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At least 325 records · Page 18

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Thermal properties of HfN-MoW surrogate cermet fuel for nuclear thermal propulsion

Here, the thermal properties of MoW-HfN, a surrogate cermet for MoW-UN nuclear thermal propulsion fuel, were characterized over a wide range of elevated temperatures. Thermal diffusivity, coefficient of thermal expansion (CTE), and heat capacity were measured. Optical and scanning electron microscopy were performed to characterize the microstructure and draw structure–property correlations. The thermal diffusivity was obtained using the laser flash method. Diffusivity values ranged from about 0.18 cm 2 /s at 200°C to 0.15 cm 2 /s at 1800°C. The CTE was measured using push-rod dilatometry up to 1600°C, giving values between 6.0 and 9.0 μm/m. A scientific rationalization of the effective material properties is made using the rule-of-mixtures and other effective properties models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

sbovqaopt: Surrogate-based optimizer for variational quantum algorithms v.0.1.0

SAND2022-13232 O The sbovqaopt package provides a surrogate-based optimizer for variational quantum algorithms as introduced in the journal publication of the same title. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗

DOE OSTI2025

Laser Powder Bed Fusion Microstructure Surrogate Model

SAND2025-11467O The Laser Powder Bed Fusion (LPBF) Microstructure Surrogate Model is a machine-learning-based tool. It predicts statistics of microstructures that are produced by the LPBF additive manufacturing process. It includes a series of codes for training, testing, and analyzing the model as well as utility scripts for handling data. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Moser, Daniel [Sandia National Lab. (SNL-CA), Live↗

DOE OSTI2022

Monitoring Noble Gases (Xe and Kr) and Aerosols (Cs and Rb) in a Molten Salt Reactor Surrogate Off-Gas Stream Using Laser-Induced Breakdown Spectroscopy (LIBS)

In this study with surrogate materials we show that laser-induced breakdown spectroscopy (LIBS) is a robust tool with promising capability toward monitoring gaseous (Xe and Kr) and aerosol (Cs and Rb) species in an off-gas stream from a molten salt reactor (MSR). MSRs will continually evolve fission products into the cover gas flowing across the reactor headspace. The cover gas entrains Xe and Kr gases, along with aerosol particles, before passing into an off-gas treatment system. Univariate models of Xe and Kr peaks showed a strong correlation to concentration indicated by their coefficients of determination of 0.983 and 0.997, respectively. Multivariate models were built for all four analytes using partial least squares regression coupled with preprocessing steps including normalization, trimming, and/or genetic algorithm derived filters. The models were evaluated by predicting the concentrations of the analytes in four validation samples, in which all calibration models were successfully validated at a confidence interval of 99.9%. Finally, pressure controllers were used to regulate the mass flow rate of Kr flowing into the measurement cell in sinusoidal and stepwise waveforms to test the real-time monitoring capabilities of the regression models. Both univariate and partial least squares Kr models were able to successfully quantify the gas concentration in the real-time evaluation. The root mean squared error of prediction (RMSEP) values for these real-time tests were calculated to be 0.051, 0.060, and 0.121 mol% demonstrating the measurement systems’ capability to perform online monitoring with acceptable accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Compressive strength of the plastic-bonded explosive surrogate idoxuridine under quasistatic and dynamic compression

Inert simulant materials, or “mocks”, are often used as surrogates for plastic-bonded explosives (PBX) in non-detonative tests in order to mitigate hazards. Mocks should reproduce as many non-detonative properties of the explosive as possible, including structural behavior in a variety of thermal and mechanical environments. Recently, the molecular crystal idoxuridine (IDOX) has been identified as an ideal mock for the main component in the explosive polymer-matrix composite PBX 9501, and has performed favorably under quasistatic loading conditions. Here, in order to assess robustness over a range of mechanical environments, plastic-bonded IDOX was compression tested from 0.001/s to 2000/s strain rates and compared to PBX 9501 historical data. Plastic-bonded IDOX showed good agreement to PBX 9501 across these strain rates, justifying continued development and production as a mock.

Burch, Alexandra C.↗

DOE OSTI2025

Implementation of stacked ensemble machine learning for the detection of surrogate plutonium contamination in soil via LIBS

Supervised machine learning methods have demonstrated increased utility for the quantification of lanthanide and actinide elements in atomic spectroscopy applications. This study implements laser-induced breakdown spectroscopy (LIBS) for the identification of plutonium surrogate material (CeO 2 ) in soil matrices by training supervised machine learning methods on the recorded spectral data. A bagged ensemble using Random Forest yields the highest sensitivity predictions with a detection limit of 0.015 wt.% CeO 2 . However, high precision in Ce content prediction required the use of a stacked ensemble regression, which provided the superlative Ce quantification model with an error of 0.107% and a detection limit of 0.022 wt.%. Furthermore, the high performance of the stacked ensemble demonstrates its potential to enhance the accuracy and sensitivity of nuclear contaminant detection using field-deployable spectroscopic analyzers in real-world scenarios.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Integrating adaptive learning with post hoc model explanation and symbolic regression to build interpretable surrogate models

Abstract We develop a materials informatics workflow to build an interpretable surrogate model for micromagnetic simulations. Our goal is to predict the energy barrier of a moving isolated skyrmion in rare-earth-free $$\hbox {Mn}_4$$ Mn 4 N. Our approach integrates adaptive learning with post hoc model explanation and symbolic regression methods. We discuss an unexplored acquisition function (information condensing active learning) within the adaptive learning loop and compare it with the known standard deviation function for efficient navigation of the search space. Model-agnostic post hoc explanation techniques then uncover trends learned by the trained model, which we then leverage to constrain the expressions used for symbolic regression. Graphical abstract

Biswas, Ankita↗

DOE OSTI2020

Incorporating physical constraints into Gaussian process surrogate models (LDRD Project Summary)

This report summarizes work done under the Laboratory Directed Research and Development (LDRD) project titled "Incorporating physical constraints into Gaussian process surrogate models?' In this project, we explored a variety of strategies for constraint implementations. We considered bound constraints, monotonicity and related convexity constraints, Gaussian processes which are constrained to satisfy linear operator constraints which represent physical laws expressed as partial differential equations, and intrinsic boundary condition constraints. We wrote three papers and are currently finishing two others. We developed initial software implementations for some approaches. This report summarizes the work done under this LDRD.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Automated Discovery of DOminaNt physics Informed Surrogates (ADDONIS) Framework for Improving Water Cycling Predictability

Focal Areas: The primary alignment of the proposed framework is with focal area 2, because it guides the construction of a hierarchy of predictive models generated from Statistical and AI/ ML techniques. There are connections to focal areas 1 and 3 because surrogates allow for coupled data assimilation techniques where the full model is too expensive (1) and the discovered balance laws can be used as an insightful analysis tool to validate the structure of models (3).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Selection and Characterization of Surrogate Material for Developing Zr Barrier Coatings for Uranium Nitride Particles

In support of UC 1-x N x fuel kernels (~ 800 micrometer diameter) development work ongoing at Oak Ridge national laboratory (ORNL), where the fuel kernels will be embedded inside a possible zirconium based matrix, Argonne National Laboratory (ANL) is contributing to help develop a robust metal diffusion barrier coating. Zirconium nitride (ZrN) and Zirconium oxide (ZrO2) were selected as possible diffusion barrier candidates, and the atomic layer deposition (ALD) has been chosen as a candidate technology to deposit the film. Development of such a barrier coating with the ALD process entails significant number of trials to study relevant parameters. Therefore, to prevent needless wastage, instead of coating directly over fuel kernels, a surrogate candidate with similar dimensions and physical properties were been selected and coated to develop the diffusion barrier coating.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Intelligent Sampling for Surrogate Modeling, Hyperparameter Optimization, and Data Analysis

Sampling techniques are used in many fields, including design of experiments, image processing, and graphics. The techniques in each field are designed to meet the constraints specific to that field such as uniform coverage of the range of each dimension or random samples that are at least a certain distance apart from each other. When an application imposes new constraints, for example, by requiring samples in a non-rectangular domain or the addition of new samples to an existing set, a common solution is to modify the algorithm currently in use, often with less than satisfactory results. As an alternative, we propose the concept of intelligent sampling, where we devise algorithms specifically tailored to meet our sampling needs, either by creating new algorithms or by modifying suitable algorithms from other fields. Surprisingly, both qualitative and quantitative comparisons indicate that some relatively simple algorithms can be easily modified to meet the many sampling requirements of surrogate modeling, hyperparameter optimization, and data analysis; these algorithms outperform their more sophisticated counterparts currently in use, resulting in better use of time and computer resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Optimization of artificial viscosity in production codes based on Gaussian Regression surrogate models

To accurately model flows with shock waves using staggered-grid Lagrangian hydrodynamics, artificial viscosity has to be introduced to convert kinetic energy into internal energy, thereby increasing the entropy across shocks. Determining the appropriate strength of the artificial viscosity is an art and strongly depends on the particular problem and experience of the researcher. The objective of this study is to pose the problem of finding the appropriate strength of artificial viscosity as an optimization problem and solve this problem using machine learning (ML) tools, specifically using surrogate models based on Gaussian Process regression and Bayesian analysis. We describe the optimization method and discuss various practical details of its implementation. The shock-containing problems for which we apply this method all have been implemented in the LANL code FLAG. First, we apply ML to find optimal values to isolated shock problems of different strengths. Second, we apply ML to optimize viscosity for a 1D propagating detonation problem based on Zel’dovich-von Neumann-Doring (ZND) detonation theory using a reactive burn model. We compare results for default (currently used values in FLAG) and optimized values of artificial viscosity for these problems demonstrating the potential for significant improvement in the accuracy of computations.

42 ENGINEERING↗

DOE OSTI2022

Benchmark and Assessment of the Surrogate Reaction Method for Determining Unknown (n,n') and (n,2n) Reaction Cross Sections

A new approach for determining (n,n' ) and (n,2n) cross sections from a combination of indirect measurements and theory is presented. The method builds on and significantly extends the surrogate reactions framework developed at LLNL. Multiple theory advances are described and the overall approach is demonstrated through an application to benchmark cross sections. The work addresses nuclear data needs of the Stockpile Stewardship Program (SSP) and this report comprises multiple completion criteria for an ASC/PEM Level-2 milestone.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Measurement of radiolytic hydrogen generation from surrogate zr-based materials in a "mini-canister"

This report describes a test campaign to investigate radiolytic hydrogen generation from surrogate materials resembling the (Zr-based) cladding of commercial spent nuclear fuel (SNF) with an inventory of residual water post-dryout. The radiolytic hydrogen generation rates from residual waters in the SNF-in-canister system are important for predicting the in-canister environment for sealed SNF dry storage canisters over time and the impact to continued safe dry storage.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Finding Electronic Structure Machine Learning Surrogates without Training

A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations – this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of machine-learning surrogate models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn-Sham density functional theory, the most popular computational method in materials science and chemistry.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Radiolytic H 2 Yield from Non-Oxidized Zr Surrogates Under Humid Helium Backfill

This report presents updates from a test campaign to investigate radiolytic hydrogen generation from surrogate materials resembling the (Zr-based) cladding of commercial spent nuclear fuel (SNF) with an inventory of residual water post-dryout. The radiolytic hydrogen generation rates from residual waters in the SNF-in-canister system are important for predicting the in-canister environment for sealed SNF dry storage canisters over time and its impact on continued safe dry storage.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

IER-479/547: Low-Temperature TEX Final Design and Surrogate Measurements [Slides]

This presentation covers the final design, and measurements of the Low-Temperature TEX. Visual Diagrams of the apparatus and detailed part explanations are included. Neutronic calculations for Low-Temperature TEX was performed. Six configurations were proposed. Thermal analysis for the boundary cases was performed. Thermal testing with surrogate materials has been underway and testing will resume shortly.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

IER-479/547: Low-Temperature TEX Surrogate Testing and Reflector Design [Slides]

This lecture includes a look into progress in surrogate testing. This includes a new chiller that has been fabricated and testing that has resumed. Additional tests are being conducted, including: using formed aluminum gaskets, extended testing with natural uranium, testing with pseudo-spindle, testing with no vacuum. This lecture includes a look into the Reflector Design. Results for detailed model are consistent with results presented in CED-2. Calculations provide flexibility in the physical design of the reflectors (e.g. reflector segment widths and heights). Provided initial CAD models of reflector components.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗