Optimization Of An Unstructured Finite Element Mesh For Tide And Storm Surge Modeling Applications In The Western North Atlantic Ocean

Recently, a highly resolved, finite element mesh was developed for the purpose of performing hydrodynamic calculations in the Western North Atlantic Tidal (WNAT) model domain. The WNAT model domain consists of the Gulf of Mexico, the Caribbean Sea, and the entire portion of the North Atlantic Ocean found west of the 60° W meridian. This high resolution mesh (333K) employs 332,582 computational nodes and 647,018 triangular elements to provide approximately 1.0 to 25 km node spacing. In the previous work, the 333K mesh was applied in a Localized Truncation Error Analysis (LTEA) to produce nodal density requirements for the WNAT model domain. The goal of the work herein is to use these LTEA-based element sizing guidelines in order to obtain a more optimal finite element mesh for the WNAT model domain, where optimal refers to minimizing nodes (to enhance computational efficiency) while maintaining model accuracy, through an automated procedure. Initially, three finite element meshes are constructed: 95K, 60K, and 53K. The 95K mesh consists of 95,062 computational nodes and 182,941 triangular elements providing about 0.5 to 120 km node spacing. The 60K mesh contains 60,487 computational nodes and 108,987 triangular elements. It has roughly 0.5 to 185 km node spacing. The 53K mesh includes 52,774 computational nodes and 98,365 triangular elements. This is a particularly coarse mesh, consisting of approximately 0.5 to 160 km node spacing. It is important to note that these three finite element meshes were produced automatically, with each employing the bathymetry and coastline (of various levels of resolution) of the 333K mesh, thereby enabling progress towards an optimal finite element mesh. Tidal simulations are then performed for the WNAT model domain by solving the shallow water equations in a time marching manner for the deviation from mean sea level and depth-integrated velocities at each computational node of the different finite element meshes. In order to verify the model output and compare the performance of the various finite element mesh applications, historical tidal constituent data from 150 tidal stations located within the WNAT model domain are collected and examined. These historical harmonic data are applied in two types of comparative analyses to evaluate the accuracy of the simulation results. First, qualitative comparisons are based on visual sense by utilizing plots of resynthesized model output and historical tidal constituents. Second, quantitative comparisons are performed via a statistical analysis of the errors between model response and historical data. The latter method elicits average phase errors and goodness of average amplitude fits in terms of numerical values, thus providing a quantifiable way to present model error. The error analysis establishes the 53K finite element mesh as optimal when compared to the 333K, 95K, and 60K meshes. However, its required time step of less than ten seconds constrains its application. Therefore, the 53K mesh is manually edited to uphold accurate simulation results and to produce a more computationally efficient mesh, by increasing its time step, so that it can be applied to forecast tide and storm surge in the Western North Atlantic Ocean on a real-time basis

Quantitative policy analysis for sustainable development in water-stressed developing countries : a case study of Morocco

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Immunohistochemical analysis of tau phosphorylation and astroglial activation with enhanced leptin receptor expression in diet-induced obesity mouse hippocampus

AbstractAccumulating evidence indicates that obesity is an independent risk factor for developing Alzheimer disease (AD). Recent studies have shown that diet-induced obesity (DIO) enhances AD-related pathologies in transgenic mouse models of the disease. DIO increases amyloid β (Aβ) deposition in amyloidogenic transgenic mice and enhances tau phosphorylation in tau transgenic mice. However, it remains unclear whether DIO also enhances AD-related pathological processes in wild-type (WT) mice. In this study, we examined the effects of DIO on Aβ and tau pathology in WT mice using immunohistochemistry. In addition, we evaluated the protective effect of voluntary exercise on the DIO-induced pathological changes. DIO caused tau phosphorylation and astroglial activation in the hippocampus in WT mice. Interestingly, these changes were associated with enhanced astrocytic leptin receptor (LepR) expression and mild microgliosis, but not Aβ accumulation. Although phosphorylated tau staining was only observed in the hippocampus, astrogliosis and microgliosis were present in both the amygdala and hippocampus. However, no apparent neuronal loss was observed. Voluntary exercise prevented these DIO-induced pathological changes. Our results demonstrate for the first time that DIO causes tau phosphorylation and that astrocytic LepR might be involved in the pathological process in WT mouse hippocampus. Our findings also suggest that physical exercise is a promising strategy for the prevention of AD in patients with obesity

The Effect of Planar Sinks on the Interstitial Loop Growth under High Temperature Neutron Irradiation

The role of planar sinks such as surfaces and grain boundaries for the defect structure developments was studied in fission neutron irradiated Ni and Ni alloys of 2 at% Si, Cu, Ge and Sn to the dose of 4x10^n/m^2 (>1MeV) at 573 K by comparison between thin foil irradiation and bulk irradiation. The number density of interstitial loops increases and then decreases with the increase of distance from planar sinks. Observed defect structure developments were interpreted in terms of the variation of point defect concentration with the change of sink efficiency. The necessity of the introduction of cascade localization induced bias effect is emphasized

Development of miniaturized pick-up amplification circuit for plasma particle detectors on board satellites

Plasma particles and waves are important observation targets in space plasmas for understanding the mechanisms of energy and momentum transfer between waves and particles because space plasmas are essentially collisionless. Multi-point observations are crucial for understanding the spatial–temporal variations of space plasmas. To realize such observations by a large number of satellites, onboard instruments should be miniaturized to reduce their required resources. This paper proposes a small amplifier for plasma particle detectors onboard satellites. This charge-sensitive amplifier converts an electron cloud emitted from the detector, for example a microchannel plate, to a current pulse that can be handled by a time-of-flight measurement circuit to determine the particle velocity and thus mass. The amplifier is realized using application-specific integrated circuit technology to minimize size. Its dimensions are estimated to be $$2120, mathrm{ mu m }times 1680, mathrm{ mu m}$$, which are much smaller than those of a conventional amplifier. The response time of the proposed amplifier has a variation of less than $$1.2, mathrm{ ns}$$ over the range of expected input levels. The amplifier can handle up to $$2times {10}^{7}$$ signals per second and has a sensitivity of $$1.5, mathrm{ V}/mathrm{pC}$$ at $$20, mathrm{^circ{rm C} }$$

Consistent map building in petrochemical complexes for firefighter robots using SLAM based on GPS and LIDAR

The objective of this study was to achieve simultaneous localization and mapping (SLAM) of firefighter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle filters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The difference between the original Fast-SLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550–380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was significant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (± 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refinery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of ± 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability