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DOE OSTI2020

Synchrophasor Measurements-based Events Detection Using Deep Learning

Deep learning algorithms have been developed for phasor measurement units (PMUs) analysis aiming at providing grid operators to observe and react to significant real-time changes in the grid associated with multiple factors (e.g., power generation and load variations, different type of faults, and equipment mailfunction), or for offline post-event system diagnostics. In this study, a Long Short-Term Memory (LSTM)-based deep neural network (DNN) is adopted and evaluated to identify the most appropriate model configurations for event detection and longer-term anomalous pattern extraction. The proposed DNN model shows the potential on long-term predictions with the ability to capture nonlinear and nonstationary mixture complex patterns in PMU datasets. Real-world PMU in the WECC system were used for model development and validation.

deep learning↗

DOE OSTI2022

Evaluation of dynamic testing of full-scale wind turbine drivetrains with hardware-in-the-loop

Dynamometer testing of full-scale wind turbine drivetrains involves subjecting integrated components to extreme static loads, fatigue loads, or dynamic loads to replicate field conditions. This paper provides a detailed evaluation of the uncertainty and errors in applied loads and measured responses, and their magnitude relative to established repeatability bounds for a drivetrain subjected to static and dynamic loads on the 7.5 MW test bench at the Clemson University Wind Turbine Drivetrain Testing Facility. An ideal drivetrain simulation model is utilized to isolate the influence of load tracking errors on test article responses. It is shown that the test article response variations are mainly driven by run out and clearances inherent to the drivetrain. For the dynamic profiles, the load tracking errors are within acceptable limits. Furthermore, a frequency analysis is used to show that the test bench controller tracking performance is acceptable for profiles with frequency content up to 1.5 Hz.

17 WIND ENERGY↗

DOE OSTI2021

2020 Cost Analysis of Hydropower Options at Non-Powered Dams

This report provides a cost analysis of potential hydropower development for representative non-powered dam (NPD) sites in the United States. The overarching objective is to support the establishment of a basis for evaluating progress towards improving the competitiveness of new hydropower development for electricity generation. The representative sites, herein referred to as “reference sites”, were selected from sites with detailed water resource data to support the cost analysis. Facility designs, capital costs, plant generation and efficiency profiles, capacity factors, and levelized costs of energy (LCOE) are presented for the sites under baseline and near-term innovation cases. The baseline case represents facility configurations using technologies that are already widely applied in the hydropower industry, whereas near-term innovations are technologies likely to reach wide acceptance within the next 5 to 10 years. Cost savings in the near-term relative to the baseline are discussed to illustrate the potential benefits to the hydropower industry from research and development (R&D) and adoption of these innovations. Medium- to long-term innovation options expected from ongoing private and government R&D efforts are also highlighted but are not quantified in this report because of a current lack of complete information on these options. The key findings of the cost analysis in this study imply that near-term innovations could reduce the baseline costs of hydropower development at US NPD sites. Thus, if remaining R&D issues with these near-term innovations can be resolved within the next 5 to 10 years, industry-wide adoption of these technologies and improved competitiveness of NPD hydropower development can be expected. Specific key findings of the analysis include the following: 1) Potential installed capacity at the NPD sites evaluated in this study vary from 200 kW to 70 MW, representing two orders of magnitude variation across nearly 20 sites; 2) Capital costs, not including development costs, for the sites evaluated in this study vary from $2,200/kW to $34,000/kW but are below $12,000/kW for most sites; 3) Baseline LCOE estimates for lake dams are from $79/MWh to $790/MWh with most sites under $400/MWh, whereas estimates for lock dams are from $134/MWh to $289/MWh with most sites under $200/MWh; 4) Reductions in capital cost per kilowatt under the near-term innovation case are from −77% to −14% for lake dams and −21% to −2.1% for lock dams (excluding one site with a 14% increase in per-kilowatt capital costs); 5) Combined changes in capital costs and capacity factors in the near-term case relative to the baseline lead to reductions in LCOE of −71% to −14% for lake dams and −21% to −14% for lock dams; 6) Overall, LCOE reductions lead to 8 of the 18 sites under the near-term case having LCOE values below $150/MWh compared with only 2 of 18 sites in the baseline case.

13 HYDRO ENERGY↗

DOE OSTI2021

Mutual Inductance Level Sensor for Use in Liquid Metals

Electromagnetic instrumentation capable of measuring the level of liquid metal has been developed at Argonne National Laboratory’s (ANL) Mechanisms Engineering Test Loop (METL). The mutual inductance level sensor (MILS) utilizes the electromagnetic coupling between two coil conductors and a surrounding liquid metal to determine the level of the liquid metal. Mineral insulated cables wrapped on a stainless steel core provide a durable sensor construction. The use of a sealed stainless steel thimble isolates the sensor from the high-temperature liquid metal, allowing for easy sensor maintenance. Modern digital electronics allow for reliable and accurate operation of the sensor. Two sensor variations have been designed and fabricated, and the first variation has seen extensive testing in a non-sodium testing stand as well as in the high-temperature sodium environment of METL. Electromagnetic finite-element analysis studies have been performed using the COMSOL Magnetic Fields Solver. Regular operation of the MILS will commence following in-situ calibration of the sensors in the METL environment.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Damage Accumulations Predictions for Boiler Components Via Microstructurally Informed Material Models

The goal of the project was to model material behavior and degradation during cyclic plasticity— with and without hold time—for nickel-based superalloys used in USC (ultra-super-critical) and A-USC (advanced-ultra-super-critical) boiler components. The study provided physically informed models, capturing the microstructural changes taking place in the industrial components under cyclic loading and long duration stress (up to 300,000 hours) and high temperature exposure (1100°F/593°C to 1400°F/760°C). The major developments were: 1) Qualitative and quantitative understanding of microstructure evolution (gamma prime precipitates), deformation (dislocation density), and damage mechanisms of Haynes ® 282 alloy. 2) Qualitative understanding of microstructural features generating local strain variations. 3) A continuum damage mechanics model (CDM) for Haynes ® 282 alloy at 1100°F to 1400°F capturing cyclic behavior with and without hold time. 4) Structural analysis for creep and LCF life predictions of an USC thick-wall Grade 91 superheater steel header and understanding life sensitivity to wall thickness of an AUSC Haynes ® 282 header.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2023

classLog: Logistic regression for the classification of genetic sequences

Introduction Sequencing and phylogenetic classification have become a common task in human and animal diagnostic laboratories. It is routine to sequence pathogens to identify genetic variations of diagnostic significance and to use these data in realtime genomic contact tracing and surveillance. Under this paradigm, unprecedented volumes of data are generated that require rapid analysis to provide meaningful inference. Methods We present a machine learning logistic regression pipeline that can assign classifications to genetic sequence data. The pipeline implements an intuitive and customizable approach to developing a trained prediction model that runs in linear time complexity, generating accurate output rapidly, even with incomplete data. Our approach was benchmarked against porcine respiratory and reproductive syndrome virus (PRRSv) and swine H1 influenza A virus (IAV) datasets. Trained classifiers were tested against sequences and simulated datasets that artificially degraded sequence quality at 0, 10, 20, 30, and 40%. Results When applied to a poor-quality sequence data, the classifier achieved between >85% to 95% accuracy for the PRRSv and the swine H1 IAV HA dataset and this increased to near perfect accuracy when using the full dataset. The model also identifies amino acid positions used to determine genetic clade identity through a feature selection ranking within the model. These positions can be mapped onto a maximum-likelihood phylogenetic tree, allowing for the inference of clade defining mutations. Discussion Our approach is implemented as a python package with code available at https://github.com/flu-crew/classLog .

Zeller, Michael A.↗

DOE OSTI2025

Frontal Slice Approaches for Tensor Linear Systems

Inspired by the row and column action methods for solving large-scale linear systems, in this work, we explore the use of frontal slices for solving tensor linear systems. In particular, this paper presents a novel approach for using frontal slices of a tensor $\mathcal{A}$ to solve tensor linear systems $\mathcal{A} ∗\mathcal{X} = \mathcal{B}$ where ∗ denotes the $t$-product. In addition, we consider variations of this method, including cyclic, block, and randomized approaches, each designed to optimize performance in different operational contexts. Our primary contribution lies in the development and convergence analysis of these methods. Experimental results on synthetically generated and real-world data, including applications such as image and video deblurring, demonstrate the efficacy of our proposed approaches and validate our theoretical findings.

Luo, Hengrui↗

DOE OSTI2021

Dynamic Probabilistic Safety Assessment Studies for Advanced Reactor Using RAVEN

Probabilistic Safety Assessment (PSA) is used extensively to evaluate the risks associated with complex engineering systems like Nuclear Power Plants (NPPs). Current PSA models are based on the Event-Tree/Fault-Tree (ET/FT) methodology. ET and FT models are static and are based on Boolean logic approaches. In the past, concerns have been raised in the literature regarding the capability of the traditional static modelling approaches to adequately account for the impact of process, hardware, software, firmware and human interactions on the stochastic system behaviour. To overcome the limitations of the traditional approach to PSA, several dynamic PSA methodologies have been proposed. One of the dynamic PSA methodologies used for dynamic evaluations is Dynamic Event Tree (DET) framework which can be used to assess the impact of the parameter variability and scenario dynamics on the PSA model for the initiating event. The DET framework couples the stochastic model (number of component/trains that start on demand, operator action timing, etc.) with a Thermal-Hydraulic (TH) model of the plant. This paper explores the use of DET along with a case study on advanced reactor. The initiating event selected for the study was Class IV power supply failure event. The TH analysis considering uncertainty in various parameters was performed using RELAP5 and Reactor Analysis and Virtual control ENvironment (RAVEN) tool. Based on the uncertainty analysis, it is concluded that the peak clad temperatures (PCT) are within the limits in all the code runs implying a high-degree of safety margin. However, variation in time to reach the PCT was observed among the code runs and the mean time to reach the PCT was found to be around 8590sec (approximately 2.4 hours). Hence, sufficient time margin is available for human intervention and the operator might have a relatively stress-free state during such an accident scenario. Due to the static nature of the traditional PSA models, the safety margin available was lesser, whereas, with the help of dynamic PSA models, one can demonstrate that the actual available safety margin is more in the present case study and is valuable input from the design point of view.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Implementation of hybrid finite element method based transport solver in GRIFFIN

A new transport solver option based on the hybrid FEM (HFEM) was implemented in GRIFFIN, the MOOSE-based reactor analysis code, as an effort to support routine core design calculations for advanced reactor applications. The HFEM formulation with P{sub N} (spherical harmonics expansion), akin to the variational nodal method, is effective for solving a spatially homogenized problem with strong transport effect. The residual and Jacobian evaluations of the HFEM weak form were derived and successfully implemented in GRIFFIN, having the diffusion and the PN options available in the new HFEM based transport solver. The performance was tested with the simplified ABTR benchmark problems. The results indicate that the HFEM-based transport solver is a feasible option for solving problems with spatially homogenized and strong streaming by providing superior accuracy with a proper p-refinement. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

A Differentiable Hydrology Approach for Modeling With Time-Varying Parameters

In the analysis of observational data with mathematical models, it is often desirable to let one or more model parameters vary with time to account for changing environmental conditions or to offer more flexibility. A standard approach for dynamic models is to allow for the number of parameters to grow as a function of time, leading to a high-dimensional inference problem for long data records. We propose using Hamiltonian Monte Carlo, a gradient-based Markov chain Monte Carlo (MCMC) method, to perform Bayesian inference for time-varying parameters (TVP) in hydrology models. As derivatives of model error functions with regard to parameters are not available in closed form, we implemented the GR4J rainfall-runoff models in Theano/PyMC3, and Jax/NumPyro, allowing for the application of automatic differentiation techniques. A simulation experiment assessing the viability of these methods for recovering underlying temporal variation in parameters indicates that a discrete Gaussian random walk-based prior appears to be best suited across a range of scenarios while the continuous-time Gaussian process compares relatively poorly. In an analysis of 20 years of daily real-world streamflow records from the Model Parameter Estimation Experiment (MOPEX) hydrology data set with increasing data sparsity and at multiple temporal resolutions, we find that TVP inference with gradient-based MCMC is a flexible and reliable approach for analyzing hydrology models with dynamic parameter sets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Analysis of beam ion driven Alfvén eigenmode stability induced by Tungsten contamination in EAST

Alfvén eigenmodes (AE) activity is observed in the EAST high β N and low B T discharge 93910, operation scenario dedicated to explore the ITER baseline scenario. AEs are triggered after the plasma is contaminated by Tungsten that causes an abrupt variation of the thermal plasma and energetic particles (EPs) profiles. The aim of the present study is to analyze the AE stability in the 93910 discharge using the gyro-fluid code FAR3d, identifying the AE stability trends by comparing the plasma before and after the Tungsten contamination. Tungsten contamination causes the destabilization of Toroidal AEs (TAE) and Energetic particle modes (EPMs) in the same frequency range and radial location with respect to the experimental observation and M3D-K/GTAW code results. Next, a set of parametric studies are performed to analyze the effect of the thermal plasma and EP parameters on the AE stability. The analysis indicates a lower EP β threshold for the AEs destabilization if the EP energy increases, an improved AE stability of on-axis NBI configurations due to the stronger continuum damping in the inner plasma region as well as a large enhancement of the EP drive as the thermal ion density increases due to a higher ratio of the EP and Alfven velocities. Consequently, the simulations indicate the increment of the thermal ion density after the Tungsten contamination could be the main cause of the AE/EPM destabilization.

AE↗

DOE OSTI2023

Water chemistry in flume channel and hyporheic zone (i.e., porewater) associated with: “Rethinking Aerobic Respiration in the Hyporheic Zone Under Variation in Carbon and Nitrogen Stoichiometry”

Dissolved oxygen (DO), total organic carbon (TOC), total nitrogen (TN), molecular data for organic matter, and biochemical reactions for surface water and porewater (i.e., hyporheic zone) collected from a water recirculating flume located at the University of Texas, Austin. The flume contained real river water from Lower Colorado River(Austin, TX) and clean sand. Hyporheic exchange in the flume was induced through The study aims to understand relationships between aerobic metabolism of organic matter and molecular characteristics of organic matter, such as thermodynamic signature and nitrogen content, through the extent of the hyporheic zone at 10 cm- resolution, and through time. During the experiment, organic matter (dry leaves) was added to the flume and removed after 24 hours. The water samples were collected before the addition of leaves, at the time of removal of leaves, and at hour 72. The water samples were analyzed using ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) and total organic carbon (TOC) and total nitrogen (TN) analysis. Dissolved oxygen content throughout the surface water and the hyporheic zone of the flume was measured with a large planar optode. This data package is associated with the publication ’ Rethinking Aerobic Respiration in the Hyporheic Zone Under Variation in Carbon and Nitrogen Stoichiometry’ published in Environmental Science and Technology (Turețcaia et al., 2023 https://doi.org/10.1021/acs.est.3c04765). The dataset is comprised of five folders (1) Diss_O2_pic, (2) input_files (3) output_files; (4) python_code; and (5) R_code . Diss_O2_pic contains siximages of dissolved oxygen distribution in a bedform at hours 0, 24, and 72 of the experiment conducted in a large recirculation flume. Images are in separate R and G channels (i.e., RGB). The input_files contains (1) a csv file with FTICR peaks identified within each sample, (2) a csv file with molecular information pertinent to FTICR data with Gibbs free energy calculations adjusted for environmental temperature, (3) a csv file containing concentrations of non-purgeable organic carbon measured throughout the experiment , (4) a csv file containing concentrations of total nitrogen measured throughout the experiment, (5) a csv file containing total biochemical reactions (i.e., transformations) identified in the dataset, (6) a csv containing transformation profiles, and (7) a csv file containing transformations with formulas, and (8) a jpg file with schematic representation of locations for sample collection. The output_files contains (1) and xlsx file containing percent biochemical reactions containing nitrogen identified across all 39 sample, (2) a csv file of merged FTICR data and molecular information files, (3) a csv files containing average Gibbs free energy within sampling domains and at each sampling location, (4) a csv file with average concentrations of dissolved oxygen across sampling locations at hour 0, (5) a csv file with average concentrations of dissolved oxygen across sampling locations at hour 24, (6) a csv file with average concentrations of dissolved oxygen across sampling locations at hour 72, (7) a csv file with percent chemical classes identified across sampling locations at hour 0, (8) a csv file with percent chemical classes identified across sampling locations at hour 24, (9) a csv file with percent chemical classes identified across sampling locations at hour 72, and (10) a csv file containing percent nitrogen containing biochemical reactions identified across sampling locations at hours 0, 24, and 72. The python_code contains seven ipynb files which are Jupyter Notebooks used for data analysis and figures generation. The R_code contains 3 R files with R code used for data analysis and figures generation. This data package contains the processed data used in the associated manuscript. This data has not been previously published.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Investigation of inter-subject variation in ultrafine particle deposition across human nasal airways: A study involving children, adults, and the elderly

Occupational and environmental exposure to toxic nanoparticles, driven by the rapid expansion of nanotechnology, raises significant respiratory health concern. Numerous studies have explored airflow and particle dynamics in adult nasal airways, but understanding the impact of age-related anatomical changes in children and the elderly remains limited. This study systematically investigates age-related anatomical variations and associated influence on nasal airflow dynamics and ultrafine particle deposition characteristics. Using Computational Fluid-Particle Dynamics (CFPD) method, simulation was conducted under diverse inhalation conditions spanning a wide age range, including: two children (5 years old), two young adults (in their twenties), and two elderly (over 77 years old). Our results reveal distinctive variations across age groups in anatomical dimensions, which affect distribution of wall shear stress where the elderly and children display unique patterns distinct from the young adults. While total deposition efficiency differs significantly between children and adults, filtration efficiency in the subregion with most deposition, main respiratory, remains consistent. However, inter-subject differences are observed in the vestibular and olfactory regions,emphasizing nuanced impact of age-related anatomical variations. Overall and subregional empirical equations for deposition efficiency were developed by incorporating the combined diffusion parameter, Sc a Δ b , corroborating the use of geometrical characteristic parameters for each specific subject in predicting nasal deposition efficiency across age groups. Our findings contribute to predictive nanoparticle exposure analysis in nasal airways across different age groups, thereby enhancing respiratory healthcare for individuals across the life span.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Simulating energy performance of buildings: a study using eQUEST and Energy Star ® portfolio manager

Commercial buildings consume significant energy in the United States and exhibit high potential for energy use reduction through retrofits. Benchmarking and energy simulation are well established tools in the industry to identify potential improvements and measure performance. Analysis to identify most sensitive retrofit parameters to energy performance can optimize investment and available energy savings. Presented study demonstrates methodology using a static model to determine sensitivity of building design and retrofit parameters with respect to energy performance. Calibrated simulation energy models (eQUEST) of two distribution centers (A, B) are presented. A fractional factorial analysis is conducted on retrofit parameters of efficiency measures targeting the highest energy consumers, and the results are benchmarked using Energy Star® Portfolio Manager. A custom Microsoft Excel® based simulation model is created to simulate occupancy levels, lighting, plug loads, and other equipment used in various spaces throughout the day. For Building A, efficient lighting was the most influential parameter for energy savings, carbon savings and benchmarking score; whereas, for Building B, HVAC efficiency was most influential for energy and demand controlled ventilation and economizers was most influential for benchmarking score. While retrofit projects can save energy and carbon emissions, variation in source-site ratios and state grid emissions, benchmarking scores may not always reflect equivalent improvement. State grid emissions factors, natural gas composition are difficult to model and hence not considered in this study. In conclusion, the synergistic analysis presented, emphasizes the importance of benchmarking and efficiency retrofits in promoting sustainable building practices to reduce energy consumption.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Shifting agricultural practices to produce sustainable, low carbon intensity feedstocks for biofuel production

The carbon intensity (CI) of biofuel's well-to-pump life cycle is calculated by life cycle analysis (LCA) to account for the energy/material inputs of the feedstock production and fuel conversion stages and the associated greenhouse gas (GHG) emissions during these stages. The LCA is used by the California Air Resources Board's Low Carbon Fuel Standard (LCFS) program to calculate CI and monetary credits are issued based on the difference between a given fuel's CI and a reference fuel's CI. Through the Tier 2 certification program under which individual fuel production facilities can submit their own CIs with their facility input data, the LCFS has driven innovative technologies to biofuel conversion facilities, resulting in substantial reductions in GHG emissions as compared to the baseline gasoline or diesel. A similar approach can be taken to allow feedstock petition in the LCFS so that lower-CI feedstock can be rewarded. Here we examined the potential for various agronomic practices to improve the GHG profiles of corn ethanol by performing feedstock-level CI analysis for the Midwestern United States. Our system boundary covers GHG emissions from the cradle-to-farm-gate activities (i.e. farm input manufacturing and feedstock production), along with the potential impacts of soil organic carbon change during feedstock production. We conducted scenario-based CI analysis of ethanol, coupled with regionalized inventory data, for various farming practices to manage corn fields, and identified key parameters affecting cradle-to-farm-gate GHG emissions. The results demonstrate large spatial variations in CI of ethanol due to farm input use and land management practices. In particular, adopting conservation tillage, reducing nitrogen fertilizer use, and implementing cover crops has the potential to reduce GHG emissions per unit corn produced when compared to a baseline scenario of corn–soybean rotation. This work shows a large potential emission offset opportunity by allowing feedstock producers a path to Tier 2 petitions that reward low-CI feedstocks and further reduce biofuels' CI. The prevalence of significant acreage that has not been optimized for CI suggests that policy changes that incentivize optimization of this parameter could provide significant additionality over current trends in farm efficiency and adoption of conservation practice.

09 BIOMASS FUELS↗

DOE OSTI2021

Analysis of Seismic Signals Generated by Vehicle Traffic with Application to Derivation of Subsurface Q-Values

Correct identification and modeling of anthropogenic sources of ground motion are of considerable importance for many studies, including detection of small earthquakes and imaging seismic properties below the surface. Here, to understand signals generated by common vehicle traffic, we use seismic data recorded by closely spaced geophones normal to roads at two sites on San Jacinto fault zone. To quantify the spatiotemporal and frequency variations of the recorded ground motions, we develop a simple analytical solution accounting for propagation and attenuation of surface waves. The model reproduces well-observed bell-shaped spectrograms of car signals recorded by geophones close to roads, and it can be used to estimate frequency-dependent Q-values of the subsurface materials. The data analysis indicates Q-values of 3–40, for frequencies up to 150 Hz for road-receiver paths at the two examined sites. The derived Q-values are consistent with attenuation factors of surface waves previously obtained with other methods. The analytical results and analysis procedure provide a highly efficient method for deriving Q-values of shallow subsurface materials.

58 GEOSCIENCES↗

DOE OSTI2020

Patience is a virtue: A data-driven analysis of rooftop solar PV permitting timelines in the United States

© 2020 The Authors Local permitting can ensure the safe installation and operation of rooftop solar photovoltaic (PV) systems. At the same time, burdensome local permitting processes and local variation in requirements may pose challenges to PV deployment. In this article, we explore new data on the durations between key steps in the PV permitting process in the United States. The data suggest that a typical customer can expect to wait around 25–100 days from permit application until an installed system passes inspection. Permit durations vary significantly across jurisdictions, due in part to differences in local permitting policies. However, permit durations vary as significantly within jurisdictions as across them, in part due to significant variation across installers, suggesting that installer strategies and practices play an important role in permitting timelines. Permit durations have declined over time, reflecting progress from permit streamlining policies and jurisdiction learning-by-doing, though durations have stabilized in recent years. The data suggest that typical PV customers still face long and uncertain permitting timelines in the United States.

O'Shaughnessy, E↗

DOE OSTI2024

Nonparametric Multiparticle Set Methods for Interpreting Environmental Samples

Collection and analysis of environmental samples is commonly used by a range of stakeholders in nuclear safeguards and security contexts. While the ubiquity of samples and their transport in the environment allow regular collection, developing and demonstrating methods for analyzing these samples is difficult. In this work, an environmental sample consists of a set of one or more individual particles. Recent advances in reactor simulation have allowed us to generate data that are more representative of real-world environmental samples, enabling statistically defensible method development and testing. The most notable of these advances is a drastic increase in the number of material depletion regions, which allows our simulations to capture the variation in isotopic composition seen at length scales consistent with environmental samples. Traditional approaches for handling multiparticle samples treat each particle in the sample individually, estimating the quantity of interest (e.g., core-average burnup) resulting from measurement and analysis of signatures (e.g., nuclide assays) from each individual particle. Individual estimates are then averaged to generate a single estimate of the quantity of interest over the entire sample. In this presentation, we introduce two novel approaches for interpreting environmental samples that comprise of multiple particles: (1) the Quantile-Quantile Comparator, which uses a multivariate generalization of quantile-quantile plots for comparing unknown statistical distributions, and (2) the Set Transformer, an attention-based neural network module designed to model interactions among elements (particles) in the input set (sample). Statistically representative sampling cannot be guaranteed as samples are passively collected and are beholden to what particles are available in the environment. These new analysis methods for set-input problems are expected to be more robust than traditional approaches to issues of sampling bias where particles are not uniformly distributed throughout regions of interest, as well as generally outperform traditional approaches by jointly considering all elements in the set. We will present results comparing the performance of traditional single particle approaches and the novel Quantile-Quantile Comparator and Set Transformer for interpretation of simulated environmental samples.

Phathanapirom, Birdy↗