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DOE OSTI2023

GRIDS-Net: Inverse shape design and identification of scatterers via geometric regularization and physics-embedded deep learning

This study presents a deep learning based methodology for both remote sensing and design of acoustic scatterers. The ability to determine the shape of a scatterer, either in the context of material design or sensing, plays a critical role in many practical engineering problems. This class of inverse problems is extremely challenging due to their high-dimensional, nonlinear, and ill-posed nature. To overcome these technical hurdles, we introduce a geometric regularization approach for deep neural networks (DNN) based on non-uniform rational B-splines (NURBS) and capable of predicting complex 2D scatterer geometries in a parsimonious dimensional representation. Then, this geometric regularization is combined with physics-embedded learning and integrated within a robust convolutional autoencoder (CAE) architecture to accurately predict the shape of 2D scatterers in the context of identification and inverse design problems. Further, an extensive numerical study is presented in order to showcase the remarkable ability of this approach to handle complex scatterer geometries while generating physically-consistent acoustic fields. The study also assesses and contrasts the role played by the (weakly) embedded physics in the convergence of the DNN predictions to a physically consistent inverse design.

42 ENGINEERING↗

DOE OSTI2024

tLaSDI: Thermodynamics-informed latent space dynamics identification

Here we propose a latent space dynamics identification method, namely tLaSDI, that embeds the first and second principles of thermodynamics. The latent variables are learned through an autoencoder as a nonlinear dimension reduction model. The latent dynamics are constructed by a neural network-based model that precisely preserves certain structures for the thermodynamic laws through the GENERIC formalism. An abstract error estimate is established, which provides a new loss formulation involving the Jacobian computation of autoencoder. The autoencoder and the latent dynamics are simultaneously trained to minimize the new loss. Computational examples demonstrate the effectiveness of tLaSDI, which exhibits robust generalization ability, even in extrapolation. In addition, an intriguing correlation is empirically observed between a quantity from tLaSDI in the latent space and the behaviors of the full-state solution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Graph-component approach to defect identification in large atomistic simulations

In this work, the graph-theoretical concept of connected components is employed to extract the evolution of defect configurations in a polycrystalline aluminum structure containing ~8.3 million atoms. This graph-component approach is applied to reveal details of defect formation, transport, and transformation in the polycrystalline Al under large shear deformation. Building upon standard nearest neighbor analysis, graph theory and associated tools are used to reduce the multi-million-atom system into discrete component subgraphs that represent distinct structural defects. This method allows the automated identification, characterization, and tracking of defective regions within large volumes of data representing atomic-scale processes. Such analysis elucidates relationships between external stimuli, such as strain, and defect distributions, which have a large influence on material properties. The Graph Analytics for Large Atomistic Simulations (GALAS) codebase that implements this analysis, together with user guidance, is openly available at https://github.com/pnnl/galas.

36 MATERIALS SCIENCE↗

DOE OSTI2020

The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio

Reducing the overall energy consumption and associated greenhouse gas emissions in the building sector is essential for meeting our future sustainability goals. Recently, smart energy metering facilities have been deployed to enable monitoring of energy consumption data with hourly or subhourly temporal resolution. This unprecedented data collection has created various opportunities for advanced data analytics involving load profiles (e.g., building energy benchmarking programs, building-to-grid integration, and calibration of urban-scale energy models). These applications often need preprocessing steps to detect daily load profile discords, such as: 1) outliers due to system malfunctions (the bad) and 2) irregular energy consumption patterns, such as those resulting from holidays (the ugly) compared to normal consumption patterns (the good). However, current preprocessing methods predominantly focus on filtering using statistical threshold values, which fail to capture the contextual discords of daily profiles. In addition, discord detection algorithms in building research are often aimed at finding individual building-level discords, which are not suitable at a large scale. Thus, here, we develop a method for automated load profile discord identification (ALDI) in a large portfolio of buildings (more than 100 buildings). Specifically, ALDI 1) uses the matrix profile (MP) method to quantify the similarities of daily subsequences in time series meter data, 2) compares daily MP values with typical-day MP distributions using the Kolmogorov-Smirnov test, and 3) identifies daily load profile discords in a large building portfolio. We evaluate ALDI using the metering data of both an academic campus and a residential neighborhood. Our results demonstrate that ALDI efficiently discovers measurement errors by system malfunctions and low energy consumption days in the academic campus portfolio, and it detects unique load shape patterns likely driven by occupant behavior and extreme weather conditions in the residential neighborhood.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Machine learning-enhanced hybrid modeling approach for better identification of a building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this word, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The hybrid model approach demonstrates an RMSE reduction of approximately 0.2–0.9 °C and 0.3–2 °C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied to experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Identification of mycotoxins by UHPLC–QTOF MS in airborne fungi and fungi isolated from industrial paper and antique documents from the Archive of Bogotá

Mold deterioration of historical documents in archives and libraries is a frequent and complex phenomenon that may have important economic and cultural consequences. In addition, exposure to toxic fungal metabolites might produce health problems. In this work, samples of broths of fungal species isolated from the documentary material and from indoor environmental samples of the Archive of Bogotá have been analyzed to investigate the presence of mycotoxins. High resolution mass spectrometry made possible to search for a large number of mycotoxins, even without reference standards available at the laboratory. For this purpose, a screening strategy based on ultra-high pressure liquid chromatography coupled to quadrupole-time of flight mass spectrometry (UHPLC–QTOF MS) under MS{sup E} mode was applied. A customized home-made database containing elemental composition for around 600 mycotoxins was compiled. The presence of the (de)protonated molecule measured at its accurate mass was evaluated in the samples. When a peak was detected, collision induced dissociation fragments and characteristic isotopic ions were also evaluated and used for tentative identification, based on structure compatibility and comparison with literature data (if existing). Up to 44 mycotoxins were tentatively identified by UHPLC–QTOF MS. 34 of these tentative compounds were confirmed by subsequent analysis using a targeted LC–MS/MS method, supporting the strong potential of QTOF MS for identification/elucidation purposes. The presence of mycotoxins in these samples might help to reinforce safety measures for researchers and staff who work on reception, restoration and conservation of archival material, not only at the Archive of Bogotá but worldwide. - Highlights: • Mold deterioration of historical documents is a frequent and complex phenomenon. • Samples of broths of fungal species isolated from Archive of Bogotá analyzed. • UHPLC–QTOF MS (MS{sup E}) applied for mycotoxins screening, without reference standards. • Customized home-made database for around 600 mycotoxins compiled. • 44 mycotoxins tentatively identified, 34 of which confirmed by LC–MS/MS.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Multi-area parameter error identification for large power systems

Power grid model parameters may contain errors due to various reasons. Detecting and correcting parameter errors typically requires significant computational effort due to the size and complexity of the parameter database. While the normalized Lagrange multiplier (NLM) method can effectively detect, identify and correct parameter errors, its computational burden could rapidly grow with increasing system size. This paper addresses this issue by proposing a multi-area parameter error identification method. Each area has its own outlier detection tool for detecting the incorrect parameters and measurements within the area. On the other hand, due to the reduced redundancy at area boundaries, parameter errors on branches incident to boundary buses may not be detected. Such errors are subsequently detected by a coordination level estimator completing the system-wide parameter detection procedure. In conclusion, performance of the developed method is demonstrated using the IEEE 118-bus and 2000-bus Texas synthetic systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Efficient derivatization of methylphosphonic and aminoethylsulfonic acids related to nerve agents simultaneously in soils using trimethyloxonium tetrafluoroborate for their enhanced, qualitative detection and identification by EI-GC–MS and GC–FPD

Trimethyloxonium tetrafluoroborate (TMO·BF 4 ) has been used here in the simultaneous derivatization of phosphonic and 2-aminoethylsulfonic acids related to nerve agents in different soils for their enhanced detection and identification by electron ionization gas chromatography-mass spectrometry (EI-GC–MS). The panel of acids consisted of five Schedule 2 phosphonic acids (methyl methylphosphonic acid, ethyl methylphosphonic acid, isopropyl methylphosphonic acid, pinacolyl methylphosphonic acid and cyclohexyl methylphosphonic acid) along with two sulfonic acids, N,N-diethyl-2-aminoethylsulfonic acid and N,N-diisopropyl-2-aminoethylsulfonic acid. The acids were converted to their corresponding methyl esters at ambient temperature when present at a 10 μg g -1 concentration in three separate soils: Virginia type A soil, Ottawa sand and Nebraska EPA soil. The concentration of the acids reflects values typically encountered during proficiency tests (PTs) administered annually by the Organisation for the Prohibition of Chemical Weapons (OPCW). Derivatization times to yield detectable signals for the methyl ester products for all the acids was found to vary among all three soil samples, however, it was found that generally the most optimal time across all the matrices involved was 3 h after the addition of TMO·BF 4 . Concomitantly, the analysis of the samples was complemented using GC coupled to flame photometric detection (GC–FPD). The inclusion of GC–FPD in the analysis yielded stronger signals for all seven methylated analytes making their detection after merely 3 h possible relative to the ones initially obtained with EI-GC–MS. Regarding the three soils employed in our study, a greater methylating efficiency was found in the Virginia type A soil and Ottawa sand yielding results that were significantly larger in magnitude to those found during the same time points for the Nebraska EPA soil sample. Prolonged reaction times (up to 72 h) were explored to find the time for the highest yield of methyl ester production were found instead to be deleterious to the process showcasing the importance of the fast yielding nature of the process specifically in situations where time-sensitive analysis is crucial (e.g. OPCW-PT).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Rapid identification of homogeneous O 2 evolution catalysts and comparative studies of Ru(II)-carboxamides vs. Ru(II)-carboxylates in water-oxidation

One of the major challenges in developing sustainable energy storage systems is to discover robust oxygen evolving catalysts (OECs) that can efficiently oxidize water and produce oxygen, electrons and protons under highly oxidizing conditions. Thus, developing new screening methodologies and practical tools for OEC discovery is essential. We report a screening protocol which allows the accurate identification, benchmarking and kinetic analysis of molecular OECs using chemical oxidants, based on an oxygen-sensitive luminescent dissolved oxygen optical probe (DOOP). To demonstrate the practicality of this protocol, we screened more than 100 reactions to benchmark and evaluate the structure-activity relationship of several transition-metal complexes 1–16 based on Ru, Fe and Cu. Water-oxidation by Ru(II)-carboxylates is compared to structurally similar and less studied Ru(II)-carboxamides. A new Ru(II) complex 6 containing the doubly deprotonated N,N'-bis(aryl)pyridine-2,6-dicarboxamide was also synthesized and its oxygen evolution rate was found to be dramatically enhanced at neutral pH.

25 ENERGY STORAGE↗

DOE OSTI2022

Identification and quantification of multinuclear Cu active sites derived from monomeric Cu moieties for dry NO oxidation over Cu-SSZ-13

The identification and quantification of multinuclear Cu-oxo cations in Cu-zeolites is a challenge amidst the dynamic nature of Cu ions in Cu-zeolites. Herein, we synthesized Cu-SSZ-13 zeolites containing varying densities of monomeric Cu cations (Z 2 Cu and ZCuOH, where Z represents an anionic site on the zeolite framework). The number of Cu-oxo cations, quantified using carbon monoxide temperature programmed reduction (CO-TPR), quadratically increased with increasing ZCuOH density, evidencing that proximal ZCuOH sites are precursors to Cu-oxo dimers. We also find that dehydrated proximal ZCuOH with overlapping diffusion radii of at most 0.5 nm can form Cu-oxo dimers. CO-TPR also revealed the presence of three distinct pools (i.e types) of Cu-oxo dimers; these pools catalyze dry NO oxidation at different turnover rates, with the fastest pool 2 to 30 times faster than the slowest pool. Finally, from reaction kinetics and rate law analysis, we also proposed a cohesive dry NO oxidation mechanism and demonstrated that nitrates detected by in situ FTIR are a spectator species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Nonnegative tensor factorization for contaminant source identification

Unsupervised Machine Learning (ML) is becoming increasingly popular for solving various types of data analytics problems including feature extraction, blind source separation, exploratory analyses, model diagnostics, etc. In this work, we have developed a new unsupervised ML method based on Nonnegative Tensor Factorization (NTF) for identification of the original groundwater types (including contaminant sources) present in geochemical mixtures observed in an aquifer. Frequently, groundwater types with different geochemical signatures are related to different background and/or contamination sources. The characterization of groundwater mixing processes is a challenging but very important task critical for any environmental management project aiming to characterize the fate and transport of contaminants in the subsurface and perform contaminant remediation. This task typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. Additionally, the application of inverse methods may introduce biases in the analyses through the assumptions made in the model development process. Here, we substitute the model inversion with unsupervised ML analysis. The ML analysis does not make any assumptions about underlying physical and geochemical processes occurring in the aquifer. Our ML methodology, called NTFk, is capable of identifying (1) the unknown number of groundwater types (contaminant sources) present in the aquifer, (2) the original geochemical concentrations (signatures) of these groundwater types and (3) spatial and temporal dynamics in the mixing of these groundwater types. These results are obtained only from the measured geochemical data without any additional site information. In general, the NTFk methodology allows for interpretation of large high-dimensional datasets representing diverse spatial and temporal components such as state variables and velocities. NTFk has been tested on synthetic and real-world site three-dimensional datasets. Finally, the NTFk algorithm is designed to work with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

58 GEOSCIENCES↗

DOE OSTI2014

Compositional modeling in porous media using constant volume flash and flux computation without the need for phase identification

The paper deals with the numerical solution of a compositional model describing compressible two-phase flow of a mixture composed of several components in porous media with species transfer between the phases. The mathematical model is formulated by means of the extended Darcy's laws for all phases, components continuity equations, constitutive relations, and appropriate initial and boundary conditions. The splitting of components among the phases is described using a new formulation of the local thermodynamic equilibrium which uses volume, temperature, and moles as specification variables. The problem is solved numerically using a combination of the mixed-hybrid finite element method for the total flux discretization and the finite volume method for the discretization of transport equations. A new approach to numerical flux approximation is proposed, which does not require the phase identification and determination of correspondence between the phases on adjacent elements. The time discretization is carried out by the backward Euler method. The resulting large system of nonlinear algebraic equations is solved by the Newton–Raphson iterative method. We provide eight examples of different complexity to show reliability and robustness of our approach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

A biorthogonal decomposition for the identification and simulation of non-stationary and non-Gaussian random fields

In this paper, a new method for the identification and simulation of non-Gaussian and non-stationary stochastic fields given a database is proposed. It is based on two successive biorthogonal decompositions aiming at representing spatio–temporal stochastic fields. The proposed double expansion allows to build the model even in the case of large-size problems by separating the time, space and random parts of the field. A Gaussian kernel estimator is used to simulate the high dimensional set of random variables appearing in the decomposition. The capability of the method to reproduce the non-stationary and non-Gaussian features of random phenomena is illustrated by applications to earthquakes (seismic ground motion) and sea states (wave heights).

58 GEOSCIENCES↗

DOE OSTI2026

Latent space dynamics identification for interface tracking with application to shock-induced pore collapse

Capturing sharp, evolving interfaces remains a central challenge in reduced-order modeling, especially when data is limited and the system exhibits localized nonlinearities or discontinuities. Here, we propose LaSDI-IT (Latent Space Dynamics Identification for Interface Tracking), a data-driven framework that combines low-dimensional latent dynamics learning with explicit interface-aware encoding to enable accurate and efficient modeling of physical systems involving moving material boundaries. At the core of LaSDI-IT is a revised autoencoder architecture that jointly reconstructs the physical field and an indicator function representing material regions or phases, allowing the model to track complex interface evolution without requiring detailed physical models or mesh adaptation. The latent dynamics are learned through linear regression in the encoded space and generalized across parameter regimes using Gaussian process interpolation with greedy sampling. We demonstrate LaSDI-IT on the problem of shock-induced pore collapse in high explosives, a process characterized by sharp temperature gradients and dynamically deforming pore geometries. The method achieves relative prediction errors below 9% across the parameter space, accurately recovers key quantities of interest such as pore area and hot spot formation, and matches the performance of dense training with only half the data. This latent dynamics prediction was 10 6 times faster than the conventional high-fidelity simulation, proving its utility for multi-query applications. These results highlight LaSDI-IT as a general, data-efficient framework for modeling discontinuity-rich systems in computational physics, with potential applications in multiphase flows, fracture mechanics, and phase change problems.

Gaussian process↗

DOE OSTI2022

Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding

The paper considers testing and signal identification for covariance matrices from two populations of marginally sub-Gaussian distributed. A multi-level thresholding procedure is proposed for testing the equality of two high-dimensional covariance matrices, which is designed to detect sparse and faint differences between the covariances. A novel -statistic composition is developed to establish the asymptotic distribution of the thresholding statistics in conjunction with the matrix blocking and the coupling techniques. It is shown that the proposed test is more powerful than the existing tests in detecting sparse and weak signals in covariances. Multiple testing procedures are constructed to discover different covariances and the sub-groups of variables with different covariance structures between the two populations. The proposed procedures are based on the multi-level thresholding test, which are able to control the false discovery proportion () with high power. Simulation experiments and a case study on the returns of the S&P 500 stocks before and after the COVID-19 pandemic are conducted to demonstrate and compare the utilities of the proposed methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Identification of Silver and Palladium in Irradiated TRISO Coated Particles of the AGR-1 Experiment

Evidence of the release of certain metallic fission product through intact tristructural isotropic (TRISO) particles has been seen for decades around the world, as well as in the recent AGR-1 experiment at Idaho National Laboratory (INL). However, understanding the basic mechanism of transport is still lacking. This understanding is important because the TRISO coating is part of the high temperature gas reactor functional containment and critical for the safety strategy for licensing purposes. Our approach to identify fission products in irradiated AGR-1 TRISO fuel using scanning transmission electron microscopy (STEM), Electron Energy Loss Spectroscopy (EELS) and Energy Filtered TEM (EFTEM), has led to first-of-a-kind data at the nano-scale indicating the presence of silver at triple points and grain boundaries of the SiC layer in the TRISO particle. Cadmium was also found in the triple junctions. In this initial study, the silver was only identified in SiC grain boundaries and triple points on the edge of the SiC-IPyC interface up to a depth of approximately 0.5 um. Palladium was identified as the main constituent of micron-sized precipitates present at the SiC grain boundaries. Additionally spherical nano-sized palladium rich precipitates were found inside the SiC grains. These nano-sized Pd precipitates were distributed up to a depth of 5 um away from the SiC-IPyC interlayer. No silver was found in the center of the micron-sized fission product precipitates using these techniques, although silver was found on the outer edge of one of the Pd-U-Si containing precipitates which was facing the IPyC layer. Only Pd-U containing precipitates were identified in the IPyC layer and no silver was identified in the IPyC layer. The identification of silver alongside the grain boundaries and the findings of Pd alongside grain boundaries as well as inside the grains, provide significant knowledge for understanding silver and palladium transport in TIRSO fuel, which has been the topic of international research for the past forty years. Additionally the usefulness of the advanced electron microscopic techniques for TRISO coated particle research is demonstrated in this paper.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

Identification of lithium hydride and its hydrolysis products with neutron imaging

In this study, lithium hydride (LiH) and its hydrolysis products were investigated non-destructively with neutron radiography and neutron computed tomography. Relative neutron transmission intensities (I/I 0 ) were measured for LiOH, Li 2 O and LiH, and their linear attenuation coefficients calculated from this data. We show that 7 Li is necessary for creating large differences in I/I 0 for facile identification of these compounds. The thermal decomposition of LiOH to Li 2 O was also observed with neutron radiography. Computed tomography shows that the samples were fairly homogeneous, with very few macroscopic defects. Lastly, the results shown here demonstrate the feasibility of observing LiH hydrolysis with neutron imaging techniques in real time.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2020

Method for identification of redox control parameters for corrosion mitigation in molten fluoride salts

Structural material corrosion is a significant concern for the development of molten salt-cooled reactors (MSR) and redox control was proposed to mitigate the corrosion. This study presents an efficient approach to identify the partial pressure of F 2 gas for fluorine driven corrosion, the concentration ratios of UF 4 /UF 3 and ZrF 4 /ZrF 2 , and the partial pressure ratio of HF/H 2 needed to limit metal corrosion and its subsequent dissolution in molten fluoride salts. The identification procedure is made by simply using auxiliary axes in Ellingham diagrams. This approach greatly simplifies the optimization of redox control methods in studying the material corrosion in molten salts.

36 MATERIALS SCIENCE↗