Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “NCSP Training”

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

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Expansion of Machine-Learning Method for Classifying Neutron Resonances

The understanding of astrophysics processes and the performance of nuclear reactors and other nuclear systems depend on a precise description of the neutron interaction cross sections for materials and nuclei present in these environments. At low neutron energies, these cross sections exhibit resonance structure represented by sharp enhancements when the neutron energy is sufficiently close to excited levels in a compound nucleus. Such resonances can be characterized by their quantum numbers relative to angular momenta, which are often deduced in an ad hoc and irreproducible manner from the shape of the cross sections. The correct assignment of the quantum numbers of neutron resonances is therefore of paramount importance. To address this we have developed a machine-learning method to automate the identification and correction of these spin assignments. The algorithm is trained from simulated data, generated from statistical properties of resonance data for a given nucleus, to mimic the errors found in real data. In this project we describe five independent approaches to further develop and expand the applicability of the machine-learning spin classifier: i) Feature impact; ii) Integration with the Atlas; iii) Training optimization; iv) Spacings systematics; and v) Validation with polarized data. The premises, methods, results, and future perspectives are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Hands-On Experimental Training [Slides]

Training provided includes: Nuclear Criticality Safety Fundamentals, Sub-Critical "Hands On" Demonstration, Hand-Stacking and Remote Approach to Critical Using the Planet Assembly, Flattop Free-Run Demonstration, and Godiva-IV Critical Assembly Demonstration.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Machine Learning for Neutron Resonance Evaluations [Slides]

The performance of nuclear reactors and other nuclear systems depends on a precise understanding of the neutron interaction cross sections for materials used in these systems. These cross sections exhibit a resonance structure whose shape is determined in part by the angular momentum quantum numbers of the resonances. The correct assignment of the quantum numbers of neutron resonances is therefore of paramount importance. In this presentation, we describe the application of machine learning to automate the quantum number assignments. Scikit-learn classifiers were trained on simulated resonance data whose statistical properties were chosen to mimic real data. We explored the use of several physics (and random matrix theory)-motivated features for training the classifiers, including the nearest neighbor spacing distribution, cumulative level distribution, and channel width distributions. Initial results demonstrated that we can determine resonance spin groups somewhat reliably. We are now investigating the application of our approach to 52 Cr resonance data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Automating neutron resonances classification with Machine Learning [Slides]

Team reported the following accomplishments: the development of a Machine-Learning method to properly assign spins to neutron resonances (automated, general, reproducible); full integration with evaluated resonances in the Atlas (automation of new editions); training and optimization in synthetic data; validation and deployment to real experimental resonances. Future perspectives include exploration of other classifiers and hyper-parameter combinations, further validation with well-known nucleus (e.g. 235 U), and publication pipeline.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Brief Overview of the DOE/NNSA Nuclear Criticality Safety Program [Slides]

The mission of the DOE/NNSA Nuclear Criticality Safety Program is to provide sustainable expert leadership, direction and the technical infrastructure necessary to develop, maintain, and disseminate the essential technical tools, training, and data required to support safe, efficient fissionable material operations within DOE. The vision of the program is to create a continually improving, adaptable, and transparent program that communicates and collaborates globally to incorporate technology, practices, and programs to be responsive to the essential technical needs of those responsible for developing, implementing, and maintaining nuclear criticality safety.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Novel machine-learning method for spin classification of neutron resonances

The performance of nuclear reactors and other nuclear systems depends on a precise understanding of the neutron interaction cross sections for materials used in these systems. These cross sections exhibit resonant structure whose shape is determined in part by the angular-momentum quantum numbers of the resonances. The correct assignment of the quantum numbers of neutron resonances is, therefore, paramount. In this project, we apply machine learning to automate the quantum number assignments using only the resonances' energies and widths and not relying on detailed transmission or capture measurements. The classifier used for quantum number assignment is trained using stochastically generated resonance sequences whose distributions mimic those of real data. Here we explore the use of several physics-motivated features for training our classifier. These features amount to out-of-distribution tests of a given resonance's widths and resonance-pair spacings. We pay special attention to situations where either capture widths cannot be trusted for classification purposes or where there is insufficient information to classify resonances by the total spin J. We demonstrate the efficacy of our classification approach using simulated and actual 52 Cr resonance data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

KENO-VI Primer: Performing Calculations using SCALE’s Criticality Safety Analysis Sequence (CSAS6) with Fulcrum

The SCALE code system developed at Oak Ridge National Laboratory is widely used and accepted around the world for criticality safety analysis. The well-known KENO-VI three-dimensional Monte Carlo criticality computer code is one of the primary criticality safety analysis tools in SCALE. The KENO-VI primer is designed to help a new user understand and use the SCALE/KENO-VI Monte Carlo code for nuclear criticality safety analysis. It assumes that the user has a college education in a technical field. There is no assumption of familiarity with Monte Carlo codes in general or with SCALE/KENO-VI in particular. The primer is designed to teach by example, with each example illustrating two or three features of SCALE/KENO-VI that are useful in criticality analysis. The primer is based on SCALE 6.2 and 6.3, which includes the Fulcrum graphical user interface. Each example uses Fulcrum to provide the framework for preparing input data and viewing output results. Starting with a Quickstart section, the primer gives an overview of the basic requirements for SCALE/KENO-VI input and allows the user to quickly run a simple criticality problem with SCALE/KENO-VI. Each following section begins with a list of basic objectives identifying the goal of the section and the individual SCALE/KENO-VI features covered in detail in the section’s sample problems. Upon completion of the primer, a new user should be comfortable using Fulcrum to set up criticality problems in SCALE/KENO-VI. The primer provides a starting point for the criticality safety analyst who uses SCALE/KENO-VI. Complete descriptions are provided in the SCALE/KENO-VI manual. Although the primer is self-contained, it is intended as a companion volume to the SCALE/KENO-VI training and documentation. The SCALE manual and training schedule are available at https://scale.ornl.gov. The primer provides specific examples of using SCALE/KENO-VI for criticality analysis; the SCALE/KENO-VI manual provides information on the use of SCALE/KENO-VI and all its modules. The primer also contains an appendix with sample input files. In addition, this primer, its errata, and sample inputs are also available at https://code.ornl.gov/scale/primers/kenovi/.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

A New Era of Nuclear Criticality Experiments: The First 10 Years of Radiation Test Object Operations at NCERC

The work presented in this paper focuses on the first 10 years (2011–2020) of radiation test object (RTO) operations at the National Criticality Experiments Research Center. RTOs are subcritical configurations of special nuclear material that are built by hand. These configurations are utilized for benchmark experiments, detector testing/characterization, and training. An overview of the types of measurements used in RTO operations is given as well as a history of RTO operations at Los Alamos National Laboratory from 1944–2011.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

KENO V.a Primer: Performing Calculations using SCALE’s Criticality Safety Analysis Sequence (CSAS5) with Fulcrum

The SCALE code system developed at Oak Ridge National Laboratory is widely used and accepted around the world for criticality safety analyses. The well-known KENO V.a three-dimensional Monte Carlo criticality computer code is one of the primary criticality safety analysis tools in SCALE. The KENO V.a primer is designed to help a new user understand and use the SCALE/KENO V.a Monte Carlo code for nuclear criticality safety analyses. It assumes that the user has a college education in a technical field. There is no assumption of familiarity with Monte Carlo codes in general or with SCALE/KENO V.a in particular. The primer is designed to teach by example, with each example illustrating two or three features of SCALE/KENO V.a that are useful in criticality analyses. The primer is based on SCALE 6.2 and 6.3, which includes the Fulcrum graphical user interface (GUI). Each example uses Fulcrum to provide the framework for preparing input data and viewing output results. Starting with a Quickstart section, the primer gives an overview of the basic requirements for SCALE/KENO V.a input and allows the user to quickly run a simple criticality problem with SCALE/KENO V.a. The sections that follow Quickstart include a list of basic objectives at the beginning that identifies the goal of the section and the individual SCALE/KENO V.a features that are covered in detail in the sample problems in that section. Upon completion of the primer, a new user should be comfortable using Fulcrum to set up criticality problems in SCALE/KENO V.a. The primer provides a starting point for the criticality safety analyst who uses SCALE/KENO V.a. Complete descriptions are provided in the SCALE/KENO V.a manual. Although the primer is self-contained, it is intended as a companion volume to the SCALE/KENO V.a training and documentation. The SCALE manual and training schedule are available at https://scale.ornl.gov. The primer provides specific examples of using SCALE/KENO V.a for criticality analyses; the SCALE/KENO V.a manual provides information on the use of SCALE/KENO V.a and all its modules. The primer also contains an appendix with sample input files. In addition, this primer, its errata and sample inputs are also available at https://code.ornl.gov/scale/primers/kenova.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Conduct of Operations and Nuclear Criticality Safety Standards

Since the beginning of the nuclear age in 1943, there have been 22 criticality accidents in process facilities worldwide in which fissionable materials were processed by hand for various purposes. Of these 22 process criticality accidents, 16 involved faulty or flawed conduct of operations. Most of these process accidents (17 of 22) occurred before 1970, mostly due to the implementation of formal conduct of operations—such as the use of operating procedures and training—and the use of consensus standards for nuclear criticality safety (NCS). Obviously, the nuclear facilities applied lessons learned from their accident history, and the rate of criticality accidents was significantly reduced as a result. Over the years, it has been emphasized in NCS training courses and other venues in the United States that the consensus standards, such as ASA N6.1-1964, were the key contributors to this reduction in accidents. The formality of operations required to implement and apply the standards at a facility is also a crucial prerequisite that must be sufficiently robust to ensure NCS. This prerequisite is crucial to success fully implement the NCS consensus and other safety standards at a site.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Development of MCNP Training Modules for Safeguards Practitioners [Abstract]

The Monte-Carlo N-Particle (MCNP) software developed at LANL is the most widely used neutron transport code in the world. It is an essential tool for a variety of applications including detector development and design, nuclear fuel burnup simulation, criticality safety, and nondestructive assay system optimization. For this reason, it is indispensable within the safeguards and materials control & accountability (MC&A) communities. Multiple MCNP training courses have been created and taught over the last several decades by the MCNP development team at LANL, however there are no existing courses that cover specialized topics considered fundamental to NDA and safeguards models. To fill this gap, the MCNP team and Safeguards Science and Technology group at LANL have co-created a set of training modules customized to meet the specialized needs of the safeguards and MC&A communities. The basic modules cover concepts such as NDA system optimization, He-specific and other capture tallies, and tools for improved theoretical understanding. An advanced module was also created to cover topics including variance reduction for active interrogation simulations, use of the LANL MCNPTools post-processor, PTRAC (particle tracking) and list-mode data simulations, and fuel burnup simulations. The training modules teach to the latest and most state-of-the-art MCNP features and tools released by the development team at LANL and are intended to be taught jointly by the developers and safeguards experts. Ultimately, we hope that creation of these modules will serve to capture and convey the safeguards modeling and MCNP expertise at LANL, and that we will be able to share the modules more broadly with the MC&A and safeguards communities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

A Subcritical Assembly for Training and Education Use at the Oak Ridge National Laboratory [Slides]

Sufficient AGN-201M fuel plate material exists at Y-12 to support the development of a new, inherently safe, subcritical assembly for use in NCS training courses at ORNL. Four experiments with the ORNL subcritical assembly are possible and feasible. Future work includes the final design of the core and reflector and split table component design specifications. The ORNL Material Demonstration Facility (MDF) may be used to 3D print ORSA stand/table components and graphite reflector. Researchers will consider fuel coatings for contamination control, rather than an Al can. A visit to Y-12 will be made to determine the location of AGN fuel plates and remaining fuel transportation considerations. Final facility location will be considered

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

High temperature nuclear data measurements of SiC, ZrC, and MgO [Slides]

Performed temperature dependent measurements of SiC and ZrC at ARCS instrument at SNS. Performed temperature dependent measurements of SiC, ZrC, and MgO at VISION instrument at SNS. Performed initial atomistic modeling of these materials using various techniques, including machine learned potentials. Future work includes temperature dependent transmission measurements of these materials, as well as improving the machine learned potentials with more training data and different machine learned frameworks.

ARCS↗
DOE OSTI2025

Development of a New Criticality Safety Training Program for College Students

Nuclear criticality safety (NCS) expertise remains a crucial workforce need within the US Department of Energy (DOE) laboratory complex. To address this challenge, a novel university/laboratory-based nuclear criticality training certificate program is being developed through a collaborative effort between the Georgia Institute of Technology, Texas A&M University, and Oak Ridge National Laboratory. This comprehensive program implements a two-tiered certification approach that combines online theoretical coursework with hands-on experimental training to create a sustainable pipeline of nuclear criticality specialists. The program specifically targets undergraduate and graduate students in engineering, physics, and mathematics disciplines across the United States. Through integration of fundamental nuclear physics principles, practical safety applications, and experiential learning opportunities, this initiative aims to establish a standardized pathway for developing the next generation of NCS professionals.

K-Effective↗
DOE OSTI2024

Embedding Neural Thermal Scattering (NeTS) Modules in SERPENT for Higher Fidelity Advanced Reactor Analysis

When a neutron born in fission thermalizes to the order of $k$ $B$ $T$, it’s de-Broglie wavelength and energy approach the order of inter-atomic spacing and elementary lattice oscillations, respectively. $S$($a,β,t$) or the scattering law, uuantify these temperature-dependent crystallographic contributions to total cross section (or reaction rate). In a Monte Carlo analysis, cumulative distribution functions (CDFs) of $S$($a,β,t$) are loaded to memory from “A Compact ENDF” (ACE) files for stochastically selecting thermal scattered neutron trajectories. In this work, novel neural thermal scattering (NeTS) modules for $S$($a,β,t$) CDFs are designed, trained, serialized and embedded within SERPENT using Python’s limited C-API for on-the-fly deployment of crystalline graphite $S$($a,β,t$) sampling. Torchscript tracing and Numba just-in-time (JIT) compilation streamline neural inference on NVIDIA GPUs with CUDA libraries. Demonstrations of bare sphere thermalization of fast and thermal sources show excellent agreement between embedded NeTS in SERPENT and MCNP. With an explicit model of the reactor, NeTS can predict on-the-fly changes in TREAT neutron spectra as a function of local temperature, which can serve to improve transient and accident predictions in a multiphysics analysis framework. This framework can be further extended to account on-the-fly for changes in local graphitic microstructure to scattering cross sections, and outlines a novel coupling of modern machine learning with state-of-the-art reactor physics methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Using the Criticality Accident Alarm System modeling capabilities in SCALE [Slides]

The following is a summary of advice for CAAS modeling in SCALE. Refer to the SCALE Criticality Safety and Radiation Shielding training slides or to the SCALE manual for exact syntax. Use a mesh for the fission source that is the most adequate for the problem to solve (coarse/fine). Don’t spend unnecessary resources; simplify the model if it does not impact the final results of interest. Be careful to deactivate secondary fissions in MAVRIC or the calculation may never end. Check that k eff and $\overline{\upsilon}$ calculated results are logical. Between KENO and MAVRIC, cross section libraries, materials, geometry, and mesh grid can be the same or different. Iterative calculations are usually complex problems that need variance reduction. It will be hard to find the best solving parameters in the first attempt; expert judgement is needed. Check each step separately. Use Fulcrum to visualize fission source, mesh source, and spatial/energy distributions to find potential errors or impactful imprecisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Evaluation of Oak Ridge National Laboratory Health Physics Research Reactor Operation Data for Critical Benchmark Creation

The Oak Ridge National Laboratory (ORNL) Health Physics Research Reactor (HPRR) was a research reactor designed and built at ORNL in 1961. The critical assembly used a highly enriched uranium and molybdenum alloy as the fuel and could be operated in steady-state or burst modes. The HPRR has recently been the object of an investigation to create a criticality benchmark. Such benchmarks are very important, as they are used primarily to show the accuracy of newly developed modeling codes and to help experimental validation and reactor licensing. The evaluated experiments considered in this paper were carried out between 1974 and 1986 from various HPRR activities such as steady-state subcritical, steady-state critical, and burst prompt super-critical operations of the reactor for dosimetry, irradiation, or training purposes. By using the HPRR experimental logbook information and the as-built drawings of the critical assembly, a highly detailed model of the HPRR was created with SCALE 6.2.4/KENO-VI, and a first version of a critical benchmark of the HPRR was developed following the International Criticality Safety Benchmark Evaluation Project (ICSBEP) guidelines for thorough description and uncertainty/sensitivity quantification. Unfortunately, in most of the evaluated experiments, the obtained difference between calculated and experimental k eff is around 1,000 pcm, corresponding to a relative error of approximately 1%, beyond the quality standards of the ICSBEP recommending a relative error below 0.1%. Moreover, the derived experimental uncertainty is high, around 4% relative, mainly due to the U-Mo fuel density uncertainty, but also from numerous other factors. For these reasons, the creation of a valuable critical benchmark from HPRR operation data is thus far compromised. In this paper, the different steps of the experiments’ evaluation are summarized, and the reasons for the experimental/calculation discrepancies and potential ways to solve them are explored. This paper also aims to remind us always to exercise considerable care when performing experimental work, and to record all the data possible for potential future uses.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Nuclear Criticality Safety Pipeline Course with Hands-On Experimental Training at Lawrence Livermore's Inherently Safe Subcritical Assembly Training Center

The Nuclear Criticality Safety Divisions at Lawrence Livermore National Laboratory (LLNL) and Los Alamos National Laboratory (LANL) have partnered with Prof. Massimiliano Fratoni of the University of California Berkeley to offer a semester long course on nuclear criticality safety. This course is part of a larger pipeline project among many of the Department of Energy (DOE) laboratories designed to stimulate student interest in the field of criticality safety. The course focuses on teaching the fundamentals of criticality safety, familiarity with national and consensus standards, and preparing criticality safety evaluations. Students also receive hands-on experience with special nuclear material by performing experiments with the Inherently Safe Subcritical Assembly (ISSA) at LLNL. Guest lectures are taught remotely and in-person by criticality safety engineers at LLNL and LANL, giving students an opportunity to interact with professionals in the field. The students complete a semester long project involving developing and writing a criticality safety evaluation. As universities tend to focus heavily on nuclear power and advanced nuclear reactor design, this course gives students a better understanding and perspective of what criticality safety entails. The goal of this pipeline course is to introduce students to criticality safety as another available field for nuclear engineers. It is also a way for criticality safety programs to identify talented students who have the interest and aptitude to work in criticality safety for hire upon graduation. LLNL and LANL have both hosted past students as summer students, participated in student's graduate projects, and hired students as criticality safety engineers. This has provided a unique opportunity for criticality safety programs to spot young talent with better retention outcomes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗