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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Optimal control of the electron temperature profile in DIII-D using machine learning surrogate models

The viability of the tokamak as a potential fusion reactor depends on the ability to keep the plasma in a stable regime while achieving temperatures, densities, and confinement times that are as high as possible. Tokamak scenario development attempts to find plasma regimes that achieve all of these conditions and are accessible with a given set of hardware constraints. This requires the ability to control plasma properties such as the normalized beta, the internal inductance, safety factor, rotation, etc. One property that has received less attention than some of the others, but is no less critical to achieving high performance, is the electron temperature (T e ) profile. In this work, Linear Quadratic Integral (LQI) control is used to develop a controller for the electron temperature profile in DIII-D. The controller is based on a linearized model derived from the transport equation that describes the evolution of the electron temperature, and includes contributions from the neural network surrogate models NubeamNet and MMMnet. Furthermore, the controller is tested in simulation using COTSIM, and is proven capable of tracking a target T e profile.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Observations of multi-ion physics and kinetic effects in a surrogate to the solar CNO reactions

The ‘CNO process’ occurs in heavier stars with finite metallicity in which hydrogen burning is catalyzed in the presence of 12 C. These reactions are more strongly dependent on temperature than the pp cycle reactions, and thus the CNO cycle dominates only in massive stars. For these types of reactions to be studied at ICF facilities such as OMEGA, an implosion platform using heavier nuclei in the fuel and capable of creating ion temperatures on the order of at least 20 keV is required. A potential route to reach these conditions is to take advantage of kinetic effects in low-convergence shock-driven ‘exploding pusher’ implosions. In this experiment, shots were conducted at the OMEGA laser facility using the surrogate reaction 13 C + D. Its cross section is substantially higher than the actual astrophysical CNO reactions. The yield of this reaction in these implosions was much lower than expected. Finally, physical explanations are discussed, with significant species stratification the likely explanation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Robust PCA-Deep Belief Network Surrogate Model for Distribution System Topology Identification with DERs

With the expansion of distribution networks and increased penetration of distributed energy resources (DERs), it is becoming increasingly important to obtain accurate distribution network topology in real-time. In this paper, a robust principal component analysis coupled deep belief network (PCA-DBN) surrogate model is proposed for distribution system topology identification. It integrates the benefits of robust feature extraction from PCA to deal with data quality issues and filter out noise, and the strength of DBN in capturing the nonlinear relationship between voltage amplitudes and the binary states of switchable connections. This also significantly reduces the DBN training complexity without loss of accuracy. It is shown that the widely used standard deviation of voltage drop and the voltage covariance matrix features yield less accuracy as compared to that of the voltage amplitudes in presence of high penetration of DERs and ZIP loads. Comparison results with other alternatives, such as the random forest (RF), multi-output regression (MOR) and the traditional DBN methods demonstrate that the proposed method can achieve a much higher topology identification accuracy while maintaining robustness to missing data and measurement noise under various penetration levels of DERs.

deep belief network↗
DOE OSTI2024

Multiple-charging effects on the CCN activity and hygroscopicity of surrogate black carbon particles

Accurate measurements of cloud condensation nuclei (CCN) activity and hygroscopicity of black carbon (BC)-containing particles are particularly important because of the positive climate forcing from these particles. Such measurements are typically conducted on particles selected by a Differential Mobility Analyzer (DMA), which in addition to singly charged particles transmits multiply charged larger particles that have the same electrical mobility. These larger particles activate at lower supersaturations than the singly charged particles, biasing measurements and resulting in overestimation of CCN activity and hygroscopicity parameter (κ). Here, we measure the CCN activity and determine κ for different BC surrogates with electrical mobility diameters from 100 to 200 nm selected 1) only by electrical mobility with a DMA, and 2) by both electrical mobility and mass using a DMA and a Centrifugal Particle Mass Analyzer (CPMA), thus allowing selection of only singly charged particles. We demonstrate the use of the DMA-CPMA system in resolving biases caused by multiply charged particles, and we show that the effect of multiple charging on the CCN activity of the BC particles is strongly influenced by morphology dispersion, i.e., the variability due to the range of morphologies of particles that have the same electrical mobility and mass. Finally, our findings show that electrical mobility-based methods alone are unlikely to lead to accurate results in measurements of CCN activation and hygroscopicity of BC particles, even for those with a more compact morphology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

In-situ microstructure observation of oxidized SiC layer in surrogate TRISO fuel particles under krypton ion irradiation

In accidental scenarios of high temperature gas-cooled reactors, both oxidation of and irradiation to the SiC layer in tri-structural-isotropic (TRISO) fuel particles can change the microstructure and integrity of the fuel elements. Here, in the present study, microstructure and defect evolution in the oxidized SiC layer of surrogate TRISO fuel particles under 1.2 MeV krypton ion irradiation was observed by in-situ transmission electron microscopy. The SiC layers oxidized in water vapor at 1200 °C were irradiated azt room temperature and 800 °C and at damage levels of 0.28–11.2 dpa, respectively. SiC and SiO 2 were found to still be in their crystal structures at the damage level of 11.2 dpa at 800 °C, while SiC was observed to have been amorphized at only 0.56 dpa irradiation at room temperature. The defect number density at 800 °C was an order of magnitude lower than that in the sample irradiated at room temperature. Also, crystalline SiO 2 had higher radiation resistance compared to SiC. A defect reaction rate theory was utilized to understand the fundamental defect evolution process and irradiation resistance difference.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Active learning using hybrid surrogate tool life modeling for machining process optimization

Here, this paper describes an active learning approach for part-to-part iterative machining process optimization using a hybrid surrogate tool life model. A probabilistic interpolating tool life model is developed by combining the empirical Taylor-type tool life equation and the model fit error. The probabilistic tool life model is then used to calculate the machining cost per part distribution. The optimal machining parameters are selected using an expected improvement in machining cost per part criterion. The method is validated numerically using experimental results; the results show a median convergence error of 2.2% after three tests over 400 simulations. The method is validated experimentally on two industrial applications for Ti-6Al-4V roughing resulting in a cost per part reduction greater than 23% after two tests. The described method is a robust solution for rapid convergence to optimal machining parameters in an industrial production environment.

Active learning↗
DOE OSTI2023

Determining the tensile strength of fuel surrogate TRISO-coated particle buffer, IPyC, and buffer-IPyC interlayer regions

A novel micro tensile sample fabrication technique for determining the tensile strength of the buffer, IPyC, and buffer -IPyC interlayer regions of surrogate (ZrO2) TRISO fuel particle layers was refined and implemented. Copper micro tensile samples served as baseline materials to verify the methods used. Tensile tests performed in this study, while limited in number, were analyzed using standard and Weibull statistics. As expected, the buffer layer was weakest, with an average ultimate tensile strength of 138.70 MPa, and the IPyC layer samples, were strongest, with an average ultimate tensile strength of 189.74 MPa. In the buffer -IPyC interface samples, all breaks occurred in the buffer region, though the average ultimate tensile strength of the samples, 159.80 MPa, was between the pure buffer and IPyC samples. These results suggest the interlayer region has unique properties, perhaps associated with pyrocarbon infiltration into the buffer layer during particle coating. All interlayer samples fractured within the buffer side; however, the stress strain behavior of some of these samples resembled the behavior of the IPyC layer samples. Here, the buffer and IPyC layer strengths had a normal distribution under Weibull analysis, while the interlayer region had a Rayleigh distribution. Further testing is needed to clarify both the standard and Weibull statistical results

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia

Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and can lead to personal and property damage. While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational complexity renders them unsuitable for real-time applications. Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The comparison of deep learning to the random forest algorithm is motivated by the desire to utilize a machine learning architecture that allows for the future inclusion of common uncertainty quantification techniques and the effective integration of relevant, multi-modal features.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Transient uncertainty quantification and Global Sensitivity Analysis of the open-source Molten Chloride Reactor Experiment (MCRE) using GP-PCA surrogate models

Uncertainties in the thermophysical properties of molten salts impact both the steady-state and transient behavior of Molten Salt Reactors (MSRs). In this work, we aim to quantify the influence of such uncertainties on the transient operation of the Molten Chloride Reactor Experiment (MCRE), utilizing the open-source specifications provided for this reactor. Seven representative transient scenarios are considered. For each scenario, we evaluate the impact of thermophysical property uncertainties on four key multiphysics model output variables of interest (VoIs): maximum power density, maximum fuel temperature, maximum reflector temperature, and average fuel velocity magnitude. In addition, we perform a Global Sensitivity Analysis (GSA) by computing Sobol’ indices for the uncertain input parameters to determine their contribution to the variability of each VoI. Conducting GSA is computationally intensive due to the large number of required evaluations of the high-fidelity multiphysics model. To mitigate this cost, we develop a surrogate modeling framework that combines Gaussian Process (GP) regression with Principal Component Analysis (PCA), enabling efficient sample generation for the GSA. Our results show that for energy-related VoIs, thermal conductivity is the dominant contributor to uncertainty. In contrast, for flow-related VoIs, density and dynamic viscosity are the primary sources of uncertainty. The specific heat of the fuel salt was found to play a secondary role in the transient analyses.

42 - ENGINEERING↗
DOE OSTI2022

Evaluation of 134 Ce as a PET imaging surrogate for antibody drug conjugates incorporating 225 Ac

The in vivo generator 134 Ce/ 134 La has the potential to serve as a PET imaging surrogate for both alpha-emitting 225 Ac and 227 Th radionuclides due to the unique Ce III /Ce IV redox couple and the relatively long half-life of 134 Ce. Furthermore, the purpose of this study was to demonstrate the compatibility of 134 Ce with DOTA-based antibody drug conjugates, which would act as therapeutic agents when incorporating 225 Ac.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Composite binder, processing, and particle size effects on mechanical properties of non-hazardous high explosive surrogates

Plastic-bonded explosives (PBX) are polymer matrix composites that are highly loaded with micron-scale explosive crystals. The thermal and mechanical properties of these materials must be well-understood so that appropriate engineering models can be developed, but testing such properties under many scenarios can be hazardous and expensive. A non-hazardous PBX surrogate (“mock”) may be used in place of a PBX in many experiments if the thermomechanical properties are well-matched. Our previous research has shown that 5-iodo-2'-deoxyuridine (IDOX) is a promising crystalline filler material for the explosive cyclotetramethylene-tetranitramine (HMX). In this study, we investigate how the binder composition, variations in the IDOX particle size distribution, and pressing temperature affect the compressive strength and elastic modulus. We observe that while the binder system used in the composite dominates the mechanical response, the particle size distribution and pressing conditions do have a measurable effect on the material compliance and strength.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

The Impact of Europium as a Fission Product Surrogate on Chromium Corrosion in Molten Chloride Salt

The rising interest in molten salts, both as thermal energy storage media and as high-temperature solvents for fissile materials in advanced nuclear reactors, has spurred significant growth in research on structural and functional salt-facing materials. Impurities, such as water, play a significant role in the degradation of materials in molten salt environments. Other impurities, such as fission products, are expected to accumulate in molten salts during normal reactor operation and can affect the properties of the salt and salt-facing materials. During normal reactor operation, rare-earth element species are expected to form as byproducts of fission reactions, potentially degrading or altering the performance of both structural and functional materials exposed to salts. Here, in this work, fission product surrogates, europium metal and EuCl 3 , were added to the KCl-MgCl 2 salt while exposing pure Cr specimens in the melt at 600 and 700 °C for 1000 h. Low reactivity was observed with the baseline salt, while both the Eu and EuCl 3 additions to the salt resulted in increased mass loss of Cr specimens. A capillary electrophoresis technique was developed to enable the detection of low parts-per-million (ppm) concentrations of europium (Eu) in chloride salt both before and after exposure of Cr metal. This technique provides a sensitive and reliable method for monitoring trace levels of Eu, which is critical for understanding the interactions and potential contamination effects in salt-facing materials during reactor operation. This quantification technique can be applied to other cationic and anionic contaminants in halide salts.

chloride salt↗
DOE OSTI2020

Electrochemical Strategy for Hydrazine Synthesis: Development and Overpotential Analysis of Methods for Oxidative N–N Coupling of an Ammonia Surrogate

Hydrazine is an important industrial chemical and fuel that has attracted considerable attention for use in liquid fuel cells. Ideally, hydrazine could be prepared via direct oxidative coupling of ammonia, but thermodynamic and kinetic factors limit the viability of this approach. Here, the present study evaluates three different electrochemical strategies for the oxidative homocoupling of benzophenone imine, a readily accessible ammonia surrogate. Hydrolysis of the resulting benzophenone azine affords hydrazine and benzophenone, with the latter amenable to recycling. The three different electrochemical N–N coupling methods are (1) a proton-coupled electron-transfer process promoted by a phosphate base, (2) an iodine-mediated reaction involving intermediate N–I bond formation, and (3) a copper-catalyzed N–N coupling process. Analysis of the thermodynamic efficiencies for these electrochemical imine-to-azine oxidation reactions reveals low overpotentials (η) for the copper- and iodine-mediated processes (390 and 470 mV, respectively), but a much higher value for the proton-coupled pathway (η ≈ 1.6 V). A similar approach is used to assess molecular electrocatalytic methods for electrochemical oxidation of ammonia to dinitrogen.

25 ENERGY STORAGE↗
DOE OSTI2022

Au as a Surrogate for F: The Case of UAu 6 vs UF 6

Here, anion photoelectron spectroscopy and first-principles quantum chemistry are used to demonstrate to what degree Au can act as a surrogate for F in UF6 and its anion. Unlike UF6, UAu6 exhibits strong ligand–ligand, i.e., Au–Au, interactions, resulting in three low-lying isomers, two of which are three-dimensional while the third isomer has a ring-like quasi two-dimensional structure. Additionally, all the UAu6 isomers have open-shell electrons, which in nearly all cases are localized on the central U atom. As a result, the adiabatic electron affinity and vertical detachment energy are measured to be 3.05 ± 0.05 and 3.28 ± 0.05 eV, respectively, and are in very good agreement with calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation

Abstract There is a growing interest in developing data‐driven reduced‐order models for atmospheric and oceanic flows that are trained on data obtained either from high‐resolution simulations or satellite observations. The data‐driven models are non‐intrusive in nature and offer significant computational savings compared to large‐scale numerical models. These low‐dimensional models can be utilized to reduce the computational burden of generating forecasts and estimating model uncertainty without losing the key information needed for data assimilation (DA) to produce accurate state estimates. This paper aims at exploring an equation‐free surrogate modeling approach at the intersection of machine learning and DA in Earth system modeling. With this objective, we introduce an end‐to‐end non‐intrusive reduced‐order modeling (NIROM) framework equipped with contributions in modal decomposition, time series prediction, optimal sensor placement, and sequential DA. Specifically, we use proper orthogonal decomposition (POD) to identify the dominant structures of the flow, and a long short‐term memory network to model the dynamics of the POD modes. The NIROM is integrated within the deterministic ensemble Kalman filter (DEnKF) to incorporate sparse and noisy observations at optimal sensor locations obtained through QR pivoting. The feasibility and the benefit of the proposed framework are demonstrated for the NOAA Optimum Interpolation Sea Surface Temperature (SST) V2 data set. Our results indicate that the NIROM is stable for long‐term forecasting and can model dynamics of SST with a reasonable level of accuracy. Furthermore, the prediction accuracy of the NIROM gets improved by almost one order of magnitude by the DEnKF algorithm.

Pawar, Suraj↗
DOE OSTI2024

Boosting Barlow Twins Reduced Order Modeling for Machine Learning‐Based Surrogate Models in Multiphase Flow Problems

Abstract We present an innovative approach called boosting Barlow Twins reduced order modeling (BBT‐ROM) to enhance the reliability of machine learning surrogate models for multiphase flow problems. BBT‐ROM builds upon Barlow Twins reduced order modeling that leverages self‐supervised learning to effectively handle linear and nonlinear manifolds by constructing well‐structured latent spaces of input parameters and output quantities. To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT‐ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. To evaluate the performance of BBT‐ROM, we conduct three three‐dimensional multiphase flow problems, including waterflooding and geologic carbon storage (GCS), with varying numbers of input parameter cases and model domain features. The results demonstrate that BBT‐ROM excels at predicting non‐wetting phase saturation (e.g., oil or saturation) and fluid pressure, with average relative errors ranging from 0.5% to 3%. Importantly, BBT‐ROM showcases robustness when faced with limited input parameter space during GCS testing.

58 GEOSCIENCES↗
DOE OSTI2024

The dynamic adsorption affinity of ligands is a surrogate for the passivation of surface defects

Abstract Surface defects in semiconducting materials, though they have been widely studied, remain a prominent source of loss in optoelectronic devices; here we sought a new angle of approach, looking into the dynamic roles played by surface defects under atmospheric stressors and their chemical passivants in the lifetime of optoelectronic materials. We find that surface defects possess properties distinct from those of bulk defects. ab initio molecular dynamics simulations reveal a previously overlooked reversible degradation mechanism mediated by hydrogen vacancies. We find that dynamic surface adsorption affinity (DAA) relative to surface treatment ligands is a surrogate for passivation efficacy, a more strongly-correlated feature than is the static binding strength emphasized in prior reports. This guides us to design targeted passivator ligands with high molecular polarity: for example, 4-aminobutylphosphonic acid exhibits strong DAA and provides defect passivation applicable to a range of perovskite compositions, including suppressed hydrogen vacancy formation, enhanced photovoltaic performances and operational stability in perovskite solar cells.

Science & Technology - Other Topics↗
DOE OSTI2024

Task-oriented machine learning surrogates for tipping points of agent-based models

We present a machine learning framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale approach, for the construction of different types of effective reduced order models from detailed agent-based simulators and the systematic multiscale numerical analysis of their emergent dynamics. The specific tasks of interest here include the detection of tipping points, and the uncertainty quantification of rare events near them. Our illustrative examples are an event-driven, stochastic financial market model describing the mimetic behavior of traders, and a compartmental stochastic epidemic model on an Erdös-Rényi network. We contrast the pros and cons of the different types of surrogate models and the effort involved in learning them. Importantly, the proposed framework reveals that, around the tipping points, the emergent dynamics of both benchmark examples can be effectively described by a one-dimensional stochastic differential equation, thus revealing the intrinsic dimensionality of the normal form of the specific type of the tipping point. This allows a significant reduction in the computational cost of the tasks of interest.

97 MATHEMATICS AND COMPUTING↗