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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Multiscale models of nuclear waste reprocessing: from the mesoscale to the plant-scale

Nuclear waste reprocessing and nonproliferation models are needed to support the renaissance in nuclear energy. This report summarizes an LDRD project to develop predictive capabilities to aid the next-generation nuclear fuel reprocessing, in SIERRA Mechanics, Sandia’s high performance computing multiphysics code suite and Cantera, an open source software product for thermodynamics and kinetic modeling.

Rao, Rekha Ranjana↗
DOE OSTI2014

Micro-Continuum Modeling of Nuclear Waste Glass Corrosion

The purpose of this contribution is to report on preliminary nano-continuum scale modeling of nuclear waste glass corrosion. The focus of the modeling is an experiment involving a French glass SON68 specimen leached for 25 years in a granitic environment (Guittonneau et al., 2011; Gin et al., 2011; Gin et al., 2013). The 25 year experiment carried out by French scientists offers a unique opportunity to examine the controls on the rate of nuclear glass corrosion under controlled experimental conditions using a borosilicate glass of the kind widely considered for nuclear waste storage. The long term experiment makes it possible to move beyond the initial rate stage, with direct measurements of the longer term, residual rates of corrosion. Important in this regard is whether the rate of corrosion shows a parabolic dependence on time, as would be expected from a conventional diffusion model in which the reaction products (amorphous silica-rich gel) create a diffusion barrier that grows in width over time, or whether the time dependence is linear (Guittonneau et al., 2011). A linear dependence on time would call into question the model of a Passive Reactive Interface (PRI) presented by Cailleteau et al. (2008), although it does not entirely preclude such a model if the interface maintains a constant width over time (forming at the leading edge next to the pristine glass and dissolving at the trailing edge). The leachate concentration measurements collected every 56 days over 25 years by Guittonneau et al. (2011) provide data to constrain the long term corrosion rate and its time dependence.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics

Large language models have revolutionized artificial intelligence by enabling large, generalizable models trained through self-supervision. This paradigm has inspired the development of scientific foundation models (FMs). However, applying this capability to experimental particle physics is challenging due to the sparse, spatially distributed nature of detector data, which differs dramatically from natural language. This work addresses if an FM for particle physics can scale and generalize across diverse tasks. We introduce a new dataset with more than 11 million particle collision events and a suite of downstream tasks and labeled data for evaluation. We propose a novel self-supervised training method for detector data and demonstrate its neural scalability with models that feature up to 188 million parameters. With frozen weights and task-specific adapters, this FM consistently outperforms baseline models across all downstream tasks. The performance also exhibits robust data-efficient adaptation. Further analysis reveals that the representations extracted by the FM are task-agnostic but can be specialized via a single linear mapping for different downstream tasks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Improving Reliability of Large Language Models for Nuclear Power Plant Diagnostics [Poster]

Large Language Models (LLMs) struggle out of the box when answering factually about detailed questions, especially in domains that are sparsely represented in their training data. This causes hallucinations and reduces reliability making it difficult for them to be used in practice. This work shows that using RAG techniques can improve factual accuracy and reliability, allowing for the application of LLMs in specialized areas, even when those areas that aren’t extensively covered in their initial training.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Advancing quantum simulations of the nuclear shell model with Gray-code–based resource-efficient protocols

Background: Some of the computational limitations in solving the nuclear many-body problem could be overcome by utilizing quantum computers. The nuclear shell-model calculations providing deeper insights into the properties of atomic nuclei are one such case with high demand for resources, as the size of the Hilbert space grows exponentially with the number of particles involved. Quantum algorithms are being developed to overcome these challenges and advance such calculations. Purpose: To develop quantum circuits for the nuclear shell-model, leveraging the capabilities of noisy intermediate-scale quantum (NISQ) devices. Here, we aim to minimize resource requirements (specifically in terms of qubits and gates) and strive to reduce the impact of noise by employing relevant mitigation techniques. Methods: We achieve noise resilience by designing an optimized Ansatz for the variational quantum eigensolver (VQE) based on Givens rotations and incorporating qubit-ADAPT-VQE in combination with variational quantum deflation (VQD) to compute ground and excited states, incorporating the zero-noise extrapolation mitigation technique. Furthermore, the qubit requirements are significantly reduced by mapping the basis states to qubits using Gray-code encoding and generalizing transformations of fermionic operators to efficiently represent many-body states. Results: By employing the resource-efficient protocols, we achieve the ground and excited state energy levels of 38 Ar and 6 Li with better accuracy. These energy levels are presented for noiseless simulations, noisy conditions, and after applying noise mitigation techniques. Results are compared for Jordan-Wigner and Gray-code encoding using VQE, qubit-ADAPT-VQE, and VQD. Conclusions: Our work highlights the potential of resource-efficient protocols to leverage the full potential of NISQ devices in scaling the nuclear shell model calculations, offering a pathway toward more complex quantum simulations in nuclear physics. This approach establishes a framework for studying other nuclear systems with improved quantum resource efficiency, marking a significant advancement in applying quantum computing to realistic nuclear physics applications.

Physics - Nuclear physics and radiation physics↗
NASA NTRS1976

Phenomenological model of nuclear primary air showers

The development of proton primary air showers is described in terms of a model based on a hadron core plus an electromagnetic cascade. The muon component is neglected. The model uses three parameters: a rate at which hadron core energy is converted into electromagnetic cascade energy and a two-parameter sea-level shower-age function. By assuming an interaction length for the primary nucleus, the model is extended to nuclear primaries. Both models are applied over the energy range from 10 to the 13th power to 10 to the 21st power eV. Both models describe the size and age structure (neglecting muons) from a depth of 342 to 2052 g/sq cm.

Tompkins, D. R., Jr.↗
DOE OSTI2014

Nuclear Fallout Models – An Overview

This presentation is an introduction to a full report, which is a tutorial for individuals using the products of the models reviewed.

nuclear fallout, models, software↗
DOE OSTI2023

Microstructure modeling of nuclear structural materials: Recent progress and future directions

Modeling and simulation of microstructures are essential to understand the complex responses and behaviors of nuclear materials in extreme environments. The needs to assess the extended life operation as well as the growing interest in accelerating nuclear materials development and qualification have stimulated the use of high-fidelity multiscale models aided by empirical and ab initio data. This paper reviews the role of various models across different length and time scales in investigating irradiation effects on microstructure evolution and degradation, in particular the embrittlement caused by radiation induced or enhanced formation of nanoscale chemical heterogeneities. The strength and limitations of these models, including classical rate theories, cluster dynamics, phase-field methods, and atomistic models informed by ab initio energies, are discussed with seminal examples. Challenges regarding the lack of thermo-kinetic data and theoretical treatments considering chemical complexities and magnetic excitations, as well as the stabilizing effect by excess point defects in nuclear structural materials are presented, along with potential solutions based on ab initio informed surrogate energy models and statistical sampling by Monte Carlo simulations. Further, the review then highlights the opportunities to leverage the advantages of different methods by establishing hybrid models by shared variables or coupled codes and applications. Finally, the review concludes with forward-looking remarks on how the use of physics-based models can aid the improvement of machine-learning models of property degradation and vice versa.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Study on Modeling of Nuclear Fuel End Drop during a Hypothetical Accident

It is well known that shell elements have been used to model the cladding of single nuclear fuel pins. These have been used, for example, to analyze the response of a fuel assembly by assuming that all fuel pins in a fuel assembly deform in the same pattern. It is increasingly significant to model the entire fuel assembly in order to capture more realistically the behavior of each individual nuclear fuel during a hypothetical accident in a manner that can account for interactions among single pins. However, modeling each individual fuel pin in an entire fuel assembly using shell elements will result in models with large amount of elements and degrees of freedom that take correspondingly larger amount of computational times to execute. Therefore, modeling the nuclear fuel assembly with beam elements may become an option. This paper presents the results of a set of analysis with the LS-DYNA finite element software that investigated the differences and similarities of modeling nuclear fuel assemblies using either shell elements or beam elements. This study provides additional insights on the appropriateness of using beam element models of the fuel pins in the simulation of a hypothetical accident. In this study, two single pin models, a single pin model using shell elements and a single beam model using beam elements, are constructed to analyze end drop scenarios with the LS-DYNA code. Each model is then analyzed for a 19 ft end drop scenario (linear behavior) and 30 ft end drop scenario (non-linear behavior). Comparisons are made between the results obtained with the single pin shell model and with the single pin beam model to study the differences and similarities resulting from the two modeling approaches. In this study, a B and W 15x15 PWR fuel is analyzed for both 30 ft and 19 ft end hypothetical accident drops using both single pin finite element model with shell elements and beam elements for the fuel cladding. It is found that for the 19 ft end drop, the difference in the Max/Min total axial strains is about 12% between the two models. For the 30 ft end drop, the maximum total axial strains calculated with both models is similar, and a difference of about 12% is observed for the minimum total axial strains calculated by the two models. Advantages of the single pin shell model are that it permits consideration of internal pressures and may capture local buckling. Therefore, use of beam elements to model the fuel cladding needs to take into account the possible significance of local buckling and internal pressurization for the problem being analyzed. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Protecting and Defending against Autonomous Control Systems and Digital Twin Cyber Attacks: Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Models in Nuclear Power Plants (Final)

Navigating through the complex tapestry of technological advancements, "Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Model in Nuclear Power Plants" stands at the intersection of cybersecurity and nuclear power plant operations, embarking on a journey through the intricacies of securing digital twins against malicious cyber activities. As nuclear power plants progressively integrate digital twin technology and machine learning models to optimize operations and ensure system reliability, they inadvertently expose themselves to a new spectrum of vulnerabilities, notably in the realm of hyperparameter attacks. Hyperparameters, integral in machine learning model tuning and optimal performance of digital twins, have emerged as a target for adversaries aiming to destabilize the predictive capabilities and therefore, the operational accuracy of these digital entities within critical infrastructures like nuclear plants. This paper, therefore, meticulously threads the needle through the development of a robust response strategy, poised to shield these digital reflections against calculated hyperparameter manipulations, ensuring that the digital twin can effectively and securely function as a reliable proxy for its physical counterpart. The ensuing sections delve into the orchestrated maelstrom of multi-rate time-changing intelligent coordinated hyperparameter attacks and the implementation of event-triggered predictive control, laying down a structured, predictive, and responsive framework that safeguards the nexus where the digital and physical realms of nuclear power plants coalesce. The operational integrity of digital twins in nuclear power plants depends critically on the security of machine learning hyperparameters. This study makes two different contributions. First, a decision-based idea known as a multi-rate time changing intelligent coordinated hyperparameter attack is put forth. In this attack, many hyperparameters are repeatedly changed using both random and intelligent optimal techniques by the attacker. These assaults introduce varied rates at different attack steps, compromise various amounts of hyperparameters, and improve stealth and flexibility. Second, a technique is developed for event triggered predictive control to rapidly respond to potential hyperparameter attacks. This control integrates a sliding window framework, retaining a history of previous data points and employing linear regression to predict the next data point from the current dataset. The control gain K is determined using the Lyapunov-Krasovskii method, and subsequently, an action is developed. Finally, the outcome of the simulation demonstrates the viability of the proposed method for defending nuclear power plant digital twins from hyperparameter attacks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1985

Electrons, muons and hadrons in extensive air showers and how do they depend on nuclear interaction model, part 2

Some of the results of Monte Carlo simulations of extensive air showers for nuclear interactions models are presented. The most significant part of scaling violation effect is generated by the inclusion of rising cross-section. Among the models considered the lowest value for Eo/N(max) is obtained when rapidly rising cross-section and charge exchange are both included (model R-F01). The value is still 1.38 GeV/electron. Except at the highest energies, the sensitivity to atomic mass of the primary is greater than to specific assumptions about multiple production.

Wrotniak, J. A.↗
DOE OSTI2026

Taming nuclear mass models with Gaussian processes

We propose a new set of nuclear mass predictions based on multiple theoretical mass models. By employing Gaussian process regression with the Matérn kernel, we achieved root-mean-square (rms) deviations below 100 keV for the training dataset. The best-performing mass models achieved rms deviations below 150 keV for the new precise mass data from AME2020, whereas the ensemble average showed robust performance across the nuclear chart. Our approach uniquely combines: (1) systematic refinement of eight mass models through their residuals, (2) physics-informed features, including magic numbers, nucleon parity numbers, neutron excess, and nuclear collectivity, and (3) theory-to-theory validation demonstrating robust extrapolation capability. We find that the Matérn kernel provides superior uncertainty quantification compared to the RBF kernel, with a length-scale analysis revealing enhanced inter-nuclei correlations. We provide complete mass predictions for all unknown nuclides in AME2020, offering valuable constraints for nuclear structure studies and astrophysical modeling when used with proper uncertainty propagation.

Gaussian processes↗
DOE OSTI2011

Extended chiral Hartree-Fock model for nuclear matter

We study the properties of nuclear matter with extended chiral Hartree-Fock model. The two-particle-two-hole (2p-2h) states are introduced in the nucleon ground wave function to take the tensor components of pion into account. We also consider the form factor effect of pion to regularize its high momentum behavior. We find that the pion contribution from 2p-2h states plays a very important role in the saturation mechanism of nuclear matter. Furthermore, the saturation properties are also influenced by the cut-off moment of form factor.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

Development and validation of instantaneous risk model in nuclear power plant's risk monitor

The instantaneous risk model is the fundament of calculation and analysis in a risk monitor. This study focused on the development and validation of an instantaneous risk model. Therefore the principles converting from the baseline risk model to the instantaneous risk model were studied and separated trains' failure modes modeling method was developed. The development and validation process in an operating nuclear power plant's risk monitor were also introduced. Correctness of instantaneous risk model and rationality of converting method were demonstrated by comparison with the result of baseline risk model. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Application of a Deep Learning Network to 3D Modeling of Nuclear Facilities - 19614

The ultimate goal of this study is to minimize (or eliminate) human intervention in the scanning and 3D CAD modeling process in the generation of 3D as-built model of nuclear facilities. This paper describes the application of the state-of-the-art deep learning technology in 3D point cloud data segmentation which is a part of the 3D modeling process. The baseline deep learning network chosen in this study is PointNet. We compare the deep learning approach with a conventional filtering approach, and show the feasibility and potential of deep learning technology. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗