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DOE OSTI2023

2022 Annual Report Laboratory Directed Research & Development

Idaho National Laboratory’s (INL’s) mission is “to discover, demonstrate and secure innovative nuclear energy solutions, other clean energy options and critical infrastructure.” INL executes this mission through research and development across the continuum from basic science to applied science to engineering demonstration and then deployment. The Department of Energy (DOE) Laboratory Directed Research and Development (LDRD) program enables INL to conduct high-risk, impactful research that enriches the laboratory capabilities in order to further its missions. INL’s LDRD portfolio specifically advances the core capabilities of the laboratory aligned with its five science and technology initiatives: 1) nuclear reactor sustainment and expanded deployment, 2) integrated fuel cycle solutions, 3) integrated energy systems, 4) advanced design and manufacturing for extreme environments, and 5) secure and resilient cyber-physical systems. The 45 projects that ended in fiscal year 2022 and highlighted in this report are just a small sample of the impressive breadth and depth of cutting-edge science, technology, and engineering ongoing at INL.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Design rules for 316H nuclear components cladded with nickel or tungsten

The existing Class A metallic materials qualified in the ASME Boiler & Pressure Vessel Code Section III, Division 5 rules for high temperature nuclear reactors are not ideally suited for long term molten salt corrosion resistance in high temperature molten salt reactors. Potentially, corrosion or corrosion driven environmentally assisted cracking could limit the design life of structural components in molten salt reactors. A solution to this limitation is the use of cladded components – overlay the Class A material with a thin layer of some non-qualified, corrosion-resistant material. However, this necessitates the development of design methods for cladded components that do not require long-term testing of clad materials in order to support the near-term deployment of molten salt reactors. This report develops such a methodology along with a complete set of rules presented in a format compatible with an ASME nuclear Code Case. These design rules are for 316H Class A components cladded with either nickel or tungsten. The report also discusses the development of a set of general criteria for selecting clad materials beyond the specific 316H/tungsten and 316H/nickel systems and general acceptance tests for checking the mechanical integrity of the clad/base metal interface. Finally, the report includes a set of fully-documented sample problems detailing the application of the rules to high temperature cladded components

36 MATERIALS SCIENCE↗

DOE OSTI2021

Review of Graphite Properties Relevant to Micro-Reactor Design

Microreactors are advanced reactors that are designed to be small, scalable, and portable with up to 20 MW power. These reactors could operate independently, or as part of a microgrid, without need of a frequent refueling. Micro-reactors are designed to be deployable to provide electric power in remote locations, therefore providing greater flexibility. Several micro-reactors are under development in the US. These reactors are based on advanced gas-cooled or heat-pipe concepts. While Graphite can be found in various applications, some high-temperature reactors plan to employ Graphite in their core components due to its role as a moderator and its ability to maintain its structural integrity when subjected to elevated temperatures and high neutron fluence. Although there is significant experience with use of Graphite in nuclear applications, there are still unknowns that should be addressed. Graphite properties depend on the graphite type and affected by the fabrication procedures. Material properties are functions of the temperature and irradiation dose. The fundamental properties also affected by the oxidation environment. Therefore, in-depth understanding of graphite properties, including the effect of irradiation, oxidation and other influencing parameters are required to evaluate the design accurately. This draft aims to provide a comprehensive review of material properties for Graphite relevant to micro reactor design. For this work, two distinct grades of graphite were considered, namely IG-110 and Mersen-2114. Physical, thermal, and mechanical properties for these grades were comparatively evaluated. The properties that are discussed in this draft aim to serve as a basis for design and safety analysis.

42 ENGINEERING↗

DOE OSTI2023

Final CRADA Report: Accelerated Burn-up Accumulation Test of Clean Core Thorium Energy Designated ANEEL Fuel

Clean Core Thorium Energy (CCTE), LLC, located in Oak Brook, Illinois, is committed to the development of alternative nuclear fuels. CCTE is focused on leveraging the inherent benefits of thorium to create a novel nuclear fuel solution for our worlds growing power demand. Unlike some other fuel cycles proposing to use thorium in advanced reactors, CCTE is focused on deploying solid fuel designs (ceramic pellets in metallic cladding) in existing pressurized heavy-water reactors (PHWRs) and Canada deuterium uranium (CANDU) reactors. CCTE’s fuel design, referred to as Advanced Nuclear Energy for Enriched Life (ANEEL), uses mixed thorium-uranium oxide ((Th,U)O2) to enhance reactor operational strategies while producing significantly low attractiveness material in terms of nuclear proliferation. The overall project objective is to investigate the performance of high burnup ANEEL fuel via an irradiation experiment to be performed in the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL). The scope of work for this CRADA was specifically aimed at performing conceptual design and analysis to support development of an irradiation test rig, development of a fuel fabrication process for the experiment pellets, fabrication and qualification of the pellets, and shipment of the pellets to INL so they can be used to construct experiment rodlets.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

First-of-a-Kind Fuel-bearing Molten Chloride Irradiation Experiment

Molten salt reactors (MSRs) have garnered increasing attention recently with several demonstration efforts on the way. A key challenge to the licensing basis for these reactors is the lack of experimental data on fueled salts. This is expected to be crucial to the safety evaluation and licensing basis of reactors of this type deployed in the future. While capability for irradiating molten salts has been reestablished in the recent decade, no enriched fuel irradiation capability has been developed and tested as of yet. A new experiment vehicle under development at Idaho National Laboratory (INL) is presented here. The Molten-salt Research Temperature-controlled Irradiation (MRTI) experiment was developed to host enriched uranium bearing salt samples to be irradiated at a test reactor within the lab complex. One of the key scientific objectives is to provide irradiated salt samples for post-irradiation examination (PIE) to study the impact of fission product generation and neutron/gamma radioactivity on the salt solution and salt-facing wall material. This paper provides a detailed overview of the mechanical design of the experiment, followed by an overview of the fabrication and assembly of an initial prototype vehicle (with non-fuel-bearing salt). A summary of the key analyses conducted as a part of the performance and safety evaluation is then provided. Lastly, an overview of the test conducted in prototypic out-of-pile (non-neutron) environment are shown. These evaluations provide the foundation for a planned irradiation of an enriched uranium-bearing chloride salt sample in the near term. The upcoming irradiation will contain 13 cm3 of UCl3-NaCl salt (93% enrichment) generating around 20 W/cm3 of fission energy during irradiation and a temperature range that can be contained between bounds of 525-900°C.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Design and testing of an enriched uranium fueled molten salt irradiation vehicle

Molten salt reactors (MSRs) have garnered increasing attention recently with several demonstration efforts on the way. A key challenge to the licensing basis for these reactors is the lack of experimental data on fueled salts. This is expected to be crucial to the safety evaluation and licensing basis of reactors of this type deployed in the future. While capability for irradiating molten salts has started being reestablished in the recent decade, no enriched fuel irradiation capability has been developed and tested as of yet. A new experiment vehicle under development at Idaho National Laboratory (INL) is presented here. The Molten-salt Research Temperature-controlled Irradiation (MRTI) experiment was developed to host enriched-uranium bearing salt samples to be irradiated at a test reactor within the lab complex. One of the key scientific objectives is to provide irradiated salt samples for post irradiation examination (PIE) to study the impact of fission product generation and neutron/gamma radioactivity on the salt solution and salt-facing wall material. This paper provides a detailed overview of the mechanical design of the experiment, followed by an overview of the fabrication and assembly of an initial prototype vehicle (with non-fuel bearing salt). A summary of the key analyses conducted as a part of the performance and safety evaluation is then provided. Lastly, an overview of the test conducted in prototypic out-of-pile (non-neutron) environment are shown. These evaluations provide the foundation for a planned irradiation of and enriched uranium-bearing chloride salt sample in the near term. The upcoming irradiation will contain 13cm 3 of UCl 3 -NaCl salt (93% enrichment) generating around 20 W/cm 3 of fission energy during irradiation and a temperature range that can be contained between bounds of 525-900°C.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Development of Thermal Tomography Imaging System for In-Service Nondestructive Evaluation of Additively Manufactured Components

36 MATERIALS SCIENCE↗

DOE OSTI2021

Performance Validation of Pulsed Thermal Imaging System for In-Service Applications

42 ENGINEERING↗

DOE OSTI2021

Performance of Pulsed Thermal Tomography Imaging with Machine Learning-Based Classification of Defects in Additively Manufactured Structures

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures non-destructively. Pulsed Thermal Tomography Imaging (PTT) provides a capability for non-destructive evaluation (NDE) of subsurface defects in arbitrary size structures. The PTT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PTT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PTT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PTT capabilities in detecting and visualizing microscopic defects in metallic specimens. The thermal tomography (TT) algorithm obtains depth reconstructions of spatial effusivity from the data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring of images with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images. CNN is trained on a database of TT images created for a set of simulated metallic structures with elliptical subsurface voids. Test of CNN performance demonstrate the ability to classify radii and angular orientation of subsurface defects in TT images. In addition, we have shown that CNN trained on elliptical defects is capable of classifying irregular-shaped defects obtained from scanning electron microscopy (SEM) of stainless steel sections printed with LPBF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Design, Construction, and Installation of In-Glovebox Pumped Actinide Molten Salt Loops

This report summarizes the work conducted in FY25 to construct and install pumped actinide-bearing molten salt loopsin a radiological glovebox. The establishment of such a capability will provide a platform in which technologies developed for Gen. IV molten salt reactors may be tested under relevant operating conditions. A review of convective molten salt systems is provided to frame Argonne’s pumped actinide loop relative to contemporary and historical systems. Moreover, the infrastructure needed to operate the system is detailed to provide additional context as to design metrics of the loops and supporting equipment. The pumps’ performance in water testing is outlined and used to inform operational activities planned in FY26. Finally, electrochemical experiments investigating fundamental reaction mechanisms governing reactive oxygen species that promote corrosion of molten salt media is also reported. In total, these combined loop installation and sensor development tasks will serve to aid in the future commercial deployment of molten salt reactors in the U.S.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components. Third Annual Progress Report

Additive manufacturing (AM) of high-strength corrosion resistance alloys for nuclear energy applications, such as stainless steel and Inconel, is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Probability of crack initiation at the pore depends on size, shape, and orientation of the defect. Pulsed Infrared Thermography Imaging (PIT) provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PIT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PIT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PIT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PIT capabilities in detecting microscopic subsurface defects in metals, and classifying shapes and orientation of pores in thermal images. For detection of microscopic defects in PIT imaging data, we have developed Spatial Temporal Denoised Thermal Source Separation (STDTSS) unsupervised machine learning (ML) image processing algorithm. We show that flat bottom hole (FBH) defects as small as 200µm in SS316 and IN718 specimens, can be detected with STDTSS algorithm. To the best of our knowledge, these are the smallest detected defects which are reported in literature. For classification of defects shapes, we have previously developed thermal tomography (TT) algorithm to obtain depth reconstructions of material defects from data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

An algorithmic approach to predicting mechanical draft cooling tower fan speeds from infrasound signals

Mechanical draft cooling towers (MDCTs) serve a critical heat management role in a variety of industries. For nuclear reactors in particular, the consistent, predictable operation of MDCTs is required to avoid damage to infrastructure and reduce the potential for catastrophic failure. Accurate, reliable measurement of MDCT fan speed is therefore an important maintenance and safety requirement. To that end, we have developed an algorithm for automatically predicting the rotational speeds of multiple, simultaneously operating fan rotors using contactless, infrasound measurements. The algorithm is based on identifying the blade passing frequencies (BPFs), their harmonics, as well as the motor frequencies (MFs) for each fan in operation. Using the algorithm, these frequencies can be automatically identified in the acoustic waveform’s short-time Fourier transform spectrogram. Attribution is aided by a set of filters that rely on the unique spectral and temporal characteristics of fan operation, as well as the intrinsic frequency ratios of the BPF harmonics and the BPF/MF signals. The algorithm was tested against infrasound data acquired from infrasound sensors deployed at two research reactors: the Advanced Test Reactor (ATR) located at Idaho National Laboratory (INL) and the High Flux Isotope Reactor (HFIR) located at Oak Ridge National Laboratory (ORNL). After manually identifying the MDCT gearbox ratio, the algorithm was able to quickly yield fan speeds at both reactors in good agreement with ground truth. Ultimately, this work demonstrates the ease by which MDCT fans may be monitored in order to optimize operational conditions and avoid infrastructure damage.

42 ENGINEERING↗

DOE OSTI2021

Performance of Compact Pulsed Thermal Imaging System for In-Service Applications. Pulsed thermal tomography nondestructive examination of additively manufactured reactor materials and components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which has the capability of melting metallic powder and net shaping the structures with relatively high precision. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to creep in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures nondestructively. Pulsed Thermography (PT) imaging provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. The data cube of PT measurements consists of surface temperature taken at sequential time intervals T(x,y,t). Material defects can be detected either by analyzing the thermograms T(x,y,t) data cube, or by using thermal tomography (TT) algorithm to obtain 3D spatial reconstruction of thermal effusivity e(x,y,z). To reduce the cost and enable in-service NDE in spatially constrained environment, it is highly desirable to develop PT with compact and inexpensive IR camera. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact PT system can also be used for in-service nondestructive evaluation (NDE) applications. However, data cube obtained with PT based on compact IR camera suffers from strong thermal noises and loss of features due to relatively low sampling rate. In this report we describe two unsupervised machine learning (ML) algorithms for enhancement of PT images obtained with compact IR camera. In one approach, we introduce Sparse Coding Discrete Cosine Transform (SC/DCT) algorithm to remove additive white Gaussian noise (AWGN) from spatial thermal effusivity reconstructions. In another approach we introduce a Spatial Temporal Denoised Thermal Source Separation (STDTSS) ML algorithm to process thermograms. The STDTSS algorithm consists of spatial and temporal denoising using Gaussian and Savitzky–Golay filtering, followed by the matrix decomposition using Principal Component Analysis (PCA), and Independent Component Analysis (ICA) to automatically detect flaws. In the work described in this report, we constructed a compact PT system using a relatively small and low-cost FLIR A65 camera, consisting on uncooled microbolometer detector. Performance of SC/DCT algorithm was demonstrated on enhancing TT images of Inconel 718 AM plate. Performance of the STDTSS methods was investigated using thermography data obtained from imaging stainless steel 316L specimens produced with LPBF method with imprinted calibrated porosity defects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Physics-based hybrid machine learning for critical heat flux prediction with uncertainty quantification

Critical heat flux (CHF) is a key quantity in nuclear system modeling due to its impact on heat transfer, safety margins, and reactor performance. This study develops and validates an uncertainty-aware hybrid modeling approach that combines machine learning with physics-based models to predict CHF in cases of dryout. The Biasi and Bowring empirical correlations were paired with three ML uncertainty quantification (UQ) techniques: deep neural network (DNN) ensembles, Bayesian neural networks (BNNs), and deep Gaussian processes (DGPs). A pure ML model without a base model was evaluated for comparison. Model performance was assessed under plentiful (7,350 points) and limited (9 points) training data scenarios using parity, uncertainty distributions, and calibration curves. Results show that the Biasi hybrid DNN ensemble achieved the best overall performance, with a mean absolute relative error of 1.846%, and well-calibrated uncertainty estimates. The BNN-based hybrids showed slightly higher error (2.14%) but superior uncertainty calibration. DGP models underperformed, with over 6% error and poor uncertainty calibration. All hybrid models outperformed pure machine learning configurations, demonstrating resistance against data scarcity. These findings indicate that hybrid modeling significantly improves predictive accuracy, interpretability, and resilience to data scarcity. The integration of uncertainty awareness provides actionable confidence in CHF predictions, which is vital for safety-critical decisions in nuclear applications. This hybrid approach offers a viable pathway for deploying ML models in reactor analysis tools while preserving domain knowledge and physical consistency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

The AGN-201 Digital Twin: A test bed for remotely monitoring nuclear reactors

Research reactors have historically provided researchers and scientists with a means for testing and understanding the workings of nuclear phenomena. With the lack of new research reactors built in the past decades, it is important now more than ever to utilize and provide evidence for the usefulness of research reactors. Here, this work explores the use of the AGN-201 research reactor at Idaho State University as a test bed for developing a digital twin (named the AGN-201 DT) to realize remote monitoring for nuclear reactors. The goal of the AGN-201 DT is to monitor the AGN-201 reactor and detect when undeclared events take place to provide information for a monitoring agency. The AGN-201 DT was able to detect (without a priori knowledge) when multiple undeclared experiments were placed in the core using on-the-fly machine learning and reactor physics analysis. The AGN-201 reactor provided a test bed for developing, deploying, and testing a digital twin for monitoring nuclear reactors.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2024

Passive Temperature Sensors for Nuclear Applications

Thermocouples are generally used to provide real-time temperature indications in instrumented tests performed at material and test reactors. Passive temperature monitors, such as Silicon Carbide (SiC) and melt wires, may be included in such tests as an independent technique of detecting peak temperatures experienced during irradiation. In less expensive static (drop-in) capsule tests, which have no leads attached for real-time data transmission, melt wires, and SiC temperature monitors (TMs) are essentially the only possibility for peak temperature indication. A melt wire involves placing materials (wires) of a known composition and melting temperature in a test. An inventory is maintained at Material Science Laboratory (MSL) for melt wires ranging in temperatures from 30°C to 1500°C. Unfortunately, melt wires are limited in that it can only detect whether a single temperature is or is not exceeded (melt wire melted or not). SiC TMs, which can also be used to detect peak irradiation temperatures, are advantageous because a single monitor can allow to determine the peak temperature reached within a relatively broad range (100 – 1200°C) resulting in accuracies within ±20°C. Irradiation temperature is determined by measuring a property change after isochronal annealing or during a continuously monitored annealing process using specialized equipment at MSL. Recent research has produced a passive monitor known as sublime temperature monitor. This passive sensor has the capability of recording temperature gradients and pinpointing exactly where a temperature is located along that gradient. Long measurement lengths are achieved with very high accuracy in the location of desired temperature measurements (±2 mm over a 1 m span); however, this sensor has not been deployed in a nuclear reactor. This article will focus only on passive temperature sensors currently being researched and implemented under the Advanced Sensors and Instrumentation (ASI) program at Idaho National Laboratory (INL).

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Clean Energy Technology Applications on US Mine Land: Technical Analysis

As the United States transitions toward a clean energy economy, an opportunity exists for redeveloping the more than 17,000 mine land sites located across the nation with clean energy technologies, which have a combined potential for generating more than 85 GW of clean electricity. This report provides an overview of the potential of demonstrating and deploying clean energy projects on current and former mine land. Clean energy project refers to a project that demonstrates one or more of the following technologies: solar; microgrids; geothermal; direct air capture; fossil-fueled electricity generation with carbon capture, utilization, and sequestration; energy storage, including pumped storage hydropower and compressed air energy storage; and advanced nuclear technologies. The report discusses the following technologies and their potential for creating jobs and generating tax revenue that would result in direct and indirect benefits to the local economy: Solar photovoltaics (PV) is being developed on current and former mine land in various parts of the world, including the United States. This approach is attractive because it requires limited infrastructure investment and would utilize the bare surfaces of mines and tailing ponds. Solar resource availability may be greater in the southern regions, including the Interior and Appalachian Basins and the southwestern United States. However, since some mine land sites include areas of significant change in elevation, the deployment of PV on mine land may require sophisticated planning to account for shading and irradiance, or may require regrading of the areas. PV does not create significant environmental risks and generally does not face public resistance; Geothermal systems are often spatially and genetically associated with ore deposits, and in some cases, they have been discovered while in search for epithermal mineral resources. Numerous diverse geothermal applications have been employed at mine land around the world, including power generation, mineral extraction from geothermal brines, process heating, direct use for other mining operations, and direct use for non-mining operations and subsurface energy storage, including geothermal heat pumps. Case studies highlighting these applications provide key lessons relating to identifying drivers and barriers to geothermal resource deployment and can be used to create screening tools for identifying the types and locations of mine land most amenable to utilizing geothermal resources; Carbon capture, utilization, and sequestration technologies include direct air capture (DAC) and enhanced weathering. DAC technologies include air contactors, regeneration systems, and CO 2 compression systems. Captured CO 2 can be converted to valuable feedstocks or possibly injected into abandoned subsurface mines where it would be absorbed by alkaline rock waste and mine tailings or by the porous minerals along the walls of the mine. DAC systems can be coupled with energy sources such as wind, solar, grid, or geothermal. Many DAC systems require a source of water or steam; however, some are expected to be net producers of water. Local impacts of DAC systems are expected to be low, and are related to land footprint, material disposal, and upstream impacts of energy and material production; Compressed air energy storage is an established energy storage technology in salt caverns. It has the potential for implementation in underground mines by pressurizing and storing a large amount of air using electrical compressors when excess electricity is available. When a need for discharge emerges, the air is used to spin turbines and produce the necessary volume of electricity. Abandoned or unused mine openings, including shafts, adits, access tunnels, and mined workings of any orientation, offer potential for vast amounts of compressed air energy storage if the site characteristics meet operational requirements; Pumped hydropower storage can be implemented in surface and subsurface mines. In surface mine applications, both reservoirs may be located in a mine pit or artificial reservoirs made of excavated materials. In subsurface mines, the lower reservoir may be implemented by waterproofing and flooding mine shafts and tunnels. The water is then pumped from the lower reservoir to the upper reservoir during periods of low load and high production, and it is discharged through the turbines during periods of peak demand. The potential environmental damages associated with acidity of mine water or the presence of toxic chemicals incentivizes the development of closed-loop technologies, in which water circulates inside the pumped hydropower facility without being discharged into the external water basins; Advanced nuclear energy technologies include small modular reactors, which can be deployed locally to produce electricity and heat. Such units require seismic stability and a supply of cooling water, but population constraints may exist in some areas. Therefore, remote mine land could represent an optimal location for siting advanced nuclear energy technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials (Second Annual Progress Report)

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size structures. Thermal tomography is a computational method for heat diffusion-based imaging of solids, which provides 3D visualization of data from flash thermography measurements. We investigate thermal tomography imaging and nondestructive evaluation of stainless steel and nickel super alloy metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing (AM) process. Metallic structures produced with LPBF contain defects, and there are limited capabilities to evaluate these structures non-destructively. Thermal tomography reconstruction of 3D apparent spatial effusivity provides information about AM structure geometry and internal material flaws. We study performance of thermal tomography in imaging of metallic structures through COMSOL computer simulations of transient heat transfer, and through reconstruction of data obtained from experimental measurements. Reconstruction of internal defects is investigated using a stainless steel 316L specimen with flat bottom hole (FBH) indentations, and Inconel 718 plate produced with laser powder bed fusion (LPBF) method, which contains imprinted hemispherical shape low density regions containing non-sintered metallic powder. The FBH’s have the same sizes as the imprinted defects in the LPBF specimens, but offer better imaging contrast. Thermal tomography reconstructions provide visualizations of internal defects, and allow for estimation of their sizes and locations. Detection sensitivity of TT is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, we investigate TT of AM stainless steel 316L specimen with imprinted internal porosity defects using relatively low-cost, small form factor infrared (IR) camera based on uncooled micro bolometer detector. Sparse coding related K-means singular value decomposition (SVD) machine learning, image processing algorithms are developed to improve quality of TT images through removal of Additive white Gaussian noise without blurring the images. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact TT can also be used for in-service nondestructive evaluation (NDE). With capability to perform in-service NDE of the AM component lifecycle, TT data can be used for development of a component digital twin.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗