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Safeguards-Informed Hybrid Imagery Dataset [Poster]

Rutkowski, Joshua; Gastelum, Zoe N.; Shead, Timothy M.; Rushdi, Ahmad; Bolles, Jason; Mattes, Arielle

Deep Learning computer vision models require many thousands of properly labelled images for training, which is especially challenging for safeguards and nonproliferation, given that safeguards-relevant images are typically rare due to the sensitivity and limited availability of the technologies. Creating relevant images through real-world staging is costly and limiting in scope. Expert-labeling is expensive, time consuming, and error prone. We aim to develop a data set of both realworld and synthetic images that are relevant to the nuclear safeguards domain that can be used to support multiple data science research questions. In the process of developing this data, we aim to develop a novel workflow to validate synthetic images using machine learning explainability methods, testing among multiple computer vision algorithms, and iterative synthetic data rendering. We will deliver one million images – both real-world and synthetically rendered – of two types uranium storage and transportation containers with labelled ground truth and associated adversarial examples.

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TYPE Conference Poster YEAR 2021
DOIOSTI

Designing New Materials for Photovoltaics: Opportunities for Lowering Cost and Increasing Performance through Advanced Material Innovations

Oreski, Gernot; Stein, Joshua; Eder, Gabriele; Berger, Karl; Bruckman, Laura S.; Vedde, Jan; Weiss, Karl-Anders; Tanahashi, Tadanori; French, Roger H.; Ranta, Samuli

Within the framework of IEA PVPS, Task 13 aims to provide support to market actors working to improve the operation, the reliability and the quality of PV components and systems. Operational data from PV systems in different climate zones compiled within the project will help provide the basis for estimates of the current situation regarding PV reliability and performance. The general setting of Task 13 provides a common platform to summarize and report on technical aspects affecting the quality, performance, reliability and lifetime of PV systems in a wide variety of environments and applications. By working together across national boundaries we can all take advantage of research and experience from each member country and combine and integrate this knowledge into valuable summaries of best practices and methods for ensuring PV systems perform at their optimum and continue to provide competitive return on investment. Task 13 has so far managed to create the right framework for the calculations of various parameters that can give an indication of the quality of PV components and systems. The framework is now there and can be used by the industry who has expressed appreciation towards the results included in the high-quality reports. The IEA PVPS countries participating in Task 13 are Australia, Austria, Belgium, Canada, Chile, China, Denmark, Finland, France, Germany, Israel, Italy, Japan, the Netherlands, Norway, Spain, Sweden, Switzerland, Thailand, and the United States of America.

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TYPE Other Report YEAR 2021
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Using Bayesian Methodology to Estimate Liquefied Natural Gas Leak Frequencies

Mulcahy, Garrett W.; Brooks, Dusty M.; Ehrhart, Brian D.

This analysis provides estimates on the leak frequencies of nine components found in liquefied natural gas (LNG) facilities. Data was taken from a variety of sources, with 25 different data sets included in the analysis. A hierarchical Bayesian model was used that assumes that the log leak frequency follows a normal distribution and the logarithm of the mean of this normal distribution is a linear function of the logarithm of the fractional leak area. This type of model uses uninformed prior distributions that are updated with applicable data. Separate models are fit for each component listed. Five order-of-magnitude fractional leak areas are considered, based on the flow area of the component. Three types of supporting analyses were performed: sensitivity of the model to the data set used, sensitivity of the leak frequency estimates to differences in the model structure or prior distributions, and sufficiency of sample sized used for convergence. Recommended leak frequency distributions for all component types and leak sizes are given. These leak frequency predictions can be used for quantitative risk assessments in the future.

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TYPE SAND Report YEAR 2021
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Advances in Alkaline Conversion Batteries for Grid Storage Applications

Lambert, Timothy N.; Schorr, Noah B.; Arnot, David J.; Lim, Matthew; Bell, Nelson S.; Bruck, Andrea M.; Duay, Jonathon; Kelly, Maria; Habing, Rachel; Ricketts, Logan S.; Vigil, Julian A.; Gallaway, Joshua; Kolesnichenko, Igor V.; Budy, Stephen M.; Ruiz, Elijah I.; Yadav, Gautam; Weiner, Meir; Upreti, Aditya; Huang, Jinchao; Nyce, Michael; Turney, Damon; Banerjee, Sanjoy; Magar, Birendra; Paudel, Nirajan; Vasiliev, Igor; Spoerke, Erik D.; Chalamala, Babu C.

Abstract not provided.

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TYPE Conference Presenation YEAR 2021
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Advanced Light-Duty Spark Ignition Engine Research: Co-Optimization of Fuels and Engines and Partnership to Advance Combustion Engines (FY2020 Annual Progress Report)

Sjoberg, Carl M.

This report covers recent progress on research tasks that support both the Co-Optimization of Fuels and Engines (Co-Optima) initiative and the Partnership to Advance Combustion Engines (PACE) consortium. The Co-Optima tasks further the science-base needed by industry stakeholders to co-evolve the next generation of highly efficient direct injection spark ignition (DISI) engines and new gasoline-type fuels. The research emphasis is on fuel effects on multimode spark ignition (SI) engine operation, which uses traditional non-dilute stoichiometric operation for peak load and power but reverts to lean operation at lower loads to provide higher fuel economy. This work focuses on determining desirable fuel specifications in terms of well-established metrics like research octane number (RON) and motor octane number, but it also involves the assessment of new fuel metrics, including fuel sooting propensity and phi-sensitivity. The PACE task supports the development of predictive computational fluid dynamics (CFD) modeling, which promises to unlock new strategies for high-efficiency combustion while minimizing tailpipe emissions. Here, the primary fuel is a regular E10 gasoline (i.e., a regular gasoline blend containing 10% ethanol), and focus is on fuel-spray dynamics and soot emissions. Soot-formation pathways are studied to determine how the pathways change with injection strategies and the thermal state of the engine (i.e., cold-starting vs. fully warmed-up operation). This PACE task also contributed to the development of an optimal E10 gasoline surrogate fuel, as reported in detail elsewhere

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TYPE Other Report YEAR 2021
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Results 12701–12800 of 99,299
Results 12701–12800 of 99,299