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DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗

DOE OSTI2023

Cesium Removal from 5.5 and 7.0 M Na AP-105 Using Crystalline Silicotitanate

The Tank Side Cesium Removal (TSCR) system, currently operational by Washington River Protection Solutions LLC (WRPS), prepares initial low-activity Hanford waste tank supernate feeds for the Hanford Waste Treatment and Immobilization Plant (WTP) Low-Activity Waste (LAW) Facility. In addition to entrained solids removal from the supernate, the primary goal of TSCR is to remove cesium-137 ( 137 Cs) by ion exchange, allowing contact handling of the liquid effluent product at the WTP as governed by a waste acceptance criterion (WAC). Specific to 137 Cs, this requirement is < 3.18E-5 Ci 137 Cs/mole of Na. Crystalline silicotitanate (CST) ion exchange media, manufactured by Honeywell UOP, LLC (product IONSIVTM R9140-B), has been selected as the ion exchange media for TSCR. CST is a non-elutable inorganic material that has demonstrated robust chemical, physical, and radiation tolerance while maintaining functionality. However, testing to date on actual tank waste samples has been limited to Na concentrations between 5 and 6 M Na (Fiskum et al. 2019a, 2021b and Westesen et al. 2021a, 2021b, 2022) while actual tank conditions can reach upwards of 9 M Na. Testing with feed from Hanford tank AP-105 incorporated testing at both 5.5 and 7 M Na in order to evaluate the impact of Na concentration on volume of waste processed before reaching the WAC. A 6-L volume of 5.5 M Na AP-105 and an 8-volume of 7 M Na AP-105 was processed through the Radioactive Waste Test Platform system, established at Pacific Northwest National Laboratory to support small-scale waste qualification efforts. The columns consisted of 6- to 9-mL CST beds (CST Lot 2002009604, sieved to screen out >30-mesh particles) placed in 1.5-cm-inner-diameter columns. Feed was processed at 1.9 bed volumes (BV) per hour; the flowrate, in terms of contact time with the CST bed, matched the expected flowrate at TSCR. Table S.1 and Figure S.1 summarize the measured AP-105 Cs load performance for each feed condition.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Filtration Performance of Simulated 200 West Area Waste Feeds

This report describes the scaled experimental system and approach used to examine dead-end filtration performance of representative 200W waste feeds. The scaled system, which was originally designed and assembled to test Tank Side Cesium Removal (TSCR) system performance with higher-than-expected solid loadings in 2021 (Schonewill et al. 2021), was repurposed to conduct the current experiments at ~1/145 of full scale (based on throughput). Six experimental runs were conducted with five different 200W waste feed simulants: three using a DEF module scaled for TSCR and three using a DEF module scaled for the 200W process modules (based on the current design for the Advanced Modular Pretreatment System). Each experiment was run continuously for multiple days with an operating approach prototypic of the full-scale system. Staff performing the experimental runs monitored performance, obtained data from calibrated process instruments, and collected samples for observation and analysis. The measured data are presented with a focus on assessing DEF performance – specifically, the filters’ differential pressure response to the five waste simulants, frequency and efficacy of backwashing, and baseline recovery between experimental runs; data related to ion exchange column performance are also discussed in cases where the opportunity arose. The experimental campaign demonstrated that the DEFs satisfied their primary function of protecting the ion exchange column from solid intrusion for all the representative simulants used. The filters readily handled solids loadings of =500 ppm (and even greater), especially the modules scaled to the 200W process modules. Adjustments to the processing flow rate and reductions in feed temperature were observed to affect the rate of differential pressure increase on the filters, but neither adversely affected the ability of the DEFs to perform their primary function. Backflushing reliably recovered filter performance in all runs, although it did not prevent irreversible fouling for one simulant. The run that exhibited irreversible fouling established that both the quantity and the nature of the solids being filtered need to be considered when projecting filter performance.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

A Decision Support Tool for Planning Neighborhood-Scale Deployment of Low-Speed Shared Automated Shuttles

Increasing interest and investment in connected, automated, and electric vehicles, and mobility-as-a-service concepts are paving the way for the next major shift in transportation through automated and shared mobility. The initial excitement towards rapid deployment and adoption of automated vehicles has subsided, and low-speed automated shuttles are emerging as a more pragmatic pathway for introducing automated mobility in geo-fenced districts. Such shuttles hold the promise to provide a viable alternative for serving short trips in urban districts with high travel densities. As interest in low-speed automated shuttle systems (to improve urban mobility) increases, the need for tools that can inform communities regarding benefits or dis-benefits of automated shuttle deployments is imminent. However, most of the existing transportation planning and simulation tools are not capable of handling emerging shared automated mobility options. This presentation presents a microscopic simulation toolkit that can be used by cities and communities to plan for the deployment of low-speed automated shuttles systems, as well as other shared mobility options. Labeled as the Automated Mobility District (AMD) modeling and simulation toolkit, the proposed decision support tool can help cities evaluate the mobility and sustainability impacts of deploying shared automated vehicles in geofenced regions. This paper presents a description of the toolkit, as well as a sample scenario analysis for the deployment of low-speed automated shuttles in Greenville, South Carolina. Results from the scenario study demonstrate the effectiveness of the proposed simulation toolkit in planning for advanced mobility systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Processing Aleatory and Epistemic Uncertainties in Experimental Data From Sparse Replicate Tests of Stochastic Systems for Real-Space Model Validation

This paper presents a practical methodology for propagating and processing uncertainties associated with random measurement and estimation errors (that vary from test-to-test) and systematic measurement and estimation errors (uncertain but similar from test-to-test) in inputs and outputs of replicate tests to characterize response variability of stochastically varying test units. Also treated are test condition control variability from test-to-test and sampling uncertainty due to limited numbers of replicate tests. These aleatory variabilities and epistemic uncertainties result in uncertainty on computed statistics of output response quantities. The methodology was developed in the context of processing experimental data for “real-space” (RS) model validation comparisons against model-predicted statistics and uncertainty thereof. The methodology is flexible and sufficient for many types of experimental and data uncertainty, offering the most extensive data uncertainty quantification (UQ) treatment of any model validation method the authors are aware of. It handles both interval and probabilistic uncertainty descriptions and can be performed with relatively little computational cost through use of simple and effective dimension- and order-adaptive polynomial response surfaces in a Monte Carlo (MC) uncertainty propagation approach. A key feature of the progressively upgraded response surfaces is that they enable estimation of propagation error contributed by the surrogate model. Sensitivity analysis of the relative contributions of the various uncertainty sources to the total uncertainty of statistical estimates is also presented. Finally, the methodologies are demonstrated on real experimental validation data involving all the mentioned sources and types of error and uncertainty in five replicate tests of pressure vessels heated and pressurized to failure. Simple spreadsheet procedures are used for all processing operations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Mass Detection for Heavy-Duty Vehicles using Gaussian Belief Propagation

Predicting vehicle mass is critical to accurately estimate energy use and emissions of commercial trucks. However, data from vehicle telematics is often not at sufficient temporal resolution or accuracy for use in model-based detection methods. In this work, a new statistical mass prediction technique is described for heavy-duty vehicles that incorporates the use Gaussian Belief Propagation (GBP) for probabilistic inference. Similar to Bayesian inference models, the GBP model typically requires less labeled training data than other contemporary machine learning techniques. First, a factor graph is constructed, and a set of Gaussian belief nodes with associated means and variances are fitted to the training data. To better handle noisy input data, the GBP mass prediction model utilizes a k-nearest factors (kNF) algorithm for probabilistic inference on unseen testing data. The proposed method is compared with a classical weighted k-nearest neighbors (kNN) regressor. This statistical kNF-GBP model works even with low-quantity, low-quality initial training data, while being capable of realtime mass estimation. Unlike the kNN regressor, the GBP model produces a measure of uncertainty with its predictions. The proposed method is validated using curve-sampled driving data collected from multiple cloud-connected Class 8 regional haul diesel trucks. Both the kNN regressor and the kNF-GBP mass prediction model were able to predict payload mass with coefficients of determination above 0.97 with minimal data preprocessing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Determining the Effects of Neutron Irradiation on the Structural Integrity of Additively Manufactured Heat Exchangers for Very Small Modular Reactor Applications, DOE Final Report (Project # 19-16980)

Auburn University (AU) teamed with the University of Missouri Research Reactor (MURR) and Kansas State University (KSU) to determine how to best use laser-powder bed fusion (L-PBF) additive manufacturing (AM) methods for generating radiation resistant nickel-based superalloys, Inconel alloy 625 and 718, for special purpose reactor (SPR) or very small modular reactor (vSMR) heat-exchangers (HeXs). Compact, conformal, and durable HeXs that are tolerant of extreme environments are needed for supporting the technical maturity of next-generation, portable compact reactors. AM is an enabler for realizing this new wave of HeXs – providing a means to make customizable hot and cold stream architectures with novel flow path geometries (e.g., tortuous channels with non-uniform, asymmetric cross-sections) and reduced layer-to-layer contact resistance (i.e., no separate bonding procedure required). AM further enables a more time/cost efficient means for fabricating SPRs by reducing the number of suppliers required for HeX assembly and allowing for on-site HeX fabrication. The project aim has been to better understand how neutron irradiation affects the microstructure and properties of additively manufactured nickel-based superalloys, to accelerate their safe, reliable use in the modular reactor industry. The major objective was to qualify/quantify the microstructure and microhardness of nickel-based superalloys (including Inconel 718 and 625) additively manufactured via the L-PBF process in the neutron-dosed (irradiated) and non-irradiated states over a course of 3 years. Effects of build orientation during L-PBF and post-AM heat treatments on neutron resistance, microstructure and mechanical properties were also investigated. Neutron damage mechanisms via hardening were characterized. This project combined subject-matter experts in AM, mechanical/microstructure metallic part characterization, and neutron irradiation, as well as unique assets and capabilities at AU and MURR at MU, to ensure project results translated to effectively addressing known gaps in nuclear science and engineering. Parts were fabricated using L-PBF systems readily available at AU. Specimens were then irradiated using MURR facilities; a manipulator equipped hot cell was also used to measure material hardness after dosing. MURR, a 10 MW, light-water nuclear reactor, is the largest, highest powered, highest-flux university owned research reactor in the U.S. The major findings in this project provide evidence that AM can serve as an alternative way to build structural components for future advanced small modular reactors using advanced materials like Inconel 625 (IN625) or Inconel 718 (IN718). After full spectrum neutron irradiation, vertically as-built AM IN625 samples were observed to display better resistance towards radiation-induced-hardening defects relative to traditionally machined metals. A Vickers microhardness tester, using settings of 1 kgf and dwell time of 15 seconds per indentation, was used to measure hardness in this study. The as-built, vertically printed samples experienced 1.2% of radiation hardening vs. 5.25% radiation hardening observed in wrought IN625. Another set of IN625 and IN718 samples were exposed to fast neutron irradiation. It was observed that IN718 showed more resistance towards radiation hardening compared to IN625 samples indicating IN718 had a better performance. Results showed that the IN718 samples experienced less change (-2.5 to 3.24 %) in microhardness. On the other hand, IN625 samples underwent more (0.9 to 7.21%) change in microhardness after fast neutron irradiation. AM IN625 samples were irradiated using an ion (proton) beam in cyclotron. The mechanical properties of AM samples post irradiation were compared with wrought samples. The irradiated region on the samples were tested using nano-hardness indention. It was observed that the beam current and time used in this study generated an annealing effect and thus reduced the hardness of the samples. The sum of the project results provide precious insight into how one may minimize radiation hardening in AM materials while maintaining material property constraints. Results should assist engineers in selecting an appropriate heat treatment for AM nickel-based superalloys for increased radiation resistance. Results should increase confidence levels for adopting AM for building nuclear reactor components which perform the same or better than conventionally manufactured components. Fast neutron irradiation testing provided an accelerated means of obtaining radiation effects without making materials radioactive and difficult to handle.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Consequences of long-term water exposure for bulk crystal structure and surface composition/chemistry of nickel-rich layered oxide materials for Li-ion batteries

Water exposure of layered nickel-rich transition metal oxide electrodes, widely used in high-energy lithium-ion batteries, has detrimental effects on the electrochemical performance, which complicates electrode handling and prevents implementation of environmentally benign aqueous processing procedures. Elucidating the degradation mechanisms in play may help rationally mitigate/circumvent key challenges. Here, the bulk structural consequences of long-term (>2.5 years) deuterated water (D2O) exposure of intercalation materials with compositions Li x Ni 0.5 Co 0.2 Mn 0.3 O 2 (NCM523) and Li x Ni 0.8 Co 0.1 Mn 0.1 O 2 (NCM811) are studied by neutron powder diffraction (NPD). Detailed inspection of the NPD data reveals gradual formation of a secondary crystalline phase in all exposed samples, not previously reported for this system. This unknown phase forms faster in liquid- compared to vapor-exposed compounds. Structural modelling of the NPD data shows a stable level of Li/Ni anti-site defects and does not indicate any significant changes in lattice parameters or hydrogen-lithium (D + /Li + ) exchange in the structure. Consequently, the secondary phase formation must take place via transformation rather than modification of the parent material. X-ray photoelectron spectroscopy data indicate formation of LiHCO 3 /Li 2 CO 3 at the surface and a Li-deficient oxide in the sub-surface region of the pristine compounds, and the presence of adsorbed water and transition metal hydroxides at the exposed sample surfaces.

25 ENERGY STORAGE↗

DOE OSTI2022

Mechanical Characterization of Loblolly Pine

Biorefineries are faced with various material flow challenges during feeding and handling, due to inherent variability and inhomogeneity of biomass feedstocks. This impacts the efficiency of refinery processes and unit operations. Proper characterization of bulk properties allows for optimal process design and reduction of overall costs. This work is aimed at evaluating the physical and mechanical properties that impact bulk flow of loblolly pine. Cyclic axial compression tests are performed to elucidate consolidation behavior under different stress conditions. The results are then used to develop correlations between material parameters (particle size, tissue type, moisture content) and Elastic Modulus of bulk samples.

09 BIOMASS FUELS↗

DOE OSTI2024

Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties

Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Sub-pixel high-resolution imaging of high-energy x-rays inspired by sub-wavelength optical imaging

We have developed and demonstrated an image super-resolution method—XR-UNLOC: X-Ray UNsupervised particle LOCalization—for hard x-rays measured with fast-frame-rate detectors that is an adaptation of the principle of photo-activated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM), which enabled biological fluorescence imaging at sub-optical-wavelength scales. We demonstrate the approach on experimental coherent Bragg diffraction data measured with 52 keV x-rays from a nanocrystalline sample. From this sample, we resolve the fine fringe detail of a high-energy x-ray Bragg coherent diffraction pattern to an upsampling factor of 16 of the native pixel pitch of 30 μ m of a charge-integrating fastCCD detector. This was accomplished by analysis of individual photon locations in a series of “nearly-dark” instances of the diffraction pattern that each contain only a handful of photons. Central to our approach was the adaptation of the UNLOC photon fitting routine for PALM/STORM to the hard x-ray regime to handle much smaller point spread functions, which required a different statistical test for photon detection and for sub-pixel localization. A comparison to a photon-localization strategy used in the x-ray community (“droplet analysis”) showed that XR-UNLOC provides significant improvement in super-resolution. We also developed a metric by which to estimate the limit of reliable upsampling with XR-UNLOC under a given set of experimental conditions in terms of the signal-to-noise ratio of a photon detection event and the size of the point spread function for guiding future x-ray experiments in many disciplines where detector pixelation limits must be overcome.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Efficient First-Order Algorithms for Large-Scale, Non-Smooth Maximum Entropy Models with Application to Wildfire Science

Maximum entropy (MaxEnt) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, MaxEnt models need efficient optimization algorithms to scale well for big data applications. State-of-the-art algorithms for MaxEnt models, however, were not originally designed to handle big data sets; these algorithms either rely on technical devices that may yield unreliable numerical results, scale poorly, or require smoothness assumptions that many practical MaxEnt models lack. In this paper, we present novel optimization algorithms that overcome the shortcomings of state-of-the-art algorithms for training large-scale, non-smooth MaxEnt models. Our proposed first-order algorithms leverage the Kullback–Leibler divergence to train large-scale and non-smooth MaxEnt models efficiently. For MaxEnt models with discrete probability distribution of n elements built from samples, each containing m features, the stepsize parameter estimation and iterations in our algorithms scale on the order of O(mn) operations and can be trivially parallelized. Moreover, the strong ℓ1 convexity of the Kullback–Leibler divergence allows for larger stepsize parameters, thereby speeding up the convergence rate of our algorithms. To illustrate the efficiency of our novel algorithms, we consider the problem of estimating probabilities of fire occurrences as a function of ecological features in the Western US MTBS-Interagency wildfire data set. Our numerical results show that our algorithms outperform the state of the art by one order of magnitude and yield results that agree with physical models of wildfire occurrence and previous statistical analyses of wildfire drivers.

Physics↗

DOE OSTI2022

Decoding Golden Eagle Movement Behavior from High-Resolution, Variable-Rate Telemetry Data Through Bayesian Filtering

The recent advances in animal tracking technology have enabled the collection of a vast amount of in situ data regarding the movement of wildlife at high spatiotemporal resolution. These data are usually available at variable time resolutions and contains noise (error) originating from GPS fixes. Decoding movement characteristics, particularly of flying animals, from telemetry data while handling these factors is a challenging yet important task for conservation purposes. Typically, this task is broken into two subtasks: resampling, and model calibration. The resampling subtask converts the variable rate positional data into a constant time interval data, while the model calibration subtask uses the resampled data to tune time-invariant parameters of the proposed models. For telemetry data at high temporal resolutions (order of 1 second), it is very challenging to decouple noise from actual movements using interpolation-based resampling techniques. Any errors introduced during resampling can significantly alter the the calibration and prediction attributes of the movement model. We address this problem through a unified Bayesian state-space framework that can handle both the resampling and calibration tasks in a single step. In addition, we use the speed and heading of the bird from telemetry data to regularize the position information of the bird. We use a Kalman filtering approach to include these nonlinearly related motion parameters within the state space framework. We cross-validated to quantify how this inclusion affects the model performance in estimating true bird movements. The relationship between the true state of the bird and environmental and topographical covariates is then represented parametrically. These parameters are then tuned using stochastic sampling strategies like Markov Chain Monte Carlo (MCMC). We use the telemetry data collected from golden eagles in the western USA to demonstrate the applicability of this approach to build a predictive, probabilistic movement model. Our preliminary results show that this approach provides improved predictive performance in terms of capturing higher-order motion parameters such as angular and horizontal accelerations, which may have simpler and more direct relationships with environmental covariates than corresponding speeds. In this talk, we will demonstrate how this state-space approach benefits the prediction capabilities of a movement model in simulating golden eagle paths through a wind power plant in Wyoming given certain atmospheric conditions. The model outcomes are aimed at informing mitigation strategies that can minimize the potential for collisions of golden eagles with wind turbines.

Bayesian methods↗

DOE OSTI2025

TOFHunter—unlocking rapid untargeted screening of inductively coupled plasma–time-of-flight–mass spectrometry data

This study provides an overview of a newly developed open source program written in Python, TOFHunter, which permits the rapid and untargeted screening of inductively coupled plasma (ICP)-time-of-flight (TOF)-mass spectrometry (MS) datasets. ICP-TOF-MS is an analytical tool capable of providing quasi simultaneous detection of all nuclides from Li to Pu. This capability has triggered an increase in studies investigating single-particle analysis in which the TOF-MS provides correlated elemental/isotopic signatures on a particle basis in time. Similarly, laser ablation mapping has seen rapid growth owing to ICP-TOF-MS's capacity to handle fast washout times (<10 ms) while providing a broad nuclide coverage. The caveat to this broad mass coverage and high time resolution comes in the form of large, overwhelming datasets. With datasets typically on the scale of gigabytes, it is easy for a user to only focus on very targeted analytes; however, this focus diminishes the opportunity offered by the TOF-MS detector. TOFHunter applies chemometric methods, principal component analysis (PCA), and interesting features finder (IFF) on ICP-TOF-MS data, allowing for investigation of correlations, major and minor variance sources, and sample screening. The unique spectra identified by the (IFF) are used to generate a list of mass peaks, which are then matched with both nuclides and potential interferences before being exported for the user to investigate. Several case studies are discussed herein, demonstrating TOFHunter's ability to screen aqueous injections, single-particle/single-cell analysis, and probe laser ablation mapping files for unique regions of interest.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Physics-Informed Active Learning With Simultaneous Weak-Form Latent Space Dynamics Identification

The parametric greedy latent space dynamics identification (gLaSDI) framework has demonstrated promising potential for accurate and efficient modeling of high-dimensional nonlinear physical systems. However, it remains challenging to handle noisy data. Here, to enhance robustness against noise, we incorporate the weak-form estimation of nonlinear dynamics (WENDy) into gLaSDI. In the proposed weak-form gLaSDI (WgLaSDI) framework, an autoencoder and WENDy are trained simultaneously to discover intrinsic nonlinear latent-space dynamics of high-dimensional data. Compared with the standard sparse identification of nonlinear dynamics (SINDy) employed in gLaSDI, WENDy enables variance reduction and robust latent space discovery, therefore leading to more accurate and efficient reduced-order modeling. Furthermore, the greedy physics-informed active learning in WgLaSDI enables adaptive sampling of optimal training data on the fly for enhanced modeling accuracy. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including viscous and inviscid Burgers' equations, time-dependent radial advection, and the Vlasov equation for plasma physics. With data that contains 5%–10% Gaussian white noise, WgLaSDI outperforms gLaSDI by orders of magnitude, achieving 1%–7% relative errors. Compared with the high-fidelity models, WgLaSDI achieves 121 to 1779x speed-up.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Controlled Conversion of Sodium Metal From Nuclear Systems to Sodium Chloride

A series of three bench-scale experiments was performed to investigate the conversion of sodium metal to sodium chloride via reactions with non-metal and metal chlorides. Specifically, batches of molten sodium metal were separately contacted with ammonium chloride and ferrous chloride to form sodium chloride in both cases along with iron in the latter case. Additional ferrous chloride was added to two of the three batches to form low melting point consolidated mixtures of sodium chloride and ferrous chloride, whereas consolidation of a sodium-chloride product was performed in a separate batch. Samples of the products were characterized via X-ray diffraction to identify attendant compounds. The reaction of sodium metal with metered ammonium chloride particulate feeds proceeded without reaction excursions and produced pure colorless sodium chloride. The reaction of sodium metal with ferrous chloride yielded occasional reaction excursions as evidenced by temperature spikes and fuming ferrous chloride, producing a dark salt-metal mixture. This investigation into a method for controlled conversion of sodium metal to sodium chloride is particularly applicable to sodium containing elevated levels of radioactivity—including bond sodium from nuclear fuels—in remote-handled inert-atmosphere environments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Fundamentals of Scalable Nanoparticle Assembly in Engineering Polymres

Systematically improving the properties of polymer nanocomposites is contingent on effectively controlling nanoparticle (NP) dispersion; further, it is necessary to attain this at scale if these methods are to impact practical applications. While most past work has focused on amorphous polymers, ≈ 70% of all polymers sold annually are semicrystalline. Inspired by this apparent disconnect, our work in the current DOE funded grant and this renewal proposal employs a novel handle, polymer crystallization, to organize NPs into the various amorphous zones of the lamellar semicrystalline morphology (interlamellar, interfibrillar and interspherulitic domains) of stereoregular polymers. We hypothesize that the isothermal crystallization rate of a polymer host, G, and NP diffusivity, are the two relevant control parameters in this context. In the case where the NPs are miscible in the melt, they remain spatially well-dispersed when the polymer is crystallized at a rate that is fast relative to NP diffusion. In contrast, monolayer-thick NP sheets with intersheet spacing of ~10-100nm form in slowly crystallized samples. This sheet stacking is remarkably similar to that seen in natural materials, e.g., Nacre. There is also ordering of the NPs at the larger interfibrillar scale. Importantly, the relative fraction of engulfed, interlamellar and interfibrillar NPs is determined by G. Further, the modulus of these materials can increase by a factor of 2-3 due to the organization state of the NPs, while leaving fracture toughness unaffected. We have established the broad applicability of this approach in two polymers, polyethylene (PE) and polyethyleneoxide (PEO). To achieve this goal we developed a method for grafting long PE chains from NPs with controlled graft density and molecular weight, giving us the ability to tailor their dispersion in the PE melt prior to crystallization. In this renewal proposal, we will develop the fundamental, quantitative understanding that will allow us to independently tailor polymer crystallization rate, NP diffusivity and NP agglomeration state in semicrystalline polymers so as to facilely control NP ordering and hence properties. A program that tightly integrates experiment and theory is proposed, and a key outcome of this research will be a three-dimensional design map for different regimes of NP organization (and hence properties) that can be achieved by polymer crystallization. A second independent outcome is the role of directional solidification and how it competes with the rates of polymer crystallization and NP mobility to affect the organization of the NP.

36 MATERIALS SCIENCE↗