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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

MITR Low-Enriched Uranium Conversion Fluid-Structure Interaction Preliminary Design Verification

The U.S. National Nuclear Security Administration (NNSA) Office of Material Management and Minimization (M 3 ) has developed and is pursuing an integrated approach to address the persistent threat posed by unintentional proliferation of nuclear materials. The NNSA M 3 approach reduces the risk of highly enriched uranium (HEU) and plutonium falling into the hands of non-state actors by minimizing the use of and, when possible, eliminating weapons-usable nuclear material around the world. In this geopolitical context, most research and test reactors, both domestic and international, have completed or started a program of conversion from the use of HEU to low-enriched uranium (LEU) as fuel. Conversion of civilian research reactors from HEU to LEU, and the return of the HEU to the country of origin, are important components of the NNSA non-proliferation program. Worldwide, 71 reactors have been converted to the use of LEU fuel, and an additional 32 have been confirmed to be permanently shut down. The U.S., with 20 reactor conversions, is among the 39 countries on six continents where conversions have occurred. With recent conversions in Ghana and Nigeria, an important milestone was reached in completing conversion of all reactors on the continent of Africa to LEU fuel. Africa thus becomes the third continent to have completed conversion of all HEU reactors to LEU, following Australia and South America

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Verification of Triso Fuel Burnup Using Machine Learning Algorithms

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134 Cs, 137 Cs, 154 Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Introduction to the Consortium for Monitoring, Technology, and Verification (MTV) [Slides]

Our mission is to develop new technologies that detect and deter nuclear proliferation activities while training the next generation of nuclear professionals. The three technical thrust areas are: 1. Fundamentals of nuclear and particle physics, 2. Signals and source terms for nuclear nonproliferation, and 3. Nuclear explosion. There are opportunities for collaborations with university faculty, possible student and post-doc work, and posting job ads. Further, MTV hosts various workshops, symposiums.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Uncertainty Analysis and Software Verification

Uncertainty analyses are an important part of calibrations and testing. They allow researchers insight on how to reduce and mitigate errors in testing. Each device used in testing introduces error in a system, as well as other sources such as environmental conditions, electronics, analog to digital, and random errors. Each source is carefully examined to identify how much error it introduces to a system. These sources are then combined using various methods of uncertainty calculations. To verify and validate software, a manual calculation is required to ensure the software is performing as intended. Using Excel to verify the calculations, we can identify discrepancies within the software. The root of the sum of the squares uncertainty (RSS) is used to find the combined uncertainty of a device at one and two standard deviations of the mean. Calibrations on accelerometers are performed using a vibration system along with a back-toback reference accelerometer. The vibration system takes a reference point at 100Hz frequency at 10g amplitude. The sensitivities are collected at each dialed in frequency. The sensitivity of the device represents the electrical output of the UUT (mV, pC, etc.) per unit of acceleration (g). The full history of sensitivities of selected accelerometers are used to find the averaged, standard deviation, and uncertainty of the device at each frequency tested. The uncertainty calculations from excel and the software are then compared.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Development, Implementation, and Verification of Partially-Ionized Collisional Multifluid Plasma Models in Drekar

We consider the development of multifluid models for partially ionized multispecies plasmas. The models are composed of a standard set of five-moment fluid equations for each species plus a description of electromagnetics. The most general model considered utilizes a full set of fluid equations for each charge state of each atomic species, plus a set of fluid equations for electrons. The fluid equations are coupled through source terms describing electromagnetic coupling, ionization, recombination, charge exchange, and elastic scattering collisions in the low-density coronal limit. The form of each of these source terms is described in detail, and references for required rate coefficients are identified for a diverse range of atomic species. Initial efforts have been made to extend these models to incorporate some higher-density collisional effects, including ionization potential depression and three- body recombination. Some reductions of the general multifluid model are considered. First, a reduced multifluid model is derived which averages over all of the charge states (including neutrals) of each atomic species in the general multifluid model. The resulting model maintains full consistency with the general multifluid model from which it is derived by leveraging a quasi-steady-state collisional ionization equilibrium assumption to recover the ionization fractions required to make use of the general collision models. Further reductions are briefly considered to derive certain components of a single-fluid magnetohydrodynamics (MHD) model. In this case, a generalized Ohm's law is obtained, and the standard MHD resistivity is expressed in terms of the collisional models used in the general multifluid model. A number of numerical considerations required to obtain robust implementations of these multifluid models are discussed. First, an algebraic flux correction (AFC) stabilization approach for a continuous Galerkin finite element discretization of the multifluid system is described in which the characteristic speeds used in the stabilization of the fluid systems are synchronized across all species in the model. It is demonstrated that this synchronization is crucial in order to obtain a robust discretization of the multifluid system. Additionally, several different formulations are considered for describing the electromagnetics portion of the multifluid system using nodal continuous Galerkin finite element discretizations. The formulations considered include a parabolic divergence cleaning method and an implicit projection method for the traditional curl formulation of Maxwell's equations, a purely- hyperbolic potential-based formulation of Maxwell's equations, and a mixed hyperbolic-elliptic potential-based formulation of Maxwell's equations. Some advantages and disadvantages of each formulation are explored to compare solution robustness and the ease of use of each formulation. Numerical results are presented to demonstrate the accuracy and robustness of various components of our implementation. Analytic solutions for a spatially homogeneous damped plasma oscillation are derived in order to verify the implementation of the source terms for electromagnetic coupling and elastic collisions between fluid species. Ionization balance as a function of electron temperature is evaluated for several atomic species of interest by comparing to steady-state calculations using various sets of ionization and recombination rate coefficients. Several test problems in one and two spatial dimensions are used to demonstrate the accuracy and robustness of the discretization and stabilization approach for the fluid components of the multifluid system. This includes standard test problems for electrostatic and electromagnetic shock tubes in the two-fluid and ideal shock-MHD limits, a cylindrical diocotron instability, and the GEM challenge magnetic reconnection problem. A one-dimensional simplified prototype of an argon gas puff configuration as deployed on Sandia's Z-machine is used as a demonstration to exercise the full range of capabilities associated with the general multifluid model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Design and Initial Verification Testing of an In Situ Load Retention Aging Fixture

Load retention is a key metric used to evaluate long-term mechanical functionality of polymeric materials. Its measurement currently requires periodically removing the aging fixtures from the ovens, removing the specimens from the fixtures, and performing load testing on the specimens. The ability to perform load measurement in situ (i.e., without having to remove the specimens from the fixtures, and possibly even the ovens) would facilitate data acquisition/analysis, improve data quality, and enable uninterrupted compression of specimens which is more representative of the actual application.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Verification of Data-Driven Models of Physical Phenomena using Interpretable Approximation

Machine-learned models, specifically neural networks, are increasingly used as “closures” or “constitutive models” in engineering simulators to represent fine-scale physical phenomena that are too computationally expensive to resolve explicitly. However, these neural net models of unresolved physical phenomena tend to fail unpredictably and are therefore not used in mission-critical simulations. In this report, we describe new methods to authenticate them, i.e., to determine the (physical) information content of their training datasets, qualify the scenarios where they may be used and to verify that the neural net, as trained, adhere to physics theory. We demonstrate these methods with neural net closure of turbulent phenomena used in Reynolds Averaged Navier-Stokes equations. We show the types of turbulent physics extant in our training datasets, and, using a test flow of an impinging jet, identify the exact locations where the neural network would be extrapolating i.e., where it would be used outside the feature-space where it was trained. Using Generalized Linear Mixed Models, we also generate explanations of the neural net (à la Local Interpretable Model agnostic Explanations) at prototypes placed in the training data and compare them with approximate analytical models from turbulence theory. Finally, we verify our findings by reproducing them using two different methods.

42 ENGINEERING↗
DOE OSTI2021

Independent Verification Report for Clearance Operation Roller Coaster Sites

The U.S. Department of Energy (DOE) is pursuing clearance of the Operation Roller Coaster sites under DOE O 458.1, Radiation Protection of the Public and the Environment. The clearance of property requirements in DOE O 458.1 must be implemented when DOE intends to facilitate future transfers of responsibility/control of real property. DOE O 458.1 prescribes that all radiological monitoring or surveys performed in support of clearance of property must use methods such as the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) or other methods approved by DOE. MARSSIM recommends using a series of surveys for collecting information required to demonstrate compliance. These surveys form the Radiation Survey and Site Investigation (RSSI) Process.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Verification of Boundary Conditions in MCNP

MCNP (Monte Carlo N-Particle) is code that is used to model the transport of different particles. To verify in MCNP6.1 that arrays formed with reflective and periodic boundary conditions produce a neutron multiplication factor (keff) that is equal to or higher than an array of the same size with no boundary conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

SIERRA Low Mach Module: Fuego Verification Manual (V.5.2)

The SIERRA Low Mach Module: Fuego, henceforth referred to as Fuego, is the key element of the ASC re environment simulation project. The fire environment simulation project is directed at characterizing both open large-scale pool fires and building enclosure fires. Fuego represents the turbulent, buoyantly-driven incompressible flow, heat transfer, mass transfer, combustion, soot, and absorption coefficient model portion of the simulation software. Using MPMD coupling, Scefire and Nalu handle the participating-media thermal radiation mechanics. This project is an integral part of the SIERRA multi-mechanics software development project. Fuego depends heavily upon the core architecture developments provided by SIERRA for massively parallel computing, solution adaptivity, and mechanics coupling on unstructured grids.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

SIERRA Low Mach Module: Fuego Verification Manual (V.5.4)

The SIERRA Low Mach Module: Fuego, henceforth referred to as Fuego, is the key element of the ASC re environment simulation project. The fire environment simulation project is directed at characterizing both open large-scale pool fires and building enclosure fires. Fuego represents the turbulent, buoyantly-driven incompressible flow, heat transfer, mass transfer, combustion, soot, and absorption coefficient model portion of the simulation software. Using MPMD coupling, Scefire and Nalu handle the participating-media thermal radiation mechanics. This project is an integral part of the SIERRA multi-mechanics software development project. Fuego depends heavily upon the core architecture developments provided by SIERRA for massively parallel computing, solution adaptivity, and mechanics coupling on unstructured grids.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

SIERRA Low Mach Module: Fuego Verification Manual - (V.5.6)

The SIERRA Low Mach Module: Fuego, henceforth referred to as Fuego, is the key element of the ASC fire environment simulation project. The fire environment simulation project is directed at characterizing both open large-scale pool fires and building enclosure fires. Fuego represents the turbulent, buoyantly-driven incompressible ow, heat transfer, mass transfer, combustion, soot, and absorption coefficient model portion of the simulation software. Using MPMD coupling, Scefire and Nalu handle the participating-media thermal radiation mechanics. This project is an integral part of the SIERRA multi-mechanics software development project. Fuego depends heavily upon the core architecture developments provided by SIERRA for massively parallel computing, solution adaptivity, and mechanics coupling on unstructured grids.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Sierra/SolidMechanics 5.6 Verification Tests Manual

Presented in this document is a small portion of the tests that exist in the Sierra / SolidMechanics (Sierra / SM) verfication test suite. Most of these tests are run nightly with the Sierra / SM code suite, and the results of the test are checked versus the correct analytical result. For each of the tests presented in this document, the test setup, a description of the analytic solution, and comparison of the Sierra / SM code results to the analytic solution is provided. Mesh convergence is also checked on a nightly basis for several of these tests. This document can be used to confirm that a given code capability is verfied or referenced as a compilation of example problems. Additional example problems are provided in the Sierra / SM Example Problems Manual. Note, many other verfication tests exist in the Sierra / SM test suite, but have not yet been included in this manual.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Verification of Boundary Conditions in MCNP

To verify in MCNP that arrays formed with reflective and periodic boundary conditions produce a neutron multiplication factor (keff) that is equal to or higher than an array of the same size with no boundary conditions

36 MATERIALS SCIENCE↗