Efficient dynamical downscaling of general circulation models using continuous data assimilation

Continuous data assimilation (CDA) is successfully implemented for the first time for efficient dynamical downscaling of a global atmospheric reanalysis. A comparison of the performance of CDA with the standard grid and spectral nudging techniques for representing long- and short-scale features in the downscaled fields using the Weather Research and Forecast (WRF) model is further presented and analyzed. The WRF model is configured at 25km horizontal resolution and is driven by 250km initial and boundary conditions from NCEP/NCAR reanalysis fields. Downscaling experiments are performed over a one-month period in January, 2016. The similarity metric is used to evaluate the performance of the downscaling methods for large and small scales. Similarity results are compared for the outputs of the WRF model with different downscaling techniques, NCEP reanalysis, and Final Analysis. Both spectral nudging and CDA describe better the small-scale features compared to grid nudging. The choice of the wave number is critical in spectral nudging; increasing the number of retained frequencies generally produced better small-scale features, but only up to a certain threshold after which its solution gradually became closer to grid nudging. CDA maintains the balance of the large- and small-scale features similar to that of the best simulation achieved by the best spectral nudging configuration, without the need of a spectral decomposition. The different downscaled atmospheric variables, including rainfall distribution, with CDA is most consistent with the observations. The Brier skill score values further indicate that the added value of CDA is distributed over the entire model domain. The overall results clearly suggest that CDA provides an efficient new approach for dynamical downscaling by maintaining better balance between the global model and the downscaled fields

Leakage of old carbon dioxide from a major river system in the Canadian Arctic

The Canadian Arctic is warming at an unprecedented rate. Warming-induced permafrost thaw can lead to mobilization of aged carbon from stores in soils and rocks. Tracking the carbon pools supplied to surrounding river networks provides insight on pathways and processes of greenhouse gas release. Here, we investigated the dual-carbon isotopic characteristics of the dissolved inorganic carbon (DIC) pool in the main stem and tributaries of the Mackenzie River system. The radiocarbon (14C) activity of DIC shows export of “old” carbon (2,380 ± 1,040 14C years BP on average) occurred during summer in sampling years. The stable isotope composition of river DIC implicates degassing of aged carbon as CO2 from riverine tributaries during transport to the delta; however, information on potential drivers and fluxes are still lacking. Accounting for stable isotope fractionation during CO2 loss, we show that a large proportion of this aged carbon (60 ± 10%) may have been sourced from biospheric organic carbon oxidation, with other inputs from carbonate weathering pathways and atmospheric exchange. The findings highlight hydrologically connected waters as viable pathways for mobilization of aged carbon pools from Arctic permafrost soils

Some issues on toughening, fire retardancy, and wear/scratch damage in polyamide-based nanocomposites

Addition of small percentage of nanoclay layers to polymers significantly improves many of their mechanical, physical and transport properties. Despite these improvements, some issues remain with the resultant nanocomposites and include concerns on fracture toughness, flame retardancy (and thermal stability), and scratch-wear resistance. It is the inadequacy of these specified properties that has curtailed potential applications of this class of new materials. Here, we present the efforts and approaches that were made to understand some facets of these issues in achieving a balance between different mechanical and physical properties, with particular emphasis on our recent and current research findings

Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable

There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server. Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones for the Hyperlink graph use distributed or external memory. Therefore, it is natural to ask whether we can efficiently solve a broad class of graph problems on this graph in memory. This paper shows that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes. We give implementations of theoretically-efficient parallel algorithms for 20 important graph problems. We also present the optimizations and techniques that we used in our implementations, which were crucial in enabling us to process these large graphs quickly. We show that the running times of our implementations outperform existing state-of-the-art implementations on the largest real-world graphs. For many of the problems that we consider, this is the first time they have been solved on graphs at this scale. We have made the implementations developed in this work publicly-available as the Graph-Based Benchmark Suite (GBBS).Comment: This is the full version of the paper appearing in the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), 201

Performance Evaluation Of Composite Sandwich Structures With Additively Manufactured Aluminum Honeycomb Cores With Increased Bonding Surface Area

Modern aerostructures, including wings and fuselages, increasingly feature sandwich structures due to their high-energy absorption, low weight, and high flexural stiffness. The face sheet of these sandwich structures are typically thin composite laminates with interior honeycombs made of Nomex or aluminum. Standard cores are structurally efficient, but their design cannot be varied throughout the structure. With additive manufacturing (AM) technology, these core geometries can be altered to meet the design requirements that are not met in standard honeycomb cores. This study used a modified aluminum honeycomb core, with increased surface area on the top and bottom, as the core material in sandwich panels. The modified honeycomb core was produced through the laser powder bed fusion method. The behavior of the modified sandwich composite panels was evaluated through three-point bend, edgewise compression, and impact tests, and their performance was compared to that of a conventional honeycomb core sandwich panel. The three-point bend test results indicated that the sandwich structure\u27s ultimate shear strength improved by 12.6% with the modified honeycomb core. Additionally, the displacement at the failure of the structure increased by 11%. The edgewise compression tests showed that the ultimate edgewise compressive strength improved by 19.1% when using the modified core. The impact test results revealed that the peak force increased by 8% and the energy-absorbing capacity of the sandwich structure increased by 20% with the use of the modified honeycomb core