National Laboratory (ORNL) and shipped to GELINA. The dimensions of the cylindrical sample are approximately 0.12 cm thick and a radius of 2.5 cm. Since 90 Zr is not chemically reactive to air, bare metallic samples were employed. The sample was measured at a flight path (FP) length of 60 m, using four C 6 D 6 detectors on FP14. The final paper will include experimental details and measured cross section data for 90 Zr compared to current evaluated nuclear data libraries.
07 ISOTOPE AND RADIATION SOURCES↗
Optimization of a Dynamic Single Bubbler in Immiscible Fluids for Nuclear Safeguards Applications
None
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
TEM Characterization of Nanostructured Grade 91 Neutron Irradiated to 6 DPA at 300 °C and 500 °C
Previously, samples of coarse grained (CG, grain diameter > 1 µm), ultra-fine grained (UFG, 1 µm> grain diameter> 100 nm), and nanocrystalline (NC, 100 nm> grain diameter) grade 91 steel were neutron irradiated in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). Grade 91 is a ferritic/martensitic steel alloy with a nominal composition of Fe-9%Cr-1%Mo by weight, and it is being studied for applications in advanced nuclear reactor designs including as a fuel cladding material in gas cooled reactors. To assess the viability of this alloy for nuclear applications, the role of grain boundary size has not been sufficiently explored as a mechanism for developing radiation-tolerant materials. To achieve an UFG microstructure the material was processed using equal channel angular pressing (ECAP), and the NC microstructure was achieved through high pressure torsion (HPT). Such samples of various geometries for different types of characterization were irradiated to a dose of 6 displacements per atom (DPA) at separate temperature conditions of 300 °C and 500 °C. Thus, there were a total of 6 experimental conditions, three grain sizes (CG, UFG, and NC), each irradiated at two different temperature conditions (300 °C and 500 °C).
36 - MATERIALS SCIENCE↗
Detecting and characterizing special nuclear material for nuclear nonproliferation applications
Abstract There is an urgent need for new, better instrumentation and techniques for detecting and characterizing special nuclear material (SNM), i.e., highly enriched uranium and plutonium. The development of improved instruments and techniques requires experiments performed with the SNM itself, which is of limited availability. This paper describes the findings of experiments performed at the National Criticality Experiments Research Center conducted using new instruments and techniques on unclassified, kg-quantity SNM objects. These experiments, performed in the framework of the Department of Energy, National Nuclear Security Administration Consortium for Monitoring, Technology, and Verification, focused on detecting, characterizing, and localizing SNM samples with masses ranging from 3.3 to 13.8 kg, including plutonium and highly enriched uranium using prototype detectors and techniques. The work demonstrates SNM detection and characterization using recently-developed prototype detection systems. Specifically, we present new results in passive detection and imaging of plutonium and uranium objects using gamma-ray and dual particle (fast neutron and gamma-ray) imaging. We also present a new analysis of the delayed neutron emissions during active interrogation of uranium using a neutron generator.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Demonstration of the Triple Bubbler for Pyrochemical Nuclear Safeguards Applications
NA
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
ICME and In-Situ Process Monitoring for Rapid Qualification of Components Made by Laser-based Powder Bed Additive Manufacturing Processes for Nuclear Structural Applications
Additive manufacturing (AM) through selective powder bed melting of successive layers is being considered as disruptive technology for rapid production of low-cost nuclear reactor internal components with complex geometries. However, there is a potential for non-uniform distribution of physical features, such as porosity or microstructural differences, due to variations of temperature across a component build. These heterogeneities make the deployment of traditional material qualification and non-destructive evaluation of AM components difficult. This research project explored the feasibility of using in-situ process monitoring methods and integrated computational materials engineering (ICME) principles as an alternate qualification methodology and approach. This project included six individual tasks: (i) design of artifacts relevant to the nuclear power industry, (ii) evaluation of laser processing and in-situ measurements, (iii) computational modeling, (iv) ex-situ microstructural characterization, (v) evaluation of scaling the methodology for large-scale structures, and (vi) development of a data package to codes and standards organizations. As a part of this research, laser powder bed fusion of metals (316L, Alloy 718, and Ti6Al4V) was explored. This document provides an overview of the research performed over three years and serves as the final U.S. Department of Energy report for this project. In the third year, the project focused on the following activities: (a) in-situ infrared and optical imaging of each layer during processing of representative stainless steel geometries; (b) development of methodologies to analyze the data; and (c) application of ICME methodologies and other experiments to estimate the relevance of defects and microstructure to tensile properties.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗