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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Mapping Analyte Distributions in Surrogate Nuclear Melt Glass Using Laser-induced Breakdown Spectroscopy and Micro X-Ray Fluorescence

Explore feasibility of portable LIBS and micro-XRF systems as methods of field screening for real debris; Develop a LIBS Capability to rapidly screen beads for production quality control; Complete 3D elemental mapping of surrogate debris to determine uranium and other elemental migration patterns during debris formation

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Development of High Temperature Out-of-Pile Experiments for Testing Nuclear Thermal Propulsion Fuel Surrogates

Nuclear thermal propulsion (NTP) technology is designed to provide “in-space” vehicle propulsion by deriving thrust from a hydrogen working fluid that is heated from a nuclear reaction. Initial NTP fuel structures (primarily carbon/carbide based materials) developed by the Rover/NERVA program were designed to withstand temperatures exceeding 2400 K and remain intact after tens of firing cycles. Current programs responsible for development of this technology expect the fuels to withstand temperatures in excess of 2550K under a similar duty cycle. Experiments that subject fuels to these conditions are being developed to provide data and performance information for optimization and potential down-selection of the candidate materials. Initial tests will be designed for laboratory benchtop operation and will not expose surrogate specimens to the hydrogen working fluid or irradiation fields. The primary focus of these out-of-pile experiments will be to study thermal performance and thermal failure limits of the candidate specimen materials.

Howard, Richard↗
DOE OSTI2019

A Tutorial on the Synthesis and Validation of a Closed-Loop Wind Farm Controller Using a Steady-State Surrogate Model

In wind farms, wake interaction leads to losses in power capture and accelerated structural degradation when compared to freestanding turbines. One method to reduce wake losses is by misaligning the rotor with the incoming flow using its yaw actuator, thereby laterally deflecting the wake away from downstream turbines. However, this demands an accurate and computationally tractable model of the wind farm dynamics. This problem calls for a closed-loop solution. This tutorial paper fills the scientific gap by demonstrating the full closed-loop controller synthesis cycle using a steady-state surrogate model. Furthermore, a novel, computationally efficient and modular communication interface is presented that enables researchers to straight-forwardly test their control algorithms in large-eddy simulations. High-fidelity simulations of a 9-turbine farm show a power production increase of up to 11% using the proposed closed-loop controller compared to traditional, greedy wind farm operation.

17 WIND ENERGY↗
DOE OSTI2021

Testing Surrogate-Based Optimization with the Fortified Branin-Hoo Extended to Four Dimensions

Some popular functions used to test global optimization algorithms have multiple local optima, all with the same value, making them all global optima. It is easy to make them more challenging by fortifying them via adding a localized bump at the location of one of the optima. In previous work the authors illustrated this for the Branin-Hoo function and the popular differential evolution algorithm, showing that the fortified Branin-Hoo required an order of magnitude more function evaluations. This paper examines the effect of fortifying the Branin-Hoo function on surrogate- based optimization, which usually proceeds by adaptive sampling. Two algorithms are considered. The EGO algorithm, which is based on a Gaussian process (GP) and an algorithm based on radial basis functions (RBF). EGO is found to be more frugal in terms of the number of required function evaluations required to identify the correct basin, but it is expensive to run on a desktop, limiting the number of times the runs could be repeated to establish sound statistics on the number of required function evaluations. The RBF algorithm was cheaper to run, providing more sound statistics on performance. A four-dimensional version of the Branin-Hoo function was introduced in order to assess the effect of dimensionality. Furthermore, it was found that the difference between the ordinary function and the fortified one was much more pronounced for the four-dimensional function compared to the two dimensional one.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Photopolymerizable Sol-gel Synthesis for Faster Manufacturing of Surrogate Nuclear Debris

Simulated debris is used to train first responders for potential nuclear detonations. Sol gel chemistry has been used to produce surrogate material for these training exercises, but the current average production time is 4 hours. Optimization using UV-polymerizable sol gel precursors can potentially decrease time as well as cost of the production process. FT-IR spectroscopy was used to measure and compare gelation kinetics of the standard and photopolymerizable sol gel synthetic procedures. Addition of the UV-polymerizable precursors and controlling the aqueous phase volume resulted in a 120-fold decrease in vitrification time.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Experiments and Simulations of Droplet Burning of Isobutanol Mixed with a Tier II Gasoline Certification Fuel and its Surrogate, Paper Number: SHTC2023-115267

This study presents an experimental and numerical results of combustion dynamics of a Tier II certification fuel/iso-butanol mixtures and its surrogate(S5)/iso-butanol mixtures. The experiments were carried out where gas transport was determined entirely by fuel evaporation to promote spherical symmetry during the burning process. The droplet initial diameters were in the range 0.55 mm to 0.62 mm. The combustion characteristics such as droplet diameters, flame standoff ratios and soot shell standoff ratios of indolene/iso-butanol mixtures are compared with S5/iso-butanol mixtures and the implications of iso-butanol addition to indolene and S5 on combustion are discussed. The presence of iso-butanol is experimentally found to reduce the formation of soot as the iso-butanol concentration was increased and the iso-butanol addition insignificantly influence the burning rate. Simulations were carried out with the detailed kinetic chemistry (324 species and 17608 reactions) from POLIMI. The detailed numerical model included unsteady gas and liquid transport, soot chemistry and radiative transport. The impact of iso-butanol on the burning rate, flame structure and soot formation are presented. Droplet burn rates were well predicted while flame diameters showed more variability depending on how the flame was defined in the numerical simulation (peak gas temperature and OH concentration). Simulations also show the influence of iso-butanol concentration on flame temperature, greenhouse gas emissions and soot formations.

Guo, Songtao↗
DOE OSTI2022

Combustion Characteristics of a Tier II Gasoline Certification Fuel and its Surrogate with Iso-butanol: experiments and detailed numerical modeling

This presentation will describe results of an experimental and numerical study of combustion of a Tier II gasoline certification fuel and its surrogate blended with iso-butanol as a potential biofuel additive. The experiments were carried out under conditions where gas transport originated entirely by fuel evaporation with external convection (both forced and natural) were minimized. The resulting near spherically symmetric droplet flames were amenable to detailed numerical modeling. The droplet initial diameters were in the narrow range of 0.55 mm to 0.62 mm while and burning is performed under standard atmosphere. The combustion characteristics such as evolution of droplet, flame and soot shell diameters for indolene/iso-butanol and surrogate/iso-butanol mixtures are compared. The presence of iso-butanol is experimentally found to reduce the formation of soot as the iso-butanol concentration was increased. Simulations are also reported using a detailed kinetic chemistry comprised of 324 species and 17608 reactions using the CRECK POLIMI mechanism. The detailed numerical model included unsteady gas and liquid transport, soot chemistry and radiative transport. Droplet burn rates were well predicted while flame diameters showed more variability depending on how the flame was defined in the numerical simulation (peak gas temperature and OH concentration). Simulations also show the influence of iso-butanol concentration on flame temperature, greenhouse gas emissions and particulate emissions.

Guo, Songtao↗
DOE OSTI2016

Inverse Uncertainty Quantification of Reactor Simulation with Polynomial Chaos Surrogate Model

Reliable uncertainty quantification (UQ) and sensitivity analysis (SA) is significant part of modelling and simulation. This is particularly important in the nuclear community due to the fact that the licensing of nuclear installations has shifted from conservative to Best Estimate plus Uncertainty (BEPU) methodologies. UQ can be broadly classified as forward UQ and inverse UQ. Forward UQ is the process of quantifying the uncertainties in simulation outputs by propagating the uncertainties in input parameters. It requires knowledge in the input uncertainty, which is often specified by ad-hoc expert judgment which lacks mathematical rigor. The inverse UQ is the process to determine the uncertainty in input parameters given observations of the output. The purpose of this paper is to replace such ad-hoc expert judgment of the statistical properties of input model parameters in nuclear reactor simulation. This summary presents work on inverse UQ of a nonlinear Point Reactor Kinetics Equations - lumped thermal-hydraulics parameter coupled problem. Bayesian inference and Markov Chain Monte Carlo (MCMC) are used for the inverse UQ. Because MCMC requires a large number of simulations which is impractical for models that are computationally expensive, surrogate models is constructed by generalized Polynomial Chaos (PC) expansion technique. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Surrogate Post-Detonation Urban Debris (SPUD): Standard Reference Materials 4600 and 4601

If a terrorist nuclear explosion were to occur in the United States, high-quality (accurate and precise) legally defensible nuclear forensics measurements will be required to assure the certainty and confidence needed, for conclusions drawn from these data will be coupled with law enforcement and intelligence information for the purposes of attribution and retribution. Standard Reference Materials (SRMs)/Certified Reference Materials (CRMs) establish the traceability, accuracy, and precision of nuclear forensic measurements and provide the ability to benchmark measurement methods essential to meeting the requirements of legal scrutiny. Currently there is a critical shortage of key SRMs/CRMs required for accurate and precise measurements that are fundamental to nuclear measurements supporting pre- and post-detonation nuclear forensics. Supplies either do not exist, are not good enough, are in short supply, and/or are very expensive. The radionuclide measurement techniques and data quality objectives for pre- and post-detonation nuclear forensics are quite different than the applications for which the current suite of available SRMs/CRMs was developed. Specifically, 1) modern mass spectrometry methods have largely replaced traditional activity-based measurement methods for quantifying small quantities of long-lived radionuclides and most trace radionuclide SRMs/CRMs are certified for activity and are thus not adequate for current applications, 2) modern instrumental methods are capable of higher precision than historic measurement techniques, and in some cases exceed the precision of the available SRMs/CRMs, and 3) the quality assurance requirements for pre- and post-detonation nuclear forensics are such that the existing cadre of SRMs/CRMs does not adequately support the needs for method validation. There are numerous governmental stakeholders both within the US (DOE, DoD) and internationally (IAEA, JRC-ITU) whose missions are either directly involved with nuclear forensics measurements or rely on the forensic data obtained to make the exacting policy decisions (DOJ (FBI), DHS, CIA, DIA, DoD) associated with disrupting the production of illicit nuclear materials, interdicted nuclear materials or a nuclear/radiological detonation event. Starting in 2008 the DHS NTNFC, in collaboration with NBL and NIST, took on the production of suitable SRMs/CRMs for the pre-detonation nuclear forensics community. Two CRMs have been released since the start of this effort and two more are slated for completion in 2016. More recently, NIST, as a part of its mission as the US National Metrology Institute (NMI) to deliver standards (providing traceability) and foster homeland security, has launched the first certification campaign of a measurement traceable post nuclear detonation SRM. NIST, in cooperation with partner labs (AFIT (DoD), FBI (DOJ), and NPL) and with support from the FBI, has developed two Surrogate Post-Detonation Urban Debris (SPUD) Standard Reference Materials (SRMs) - SRM 4600 and 4601. These SRMs have been developed to simulate the expected debris mixture from the detonation of an Improvised Nuclear Device (IND) in an urban environment, which is capable of producing fresh fission as well as activation products. The elemental composition of the vitrified debris materials is a composite of minerals and metals characteristic of the concrete and steel, the building materials that would be exposed to a nuclear detonation and whose resulting activation products would be found in rubble samples collected for forensics evidence and attribution. Additionally, because post-detonation samples would also contain short-lived fission products, the debris materials have been doped with Uranium (U). To create the nuclear fission products, samples of the SPUD materials can be placed in a well-characterized nuclear reactor and exposed to a set neutron flux and energy, causing the encased U atoms to split and yield short-lived fission products. Because a known amount of U is added to the SPUD materials it is feasible to determine the concentration of the resulting fission products. The subsequent irradiated SPUD glass would be chemically similar to samples that would be generated in an actual detonation, and thus can serve as a standardized reference material for nuclear forensic post detonation measurements. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Equipping Neural Network Surrogates with Uncertainty for Propagation in Physical Systems

Coarse-grained or filtered models typically rely on closure models to account for unresolved scales. For instance, large eddy simulation for modeling turbulent fluid flows explicitly resolves the largest scales, but requires modeling closure terms to account for the sub-filter scales. With the vast amount of data available from high-fidelity simulations, there are unique opportunities to leverage data-driven modeling techniques to formulate expressive and flexible closure models. Despite their flexibility, data-driven models struggle in domain shift settings, i.e. when deployed in configurations not captured in the training dataset. In particular, the efficacy of neural network surrogates is difficult to assess a priori due to the deterministic, point-estimate nature of predictions. In high-consequence applications, such models require reliable uncertainty estimates in the data-informed and out-of-distribution regimes. To quantify uncertainties in both regimes, we employ Bayesian neural networks which are able to capture both epistemic and aleatoric uncertainties. We will discuss challenges associated with the training and evaluation of these networks. Furthermore, we will discuss uncertainty embedding strategies to enable efficient sampling and propagation of uncertainty through high-fidelity simulations.

Bayesian neural networks↗
DOE OSTI2024

A Graph Neural Network Surrogate Model for hls4ml

Recent advancements in use of machine learning (ML) techniques on field-programmable gate arrays (FPGAs) have allowed for the implementation of embedded neural networks with extremely low latency. This is invaluable for particle detectors at the Large Hadron Collider, where latency and used area are strictly bounded. The hls4ml framework is a procedure that converts trained ML model software to a synthesis result to can be used on an FPGA. However, running the pipeline is a time-consuming procedure, and there is a strong risk of failure. In particular, it may not be possible to successfully convert a model into a synthesis result, or the resource consumption of the model may exceed the resources of the target FPGA. To aid with this development, we introduce wa-hls4ml, a surrogate model using a graph neural network to emulate the structure of the source models. The goal is to estimate the chance of success and resource consumption of a given model when passed through the hls4ml pipeline, without needing to run the pipeline.

Plotnikov, Dennis↗
DOE OSTI2024

Milestone 1.2.16. Reconciling the Impacts of Thermal Pretreatment on Radiation-Induced H2 Generation from Aluminum-Clad Spent Nuclear Fuel Surrogate Materials

To support the technical basis for the extended dry storage of aluminum-clad spent nuclear fuel (ASNF), thermal pretreatment procedures to minimize the radiation-induced generation of molecular hydrogen (H2) have been investigated. The aim of thermal pretreatment is to eliminate the residual adsorbed water content on the ASNF’s corrosion layers, precursors for H2 generation. To date, irradiation studies in this area have found conflicting results for the effectiveness of thermal pretreatment procedures. The aim of this study was to reconcile those differences. However, the presented results, which utilized a modified in situ thermal pretreatment procedure, afforded H2 yield data that further indicates that thermal pretreatment does not significantly reduce the radiation-induced yield of H2 from gamma irradiated ASNF surrogate materials. Assessment of the differences between thermal pretreatment studies suggests that stainless-steel—present in the irradiation setup of studies that demonstrated a reduction in the yield of H2 with thermal pretreatment—may afford not only unanticipated interfacial chemistry, but also the formation and radiolytic contribution of iron oxides to the chemistry underpinning the formation of H2 in these systems. Given the Department of Energy Standard Canister—proposed for the extended dry storage of ASNF—is predominantly composed of stainless-steel, the potential contribution of stainless-steel and its corrosion layers to radiolytic H2 production should be further investigated. This research was funded by the U.S. Department of Environmental Management, Office of Technology Development, under contract DE-AC07-05ID14517.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural Architecture Search is a powerful approach for automating model design, but existing methods struggle to accurately optimize for real hardware performance, often relying on proxy metrics such as bit operations. We present Surrogate Neural Architecture Codesign Package (SNAC-Pack), an integrated framework that automates the discovery and optimization of neural networks focusing on FPGA deployment. SNAC-Pack combines Neural Architecture Codesign's multi-stage search capabilities with the Resource Utilization and Latency Estimator, enabling multi-objective optimization across accuracy, FPGA resource utilization, and latency without requiring time-intensive synthesis for each candidate model. We demonstrate SNAC-Pack on a high energy physics jet classification task, achieving 63.84% accuracy with resource estimation. When synthesized on a Xilinx Virtex UltraScale+ VU13P FPGA, the SNAC-Pack model matches baseline accuracy while maintaining comparable resource utilization to models optimized using traditional BOPs metrics. This work demonstrates the potential of hardware-aware neural architecture search for resource-constrained deployments and provides an open-source framework for automating the design of efficient FPGA-accelerated models.

Weitz, Jason [UC, San Diego] (ORCID:00090004631535↗