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Innovative Development Selection and Testing to Reduce Cost and Weight of Materials for BOP Components

Zimmerman, Jonathan A.; San Marchi, Chris

The primary objective of this effort is to identify alloys to replace type 316/316L in hydrogen service for balance of plant (BOP) applications onboard fuel cell electric vehicles (FCEVs). Type 316/316L austenitic stainless steels are used extensively in hydrogen systems for their resistance to hydrogen embrittlement, which is attributed to the relatively high nickel content of type 316/316L alloys. Nickel content, however, drives the cost of austenitic stainless steels, thus type 316/316L alloys impose a cost premium compared to similar alloys with lower nickel content. Since the cost of BOP components is a large fraction of the cost of hydrogen fuel systems (even dominating the cost at low production volumes [1]), alternative materials are desired. In addition, type 316/316L alloys are relatively low strength, thus high-pressure components tend to be heavy to accommodate the stresses associated with the pressure loads. Higher-strength materials will reduce weight of the components (an added benefit for onboard components) and contribute to lower cost since less material is needed. However, engineering data to justify selection of lower cost and higher strength alloys for high-pressure hydrogen service are currently unavailable. Moreover, alloy design could enable low cost solutions to the specific needs of onboard hydrogen storage.

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TYPE Other Report YEAR 2016
DOIOSTI

Optimizing Viral Detection: Creating Fluorescent Chikungunya Virus Infectious Clones

Kaszubinski, Sierra

Human infection with Chikungunya virus (CHIKV) results in debilitating joint pain and arthritis that can persist for months or even years. CHIKV is a re-emerging virus responsible for large epidemics in Asia, Africa, Europe, and most recently South and Central America. Yet, CHIKV has no specific treatment or vaccine. Therefore, steps towards eradicating the virus are important to public safety. Tools to facilitate drug screens are especially important when searching for viable treatments. Therefore, our lab sought to create reporter CHIKV encoding fluorescent markers, to detect infection in cells. Cloning was used to develop a CHIKV infectious clone encoding GFP. Creation of this tool required use of PCR, Gibson assembly, and the CRISPR Cas9 system. We are also working to create a CHIKV expressing mCherry. Next steps include determining the efficiency of these infectious clones compared to wildtype virus and performing drug screen studies. I also made contributions to other projects including development of a CRISPR Cas9 guide RNA library, a bioinformatics project using new software, and CRISPR Cas9 expressing cell line, that will be used in Zika virus studies. Finally, I assisted in development of a market survey focused on biodefense technologies for DHS.

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TYPE Other Report YEAR 2016
DOIOSTI

Next Day Building Load Predictions based on Limited Input Features Using an On-Line Laterally Primed Adaptive Resonance Theory Artificial Neural Network

Buildings and Energy

Jones, Christian B.; Robinson, Matt; Yasaei, Yasser; Caudell, Thomas; Martinez-Ramon, Manel; Mammoli, Andrea

Optimal integration of thermal energy storage within commercial building applications requires accurate load predictions. Several methods exist that provide an estimate of a buildings future needs. Methods include component-based models and data-driven algorithms. This work implemented a previously untested algorithm for this application that is called a Laterally Primed Adaptive Resonance Theory (LAPART) artificial neural network (ANN). The LAPART algorithm provided accurate results over a two month period where minimal historical data and a small amount of input types were available. These results are significant, because common practice has often overlooked the implementation of an ANN. ANN have often been perceived to be too complex and require large amounts of data to provide accurate results. The LAPART neural network was implemented in an on-line learning manner. On-line learning refers to the continuous updating of training data as time occurs. For this experiment, training began with a singe day and grew to two months of data. This approach provides a platform for immediate implementation that requires minimal time and effort. The results from the LAPART algorithm were compared with statistical regression and a component-based model. The comparison was based on the predictions linear relationship with the measured data, mean squared error, mean bias error, and cost savings achieved by the respective prediction techniques. The results show that the LAPART algorithm provided a reliable and cost effective means to predict the building load for the next day.

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TYPE Journal Article YEAR 2016
OSTI

2015 Annual Site Environmental Report for Sandia National Laboratories New Mexico

Griffith, Stacy

Sandia National Laboratories, New Mexico, is a government-owned/contractor-operated facility. Sandia Corporation (Sandia), a wholly owned subsidiary of Lockheed Martin Corporation, manages and operates the Laboratories for the U.S. Department of Energy (DOE), National Nuclear Security Administration (NNSA). The DOE/NNSA Sandia Field Office administers the contract and oversees contractor operations at the site. This Annual Site Environmental Report (ASER) summarizes data and the compliance status of Sandia's sustainability, environmental protection, and monitoring programs during calendar year 2015. Major environmental programs include air quality, water quality, groundwater protection, terrestrial surveillance, waste management, pollution prevention, environmental restoration, oil and chemical spill prevention, and implementation of the National Environmental Policy Act.

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TYPE Other Report YEAR 2016
DOIOSTI
Results 41201–41400 of 99,299
Results 41201–41400 of 99,299