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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Representative Source Terms and the Influence of Reactor Attributes on Functional Containment in Modular High-Temperature Gas-Cooled Reactors

Modular high-temperature gas-cooled reactors (MHTGRs) offer a high degree of passive safety. The low power density of the reactor and the high heat capacity of the graphite core result in slow transients that do not challenge the integrity of the robust TRISO fuel. Another benefit of this fuel form and the surrounding graphite is their superior ability to retain fission products under all anticipated normal and off-normal conditions, which limits reactor accident source terms to very low values. In this paper, we develop estimates of the source term for a generic MHTGR to illustrate the performance of the radionuclide barriers that comprise the MHTGR functional containment. We also examine the influence of initial fuel quality, fuel performance/failure, reactor outlet temperature, and retention outside of the reactor core on the resultant source term to the environment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Frequent Subgraph Discovery in Large Attributed Streaming Graphs

The problem of finding frequent subgraphs in large dynamic graphs has so far only consid- ered a dynamic graph as being represented by a series of static snapshots taken at various points in time. This representation of a dynamic graph does not lend itself well to real time processing of real world graphs like social networks or internet traffic which consist of a stream of nodes and edges. In this paper we propose an algorithm that discovers the frequent subgraphs present in a graph represented by a stream of labeled nodes and edges. Our algorithm is efficient and consists of tunable parameters that can be tuned by the user to get interesting patterns from various kinds of graph data. In our model updates to the graph arrive in the form of batches which contain new nodes and edges. Our algorithm con- tinuously reports the frequent subgraphs that are estimated to be found in the entire graph as each batch arrives. We evaluate our system using 5 large dynamic graph datasets: the Hetrec 2011 challenge data, Twitter, DBLP and two synthetic. We evaluate our approach against two popular large graph miners, i.e., SUBDUE and GERM. Our experimental re- sults show that we can find the same frequent subgraphs as a non-incremental approach applied to snapshot graphs, and in less time.

Dynamic graph, Frequent subgraph mining, pattern d↗