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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Characterization of Surrogate Molten Salt Reactor Aerosol Streams

Measuring the aerosol evolution from MSRs is important for monitoring the off-gas system of the reactor and is particularly important for detecting off-normal conditions. In a molten salt reactor (MSR) accident scenario, an aerosol release would be a major factor in the source term. This aerosol stream would likely be generated from a breach in the cover gas system, which causes particles produced from fission itself to escape, or from a salt spill that produces aerosols through splashing and secondary reactions. The particle size of the produced aerosols is anticipated to vary greatly and range from 0.01 to 10 µm. The transport of these aerosols would be dependent on the particle size. A better understanding of aerosol generation, size, and monitoring methods are needed to inform estimation and mitigation of potential aerosolized source terms from MSRs. While salt spill experiments are being performed at Argonne National Laboratory, the development of aerosol characterization and monitoring methods are being developed at Oak Ridge National Laboratory. To generate prototypic aerosols for use in testing monitoring instruments and mitigation methods, a surrogate aerosol stream was produced with a Collison nebulizer, and the particle size distributions were measured with a cascade impactor. The results demonstrated that by changing the nebulizer pressure, the aerosol particle size distribution can be adjusted to best match the region of interest for experiments with higher pressures, driving the particle size down. However, nearly all aerosols formed exceeded 1 µm in diameter, providing a lower bound for the surrogate aerosol stream. In addition to verifying the applicability of this surrogate aerosol stream, this work has shown that a laser induced breakdown spectroscopy monitoring system that is under development is resilient to changes in particle sizes, increasing its robustness for off-gas monitoring.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Spatio-Temporal Surrogates for Interaction of a Jet with High Explosives: Part II - Clustering Extremely High-Dimensional Grid-Based Data

Building an accurate surrogate model for the spatio-temporal outputs of a computer simulation is a challenging task. A simple approach to improve the accuracy of the surrogate is to cluster the outputs based on similarity and build a separate surrogate model for each cluster. This clustering is relatively straightforward when the output at each time step is of moderate size. However, when the spatial domain is represented by a large number of grid points, numbering in the millions, the clustering of the data becomes more challenging. In this report, we consider output data from simulations of a jet interacting with high explosives. These data are available on spatial domains of different sizes, at grid points that vary in their spatial coordinates, and in a format that distributes the output across multiple files at each time step of the simulation. We first describe how we bring these data into a consistent format prior to clustering. Borrowing the idea of random projections from data mining, we reduce the dimension of our data by a factor of thousand, making it possible to use the iterative k-means method for clustering. We show how we can use the randomness of both the random projections, and the choice of initial centroids in k-means clustering, to determine the number of clusters in our data set. Our approach makes clustering of extremely high dimensional data tractable, generating meaningful cluster assignments for our problem, despite the approximation introduced in the random projections.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

R&D Outcomes of Surrogate Environmental Swipe Manufacturing Technique Method Development for Arms Control and Treaty Verification Applications

The analysis of environmental samples is a critical component of nuclear compliance verification and safeguards activities. While the associated particle analyses have benefitted from recent advances in the production of actinide-bearing particulates with uniform and well-characterized particle size, material phase, and isotopic ratios to serve as reference materials for calibration and interlaboratory comparisons, there is an outstanding need for surrogate environmental samples, which resemble field-collected samples, with the aforementioned reference particulates to support future nuclear compliance verification exercises and method development. This effort seeks to leverage the recent advances in tailored reference particle production to generate surrogate environmental swipe samples via parallel production techniques (direct and indirect) which are uniquely suited to the hydrothermal and aerosol-based synthesis routes developed at PNNL and SRNL, respectively. PNNL’s hydrothermal particle production method was used to generate surrogate reference particles for the indirect production of test swipes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Conceptual Design of Integrated Energy Systems with Market Interaction Surrogate Models

Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with market and can result in overestimated economic values. In this work, we pro-pose a machine learning surrogate-assisted optimization framework to quantify the IES/market interactions and thus go beyond price taker. We use time series clustering to generate representative IES operation profiles for the IES optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.

Chen, Xinhe↗
DOE OSTI2024

Graph-Based Modeling for the Detection and Tracking of Sarin-Surrogate-Induced Neurotoxicity Using a Human-Relevant, In-Vitro Brain Model

Organophosphorus (OP) nerve agents are a chemical threat to the United States, to the civilian population (e.g., pesticides) and historically weaponized (e.g., sarin) as chemical warfare agents. The unprecedented, accelerated process from “bench-to-bedside” during the SARSCov2 pandemic has made it clear that technology and tools need to be readily available for immediate response. Advances in human organ tissue mimetic systems are a promising technology to evaluate the human-relevant response in vitro for basic and applied research and drug screening. In particular, current brain microphysiological systems (MPS) have the capability to monitor and detect changes in engineered human neural circuit activity. However, current data analytics approaches for these systems lack the granularity to functionally detect and distinguish the different mechanisms that occur in the brain following neurotoxicity, injury, and disease. The goal of this project was to advance the computational analytical capabilities of the brain MPS to detect functional changes in neural circuit structure at different stages of Sarin surrogate-induced neurotoxicity. We developed graph-based models to (1) identify the composition of the neural circuit structure; (2) detect and monitor how this structure changes following sarin-induced neurotoxicity; and (3) evaluate the analytical pipeline using known/promising oxime reactivators. Through experiments on the bMPS where in vitro neuronal cultures were exposed to a sarin surrogate, we demonstrated the capabilities of our computational pipeline to identify different responses in the functional networks of brain cells exposed to low and high concentrations of the nerve agent. We identified a biphasic response of human neural network activity following exposure to a sarin-surrogate that had not been reported in the literature before. The graph-based models and software developed in this project can be used for future studies that leverage the brain MPS technology, such as treatment efficacy assessment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Application and Evaluation of Surrogate Models for Radiation Source Search

Surrogate models are increasingly required for applications in which first-principles simulation models are prohibitively expensive to employ for uncertainty analysis, design, or control. They can also be used to approximate models whose discontinuous derivatives preclude the use of gradient-based optimization or data assimilation algorithms. We consider the problem of inferring the 2D location and intensity of a radiation source in an urban environment using a ray-tracing model based on Boltzmann transport theory. Whereas the code implementing this model is relatively efficient, extension to 3D Monte Carlo transport simulations precludes subsequent Bayesian inference to infer source locations, which typically requires thousands to millions of simulations. Additionally, the resulting likelihood exhibits discontinuous derivatives due to the presence of buildings. To address these issues, we discuss the construction of surrogate models for optimization, Bayesian inference, and uncertainty propagation. Specifically, we consider surrogate models based on Legendre polynomials, multivariate adaptive regression splines, radial basis functions, Gaussian processes, and neural networks. We detail strategies for computing training points and discuss the merits and deficits of each method.

Cook, Jared A.↗
DOE OSTI2019

Surrogate Models for Estimating Failure in Brittle and Quasi-Brittle Materials

In brittle fracture applications, failure paths, regions where the failure occurs and damage statistics, are some of the key quantities of interest (QoI). High-fidelity models for brittle failure that accurately predict these QoI exist but are highly computationally intensive, making them infeasible to incorporate in upscaling and uncertainty quantification frameworks. The goal of this paper is to provide a fast heuristic to reasonably estimate quantities such as failure path and damage in the process of brittle failure. Towards this goal, we first present a method to predict failure paths under tensile loading conditions and low-strain rates. The method uses a k-nearest neighbors algorithm built on fracture process zone theory, and identifies the set of all possible pre-existing cracks that are likely to join early to form a large crack. The method then identifies zone of failure and failure paths using weighted graphs algorithms. We compare these failure paths to those computed with a high-fidelity fracture mechanics model called the Hybrid Optimization Software Simulation Suite (HOSS). A probabilistic evolution model for average damage in a system is also developed that is trained using 150 HOSS simulations and tested on 40 simulations. A non-parametric approach based on confidence intervals is used to determine the damage evolution over time along the dominant failure path. For upscaling, damage is the key QoI needed as an input by the continuum models. This needs to be informed accurately by the surrogate models for calculating effective moduli at continuum-scale. We show that for the proposed average damage evolution model, the prediction accuracy on the test data is more than 90%. In terms of the computational time, the proposed models are ≈ O ( 10 6 ) times faster compared to high-fidelity fracture simulations by HOSS. These aspects make the proposed surrogate model attractive for upscaling damage from micro-scale models to continuum models. We would like to emphasize that the surrogate models are not a replacement of physical understanding of fracture propagation. The proposed method in this paper is limited to tensile loading conditions at low-strain rates. This loading condition corresponds to a dominant fracture perpendicular to tensile direction. The proposed method is not applicable for in-plane shear, out-of-plane shear, and higher strain rate loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Surrogate Constructed Scalable Circuits ADAPT-VQE in the Schwinger model

Inspired by recent advancements of simulating periodic systems on quantum computers, we develop a new approach, (SC)$^2$-ADAPT-VQE, to further advance the simulation of these systems. Our approach extends the scalable circuits ADAPT-VQE framework, which builds an ansatz from a pool of coordinate-invariant operators defined for arbitrarily large, though not arbitrarily small, volumes. Our method uses a classically tractable ``Surrogate Constructed'' method to remove irrelevant operators from the pool, reducing the minimum size for which the scalable circuits are defined. Bringing together the scalable circuits and the surrogate constructed approaches forms the core of the (SC)$^2$ methodology. Our approach allows for a wider set of classical computations, on small volumes, which can be used for a more robust extrapolation protocol. While developed in the context of lattice models, the surrogate construction portion is applicable to a wide variety of problems where information about the relative importance of operators in the pool is available. As an example, we use it to compute properties of the Schwinger model - quantum electrodynamics for a single, massive fermion in $1+1$ dimensions - and show that our method can be used to accurately extrapolate to the continuum limit.

Gustafson, Erik [RIACS, Mtn. View] (ORCID:00000001↗
DOE OSTI2024

Conceptual Design of Integrated Energy Systems with Market Interaction Surrogate Models

Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with the market and can result in overestimated economic values. In this work, we propose a machine learning surrogate-assisted optimization framework to quantify IES/market interactions and thus go beyond price-taker. We use time series clustering to generate representative IES operation profiles for the optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with a polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.

Chen, Xinhe↗
NASA NTRS2009

Unsteady Extinction of Opposed Jet Ethylene/Methane HIFiRE Surrogate Fuel Mixtures vs Air

A unique idealized study of the subject fuel vs. air systems was conducted using an Oscillatory-input Opposed Jet Burner (OOJB) system and a newly refined analysis. Extensive dynamic-extinction measurements were obtained on unanchored (free-floating) laminar Counter Flow Diffusion Flames (CFDFs) at 1-atm, stabilized by steady input velocities (e.g., U(sub air)) and perturbed by superimposed in-phase sinusoidal velocity inputs at fuel and air nozzle exits. Ethylene (C2H4) and methane (CH4), and intermediate 64/36 and 15/85 molar percent mixtures were studied. The latter gaseous surrogates were chosen earlier to mimic ignition and respective steady Flame Strengths (FS = U(sub air)) of vaporized and cracked, and un-cracked, JP-7 "like" kerosene for a Hypersonic International Flight Research Experimentation (HIFiRE) scramjet. For steady idealized flameholding, the 100% C2H4 flame is respectively approx. 1.3 and approx.2.7 times stronger than a 64/36 mix and CH4; but is still 12.0 times weaker than a 100% H2-air flame. Limited Hot-Wire (HW) measurements of velocity oscillations at convergent-nozzle exits, and more extensive Probe Microphone (PM) measurements of acoustic pressures, were used to normalize Dynamic FSs, which decayed linearly with pk/pk U(sub air) (velocity magnitude, HW), and also pk/pk P (pressure magnitude, PM). Thus Dynamic Flame Weakening (DFW) is defined as % decrease in FS per Pascal of pk/pk P oscillation, namely, DFW = -100 d(U(sub air)/U(sub air),0Hz)/d(pkpk P). Key findings are: (1) Ethylene flames are uniquely strong and resilient to extinction by oscillating inflows below 150 Hz; (2) Methane flames are uniquely weak; (3) Ethylene / methane surrogate flames are disproportionately strong with respect to ethylene content; and (4) Flame weakening is consistent with limited published results on forced unsteady CFDFs. Thus from 0 to approx. 10 Hz and slightly higher, lagging diffusive responses of key species led to progressive phase lags (relative to inputs) in the oscillating flames, and caused maximum weakening. At 20 to 150 Hz, diffusion-rate-limited effects diminished, causing flames to "regain strengnth," and eventually become completely insensitive beyond 300 Hz. Detailed mechanistic understanding is needed. Overall, ethylene flames are remarkably resilient to dynamic extinction by oscillating inflows. They are the strongest, with the notable exception of H2. For HIFiRE tests, the 64%/36% surrogate disproportionally retains the high dynamic FS of ethylene, so the potential for loss of scramjet flameholding (flameout) due to low frequency oscillations is significantly mitigated.

Vaden, Sarah N.↗
NASA NTRS2011

Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

A surrogate model methodology is described for predicting, during flight, the residual strength of aircraft structures that sustain discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. Two ductile fracture simulations are presented to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data does, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high fidelity fracture simulation framework provide useful tools for adaptive flight technology.

Spear, Ashley D.↗
NASA NTRS2011

Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

A surrogate model methodology is described for predicting in real time the residual strength of flight structures with discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. A residual strength test of a metallic, integrally-stiffened panel is simulated to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data would, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high-fidelity fracture simulation framework provide useful tools for adaptive flight technology.

Spear, Ashley D.↗
NASA NTRS2020

Simulation of Hydrogen Flow in a Nuclear-Thermal Propulsion Reactor Subsystem: Conjugate Heat Transfer in Surrogate Fuel Elements and Flow Through the Dome Inlet to the Fuel Elements

NASA is performing research related to a Nuclear-Thermal Propulsion (NTP) engine as a step toward future manned missions to Mars. BWX Technologies (BWXT), in conjunction with NASA, is developing the reactor subsystem for that NTP engine design. As a subset of the reactor subsystem, the fuel elements have continued to evolve in design, as well. A set of experiments within the Nuclear Thermal Reactor Element Environmental Simulator at NASA Marshall has been developed to compliment that design process, including a series of surrogate fuel elements. Before the experiments of each surrogate can be completed, they must be deemed safe using structural analysis, which itself requires thermal and conjugate heat transfer analyses of the articles. Therefore, conjugate heat transfer simulations of the hydrogen flow through the test article fuel element surrogates were developed to provide estimates of heating, as well as to provide assessments of the fluid flow through the elements. The set of simulations involved not only the separate test conditions, but also a sensitivity study to estimate the effect of physical parameter uncertainty on the selected quantities of interest. Additionally, the fluid flow through the fore section inner plenum dome to the entrances of the fuel elements, another section of the reactor subsystem still under active development, was simulated and analyzed. The simulations show the extent of non-uniformity in the mass flow distribution across the dome outlets. Furthermore, additional analyses estimate the effects of required flow blockages on the potential development of flow dynamics. These analyses were used in the design cycle to make improvements to the reactor subsystem design.

Kalen E. Braman↗
DOE OSTI2014

Reversible Bending Fatigue Testing on Zry-4 Surrogate Rods

Testing high-burnup spent nuclear fuel (SNF) presents many challenges in areas such as specimen preparation, specimen installation, mechanical loading, load control, measurements, data acquisition, and specimen disposal because these tasks are complicated by the radioactivity of the test specimens. Research and comparison studies conducted at Oak Ridge National Laboratory (ORNL) resulted in a new concept in 2010 for a U-frame testing setup on which to perform hot-cell reversible bending fatigue testing. Subsequently, the three-dimensional finite element analysis and the engineering design of components were completed. In 2013 the ORNL team finalized the upgrade of the U-frame testing setup and the integration of the U-frame setup into a Bose dual linear motor test bench to develop a cyclic integrated reversible-bending fatigue tester (CIRFT). A final check was conducted on the CIRFT test system in August 2013, and the CIRFT was installed in the hot cell in September 2013 to evaluate both the static and dynamic mechanical response of SNF rods under simulated loads. The fatigue responses of Zircaloy-4 (Zry-4) cladding and the role of pellet pellet and pellet clad interactions are critical to SNF vibration integrity, but such data are not available due to the unavailability of an effective testing system. While the deployment of the developed CIRFT test system in a hot cell will provide the opportunity to generate the data, the use of a surrogate rod has proven quite effective in identifying the underlying deformation mechanism of an SNF composite rod under an equivalent loading condition. This paper presents the experimental results of using surrogate rods under CIRFT reversible cyclic loading. Specifically, monotonic and cyclic bending tests were conducted on surrogate rods made of a Zry-4 tube and alumina pellet inserts, both with and without an epoxy bond.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Validating (d,p gamma) as a Surrogate for Neutron Capture

The r-process is responsible for creating roughly half of the elements heavier than iron. It has recently become understood that the rates at which neutron capture reactions proceed at late times in the rprocess may dramatically affect the final abundance pattern. However, direct measurements of neutron capture reaction rates on exotic nuclei are exceptionally difficult, necessitating the development of indirect approaches such as the surrogate technique. The (d,py) reaction at low energies was identified as a promising surrogate for the (n,y) reaction, as both reactions share many characteristics. We report on a program to validate (d,py) as a surrogate for (n,y) using 95Mo as a target. The experimental campaign includes direct measurements of the y-ray intensities from the decay of excited states populated in the 95Mo(n,y) and 95Mo(d,py) reactions.

Ratkiewicz, A.↗
DOE OSTI2020

Modeling Time-Dependent Surrogates of Additive-Manufactured Nuclear Fuels Processes

Additive manufacturing (AM) technology is being increasingly adopted in a wide variety of application areas for its ability to rapidly produce, prototype, and customize designs. Recently, a hybrid AM technique was successfully developed at Idaho National Laboratory (INL) to manufacture nuclear fuels [1]. Despite the advantages, this AM technique needs optimization due to defects from a highly complex melting and sintering process. The complex metallurgical phenomena during AM processes are strongly related to parameters such as applied laser power, traveling speed, and scan style, which could lead to differences in density, residual stress, crystallographic texture, and mechanical properties. In addition, stochastic variations in laser energy interaction and associated multiscale/multiphysics phenomena cause variations in microstructure evolution and mechanical properties. Currently, researchers at INL are focusing on developing a comprehensive modeling framework, leveraging INL’s simulation tools MOOSE/MARMOT/BISON/RAVEN [2-4] to describe all steps of this AM process across multiple length scales. Although this advanced framework plays a critical role in enabling enhancements to traditional trial and error approaches for design and optimization of nuclear fuel materials, it remains computationally intense, limiting its use in sensitivity and optimization analysis. In this case, an accurate and inexpensive surrogate becomes an effective tool for providing a tractable approximation of the underlying underline physics. Surrogate models generally not based on the physics of a system are purely mathematical models used to capture the relationships between specific system inputs and outputs. Popular approaches, including neural networks [5], response surfaces [6], and subspace-based reduced order models [7], have been applied to a wide range of disciplines, such as nuclear reactor design, aerospace design and automotive design. In this summary, we employ advanced time-dependent surrogate models such as high-dimensional model representation (HDMR) [8] and physics-informed deep neural network (PINNs) [9] to accelerate the design and optimization of AM process.

42 ENGINEERING↗
DOE OSTI2022

Experiments and numerical modeling of surrogate droplet combustion for a 93 octane gasoline calibration fuel

Gasoline is the world’s most prolific liquid transportation fuel for ground transportation vehicles. Yet, a fundamental understanding of its burning characteristics is still lacking. Complicating this matter is the highly multicomponent nature of gasoline (comprised of hundreds of chemical species) and the seasonable variability of its composition due to variations in the crude oil feedstocks used in refining. The need to bring order to this complex problem has led to development of calibration fuels (Tier II in presented study) which on average will provide repeatable metrics that replicate performance of refined gasoline for various engine designs. This study presents results of experiments using the configuration of an isolated droplet to study the combustion dynamics of calibration fuel and three different surrogates (S4, S5, S6) for the calibration fuel. Droplet burning is under microgravity conditions, where gas transport is determined entirely by fuel evaporation in order to promote spherical symmetry in the burning process. The initial droplet diameters ranged between 0.55 mm and 0.60 mm. The droplets were spark-ignited and their burning histories were recorded by digital video cameras to obtain quantitative data. For each fuel, the evolutions of droplet, flame, and soot-shell diameters were measured to calculate the burn rates, flame-droplet diameter ratios (FSR) and soot-droplet diameter ratios. Additionally, a detailed numerical modeling is presented. This simulation includes multicomponent phase equilibrium, unsteady gas and liquid transport, radiative heat transfer, internal liquid diffusion and detailed combustion chemistry of different fuels during their burning process. Experiment results show that the calibration fuel and surrogates have a close burn rates and FSR, and S5 presents a relatively similar results with calibration fuel. Predictions from the numerical simulation using the surrogate are compared with experiment measurements, which showed a good agreement with the experiment results, including the droplet burning rate and FSR.

Guo, Songtao↗
DOE OSTI2016

Surrogate materials options for single phase conjugate heat transfer scaled experiments of light water reactors at prototypical conditions

In light water reactors, the conjugate heat transfer between heating rods and cooling water is a problem of paramount importance that has been studied for decades. The design of nuclear reactors depends on correlations for the heat transfer coefficient between heat source and coolant, and experimental studies are needed to improve the existing correlations, develop new ones that are adapted to the new reactors designed and their components, and develop high fidelity turbulence models. No studies have been carried out yet at prototypical conditions where the liquid velocity and temperature fields are measured, because of the difficulty to find instruments that can resist the extreme operating conditions. Here, two surrogate gases, sulfur hexafluoride and cryogenic nitrogen, are proposed to conduct conjugate heat transfer experiments at prototypical conditions. Surrogate structural materials are also investigated to favorably reproduce conjugate heat transfer for steady and transient operations. The operating conditions are now less demanding than high-pressure, high-temperature water, which allows the deployment of many proven diagnostics. To demonstrate these surrogate materials, a sample scaling analysis is applied to one pressurized water reactor fuel rod under axial flow and vibrations, showing a very close matching between prototype and model for the conjugate heat transfer. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗