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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Joint Topology Identification and State Estimation in Unobservable Distribution Grids

Many distribution system operations (e.g., state estimation, control, fault detection/localization) rely on the assumption that the underlying topology is accurately defined. In general, topology identification is a challenging problem in distribution systems as these systems are unobservable with a very limited number of available measurements. In this paper, we tackle this problem by designing a compressive sensing framework that jointly estimates the systems states and network topology via an integrated mixed integer nonlinear program (MINLP) formulation. Here, two reformulations of the original MINLP problems are investigated. Firstly, in order to remove the nonlinearity in the MINLP formulation, a mixed integer linear programming (MILP) problem that employs auxiliary variables is derived. Furthermore, to achieve a faster solution, convex relaxation of the original formulation is derived. Finally, using a Markovian model for topology changes, prior information about system topology is used to improve topology identification particularly when a limited amount of measurements is available. Simulation results on IEEE 37-bus test feeder and IEEE 123-bus test feeder illustrate the efficiency and scalability of the proposed approaches from both state estimation and topology identification point of view (even with 30% of available data).

42 ENGINEERING↗

DOE OSTI2021

Contributions to the BeEST sterile neutrino search

Sterile neutrinos are hypothetical right-handed leptons that have been postulated as extensions to the Standard Model to help explain the origin of neutrino masses and the matter-antimatter asymmetry in the Universe. Sterile neutrinos with keV-scale masses are also a promising candidate for dark matter. LLNL and the Colorado School of Mines are developing an experiment to search for sterile neutrinos by measuring the \Beryllium-7 Electron capture in Superconducting Tunnel junctions (STJs)" (the \BeEST" Experiment). When 7 Be implanted into STJ radiation detectors decays by electron capture, the neutrino escapes and the energy of the recoiling 7 Li nucleus can be measured with high accuracy. Using conservation of energy and momentum, the mass of the emitted neutrino can then be found. Sterile keV neutrinos would generate additional peaks in the spectrum, and the experiment uses high-resolution STJs to observe such a potential signal. During my internship, I contributed to different aspects of the BeEST experiment. I designed a UHV setup for X-ray spectroscopy analysis with STJs. It will allow high-resolution X-ray spectroscopy of different materials to examine the broadening in the BeEST spectra, the origins of which are unknown but may be related to material effects inside the STJ detectors. The design accounts for the specific demands of the BeEST project, but provides a degree of flexibility that makes it useful for other potential STJ projects. Additionally, to test the accuracy of the electronics and data analysis routine, an arbitrary waveform generator (AWG) will be used to generate simulated spectra that contain sterile neutrino signals of a specified mass and admixture. For this I programmed an AWG to output random STJ signals whose amplitudes follow the appropriate user-de ned probability distribution. The AWG signals can be input into the 32-channel preamplifier to simulate any spectrum of interest and test the uniformity of the amplifier chain, as well as the sensitivity of the data analysis.

42 ENGINEERING↗

DOE OSTI2023

Extending PETSc's Composable Hierarchical Solvers (Final Technical Report)

This report documents research activities conducted at CU Boulder as part of Extending PETSc’s Composable Hierarchical Solvers, which has been part of a collaboration with Argonne National Laboratory (separate award). Our work has focused on performance-portable end-to-end GPU solvers demonstrated via exemplary applications in nonlinear fluid and structural mechanics. We describe advances in algorithmic composition and analysis in the context of these applications, but the implementations are fully documented and decoupled, and in use by other projects. We believe the vertical integration achieved through collaboration with ECP’s CEED and the PSAAP center at CU was necessary to take risks with data structures and algorithms.

42 ENGINEERING↗

DOE OSTI2024

Manufacturing Supply Chain Development for Modular Solar-Thermochemical Conversion Platform [Abstract]

We will develop a tunable pulsed electron source capable of generating variable intensity highly-focused electron pulses at high repetition rates. Specifically, we need to demonstrate a high-speed variable electron gun capable of generating a variable intensity electron pulse containing between 102 and 105 electrons with an accelerating voltage of 1kV focused into a spot size <100nm in diameter at with a pulse rate >10MHz (with path to 300MHz). This combined performance cannot be achieved by commercially available electron sources. The primary technology path selected for this work will be to use a photocathode approach, where an easily modulated UV laser is used to stimulate electron emission. The approach utilizes the advances made in the development of the Dynamic TEM (DTEM), though this project will be simpler in that the pulse intensity is lower and there is no requirement for coherency. Alternate paths, including using a traditional electron beam modulated using a v-shaped slit and electrostatically deflecting the beam to adjust intensity; will also be considered for future capabilities. The electron source will then be used generate optically stimulated luminescence (OSL) in lithium fluoride (LiF) substrates to demonstrate the potential for OSL to serve as a basis technology for next generation data storage systems.

42 ENGINEERING↗

DOE OSTI2024

Optically Stimulated Luminescence Data Storage (Abstract)

42 ENGINEERING↗

DOE OSTI2020

Solid State NMR data

Applying multi-dimensional solid state NMR to explore the nanoarchitecture of native and engineered plant cell walls

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Systems, devices and methods for communicating data over circularly pulse-shaped waveforms

Circularly pulse-shaped waveforms for communication systems are disclosed herein, including a single carrier modulation in which pulse-shaping is performed using a circular convolution by the transmitter for various modulation schemes. A transmitter, related method, and corresponding receiver are also disclosed for demodulation of the single carrier circularly pulse-shaped signal and data extraction.

42 ENGINEERING↗

DOE OSTI2022

A Structural engineering Graduate Degree Program with specialization in structural health monitoring & non-destructive evaluation [Slides]

The promotional presentation discusses the graduate degree program at the University of California, San Diego, Jacobs School of Engineering, related to structural engineering. Structural Health Monitoring (SHM) encompasses the integration of a broad range of technologies from sensing/actuating, data interrogation, and statistical modeling to enable system-level or component-level health assessment and predictive modeling. The course is available via distance learning to Los Alamos National Laboratory engineers.

42 ENGINEERING↗

DOE OSTI2022

Development of Methane Emissions Model to Assess Fuel Recovery Potential at Gas Well Sites Using On Site Compression

The U.S. natural gas production and consumption has increased 85.5% since 2005 primarily due to the unconventional production methods of horizontal drilling and hydraulic fracturing. Natural gas used as a fuel has a lower greenhouse gas (GHG) footprint than coal and petroleum due to lower Carbon Dioxide (CO2) emissions when combusted. However, the “greener” benefit to natural gas may be negated by leaks in production and transmission systems. Methane (CH4), the primary hydrocarbon in natural gas, has an estimated Global Warming Potential (GWP) of 28-36 over 100 years, meaning it can absorb 28-36 more energy than CO2 which has a GWP of 1.0. Natural gas well sites are prone to methane emissions, or leaks and irregular gas releases, vented to atmosphere throughout production and transmission. The U.S. Department of Energy (DOE) and the National Energy Technology Laboratory (NETL) has recently granted West Virginia University (WVU) funding under agreement DE-FOA-0002005, to “Advance technologies to mitigate methane emissions and increase the efficiency of the natural gas transportation infrastructure”. As part of this funding WVU was tasked with identifying and quantifying sources of methane emissions at unconventional well sites, processing this data, and developing a system to recapture these emissions. A 0-D Simulink model was developed, utilizing standardized methodologies, data from previously conducted studies, as well as collected data from well sites in the Marcellus shale play region. The model was developed to predict emission rates from various components at natural gas well sites as well as the potential to utilize these emissions as fuel for the natural gas powered compressor engines on-site. This model was utilized to run high, medium, and low cases for four identified emission sources, engine size, pneumatic controller count, liquid level production which dictates tank emissions, and compressor packing vent emissions. Due to discrepancies in transient tank emission data, a high and low emission factor for tanks was used, resulting in two sets of 81 executed cases, and 162 unique cases of total site emissions and potential for fuel consumption. Each of the cases were run over 86,400 seconds at a 1 Hz, representative of a full 24 hour day of operation. The fuel consumption offset an average of 557% of fuel consumption on an energy density basis across all 81 cases with the high tank emission factor with a maximum offset of 2334%. The fuel consumption offset was an average of 82.9% for all 81 cases with the low tank emission factor with a maximum offset of 337%. This study highlights flaws in the use of publicly available methane number calculations to determine natural gas’s suitability as an engine fuel as well as the lack of public data for transient liquid storage tank emissions.

03 NATURAL GAS↗

DOE OSTI2020

Survey of Dynamic Mode Decomposition Methods

Dynamic mode decomposition (DMD) is a data-driven reduced order modeling (ROM) technique used for dynamic systems. The widely adopted algorithm was first introduced and demonstrated on fluid flow data by Schmid. In recent years, various other fields, such as nuclear engineering, have begun to adopt this method. For example, DMD has been used for estimating α-eigenvalues, as an ROM for pulsed neutron problems, for predicting isotopic composition in burnup calculations, as acceleration techniques for iterative methods, and in capturing dynamic behaviors in molten salt reactor transients. This report seeks to demonstrate the capabilities and limits of the standard DMD algorithm, and identify problem spaces where variants may be better suited. The primary variant this report considers is Multi-Resolution DMD (mrDMD). Because this serves as a survey, synthetically produced data is used in lieu of simulation results. The remainder of this report will go into detail on the DMD theory, outline the standard DMD and mrDMD algorithms, present test cases highlighting the applicability of each, and finally present a discussion on how to determine the best suited algorithm for a given problem. All calculations performed in this report are carried out using the open source DMD library, PyDMD.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Sparse Matrix-Based HPC Tomography

Tomographic imaging has benefited from advances in X-ray sources, detectors and optics to enable novel observations in science, engineering and medicine. These advances have come with a dramatic increase of input data in the form of faster frame rates, larger fields of view or higher resolution, so high performance solutions are currently widely used for analysis. Tomographic instruments can vary significantly from one to another, including the hardware employed for reconstruction: from single CPU workstations to large scale hybrid CPU/GPU supercomputers. Furthermore, flexibility on the software interfaces and reconstruction engines are also highly valued to allow for easy development and prototyping. This paper presents a novel software framework for tomographic analysis that tackles all aforementioned requirements. The proposed solution capitalizes on the increased performance of sparse matrix-vector multiplication and exploits multi-CPU and GPU reconstruction over MPI. Furthermore, the solution is implemented in Python and relies on CuPy for fast GPU operators and CUDA kernel integration, and on SciPy for CPU sparse matrix computation. As opposed to previous tomography solutions that are tailor-made for specific use cases or hardware, the proposed software is designed to provide flexible, portable and high-performance operators that can be used for continuous integration at different production environments, but also for prototyping new experimental settings or for algorithmic development. The experimental results demonstrate how our implementation can even outperform state-of-the-art software packages used at advanced X-ray sources worldwide.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Machine Learning Based Software Package for Reproducible Data Analysis for Electron Microscopy

Advances in hardware and computation technologies are enabling large hyperspectral imaging during Electron Microscopy (EM), resulting in datasets being produced at rapidly increasing spatial and temporal resolutions. This presents exciting opportunities for microstructure analysis, but also poses great challenges in data analysis. Under this STTR program, QuesTek Innovations LLC, a leader in the field of integrated computational materials engineering (ICME), collaborated with Lawrence Livermore National Laboratory and Argonne National Laboratory. The team developed an open-source machine learning (ML) powered tool for EM data analysis of multiple systems and data types, and collected EM data for ML model development using the cutting-edge PicoProbe equipment at Argonne National Laboratory.

Lin, Yu↗

DOE OSTI2025

Developing an Automated Microscopic Traffic Simulation Scenario Generation Tool

Traffic simulation is an effective tool for urban planners, traffic engineers, and researchers to study traffic. In particular, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, has demonstrated its importance in analyzing and managing transportation systems. However, integrating data from various sources, generating traffic scenarios, and importing information into traffic simulators to conduct microscopic simulations have always been a challenge. This paper presents a solution to overcome this challenge: RealTwin, a comprehensive tool for automated scenario generation for microscopic traffic simulation. Following a streamlined scenario generation and calibration workflow, RealTwin effectively bridges gaps between traffic data from various sources and traffic simulators, making microscopic traffic simulation more accessible for researchers and engineers across various levels of expertise. Using RealTwin to generate a real-world traffic scenario in Simulation of Urban Mobility (SUMO), VISSIM, and AIMSUN, RealTwin’s ability is demonstrated in the construction of realistic and consistent traffic scenarios in different simulators. Furthermore, this paper introduces and illustrates RealTwin’s capability for technology (e.g., autonomous vehicle) scenario generation. This feature can contribute to more comprehensive microscopic simulations, facilitating the analysis of potential effects of various technological innovations on mobility, energy efficiency, and safety. Finally, RealTwin is used to calibrate a simulation in SUMO. In conclusion, the calibration module enhances RealTwin’s ability to generate consistent simulations across different platforms and more realistic simulations that reflect real-world traffic operations.

autonomous vehicle↗

DOE OSTI2022

Challenge Problem 1: Preliminary Model Development and Assessment of Flexible Heat Transfer Modeling Approaches

This report presents the modeling progress of a university consortium studying flexible modeling for turbulent heat transfer. In the context of advanced nuclear reactor design, where challenges include non-unity Prandtl fluids, free and mixed convection, and transitional flow, computational fluid dynamics methods are underutilized due to low confidence in modeling approaches and high computational cost. This report evaluates the performance of engineering CFD models in diabatic, buoyant turbulent flow. It finds that all turbulence models including several variants of the k-ε and k-ω models struggle to predict accurate turbulent momentum and heat transfer in such flows. The Nusselt numbers have been compared between the models and the DNS data, where calculations have been performed for each case and trends have shown a good agreement between DNS estimated Nusselt numbers and available correlations as well as experimental data. A novel DNS correlation for high Pr cases as they are transitioning from mixed convection to natural convection has been proposed. While Nusselt number errors relative to DNS range from 20% to 50%, the models capture similar trends to DNS with respect to Nusselt suppression and amplification under varying levels of buoyancy effect. This report also highlights model form error as a significant contributor to CFD predictions and proposes a framework for quantifying model error and improving confidence in CFD calculations. To improve the predictive capability of engineering CFD models, data-driven approaches for turbulence models are investigated. Theoretical frameworks based on the invariant tensor / vector basis neural networks for prediction of Reynolds stresses and turbulent heat fluxes are employed. The models are developed using direct numerical simulations data for forced convection flows of different fluids in vertical planar channel domain. The framework is implemented in spectral element solvers Nek5000 / nekRS and has shown a potential for future development and consideration of mixed convection flows.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Microgrid Conceptual Design Guidebook | 2022

This guide is meant to assist communities – from residents to energy experts to decision makers – in developing a conceptual microgrid design that meets site-specific energy resilience goals. Using the framework described in this guidebook, stakeholders can come together and start to quantify site-specific vulnerabilities, identify the most significant risks to delivery of electricity, and establish electric outage tolerances across the community. In addition to establishing minimum service needs, this framework encourages communities to consider broader sustainability goals and policy constraints and begin to estimate up-front costs associated with the installation of alternative microgrid solutions. The framework guides a community through data collection and a high-level assessment of its needs, constraints, and priorities, prior to engaging engineers, vendors, and contractors. The first sections of this guidebook provide a high-level primer on electric systems. The latter sections include guidance for step-by-step data gathering and analysis of site conditions. The ultimate product resulting from the stepwise approach is a conceptual microgrid design. A conceptual design is defined as an initial design (10%-20% complete) that considers the specific threats, needs, limitations, and investment options for a given location. Going through this exercise and developing the conceptual microgrid design as a community ensures the same community members who will ultimately live with the solution are the developers of its foundational design. Often, these are also the very same people who understand system tolerances and needs the best and are therefore the ideal candidates for establishing these criteria. Especially when it comes to evaluating critical infrastructure, it is the community that best understands the most critical services. The framework is intended to facilitate a systematic approach to planning for resilience and provide a deeper understanding of how to use a framework to make decisions around microgrid solutions. Like many processes where tradeoffs need to be considered, this is often an iterative process. If this guide serves to help educate and empower communities who are beginning the process of deploying a microgrid, it has met the goal of its authors.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

The Effect of Spark-Plug Heat Dispersal Range and Exhaust Valve Opening Timing on Cold-Start Emissions and Cycle-to-Cycle Variability

The partnership for advancing combustion engines (PACE) is a US Department of Energy consortium involving multiple national laboratories and includes a goal of addressing key efficiency and emission barriers in light-duty engines fueled with a market-representative E10 gasoline. A major pillar of the initiative is the generation of detailed experimental data and modeling capabilities to understand and predict cold-start behavior. Cold-start, as defined by the time between first engine crank and three-way catalyst light-off, is responsible for a large percentage of NOx, unburned hydrocarbon and particulate matter emissions in light-duty engines. Minimizing emissions during cold-start is a trade-off between achieving faster light-off of the three-way catalyst and engine out emissions during that period. In this study, gaseous and soot emissions were measured at a distance representative of the three-way catalyst position downstream of the engine at a 2 bar net indicated mean effective pressure (NIMEP) steady-state operating condition representative of cold-start. The test matrix included sweeps of ignition timing 15 degrees-before to 10 degrees-after top dead center firing (TDCf) across three different spark-plug heat dispersal ranges (HR). Additionally, the effect of varying exhaust valve opening (EVO) timing on combustion stability and emissions was also studied. Results show that the spark plug HR affects the coefficient of variation (COV) of NIMEP under all cold-start conditions, while the impact on emissions was found to be minimal. At very retarded spark timings, colder spark plugs required higher air and fuel flow to maintain the desired 2bar NIMEP load, but the fraction of fuel energy going into the exhaust was found to be similar for all spark plugs. Finally, retarding exhaust valve timings showed a simultaneous reduction in emissions while increasing the fraction of fuel energy being fed into the exhaust. However, engine COV was also observed to increase with retarded exhaust timings.

42 ENGINEERING↗

DOE OSTI2022

Data-driven Minimum Entropy Control for Stochastic Nonlinear Systems using the Cumulant-Generating Function

Here, we present a novel minimum entropy control algorithm for a class of stochastic nonlinear systems subjected to non-Gaussian noises. The entropy control can be considered as an optimization problem for the system randomness attenuation, but the mean value has to be considered separately. To overcome this disadvantage, a new representation of the system stochastic properties was given using the cumulant-generating function based on the moment-generating function, in which the mean value and the entropy was reflected by the shape of the cumulant-generating function. Based on the samples of the system output and control input, a time-variant linear model was identified, and the minimum entropy optimization was transformed to system stabilization. Then, an optimal control strategy was developed to achieve the randomness attenuation, and the boundedness of the controlled system output was analyzed. The effectiveness of the presented control algorithm was demonstrated by a numerical example. In this paper, a data-driven minimum entropy design is presented without pre-knowledge of the system model; entropy optimization is achieved by the system stabilization approach in which the stochastic distribution control and minimum entropy are unified using the same identified structure; and a potential framework is obtained since all the existing system stabilization methods can be adopted to achieve the minimum entropy objective.

42 ENGINEERING↗

DOE OSTI2023

Conceptual Design of Engineering-Scale Molten Salt Spill Tests to Support Molten Salt Reactor Licensing

Nuclear accident progression analysis codes for reactor licensing must be validated using experimental data that was generated at a large enough scale so that the results are representative of full-scale accidents. This report describes a conceptual design of engineering-scale molten salt spill tests that will provide the necessary datasets to support molten salt reactor (MSR) licensing. Design considerations, salt compositions, test variables, processes to be quantified, and suitable measurement techniques are presented and discussed. Aspects of the conceptual engineering-scale molten salt spill test design presented in this report were made using insights from recent laboratory-scale molten salt spill tests conducted at Argonne. Additional input from model developers, MSR vendors, and technical experts will benefit the design of the proposed engineering-scale molten salt spill tests.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗