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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Dose Assessment Model for Radioactive Waste Exposed by Gully Erosion at West Valley - 20513

The Western New York Nuclear Service Center (WNYNSC), located approximately 48 km south of Buffalo, New York, is the site of a former nuclear fuel reprocessing and radioactive waste disposal facility. Spent nuclear fuel was processed there from 1966 to 1972, leaving behind radioactive and chemical wastes in two disposal areas and a waste tank farm. Site operations also resulted in releases of radioactivity to site soils, groundwater, and to surface waters draining the site. The New York State Energy Research and Development Authority (NYSERDA) and the U.S. Department of Energy (DOE) are collaborating in a process of decision making for decommissioning those facilities remaining at the WNYNSC following the completion of Phase 1 decommissioning. Neptune and Company, Inc. (Neptune) was contracted to develop a probabilistic performance assessment (PPA) computer model to assist the agencies in this process. The PPA Model includes a contaminant transport component focusing on the movement of contaminants within and among environmental media including groundwater and surface water transport, contaminant translocation by plants and animals, diffusion, and erosion. The model also includes evaluation of potential exposure and health effects for a Resident Farmer exposure scenario, where the Resident Farmer represents a critical group, described as that group of individuals reasonably expected to receive the greatest exposure to residual radioactivity for any applicable set of circumstances. The West Valley Site is located in the glaciated Allegheny Plateau region of western New York State. The waste reprocessing and disposal areas were constructed on a relatively fat area of plateau dissected by drainages of Buttermilk Creek, including Erdman Brook, Franks Creek, and Quarry Creek. An important aspect of the contaminant transport component of the PPA model is consideration of erosive processes such as slumping of the stream slopes and the advance of gullies from these streams. These erosion processes remove material from the plateau, growing the size of the creek valleys and making them wider and deeper. Of particular interest for the impact of erosion is radioactive waste contained in the Nuclear Regulatory Commission (NRC)-Licensed Disposal Area (NDA), the New York State-Licensed Disposal Area (SDA), and residual radiological inventory in the underground storage tanks at the Waste Tank Farm (WTF). The PPA Model is organized around geographically-defined facilities which were constructed upon the plateau, including the NDA, SDA, and WTF. Ongoing stream erosion processes will potentially transfer radioactive waste and residual inventory from these facilities to the ground surface on adjacent hillslope areas where erosion has breached the facility. Hence, it is important to evaluate the consequences of potential exposures to a Resident Farmer on the hillslopes below a breached facility. Two interrelated aspects of the dose assessment model related to hillslope exposure are discussed: 1) representation of the physical processes related to transport of radionuclides from facilities onto the hillslopes, and from the hillslopes into adjoining creeks where contaminated material migrates downstream with surface water and sediment, and; 2) adaptation of the activities associated with the Resident Farmer scenario to assess potential exposures to contamination in the hillslope areas. This discussion will cover the conceptual basis of the hillslope exposure and transport models, and also implementation in the PPA computer model. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Myna

The additive manufacturing (AM) community has been developing digital factory tools over the past decade to better leverage the multi-modal process data coming out of the advanced manufacturing process. As a result, numerous databases of additive manufacturing process data exist in the literature and in the archival storage of disparate research groups. While some efforts have been made to create a standard ontology for storing and sharing AM data, in practice a variety of data structures are used to store AM build data, even within a single institution. This causes many problems for maintainability and extensibility when attempting to integrate computational modeling tools with experimental data to either validate models or to provide further insight into results and trends. Myna is a Python-based framework that aims to decrease the effort needed to connect individual computational models to the variety of AM process data that exist in different research groups and institutions. This type of software is sometimes referred to as "middleware" or “glueware,” in that it connects disparate databases and applications into a single computational ecosystem. Instead of maintaining unique interfaces between each application and each database, developers can create a single interface from each application to Myna and thereby gain access to the implemented database connections. Similarly, developing a database connection in Myna provides access to the developed simulation applications. This framework greatly simplifies the maintainability of model applications that rely on experimental data. Using external simulation tools, users will also be able to run pre-configured workflows using the built-in workflow manager. Several examples of input files are provided with Myna for different workflows, including melt pool geometry predictions and detailed melt pool and solidification microstructure predictions.

Knapp, GerryL. [Oak Ridge National Laboratory (ORN↗
DOE OSTI2022

Report on Year-4 of Water NSTF Matrix Testing: Facility Maintenance and Accident Testing

Under support from the Department of Energy (DOE) and the Office of Advanced Reactor Technologies (ART), a large-scale test facility has been constructed at Argonne National Laboratory to generate NQA-1 qualified validation data for passive decay heat removal systems in advanced reactors. The Natural convection Shutdown heat removal Test Facility (NSTF) reflects key features of a ½ scale, water-based, Reactor Cavity Cooling System (RCCS) and is intended to study the behavior, bound performance, and ultimately guide design decisions for passive decay heat removal systems for advanced reactors. In addition to the experimental activities detailed in this report, a supportive computational modeling effort is on-going which has been demonstrated to significantly strengthen the experimental program while also improving accuracy of the computer models. Together these create a mutually beneficial relationship integral to meeting the overall program objective of examining the heat removal performance of the RCCS concept. This report serves as a summary of maintenance and experimental activities during the program’s fourth year of water-based operation. A planned six-month maintenance period began in August 2021, during which major inspections, repairs, cleaning, and installation of new instrumentation and data acquisition hardware were conducted. Most significantly, two heaters that faulted during Year-3 were repaired, allowing the facility to resume use of the full heated section area and full range of available electric power. The remainder of the year consisted of eight months of test operations, during which the facility logged 211 hours of active heating across one bake-out (following the maintenance period) and seven matrix tests; five classifieds as Accepted per NQA-1, one as Trending, and one as Failed. Testing began by performing two repeat cases to confirm expected facility response and behavior during both single- and two-phase flow conditions, ensuring no changes were introduced during the maintenance period that might have altered the thermal-hydraulic characteristics of the facility. In continuation of the power parametric series initiated in previous years, a high-power test case was then performed examining heat removal performance at a decay heat load equivalent to 2.4 MWt, full-scale, a level exceeding maximum design targets. Additional testing then introduced various blockage scenarios along the network piping, examining the effects of partial and complete blockages of the flow paths on the system behavior and heat removal performance. A study of static boiling tests directed at understanding the geysering two-phase instability was also conducted. The loop was filled only to the bottom of the tank outlet, creating an open loop configuration that prevents any natural circulation flow from occurring, and the heaters were powered on until the facility reached saturation conditions. Following, a series of quasi-steady-state conditions were introduced by adjusting the inventory level in the adiabatic chimney piping at decreasingly lower elevations above the heated region. A strong correlation of geysering characteristics and loop level was observed, with flow and temperature excursions decreasing in intensity, but increasing in frequency, as the fill were reduced to lower elevations along the chimney piping. Once the level fell very low in the chimney, at points near the top of the heated section, the system reached a stable state of continuous boiling without any occurrence of geysering eruptions. A final significant testing accomplishment this year was successful completion of an “accident scenario” test, whose operating conditions were based on a prototypic decay heat curve provided by Framatome and scaled for the NSTF. This test began by establishing steady-state, single-phase “normal operation” conditions, before simulating an accident trip where the availability of active cooling systems was lost. Loop temperatures gradually increased until reaching saturation and subsequent two-phase boiling flow. Over the course of an extended operational period along the defined decay heat curve, steam boil-off caused gradual but continued depletion of liquid inventory until reaching a critically low level causing flow stagnation and cessation of natural circulation heat removal. At this point, after nearly 72 hours of continuous operation, a cold refill was performed to replenish the system inventory and allow the facility to re-establish closed loop natural circulation flow and return to a safe operational state.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

FY23 Report on Water NSTF Testing at Two-Phase Conditions: Off-normal Scenarios

Under support from the Department of Energy (DOE) and the Office of Advanced Reactor Technologies (ART), a large-scale test facility has been constructed at Argonne National Laboratory to generate NQA-1 qualified validation data for passive decay heat removal systems in advanced reactors. The Natural convection Shutdown heat removal Test Facility (NSTF) reflects key features of a ½ scale, water-based, Reactor Cavity Cooling System (RCCS) and is intended to study the behavior, bound performance, and ultimately guide design decisions for passive decay heat removal systems for advanced reactors. In addition to the experimental activities detailed in this report, a supportive computational modeling effort is on-going which has been demonstrated to significantly strengthen the experimental program while also improving accuracy of the computer models. Together these create a mutually beneficial relationship integral to meeting the overall program objective of examining the heat removal performance of the RCCS concept. This report details the experimental activities and upgrades performed during the program’s fifth year of water-based operation. The theme of this year of testing was examining off-normal scenarios. In practice, that meant examining scenarios with blockages or other design basis conditions. From a maintenance and capability standpoint that meant assessing valving options for implementing blockages, assessing drain and refill capabilities, and ensuring in all cases that operators could safely, efficiently, and repeatably bring the facility to an off-normal condition and return it to the normal operating state. In one notable case, no solution existed, nor was a solution commercially available, that met the facility needs for implementing blockages within the two-phase region of the chimney. A valve and feedthrough were custom-designed for NSTF to operate submerged in saturated water while being safely actuated from outside the tank. The fifth year of testing included eight matrix tests consisting of 162 hours of active heating, 9,735 kWh of electrical heating, with seven tests classified as Accepted per NQA-1 and one as Trending. All matrix tests were performed at two-phase flow conditions. The power parametric series from previous years was extended to include another scenario with a decay load equivalent to 1.75 MWt, full scale, further resolving the system flow oscillation response as a function of input power. A depletion test was performed to extend previous work and examine the system’s approach to stagnation and geysering that were not previously observed. This test began at an initial inventory level of 50%, prototypic power of 2.1 MWt, and employed an accelerated drain of 0.75 gpm plus boiloff until stagnation conditions were achieved and multiple geysering events were recorded. Further exploring the geysering phenomenon was a separate effects test examining static boiling in the risers and chimney. This static boiling test was performed at 48 kWe, a higher power than testing in the previous year, and included enhancements to the drain system to remove inventory in a better-controlled and more reliable manner. Two tests were performed, at prototypic conditions of 1.4 MWt and 2.4 MWt, respectively, examining the system response to throttled conditions in the singlephase region of the piping. A ball valve immediately before he riser inlet header was increasingly throttled, progressing the system through a repeatable pattern of two-phase oscillations and stable regimes before ultimately inducing stagnation and geysering. Concluding the year, two tests were performed at baseline conditions of 70% initial inventory level and 2.1 MWt of prototypic decay heat to examine both repeatability and the response to throttling within the two-phase region, at the inventory tank inlet using the custom valve mentioned above. The first such test repeated transient operating conditions and revealed significant sensitivity t

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Additive Manufacturing Evaporative Casting

Traditional lost foam casting has been around for decades. The process uses foam forms blown by an injection molding like process or CNC milled into the desired shape, then pouring molten metal over the foam pattern to create a metallic object. Additive Manufacturing Evaporative Casting (AMEC) is a new process that eliminates foam forms by using additive manufacturing to 3D print the desired cast geometry. This not only saves time and money but allows for more advanced and complex designs that can’t be achieved by carving foam. Additionally, AMEC doesn’t require molds and tooling like other casting and foundry options. Because the AMEC process is new, extensive testing is needed to develop a better understanding of the process to minimize defects, quantify material properties, and start computer modeling for the process. This CRADA (collaborative research and development agreement) between ORNL and Skuld seeks to improve the process, develop a computational model, characterize material properties, and explore new applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

ACCRUE—An Integral Index for Measuring Experimental Relevance in Support of Neutronic Model Validation

A key challenge for the introduction of any design changes, e.g., advanced fuel concepts, first-of-a-kind nuclear reactor designs, etc., is the cost of the associated experiments, which are required by law to validate the use of computer models for the various stages, starting from conceptual design, to deployment, licensing, operation, and safety. To achieve that, a criterion is needed to decide on whether a given experiment, past or planned, is relevant to the application of interest. This allows the analyst to select the best experiments for the given application leading to the highest measures of confidence for the computer model predictions. The state-of-the-art methods rely on the concept of similarity or representativity, which is a linear Gaussian-based inner-product metric measuring the angle—as weighted by a prior model parameters covariance matrix—between two gradients, one representing the application and the other a single validation experiment. This manuscript emphasizes the concept of experimental relevance which extends the basic similarity index to account for the value accrued from past experiments and the associated experimental uncertainties, both currently missing from the extant similarity methods. Accounting for multiple experiments is key to the overall experimental cost reduction by prescreening for redundant information from multiple equally-relevant experiments as measured by the basic similarity index. Accounting for experimental uncertainties is also important as it allows one to select between two different experimental setups, thus providing for a quantitative basis for sensor selection and optimization. The proposed metric is denoted by ACCRUE, short for Accumulative Correlation Coefficient for Relevance of Uncertainties in Experimental validation. Using a number of criticality experiments for highly enriched fast metal systems and low enriched thermal compound systems with accident tolerant fuel concept, the manuscript will compare the performance of the ACCRUE and basic similarity indices for prioritizing the relevance of a group of experiments to the given application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Modeling of advanced accelerator concepts

Computer modeling is essential to research on Advanced Accelerator Concepts (AAC), as well as to their design and operation. This paper summarizes the current status and future needs of AAC systems and reports on several key aspects of (i) high-performance computing (including performance, portability, scalability, advanced algorithms, scalable I/Os and In-Situ analysis), (ii) the benefits of ecosystems with integrated workflows based on standardized input and output and with integrated frameworks developed as a community, and (iii) sustainability and reliability (including code robustness and usability).

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Mesoscale modeling and semi-analytical approach for the microstructure-aware effective thermal conductivity of porous polygranular materials

Here we established a comprehensive modeling approach for investigating the microstructure-aware effective thermal conductivity ($κ_{eff}$) for porous microstructures containing solid particles and gaseous pores. Our approach combines the mesoscale computational modeling framework and the semi-analytical method, allowing for efficient prediction of $κ_{eff}$ for realistic porous microstructures, while considering complicated microstructural thermal conduction pathways effectively in the prediction. We used the diffuse-interface mesoscale computational model to generate extensive simulated $κ_{eff}$ data for realistic digital representations of microstructures with wide ranges of porosity ($f_p$), thermal conductivity of the gas phase ($κ_g$), and thermal conductivity of the solid phase ($κ_s$). From the simulated data, we identified two property variation regimes for $κ_{eff}$: (1) a slow $κ_{eff}$ increase for $κ_s ~ κ_g$; and (2) a faster $κ_{eff}$ increase for $κ_s \gg κ_g$. To capture the key features of the relationship between the microstructure and $κ_{eff}$, we derived a semi-analytical model by introducing structure and intensification factors. The two new factors incorporate the calibrated effective contribution of the solid volume with $κ_s$ and additional interfacial effects into the prediction of $κ_{eff}$, respectively, allowing for consideration of parallel, serial, and interfacial conduction mechanisms effectively. Using the selected simulation data, we quantified key model parameters within the semi-analytical model and verified that the parameterized model exhibits excellent agreement with simulated $κ_{eff}$ for the entire range of the parameter space.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Temperature Field Reconstruction of Surfaces Heated Through Radiative Heat Transfer Using Convolutional Neural Networks

Microreactors could play a crucial role in decarbonizing our energy portfolio. However, their development and implementation come with specific challenges, particularly regarding cost. Due to their compact size and the harsh operational environment, collecting real-time data on reactor operation can be challenging. Many probe designs are unable to withstand extreme conditions (e.g., temperature, radiation) in the reactor. In this context, using convolutional neural networks (CNNs) can pave the way for developing a nonintrusive approach that relies solely on ex-core sensors. A well-trained physics-informed CNN can reconstruct the distribution of a given physical quantity over a domain using only a few sensors, allowing us to reconstruct the desired field distribution even in a limited space or complex geometries where a large array of sensors is impractical. In this work, we present the initial steps toward developing a real-time tool for monitoring the thermal behavior of nuclear reactor pressure vessels. Based on an experimental setup, a computational model using the Multiphysics Object-Oriented Simulation Environment (moose) framework was built, where the Ray Tracing and Heat Conduction modules were used to evaluate the temperature distribution over a convex metal surface heated through radiative heat transfer. This metal surface represents a section of a heated nuclear reactor vessel wall. The model also accounts for solid mechanics physics through the moose Solid Mechanics module. In situ experimental data, acquired from a Texas A&M facility, were used to validate the computational model. Part of the data generated by the moose model was used to train the convolutional neural network to reconstruct the vessel wall's outer surface temperature. The CNN generalization was then compared against the experimental and computational data.

Aldeia Machado, Luiz Carlos↗
DOE OSTI2020

Distributed Computing for the Project 8 Experiment

The Project 8 collaboration aims to measure the absolute neutrino mass or improve on the current limit by measuring the tritium beta decay electron spectrum. We present the current distributed computing model for the Project 8 experiment. Project 8 is in its second phase of data taking with a near continuous data rate of 1Gbps. The current computing model uses DIRAC (Distributed Infrastructure with Remote Agent Control) for its workflow and data management. A detailed meta-data assignment using the DIRAC File Catalog is used to automate raw data transfers and subsequent stages of data processing. The DIRAC system is deployed on containers managed using a Kubernetes cluster to provide a scalable infrastructure. A modified DIRAC Site Director provides the ability to submit jobs using Singularity on opportunistic High-Performance Computing (HPC) sites.

Distributed Computing, Kubernetes, DIRAC, Project ↗
DOE OSTI2021

Magnetic-Field-Driven Electron Dynamics in Graphene

Graphene exhibits unique optoelectronic properties originating from the band structure at the Dirac points. It is an ideal model structure to study the electronic and optical properties under the influence of the applied magnetic field. In graphene, electric field, laser pulse, and voltage can create electron dynamics which is influenced by momentum dispersion. However, computational modeling of momentum-influenced electron dynamics under the applied magnetic field remains challenging. In this work, we perform computational modeling of the photoexcited electron dynamics achieved in graphene under an applied magnetic field. Our results show that magnetic field leads to local deviation from momentum conservation for charge carriers. With the increasing magnetic field, the delocalization of electron probability distribution increases and forms a cyclotron-like trajectory. Our work facilitates understanding of momentum resolved magnetic field effect on non-equilibrium properties of graphene, which is critical for optoelectronic and photovoltaic applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Crystal Engineering of Hydrogen Bonding for Direct Air Capture of CO2: A Quantum Crystallography Perspective

Rising atmospheric CO2 levels demand efficient and sustainable carbon capture solutions. Direct air capture (DAC) via crystallizing hydrogen-bonded frameworks such as carbonate salts has emerged as a promising approach. This review explores the potential of crystal engineering, in tandem with advanced quantum crystallography techniques and computational modeling, to unlock the full potential of DAC materials. We examine the critical role of hydrogen bonding and other noncovalent interactions within a family of bis-guanidines that governs the formation of carbonate salts with high CO2 capture capacity and low regeneration energies for utilization. Quantum crystallography and charge density analysis prove instrumental in elucidating these interactions. A case study of a highly insoluble carbonate salt of a 2,6-pyridine-bis-(iminoguanidine) exemplifies the effectiveness of these approaches. However, challenges remain in the systematic and precise determination of hydrogen atom positions and atomic displacement parameters within DAC materials using quantum crystallography, and limitations persist in the accuracy of current energy estimation models for hydrogen bonding interactions. Future directions lie in exploring diverse functional groups, designing advanced hydrogen-bonded frameworks, and seamlessly integrating experimental and computational modeling with machine learning. This synergistic approach promises to propel the design and optimization of DAC materials, paving the way for a more sustainable future.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries

The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark—A Bayesian Inverse UQ-Based Approach for Data Assimilation

The Organisation for Economic Co-operation and Development Working Party on Nuclear Criticality Safety has proposed a benchmark exercise to assess the performance of current nuclear data adjustment techniques applied to nonlinear applications and experiments with low correlation to applications. This work introduces Bayesian inverse uncertainty quantification (IUQ) employing scientific machine learning surrogate models as a method for nuclear data adjustments in this benchmark, and compares IUQ to the more traditional methods of generalized linear least squares (GLLS) and Monte Carlo Bayes (MOCABA). Posterior predictions from IUQ showed agreement with GLLS and MOCABA for linear applications. Here, when comparing GLLS, MOCABA, and IUQ posterior predictions to computed model responses using adjusted parameters, we observe that the GLLS predictions failed to replicate the computed response distributions for nonlinear applications, while MOCABA showed near agreement, and IUQ used the computed model responses directly. We also discuss observations on why experiments with low correlation to applications can be informative to nuclear data adjustments and identify some properties useful in selecting experiments for inclusion in nuclear data adjustment. Performance in this benchmark indicates potential for Bayesian IUQ in nuclear data adjustments.

Bayesian calibration↗
DOE OSTI2023

Multifidelity Active Learning for Failure Estimation of TRISO Nuclear Fuel

The Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel proposed to be used for multiple modern nuclear technologies. Therefore, characterizing its safety is vital for the reliable operation of nuclear technologies. However, the TRISO fuel failure probabilities are small and the computational model is time consuming to evaluate them using traditional Monte Carlo-type approaches. In the paper, we present a multifidelity active learning approach to efficiently estimate small failure probabilities given an expensive computational model. Active learning suggests the next best training set for optimal subsequent predictive performance and multifidelity modeling uses cheaper low-fidelity models to approximate the high-fidelity model output. After presenting the multifidelity active learning approach, we apply it to efficiently predict TRISO failure probability and make comparisons to the reference results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Passive heat removal in horizontally oriented micro-HTGRs

There are novel, horizontally oriented high-temperature gas-cooled micro-reactor (HTGR) designs under consideration because of their portability, simplicity, and reliable operation. These features of the micro-HTGR design can meet the fission battery attributes, such as economic, standardize, installed, unattended and reliable. Although there have been HTGR related safety studies, micro-HTGRs when horizontally oriented are expected to exhibit significantly different thermal physics. During the unavailability of helium circulation, the internal reactor core is designed to cool by block-to-block conduction and radiation, and the reactor vessel surface is cooled by the ambient air. This scenario is anticipated during the transport of the micro-HTGR in a shipping container. The conduction and radiation between the prismatic micro-HTGR blocks in the core can be influenced by variances in the thermal contacts. This work investigated the conduction within a simulated horizontal HTGR core. An experimental setup was used to validate a numerical model. The experimental setup consisted of a hexagonal assembly with scaled prismatic blocks placed within a high-temperature vacuum environment. The gaps between the blocks were well controlled and monitored. The experimental setup was designed in such a way that the temperature variation in the axial direction was minimal, such that the experiment could be observed as a 2D (r, ) heat transfer problem. The pressurized tube was equipped with Infra-Red (IR) transparent windows, and an IR camera was used to capture the spatio-temporal temperature evolution in the hexagonal block assembly during the cooldown experiments. The experimental scenario was computationally modeled with a finite element analysis (FEA) program. Once validated, the computational model was used to investigate the impact of gap conductance on overall decay heat removal. Using a conservative estimate for gap conductance value (100 W/m 2 -K) between the prismatic blocks, there is a negligible increase in temperature observed during decay heat generation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗