Publications

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Leaks in a hydrogen system can have destructive effects on other components within the system, leading to cascading leaks. The risk of cascading leaks is not currently quantified in many existing risk frameworks, but the prevalence of cascading failures in historical hydrogen facility accidents necessitates further study. A method for quantifying the probability, frequency, and risk of cascaded leaks is proposed. The method provides example scenarios of metrics that would set off cascading failures from each physical effect, including a jet fire melting the O-ring of another component, and an overpressure event from an initial explosion shearing off another component from the system. Cascading leak frequencies and individual risk are calculated for an example hypothetical system. While cascading leaks are quantitatively demonstrated to add to the overall risk, their contributions are small and may not add value to a risk assessment when analyzed in this rigorous quantitative framework.

To extend NUREG-1465 and high burnup fuel source term (SAND2023-01313) recommendations, representative radiological releases to containment – patterned after NUREG-1465 – have been evaluated for LWRs utilizing the chromium-coating on major zircaloy structures (cladding and fuel canisters) and high burnup fuel with enrichments of 8% and 10% for PWRs and BWRs, respectively. Representative radionuclide releases are generated for this accident tolerant fuel concept by applying non-parametric bootstrap methods to MELCOR simulation results. Accident scenarios considered in this analysis include principle contributors to historical core damage frequency estimates for a range of nuclear reactor technologies representative of the operating U.S.A. fleet of nuclear reactors.

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Cybersecurity certification programs for Electric Vehicle Supply Equipment (EVSE) are fragmented due to no single certification covering all aspects of the device and additionally the existence of multiple programs and under different levels of regulation. These devices are also confronted by the intricate assembly of product software, firmware, and hardware. Devices contain both logical and physical interfaces. These multifaceted devices have vulnerabilities at many levels and interconnect with other potentially vulnerable systems including the electric vehicle, the cloud where data and payment information are stored, and the electric grid and electric grid equipment including utilities. Of the EVSE certification programs that are found, none are directly for the cybersecurity of EVSE. Many standards are for safety, specifically battery safety, some are cybersecurity standards for other types of equipment and can be modeled for EVSE. In specific, ISA/IEC 62443 is found to be significantly in line with EVSE security needs and will be used in future testing to certify EVSE and help guide the project to demonstrate where gaps exist, where strengths lie in the standard and how this can be used to lead the certification efforts in harmonizing EVSE cybersecurity standards. In addition, there are multiple efforts that are currently seeking to build EVSE standards or revise existing standards to address gaps. This effort is seeking to establish a cybersecurity program for EVSE that will inform customers and help increase the level of security across products and state EVSE procurements to achieve consistency across different jurisdictions.

Presentation for NACTI 2024

Short and sweet three-minute presentation on CSP metrology techniques

We have attempted to capture a sense of the scientific state of the art in studying social media platforms, including data collection from platforms, understanding platform behavior, known adversarial uses, and adverse content detection, classification, and quantification. Our coverage of the field is backed up by roughly two hundred citations, and it concludes with a comparative analysis and a list of apparent gaps and potential paths forward.

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Poster for the intern symposium

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The slides will be presented at a DOE CESER Peer Review for the Risk Management Tools and Technology (RMT) that provide an overview of the ESSec project. The slides discuss the containerization and orchestration tools for energy storage systems and our results along with an overview of the demonstration performed for this project. The peer review is scheduled for August 27-29.

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In recent years, microalgae have been identified as potential source of biofuel due to their ability to grow abundantly in little time. As photosynthetic organisms, microalgae only need water, light, micronutrients, and carbon dioxide to yield algal biomass. Several applications for this biomass are being studied, such as the extraction of biofuel (Zhang et al. 2022) as well as wastewater treatment (Rude et al. 2022). Two specific microalgae species have been identified as uniquely prolific: Picochlorum celeri, a saltwater species, and Chlorella sorokiniana 1116, a freshwater species. Both species showcased survival in less-than-ideal conditions and showcased hardiness in their respective waters (Krishnan et al. 2021), making them popular specimen to be studied for a variety of applications. This project involved two components, the first component focusing on the development of a growth curve for P. celeri and the second studying varying salt concentration in both species.

This work will presented at the DOE OE energy storage peer review conference.

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This document provides a technical description of algorithms used in the OpenCSP code repository that are common to deflectometry. This document is meant to be a technical reference that describes the internal algorithms of OpenCSP. An example program that uses these algorithms is SOFAST 2.0 [1].

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Climate and sea level change is causing numerous challenges across the globe to human societies and the cultural and infrastructure investments they have made over hundreds of years based on previous modalities in climate and sea level. Decarbonizing our global economy is therefore essential to stopping additional emissions of CO₂ to the atmosphere. One proposed decarbonization technology that has been advanced as a replacement for the “hydrocarbon economy” that exists today is the “hydrogen economy.” In the hydrogen economy, hydrogen is both an energy carrier and an industrial feedstock that can replace hydrocarbons’ traditional roles in these systems. While most hydrogen is produced from conventional, fossil-based feedstocks, hydrogen comes with the added benefits of being able to be made from water and electricity providing a promising way to store renewable energy from wind and solar developments.

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Study of subcooled pool boiling experiments performed using a dielectric coolant to test effects of variations in heater surface configuration on pool boiling characteristics.

Nuclear power plant (NPP) risk assessment is broadly separated into disciplines of nuclear safety, security, and safeguards. Different analysis methods and computer models have been constructed to analyze each of these as separate disciplines. However, due to the complexity of NPP systems, there are risks that can span all these disciplines and require consideration of safety-security (2S) interactions which allows a more complete understanding of the relationship among these risks. A novel leading simulator/trailing simulator (LS/TS) method is introduced to integrate multiple generic safety and security computer models into a single, holistic 2S analysis. A case study is performed using this novel method to determine its effectiveness. The case study shows that the LS/TS method avoided introducing errors in simulation, compared to the same scenario performed without the LS/TS method. A second case study is then used to illustrate an integrated 2S analysis which shows that different levels of damage to vital equipment from sabotage at a NPP can affect accident evolution by several hours.

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In magnetized liner inertial fusion (MagLIF), a cylindrical liner filled with fusion fuel is imploded with the goal of producing a one-dimensional plasma column at thermonuclear conditions. However, structures attributed to three-dimensional effects are observed in self-emission x-ray images. Despite this, the impact of many experimental inputs on the column morphology has not been characterized. We demonstrate the use of a linear regression analysis to explore correlations between morphology and a wide variety of experimental inputs across 57 MagLIF experiments. Results indicate the possibility of several unexplored effects. For example, we demonstrate that increasing the initial magnetic field correlates with improved stability. Although intuitively expected, this has never been quantitatively assessed in integrated MagLIF experiments. We also demonstrate that azimuthal drive asymmetries resulting from the geometry of the “current return can” appear to measurably impact the morphology. In conjunction with several counterintuitive null results, we expect the observed correlations will encourage further experimental, theoretical, and simulation-based studies. Finally, we note that the method used in this work is general and may be applied to explore not only correlations between input conditions and morphology but also with other experimentally measured quantities.

This a presentation for the 2024 Signal Processing Applied to Nonproliferation (SPAN) 2024 workshop. It discusses seismic research conducted at the Redmond Mine as part of the NA-22 STILGAR project.

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These instructions show how to use the OpenCSP Scene Reconstruction application. Scene Reconstruction calculates the three-dimensional positions of Aruco markers placed around a scene using photogrammetry. This has multiple applications within OpenCSP; this document is referenced by multiple other OpenCSP documents.

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The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. We are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

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