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DOE OSTI2025

Block Lanczos algorithm for lattice QCD spectroscopy and matrix elements

Recent work introduced a new framework for analyzing correlation functions with improved convergence and signal-to-noise properties, as well as rigorous quantification of excited-state effects, based on the Lanczos algorithm and spurious eigenvalue filtering with the Cullum-Willoughby test. Here, we extend this framework to the analysis of correlation-function matrices built from multiple interpolating operators in lattice quantum chromodynamics (QCD) by constructing an oblique generalization of the block Lanczos algorithm, as well as a new physically motivated reformulation of the Cullum-Willoughby test that generalizes to block Lanczos straightforwardly. The resulting block Lanczos method directly extends generalized eigenvalue problem (GEVP) methods, which can be viewed as applying a single iteration of block Lanczos. Block Lanczos provides qualitative and quantitative advantages over GEVP methods analogous to the benefits of Lanczos over the standard effective mass, including faster convergence to ground- and excited-state energies, explicitly computable two-sided error bounds, straightforward extraction of matrix elements of external currents, and asymptotically constant signal-to-noise. No fits or statistical inference are required. Proof-of-principle calculations are performed for noiseless mock-data examples as well as two-by-two proton correlation-function matrices in lattice QCD.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

CRCNS22 Learning Rules in the Hippocampus and their Mapping to Neuromorphic Systems (Final Technical Report)

Large scale biologically-realistic computational models are key to investigating the interplay between structure and function in nervous systems, thus paving the way to new clinical methods and neuro-inspired computing solutions. This project focuses on the hippocampus, in particular the CA3-CA1 regions, due to their role in associative learning and memory, pattern separation and completion, and spatial navigation. Investigations into the neuronal organization and learning rule(s) of this circuit can shed light into how declarative memories are formed, stored, recalled and forgotten and inform computational, experimental and clinical neuroscience work. Our project aims at developing a novel data-driven methodology supported by a broad heterogeneous base of neuroscience experimental knowledge and inspired from advances in computer science and engineering. Specifically, this work will benchmark existing and new learning rules within a full-scale spiking neural network simulation of the CA3-CA1 region. The model will be based on an open-source repository, called the Hippocampome, which contains neuronal morphologies, firing patterns, synapse probabilities, and most other required parameters for all known neuron types in the rodent hippocampal formation. The model will be first trained in a supervised fashion for associative memory tasks using backpropagation through time traditionally used in computer science, enhanced with a new technique called the surrogate gradient method. This optimization method will be used to obtain a global loss minimization, but it is not biologically inspired as it assumes the use of data not locally available to the synapses. However, we propose its use as a benchmarking tool, to compare the training performance of local biologically plausible and hardware-mappable learning rules at scale. New rules or combinations will be proposed and tested as needed, based on the obtained results. Progress in this area will also drive the development of novel hardware-mappable algorithms for continual lifelong learning and categorization of new events from few presented examples. This project goes beyond the existing state-of-the-art by looking at large scale realistic neuronal circuits as networks trainable via global optimization methods such as surrogate gradient descent. The objective function of the brain that supports learning is largely unknown, but it is likely that it operates through local learning rules. Studying network trajectories around local minima as proposed in this work represents a useful strategy for understanding whether a network is training by using a specific (set of) learning rule(s). Starting from a completely untrained network is a challenging test since it is difficult to determine how the learning rule affects the trajectory of the network. This interdisciplinary project will help understand what rule governs learning in these regions or if multiple learning rules are involved. The work will develop a robust methodology to measure if the network is converging to the target solution, oscillating around it, or diverging away.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Stellar populations with MEGARA: The inner regions of NGC 7025

This paper aims to determine the capabilities of the MEGARA spectrograph at the Gran Telescopio Canarias (GTC), which is an optical integral-field unit, for studying stellar populations. We also aim to exploit its combination of high spectral (R ~ 6000, 12 000 and 20 000) and spatial (0.62") resolution within its 12".5 × 11".3 field of view. We do this by analysing the commissioning data of the nearby S0a galaxy NGC 7025. We establish a systematic method through which we can determine the properties of the stellar populations in the observations made with MEGARA, more specifically, within the MEGADES legacy project. For this paper in particular, we determine the properties of the stellar populations of NGC 7025. We used MEGARA observations of galaxy NGC 7025 that were taken during the commissioning phase of the instrument. We applied different approaches to estimate the properties of the stellar populations with the highest possible certainty. In addition to the specific study of NGC 7025 and in the context of the MEGADES survey, we have carried out a number of tests to determine the expected errors (including potential biases) in these star formation history (SFH) derivations as a function of these parameters, namely spectral setup, signal-to-noise ratio, σ, and the SFH itself. All the studies we conduct (both full spectral fitting and absorption line indices) of the stellar populations of NGC 7025 indicate that the stars that form its bulge have supersolar metallicity and considerably old ages (~10 Gyr) in general. Using three different combinations of MEGARA spectral setups, we determined that the bulge of NGC 7025 has smild negative mass-weighted age gradient. For the more detailed SFH, our results indicate that in addition to a rather constant star formation at early epochs, a peak in the formation history of the stars in the bulge is also found 3.5–4.5 Gyr ago. This partly explains the mass-weighted age gradients we measured. The scenario presented in NGC 7025 is that of an isolated galaxy under secular evolution that about 3.5–4.5 Gyr ago likely experienced a minor merger (mass ratio 1/10) that induced an increase in star formation and also perturbed the morphology of its outer disc. In addition to these specific results for NGC 7025, we report different lessons learned for the ongoing exploitation of the MEGADES survey with the GTC, such as the need to obtain combined observations in the LR-B + LR-V setups and a signal-to-noise ratio of at least 20 per Å.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Final Report to DOE EERE – Geothermal Technologies Program Project Title: Monitoring and modeling of fluid flow in a developing enhanced geothermal system (EGS) reservoir

The primary objective of this project was to improve our ability to predict performance of an Enhanced Geothermal System (EGS) reservoir over time by relating, in a quantitative manner, microseismic imaging with fluid and temperature changes within the reservoir. Historically, microseismic data have been used qualitatively to place bounds on the growth of EGS reservoirs created by large hydraulic fracturing experiments. Previous investigators used an experimentally based fracture opening relationship (fracture aperture as a function of pressure), the spatial extent of microseismic events, and some assumptions about fracture frequency to determine the size of an EGS reservoir created during large pumping tests. We addressed a number of issues (1) locating microearthquakes that occur during hydraulic fracturing, (2) obtaining more information about a reservoir than the microearthquake locations from the microearthquake data, for example, information about the seismic velocity structure of the reservoir or the scattering of seismic waves within the reservoir, (3) developing an improved methodology for estimating properties of fractures that intersect wellbores in a reservoir, and (4) developing a conceptual model for explaining the downward growth of observed seismicity that accompanies some hydraulic injections into geothermal reservoirs. We used two primary microseismic datasets for our work. The work was motivated by a dataset from the Salak Geothermal Field in Indonesia where seismicity accompanying a hydraulic injection was observed to migrate downward. We also used data from the Soultz EGS site in France. We also used Vertical Seismic Profiling data from a well in the United States. The work conducted is of benefit for characterizing reservoirs that are created by hydraulic fracturing for both EGS and for petroleum recovery.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Identification and overexpression of endogenous transcription factors to enhance lipid accumulation in the biotechnologically relevant species Chlamydomonas pacifica

Sustainable low-carbon energy solutions are critical to mitigating global carbon emissions. Algae-based platforms offer potential by converting carbon dioxide into valuable products while aiding carbon sequestration. However, scaling algae cultivation faces challenges like contamination in outdoor systems. Previously, our lab evolved Chlamydomonas pacifica, an extremophile green alga, which tolerates high temperature, pH, salinity, and light, making it ideal for large-scale bioproduct production, including biodiesel. Here, we enhanced lipid accumulation in evolved C. pacifica by identifying and overexpressing key endogenous transcription factors through genome-wide in-silico analysis and in-vivo testing. These factors include Lipid Remodeling Regulator 1 (CpaLRL1), Nitrogen Response Regulator 1 (CpaNRR1), Compromised Hydrolysis of Triacylglycerols 7 (CpaCHT7), and Phosphorus Starvation Response 1 (CpaPSR1). Under nitrogen deprivation, CpaLRL1, CpaNRR1, and CpaCHT7 overexpression enhanced lipid accumulation compared to wild-type. However, CpaPSR1 increased lipid accumulation compared to wild-type in normal media and did not increase further under nitrogen deprivation, highlighting the difference in function based on media conditions. Notably, lipid analysis of CpaPSR1 under normal media conditions revealed a 2.4-fold increase in triglycerides (TAGs) compared to the wild-type, highlighting its potential for biodiesel production. This approach provides a framework for transcription factor-focused metabolic engineering in algae, advancing bioenergy and biomaterial production.

Biofuels↗

DOE OSTI2025

Robust Synthetic Biology Toolkit to Advance Carboxysome Study and Redesign

Carboxysomes are polyhedral protein organelles that microorganisms use to facilitate carbon dioxide assimilation. They are composed of a modular protein shell that envelops an enzymatic core mainly composed of physically coupled Rubisco and carbonic anhydrase. While the modular construction principles of carboxysomes make them attractive targets as customizable metabolic platforms, their size and complexity can be a hindrance. In this work, we design and validate a plasmid set, the pXpressome toolkit, in which α-carboxysomes are robustly expressed and remain intact and functional after purification. We tested this toolkit by introducing mutations that influence carboxysome structure and performance. We find that deletion of vertex-capping genes results in formation of larger carboxysomes, while deletion of facet forming genes produces smaller particles, suggesting that adjusting the ratio of these proteins can rationally affect morphology. Through a series of fluorescently labeled constructs, we observe that this toolkit leads to more uniform expression and better cell health than previously published carboxysome expression systems. Overall, the pXpressome toolkit facilitates the study and redesign of carboxysomes with robust performance and improved phenotype uniformity. The pXpressome toolkit will support efforts to remodel carboxysomes for enhanced carbon fixation or serve as a platform for other nanoencapsulation goals.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Towards a Unified Low-Cost Flow Plate, Flow-Field, PTL Solution for Proton Exchange Membrane Electrolyzers

Proton exchange membrane (PEM) water electrolysis is a highly efficient method for hydrogen production. Research cells typically consist of one proton exchange membrane, two catalyst layers, two porous transport layers, two flow-field plates, and two endplates. In commercial systems, the machined flow-field plates that are employed in research cells are typically replaced by stamped parts or open mesh material solutions to reduce manufacturing cost at scale. Nonetheless, the cell contains about 8 total interfaces: bipolar plate / flow plate material / porous transport medium / electrode / membrane / electrode / porous transport medium / flow plate material / bipolar plate. All these materials and interfaces need to be optimized for maximum performance and efficiency. Reducing the amount of interfaces by combining individual cell components directly benefits the fabrication cost (by reducing the parts count and the needs for surface coatings) and the electrochemical performance (by reducing ohmic losses). We have designed a novel PEM electrolysis cell with a piece of channeled titanium felt functioning as both the anode flow-field and the PTL, referred to as the channeled diffusion layer (CDL). The pores of the felt facilitate both in-plane and through-plane diffusion, ensuring maximum catalyst utilization while also minimizing mass transport loss. The titanium felt can be mass manufactured with existing stamping and forming methods and is therefore a promising candidate to reduce the capital cost of PEM electrolyzers whilst improving hydrogen production efficiency. Experiments conducted with 3mg IrOx/cm2 loading MEAs have shown a approximately 40% boost in peak current by implementing the CDL design. Low catalyst-loading MEAs are being tested in ongoing experiments and their results will be discussed and compared.

08 HYDROGEN↗

DOE OSTI2023

Comparison of metagenomes from fermentation of various agroindustrial residues suggests a common model of community organization

The liquid residue resulting from various agroindustrial processes is both rich in organic material and an attractive source to produce a variety of chemicals. Using microbial communities to produce chemicals from these liquid residues is an active area of research, but it is unclear how to deploy microbial communities to produce specific products from the different agroindustrial residues. To address this, we fed anaerobic bioreactors one of several agroindustrial residues (carbohydrate-rich lignocellulosic fermentation conversion residue, xylose, dairy manure hydrolysate, ultra-filtered milk permeate, and thin stillage from a starch bioethanol plant) and inoculated them with a microbial community from an acid-phase digester operated at the wastewater treatment plant in Madison, WI, United States. The bioreactors were monitored over a period of months and sampled to assess microbial community composition and extracellular fermentation products. We obtained metagenome assembled genomes (MAGs) from the microbial communities in each bioreactor and performed comparative genomic analyses to identify common microorganisms, as well as any community members that were unique to each reactor. Collectively, we obtained a dataset of 217 non-redundant MAGs from these bioreactors. This metagenome assembled genome dataset was used to evaluate whether a specific microbial ecology model in which medium chain fatty acids (MCFAs) are simultaneously produced from intermediate products (e.g., lactic acid) and carbohydrates could be applicable to all fermentation systems, regardless of the feedstock. MAGs were classified using a multiclass classification machine learning algorithm into three groups, organisms fermenting the carbohydrates to intermediate products, organisms utilizing the intermediate products to produce MCFAs, and organisms producing MCFAs directly from carbohydrates. This analysis revealed common biological functions among the microbial communities in different bioreactors, and although different microorganisms were enriched depending on the agroindustrial residue tested, the results supported the conclusion that the microbial ecology model tested was appropriate to explain the MCFA production potential from all agricultural residues.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network

Machine learning (ML) is emerging as a promising tool for modeling hydro-ecological processes due to the increasing availability of large environmental data. However, the use of ML requires sufficient programming knowledge due to a lack of a graphical user interface (GUI). In this study, we introduced a GUI package, named HydroEcoLSTM, with the long short-term memory network (LSTM) as the core model, that allows non-ML experts to utilize their domain knowledge to construct complex ML models. We demonstrated the functionalities of HydroEcoLSTM with two practical examples, including (1) predictions of streamflow in both gauged and ungauged catchments and (2) predictions of multiple outputs (i.e., streamflow and isotope transport from two catchments). The simulation results obtained in both case experiments are satisfactory. In the first example, the average Nash–Sutcliffe Efficiency (NSE) for streamflow simulation during the testing period is 0.79 while the application of the trained model in two assumed ungauged catchments also achieves the average NSE of 0.68. In the second example, the average NSE for streamflow and instream isotope simulation during the testing period is 0.71. Ultimately, applications of HydroEcoLSTM with real-world examples demonstrate its potential use for practical applications and research without requiring extensive coding skills.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Reducing Mass of Steel Auto Bodies using Thin Advanced High Strength Steel with Carbon-Fiber Reinforced Epoxy

Diversitak, a company based in Detroit, MI, has developed a proprietary, low specific gravity, carbon fiber-reinforced epoxy (CFRE) under U.S. patent number 9,963,58832. Preliminary testing on this new material conducted in collaboration with ArcelorMittal Steel Company proved out the CFRE concept. A thin layer of this CFRE was applied to a stamped sheet of steel with residual stamping oils from a mill, in a time corresponding to automotive processing (e.g., ~15 seconds), and processed following automotive e-coat procedures (phosphating + 175–200°C heating), to complete the curing. No problems with adherence or performance were noted. While the CFRE does add weight to a thin gauge steel panel, it weighs much less than what is displaced by using thicker conventional mild steel gauges. The application of the coating showed a significant increased dent resistance, oil canning resistance, and part stiffness.This current two-year project was designed to mature this new technology to near manufacturing readiness to reduce the weight of a vehicle and lower the cost of weight reduction. The process involves the use of thinner gauge steels than are currently used. The collaborative development team included two industrial manufacturers: Diversitak and ArcelorMittal Steel Company; LightMAT; and two National Laboratories: Oak Ridge National Laboratory (ORNL) and Idaho National Laboratory (INL). The team developed a new manufacturing process to reduce the weight of a vehicle and lower the cost of weight reduction, as well as a better understanding of how to apply the coating so that it will perform to a high standard in-service. The team also performed an in-depth study to determine the long-term durability of the materials manufactured using this technology and well-known automotive industry standard tests.The overall process involved stiffening the thinner gauge steel by applying the CFRE on only one side. To accomplish this goal, the optimal reinforcement fiber length and fiber concentration was first determined. This was followed by measuring the coefficient of thermal expansion (CTE) in all three directions, so it could be fed into manufacturing models and methods for rapidly and inexpensively applying the coating. This was followed by panel level evaluations of the coating and steel combination, and then by full part demonstration of the technology on door panels. The final step was corrosion testing of the parts.ArcelorMittal characterized the advanced high strength steel (AHSS) (e.g., metallurgy-heat treatment for required AHSS properties as a function of the sheet thickness, state of internal stress) and quantified CFRE adhesion to the steel as a function of sheet preparation (e.g., rolling and stamping).ORNL optimized the fiber length, fiber concentration, and coating thickness for best vehicle function and performance at the least cost. Along with the suppliers, ORNL developed a durable CFRE application process (e.g., gun material, design, robotic dispensing process) and identified the adhesion stability of the CFRE during process holding. An approach to ensure that application/curing timing conforms to conventional assembly line speed and plant cycle times was determined. ORNL also determined the CTE of the material in all three directions and performed material scanning electron microscopy (SEM) analyses.INL characterized the corrosion properties of the steel panels coated with CFRE. The panels were investigated for corrosion resistance and stability as replacement materials used in automotive body panels to reduce mass. The coupons tested at INL were supplied by Diversitak after an optimized CFRE formulation was achieved in the already coated form for corrosion testing.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Divide and conquer: separating the two probabilities in seismic phase picking

There are two fundamental probabilities in the seismic phase picking process—the probability of the existence of a seismic phase (detection probability) and the probability associated with the phase arrival time estimation (timing probability). The nearly ubiquitous approach in developing deep learning phase picking models is to use a kernel, such as a truncated Gaussian, to mask the labelled phase arrival time and train a segmentation model. Once a model is trained, the times of the peaks in the output are taken as phase arrival times (picks), and the height of the peaks are taken as ‘probability’ of the picks. Here, we show that this ‘probability’ represents neither the detection nor the timing probability because this approach forces the output to follow the shape of the kernel. We introduce an approach using two models to estimate these two distinct probabilities. We use a binary classifier with a calibrated confidence to address the detection probability and a multiclass classifier to obtain a probability mass function to address the timing probability. This new approach can make the deep learning-based phase picking process more interpretable and provide options to logically control seismic monitoring workflows.

58 GEOSCIENCES↗

DOE OSTI2026

The Explosive Hazards of Icosahedral Carboranes and Dodecaborate with Common Oxidizers and Salts

Carboranes find use in a wide variety of applications ranging from specialty catalysts to medicinal uses. Certain functionalized carboranes are described as explosive materials, although their nonfunctionalized precursors are regarded as inherently stable and not explosive. Herein, we report new findings on the sensitization of nonfunctionalized 12-vertex ortho-, meta-, and para-carboranes as well as dodecaborate in physical mixtures with several common inorganic salts. These mixtures were evaluated for explosive properties with drop hammer impact tests, friction tests, and differential scanning calorimetry thermal gravimetric analysis (DSC-TGA). Results indicate explosive sensitization to mechanical stimuli for the nonfunctionalized carboranes tested with a wide variety of inorganic salts, especially oxidizers. Sensitization of these compounds has not previously been reported, and raising awareness to the broader scientific community on this matter is warranted, especially as several of the mixtures evaluated are more sensitive to mechanical stimuli than primary explosives such as lead azide or pentaerythritol tetranitrate.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Simulink Modeling and Dynamic Study of Fixed-Speed, Variable-Speed, and Ternary Pumped Storage Hydropower

Pumped Storage Hydropower (PSH) is one of the most popular energy storage technologies in the world. It uses an upper reservoir to store water which can be later used during high-demand. In the United States, most of the energy storage capability actually corresponds to PSH. Moreover, PSH also brings multiple benefits to grid operation. This report presents the Simulink models of three common PSH technologies: Fixed-Speed (FS), Variable-Speed (VS), and Ternary (T)-PSH. These models are available to the general public on this GitHub repository, which contains the MATLAB model initialization files, the Simulink model files, and supplementary MATLAB code used to obtain the figures in this work. For each PSH model, an introductory description of the model components and other relevant functionalities are provided. For further information regarding the models and the initialization parameters, the reader is referred to the shared files in the repository. This report also presents the dynamic behavior of each model. The response of such models to a load event is analyzed and matched with each model's features. A custom IEEE 39 bus case is employed for the FS and T-PSH simulations, while the VS-PSH is simulated on a simplified three-bus test system due to the computational complexity of the model. For the T-PSH, the steady-state and the switching between several operating modes are also studied in this work.

13 HYDRO ENERGY↗

DOE OSTI2025

Compact in-vacuum gamma-ray spectrometer for high-repetition rate PW-class laser–matter interaction

With the advent of high repetition rate laser facilities, novel diagnostic tools compatible with these advanced specifications are required. This paper presents the design of an active gamma-ray spectrometer intended for these high repetition rate experiments, with particular emphasis on functionality within a PW level laser-plasma interaction chamber’s extreme conditions. The spectrometer uses stacked scintillators to accommodate a broad range of gamma-ray energies, demonstrating its adaptability for various experimental setups. In addition, it has been engineered to maintain compactness, electromagnetic pulse resistance, and ISO-5 cleanliness requirements while ensuring high sensitivity. The spectrometer has been tested in real conditions inside the PW-class level interaction chamber at the BELLA center, LBNL. The paper further details the calibration process, which utilizes a 60 Co radioactive source, and describes the unfolding technique implemented through a stochastic minimization method.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Scan2Sim: Software to Convert Network Scans to Emulations

Within operational technology (OT) systems design, the construction of testing environments for simulation is often a tedious, manual process that slows down safety and security evaluations. This document details the design and functionality of Scan2Sim, a program designed to construct high-fidelity topological schematics for OT systems without significant manual human input. Scan2Sim may take as input a detailed network scan of a system, and produces an instruction set to re-create the original scanned network within a virtualized simulation network. This construction is achieved via heuristic methods of machine template selection, which allows for a fast, performant approach to automated environment construction. The current tool is designed to produce topology schematics compatible with the Minimega, a tool designed by Sandia National Laboratories for repeatable experimentation management.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗

DOE OSTI2025

Testing the lepton content of the proton at HERA and EIC

Although protons are baryons with an overall vanishing lepton number, they possess a non-trivial leptonic content arising from quantum fluctuations which can be described by lepton parton distribution functions (PDFs) of the proton. These PDFs have been recently computed and can be used to define lepton-induced processes at high-energy colliders. In this article, we propose a novel way to test the computation of lepton PDFs of the proton by analyzing both non-resonant di-lepton and resonant Z gauge boson production processes induced by leptons within the proton at proton-electron colliders like HERA and EIC. Despite the fact that lepton PDFs of the proton are known to be small, this work demonstrates that both processes imply a measurable yield of events at HERA and EIC, which could be used to test these PDFs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software

A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗