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DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗

DOE OSTI2020

Interpenetrating Plasma Simulations

This project investigated multifluid models for simulating interpenetrating plasmas. These models solve the Euler equations for each plasma stream along with an appropriate model for electron dynamics. The project developed a novel code, Euclid, that simulates these models using high-order conservative spatial discretization methods and explicit and semi-implicit time integration methods. Euclid was used to solve several multifluid test cases representative of plasma dynamics in high-energy density physics experiments. Kinetic simulations of interpenetrating flows using the LOKI code revealed novel physical effects of the ion-ion streaming instability. A new multi-flow collision operator was developed for the LOKI code. A simple model of the ion-ion streaming instability was also developed. Several presentations were given on the project results; three journal articles have been published, one has been submitted, and two are planned.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Assessment Framework of Marine Hydrokinetic Technologies for Microgrid Applications

Communities in Alaska that are not connected to a regional grid (referred to hereafter as grid-islanded Alaskan communities) rely heavily on stand-alone generators with imported diesel fuel as the primary source of energy. Several of these grid-islanded Alaskan communities have the potential to harness significant wave, tidal, and hydrokinetic power; many also have hydropower or wind potential that could complement these resources. The implementation of marine and hydrokinetic energy focused microgrids in these communities would diversify their energy profiles, with the potential in many communities to keep costs flat while reducing dependance on diesel. This would enhance resilience and reduce environmental impacts. This seedling proposal will systematically identify grid-islanded coastal communities in Alaska with wave, tidal, and hydrokinetic energy potential to add fuel diversity to generation, applying microgrid integration methods and strategies that will meet the community’s needs. This proposal is in partnership with the Alaska Center for Energy and Power (ACEP) and XENDEE Corporation who bring extensive expertise in Alaskan communities and microgrid technoeconomic assessment, respectively. This project will deliver: (1) a database of grid-islanded communities that are candidates for MHK based microgrids, (2) an assessment of possible microgrid integration methods and strategies related to tidal, wave, and hydrokinetic technologies, and (3) a MHK development plan for the XENDEE Microgrid design platform and planning tool that can serve these grid-islanded communities and project developers. In the final stage, the planning tool will allow for the comprehensive comparison between diesel based and MHK based renewable generation.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

Implementation of an Experimental Setup Based on the Three-Omega Method for Thermal Conductivity Measurements of Molten Salts

Molten salt reactors (MSRs) are one of the most promising next-generation nuclear reactor designs which use molten salts as the fuel and coolant. To design, model, predict, license, and operate MSRs, the behavior of the fuel and coolant must be understood as a function of temperature and irradiation, i.e., burnup. Of particular importance and complexity is the reliable and reproducible measurement of thermal conductivity of molten salts at high temperatures (> 500 °C). Thermal conductivity provides information on the rate at which heat passes through a specified material and is dependent on chemical composition. Currently, in the national laboratory complex, several methods for measuring thermal conductivity on molten salt systems are under development, however, to date, consistent data have not been produced. Typically, thermal conductivity measurements of high-temperature melts have been conducted by using the Transient Hot Wire (THW) method where thermal conductivity is determined by the change in temperature over time at a certain distance from a heating wire. Another time-dependent technique is the laser flash method, commonly used for solid samples. Laser flash requires prior measurements of density and heat capacity to calculate the thermal conductivity of the sample, introducing additional measurement errors. Steady state and time-dependent measurements such as THW and laser flash have errors from convection and ambient temperature fluctuations (heat radiation). In order to produce high fidelity thermal conductivity data, a measurement technique needs to be developed that will reduce the effect of convection at high temperatures by measuring in a very fast time window. In addition, to measure on actinide and irradiated salts the sample size should be small. Therefore, this project aims to develop a frequency-domain technique based on the three-omega (3?) method, previously proven to work on solids, nanofluids and low temperature solar salts (25 to 250 °C). If the 3?-device can be designed with materials that can withstand high temperatures (>500 °C) and can be configured to reduce errors due to convection, then it would prove to be a valuable technique for ensuring thermal conductivity on molten salt systems needed for MSR development. For this proposal, the 3? instrument will be designed, assembled, and tested on non-radioactive salts before being used on actinide bearing salts. In addition, a thermal model will be developed to determine the thermal conductivity of the sample from the measured thermal oscillation of the sensor. Because 3? is a direct method that requires a small volume of sample and works in a very fast time window, it will aid in suppressing the interference due to convection which is ideal when working with high temperature liquid samples.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

SECARB-USA: Data Quality Methodology

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Data Quality Methodology (4.2.1)

SECARB-USA Deliverable 4.2.1 utilizes the outputs from the Needs Assessment (Subtask 2.1) to develop a data quality methodology. Many types of data are needed to evaluate a site for technical and financial viability. Several inventories of data types have been produced (for example NETL, 2010). The objective here is to organize the data types so that the needs met are specified. From this cross index (Table 1 in the Appendix), it will be possible in future tasks to (1) determine, on a site-specific basis, how much of each data type is required at each stage of a project to meet the need, and conversely (2) to further specify and define the data collection methods applied such that the data are tailored to fit that need. Derivative tables can then be developed to semi-quantitively evaluate the extent to which need is critical for early go/no decision points, or if it is more important than average, requiring faster or larger capitalization and spend to meet the need. In an additional step, the current availability of data for a site can be semi-quantitively assessed. From the table of data criticality and the table of data availability, site-specific cost for meeting the data needs can be determined, and allow a pre-permit spend estimated for a portfolio of projects. All of the data needs to evaluate a site are somewhat interconnected. We used criterion (2) above to determine if the data collection design would have to be modified to meet the need; if this was commonly true a linkage was shown. The application of this cross index to sites in the subsequent tasks will demonstrate that the demand for data types varies site-to-site and project-to-project. Examples of factors to be considered are the complexity of the geology, the injection goals such as rate and duration of the injection, and the types of risk and risk tolerance of key stakeholders. In future tasks the team will compare the demand for data with existing data availability. This will, in turn, determine when data needs to be acquired to support project development. For example, in a project area with complex structure, 3-D seismic data may be needed earlier and more urgently than in an area with simple rock body geometries. In some locations, a 3-D seismic survey has already been collected and can be purchased. In other locations the project developer will need to collect these data. For another example, a project near an urban area or near to a park may generate earlier and more substantive public concern than a site that is developed in mined lands. A calculation using the derivative from table 1 will show the different investment needs. Project cost will vary corresponding to data criticality and data availability.

42 ENGINEERING↗

DOE OSTI2024

Development of multi-scale computational frameworks to solve fusion materials science challenges

Over the past two decades, the US-DOE has funded multiple projects that rely on high-performance computing and exascale computing platforms to accelerate scientific discoveries and address grand scientific challenges, such as harnessing fusion energy. In this article, we review in detail one of these efforts aimed at enhancing our capability to model plasma-facing materials subject to plasma and high-energy ion/neutron irradiation. The plasma surface interactions project has built a multi-scale modeling framework where many of the plasma- and high-energy ion/neutron irradiation-induced effects occurring in tungsten are explored. Here, this knowledge is used to develop atomistically-informed, high-fidelity continuum and meso-scale models that can be validated against experiments. We review the developments within this project, with attention to experimental validation efforts, and specifically highlight activities associated with: helium bubble bursting and equation of state, and hydrogen-helium interactions in tungsten; atomistically-informed model development for beryllium-tungsten material mixing; coupling of scrape-of-layer plasma, sheath and material models; and coupling of stochastic cluster-dynamics and crystal plasticity models to address radiation effects in tungsten under stress. Finally, we present how the project is preparing for future computational architectures, for instance through efforts to adapt atomistic methods to exascale computing.

36 MATERIALS SCIENCE↗

DOE OSTI2021

py4DSTEM: A Software Package for Four-Dimensional Scanning Transmission Electron Microscopy Data Analysis

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full two-dimensional (2D) image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields, and other sample-dependent properties. However, extracting this information requires complex analysis pipelines that include data wrangling, calibration, analysis, and visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open-source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail and present results from several experimental datasets. We also implement a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open-source HDF5 standard. We hope this tool will benefit the research community and help improve the standards for data and computational methods in electron microscopy, and we invite the community to contribute to this ongoing project.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

X-ray phase contrast and absorption imaging for the quantification of transient cavitation in high-speed nozzle flows

In this work, high-flux synchrotron radiation has been employed in a time-resolved manner to characterise the distinct topology features and dynamics of different cavitation regimes arising in a throttle orifice with an abrupt flow-entry contraction. Radiographs obtained though both X-ray phase- contrast and absorption imaging have been captured at 67,890 frames per second. The flow lied in the turbulent regime (Re=35,500), while moderate (CN=2.0) to well-established (CN=6.0) cavitation conditions were examined encompassing the cloud and vortical cavitation regimes with pertinent transient features, such as cloud-cavity shedding. X-ray Phase-Contrast Imaging (XPCI), exploiting the shift in the X-ray wave phase during interactions with matter, offers sharp-refractive index gradients in the interface region. Hence, it is suitable for capturing fine morphological fluctuations of transient cavitation structures. Nevertheless, the technique cannot provide information on the quantity of vapour within the orifice. Such data have been obtained utilising absorption imaging, where beam attenuation is not associated with scattering and refraction events, and hence can be explicitly correlated with the projected vapour thickness in line-of-sight measurements. A combination of the two methods is proposed, as it has been found is capable of quantifying the vapour content arising in the complex nozzle flow, while also faithfully illustrating the dynamics of the highly-transient cavitation features.

42 ENGINEERING↗

DOE OSTI2020

Veridical data science

Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework, composed of both a workflow and documentation, aims to provide responsible, reliable, reproducible, and transparent results across the data science life cycle. The PCS workflow uses predictability as a reality check and considers the importance of computation in data collection/storage and algorithm design. It augments predictability and computability with an overarching stability principle. Stability expands on statistical uncertainty considerations to assess how human judgment calls impact data results through data and model/algorithm perturbations. As part of the PCS workflow, we develop PCS inference procedures, namely PCS perturbation intervals and PCS hypothesis testing, to investigate the stability of data results relative to problem formulation, data cleaning, modeling decisions, and interpretations. We illustrate PCS inference through neuroscience and genomics projects of our own and others. Moreover, we demonstrate its favorable performance over existing methods in terms of receiver operating characteristic (ROC) curves in high-dimensional, sparse linear model simulations, including a wide range of misspecified models. Finally, we propose PCS documentation based on R Markdown or Jupyter Notebook, with publicly available, reproducible codes and narratives to back up human choices made throughout an analysis. The PCS workflow and documentation are demonstrated in a genomics case study available on Zenodo.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Score-based diffusion models for generating liquid argon time projection chamber images

For the first time, we show high-fidelity generation of Liquid Argon Time Projection Chamber (LArTPC-like) data using a generative neural network. This demonstrates that methods developed for natural images do transfer to LArTPC-produced images, which, in contrast to natural images, are globally sparse but locally dense. We present the score-based diffusion method employed. We evaluate the fidelity of the generated images using several quality metrics, including modified measures used to evaluate natural images, comparisons between high-dimensional distributions, and comparisons relevant to LArTPC experiments. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2025

Latent diffusion can map beam loss to two-dimensional phase-space projections

Beam loss monitors (BLMs) and beam current monitors (BCMs) are ubiquitous at particle accelerators around the world. These simple devices provide noninvasive high-level beam measurements but give no insight into the detailed 6D (𝑥,𝑦,𝑧,𝑝 𝑥 ,𝑝 𝑦 ,𝑝 𝑧 ) beam phase-space distributions or dynamics. We show that generative conditional latent diffusion models can learn intricate patterns to solve the extreme inverse problem of mapping waveforms of tens of BLMs or BCMs along an accelerator to detailed 2D projections of a charged particle beam’s 6D phase-space density. This transformational method can be used at any particle accelerator to transform simple noninvasive devices into detailed beam phase-space diagnostics. We demonstrate this concept via multiparticle simulations of the high-intensity beam in the kilometer-long Los Alamos Neutron Science Center linear proton accelerator.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Data-Driven Generic Turbines for Distributed Wind Modeling, Optimization, and Economic Studies

As distributed energy resources (DER) become less expensive and more popular, utilities, project developers, and customers have an increasing need to model the performance of existing and proposed DER systems. Distributed wind has been shown to have widespread economic potential but is often represented by a simplified model in - or excluded from - DER modeling tools and studies. There is often no economic imperative to extend models and studies to give full consideration to distributed wind. We present a set of data-driven generic turbines derived from 16 years of annual distributed wind market survey data. The proposed methodology can be used to derive generic turbines from separate or updated data sets. Finally, a mixed-integer linear programming approach to optimal distributed wind project sizing is used to demonstrate the generic turbine models. Combined, these models and methods can reduce barriers to considering distributed wind in modeling tools and studies.

Reiman, Andrew P.↗

DOE OSTI2021

Structure Perception in 3D Point Clouds

Understanding human perception is critical to the design of effective visualizations. The relative benefits of using 2D versus 3D techniques for data visualization is a complex decision space, with varying levels of uncertainty and disagreement in both the literature and in practice. This study aims to add easily reproducible, empirical evidence on the role of depth cues in perceiving structures or patterns in 3D point clouds. We describe a method to synthesize a 3D point cloud that contains a 3D structure, where 2D projections of the data strongly resemble a Gaussian distribution. We performed a within-subjects structure identification study with 128 participants that compared scatterplot matrices (canonical 2D projections) and 3D scatterplots under three types of motion: rotation, xy-translation, and z-translation. We found that users could consistently identify three separate hidden structures under rotation, while those structures remained hidden in the scatterplot matrices and under translation. This work contributes a set of 3D point clouds that provide definitive examples of 3D patterns perceptible in 3D scatterplots under rotation but imperceptible in 2D scatterplots.

data analysis↗

DOE OSTI2020

Comprehensive Chemical Fingerprinting by Multidimensional GC and Supervised Machine Learning

This project leverages advances in machine learning based data analysis techniques and untargeted omic analytical methods to progress nuclear nonproliferation technologies beyond current capabilities. The developed approaches can be used to identify and detect complex chemical fingerprints of facilities of interest. These techniques have been developed for fields such as metabolomics and genomics but have not been applied to nuclear nonproliferation applications. Adaptation of these techniques for volatile organic compound analysis has far reaching application within the scientific community including environmental chemistry, atmospheric physics, and climate sciences.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Optimal Header Design for Diffusion Bonded Heat Exchangers

Microchannel heat exchangers have seen increasing adoption in many high-pressure applications in recent decades but are subject to particulate fouling from the relatively small channel size compared to traditional designs. Typical cleaning methods require process shutdown, heat exchanger removal, cleaning, then reassembly. The objective of this project was to refine and transfer technology to enable header design improvements for Cleaning-in-Place (CIP), allowing for reduced/negligible process interruption for the cleaning process. The technology transfer was from Sandia National Laboratories (Sandia) to Vacuum Process Engineering, Inc. (VPE). This primary purpose of CIP was developed while considering channel flow uniformity and heat exchanger cost. The project phases were to 1) capture and define potential improvement options, 2) evaluate options with both simulation and experiments, and 3) transfer design knowledge to the industry partner. These efforts resulted in improved header designs from the first known focused effort in this area. The improved designs will help the entire microchannel heat exchanger field that has applications in supercritical CO 2 power cycles, hydrogen (fuel cell) vehicle fueling, liquified natural gas processing, and more.

42 ENGINEERING↗

DOE OSTI2021

Structure-preserving numerical discretizations for domains with boundaries

This SAND report documents Exploratory Express LDRD Project 223790, "Structure-preserving numerical discretizations for domains with boundaries", which developed a method to incorporate consistent treatment of domain boundaries and arbitrary boundary conditions in discrete exterior calculus (DEC) for arbitrary polygonal (2D) and tensor-product structure prism (3D) grids. The new DEC required the development of novel discrete exterior derivatives, boundary operators, wedge products and Hodge stars. This was accomplished through the use of boundary extension and the blending of known 2D operators on the interior with 1D operators on the boundary. The Hodge star was based on the Voronoi Hodge star, and retained the limitation of a triangular circumcentric primal or dual grid along with low-order accuracy. In addition to the new DEC, two related software packages were written: one for the study of DEC operators on arbitrary polygonal and polyhedral grids using both symbolic and numerical approaches and one for a (thermal) shallow water testbed using TRiSK-type numerics. Immediately relevant (already funded, through CANGA) followup work is the development of a high-order, geometrically flexible Hodge star and structure-preserving, high-order, oscillation-limiting transport operators (using WENO) for n-forms on arbitrary 2D and 3D grids. This will provide all of the machinery required for a high-order version of TRiSK with boundaries on arbitrary 2D and tensor-product 3D grids, which is applicable to both the atmospheric (CRM in E3SM-MMF) and oceanic (MPAS-O) components of E3SM.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Resilience Enhancements through Deep Learning Yields

This report documents the Resilience Enhancements through Deep Learning Yields (REDLY) project, a three-year effort to improve electrical grid resilience by developing scalable methods for system operators to protect the grid against threats leading to interrupted service or physical damage. The computational complexity and uncertain nature of current real-world contingency analysis presents significant barriers to automated, real-time monitoring. While there has been a significant push to explore the use of accurate, high-performance machine learning (ML) model surrogates to address this gap, their reliability is unclear when deployed in high-consequence applications such as power grid systems. Contemporary optimization techniques used to validate surrogate performance can exploit ML model prediction errors, which necessitates the verification of worst-case performance for the models.

24 POWER TRANSMISSION AND DISTRIBUTION↗