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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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799 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Multi-Scale Modeling of the Evolution of Structure and Properties in Materials for Nuclear Energy Applications [Slides]

Nuclear energy is an important component of an overall strategy to address climate change. Idaho National Laboratory (INL) is the U.S. Department of Energy’s primary facility for research and development in nuclear science and technology for energy generation, supporting the improvement and life extension of the existing reactor fleet and the development and licensing of new reactor designs. Computational modeling is an important component of these activities, particularly in the area of materials for nuclear applications, where experimental data can be very challenging and expensive to acquire, and where data is especially scarce for new reactor designs. INL has used multi-scale modeling – linking atomistic, mesoscale, and engineering scales – to improve the ability to predict the performance of materials for nuclear energy applications. In this talk, I will give an overview of the approach and tools used, and several examples of application, including performance of nuclear fuels, understanding radiation-driven formation of nanoscale void and gas bubble superlattices, and powder densification through electric field assisted sintering.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Nuclide Inventory Benchmark for BWR Spent Nuclear Fuel: Challenges in Evaluation of Modeling Data Assumptions and Uncertainties

This work discusses challenges and approaches to uncertainty analyses associated with the development of a nuclide inventory benchmark for fuel irradiated in a boiling water reactor. The benchmark under consideration is being developed based on experimental data from the SFCOMPO international database. The focus herein is on how to address missing data in fuel design and operating conditions that are important for adequately simulating the time-dependent changes in fuel during irradiation in the reactor. The effects of modeling assumptions and uncertainties in modeling parameters on the calculated nuclide inventory were analyzed and quantified through computational models developed using capabilities in the SCALE code system. Particular attention was given to the impact of the power history and water coolant density on the calculated nuclide inventory, as well as to the effect of geometry modeling considerations not usually addressed in a nuclide inventory benchmark. These considerations include gap closure, channel bow, and channel corner radius, which do not usually apply to regular reactor operation but are relevant for assessing impacts of potential anomalous operating scenarios.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Machine Learning-based Prediction of Departure from Nucleate Boiling Power for the PSBT Benchmark

Machine Learning (ML) has seen an exponential growth in its applications due to its advanced data driven prediction capabilities. The study presents a data-driven approach as a preliminary attempt to predict the power at which departure from nucleate boiling (DNB) occurs in pressurized water reactors (PWRs) by constructing an advanced ML algorithm that takes outlet pressure, inlet temperature and inlet mass flux as the input features. DNB is a critical heat flux (CHF) phenomenon seen in PWRs. The experimental data from the PWR subchannel and bundle tests (PSBT) benchmark is first used to train an artificial neural network (ANN) to predict the DNB power, which produces a root mean square error (RMSE) of 6.89 kW/m when tested on a blind subset of the PSBT data. Since the PSBT dataset is relatively small to train an accurate ANN, a data augmentation methodology based on generative adversarial networks (GANs) is used to expand the training dataset. By assuming that the real data follows a certain distribution, GANs try to learn that underlying distribution to generate similar synthetic data to augment the database and to improve the predictive capabilities of the ANN. The data generated from GANs are validated using 1-nearest neighbor and kernel maximum mean discrepancy. To further ensure data from GAN is similar to PSBT, the data is tested and filtered out using the sub-channel thermal-hydraulic code CTF. The results indicate that with the addition of 120 data points from GAN the RMSE reduces to 4.84 kW/m showing promising results for future developments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Proceedings of the international conference on physics of reactors - Physor 2022

The theme for this year's conference is 'Making Virtual a Reality: Advancements in Reactor Physics to Leap Forward Reactor Operation and Deployment'. As we leap forward to the future of nuclear technology, micro-reactors, small modular reactors, advanced reactors, and high energy LWR fuel are at the forefront of the topics.The Physor 2022 will cover a broad range of topics in thirty standard and special sessions tracks, and ten panel sessions gathering more than 350 papers.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

An in-line burn-up algorithm: proposal and preliminary analysis

A new burn-up algorithm is proposed for coupling neutronics and isotopic depletion. The algorithm is analogous to those proposed for enforcing xenon equilibrium and converging neutronics and thermal-hydraulics. During the corrector step of standard predictor-corrector methods, the corrector nuclide density is updated as neutronics converges. This is expected to improve stability while reducing computational expense compared to other implicit algorithms. The stability of the algorithm is investigated by Fourier analysis for a simple case whose results have been successfully compared with numerical experiments.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Phase-field simulations to inform nuclear fuel performance modeling

Software tools to simulate nuclear fuel performance at the engineering scale, such as Idaho National Laboratory (INL)’s BISON code, are increasingly relied upon in regulatory and economic decision-making. However, accurate results from these tools depends on the availability of materials parameters that are used as input. In recent years, atomistic and mesoscale simulation methods have emerged as a cost-effective, expedient means to obtain such input parameters. Phase-field simulations using INL’s Marmot application have been used to obtain microstructure-level parameters and to improve material models for fuel performance modeling using BISON. In this talk, recent examples of this process are given, including applications in UO2, U3Si2, and UZr fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗