Seer: Using Mochi for In Situ Analysis
Seer is an in situ framework that allows steering of in situ analysis. Seer works with other existing in situ software such as ParaView Catalyst. It supports multi-user analysis
97 MATHEMATICS AND COMPUTING↗
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Results for “insitu analysis”
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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Cloud microphysical structure analysis based on high-resolution insitu measurements
This study investigates the microphysical parameters and shapes of droplet size distributions (DSDs) along three aircraft traverses of developing convective clouds during Cloud Aerosol Interactions and Precipitation Enhancement Experiment (CAIPEEX) Phase III 2015 at a sampling frequency of 25 Hz. The droplet number concentration (Nc, cm -3 ), and liquid water content (LWC, g m -3 ) present steep gradients within zones of a few tens of meters near the cloud edges and relatively gentle gradients in the strong updraft zones. Sometimes, the horizontal LWC distribution resembles a trapezoidal shape with steep LWC and Nc gradients near the cloud edges. The LWC maximums (LWC max ) are lower than the adiabatic LWCs, but the high adiabatic fractions in the cloud core indicate low dilution. High LWC/LWC max , largest droplets, and narrow and similarly shaped DSDs are found in the regions of high updrafts. Zones of low LWC/LWC max are found close to the cloud edges, where DSDs are highly diverse, containing both large and small droplets. Finally, we analyze the mixing diagrams. Significant in-phase turbulent fluctuations in LWC and N c were found. The effective radii change slightly across cloud updraft zones but decrease at the zone of low LWC/LWC max ratio close to cloud edges. The spectra of LWC and N c obey Kolmogorov -5/3 turbulence law. The radii of the correlation of LWC and N c in updraft zones are of several tens of meters. Filaments up to 120–175 m in size are also noticed.
54 ENVIRONMENTAL SCIENCES↗
Geophysical Imaging of Flow and Transport (GIFT): FY24 Status Report
The GIFT (Geophysical Imaging of Flow and Transport) system will improve predictive capabilities as a platform for collecting multi-physical (MP) time-lapse datasets from well-controlled laboratory experiments. This will enable more accurate prediction of multi-phase fluid flow during delivery and transport of insitu remedial amendments as important components of the overall Hanford Site cleanup mission. Design and construction of the GIFT system, which consists of a meter-scale test cell and a network of custom gas circulation and analysis systems, was accomplished in fiscal year (FY) 2024, with plans to add liquid circulation to the system and use this new capability for multi-phase deep vadose experiments starting in FY25.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Thermal diffusivity recovery and defect annealing kinetics of self-ion implanted tungsten probed by insitu transient grating spectroscopy
Tungsten is a promising candidate material for plasma-facing armour components in future fusion reactors. A key concern is irradiation-induced degradation of its normally excellent thermal transport properties. In this comprehensive study, thermal diffusivity degradation in ion-implanted tungsten and its evolution from room temperature (RT) to 1073 K is considered. Five samples were exposed to 20 MeV self-ions at RT to achieve damage levels ranging from 3.2 x 10 -4 to 3.2 displacements per atom (dpa). Transient grating spectroscopy with in-situ heating was then used to study thermal diffusivity evolution as a function of temperature. Using a kinetic theory model, an equivalent point defect density is estimated from the measured thermal diffusivity. The results showed a prominent recovery of thermal diffusivity between 450 K and 650 K, which coincides with the onset of mono-vacancy mobility. After 1073 K annealing samples with initial damage of 3.2 x 10 -3 dpa or less recover close to the pristine value of thermal diffusivity. For doses of 3.2 x 10 -2 dpa or higher, on the other hand, a residual reduction in thermal diffusivity remains even after 1073 K annealing. Transmission electron microscopy reveals that this is associated with extended, irradiation-induced dislocation structures that are retained after annealing. A sensitivity analysis shows that thermal diffusivity provides an efficient tool for the assessing total defect content in tungsten up to 1000 K.
36 MATERIALS SCIENCE↗
DownScaleBench for developing and applying a deep learning based urban climate downscaling- first results for high-resolution urban precipitation climatology over Austin, Texas
Abstract Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 – 10 km) and neighborhood (order of 0.1 – 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-based dynamical models. In this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This ‘DownScaleBench’ tool can aid the process of downscaling to any location. The DownScaleBench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-based product (JAXA GsMAP). The high-resolution gridded precipitation datasets is compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. The creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities.
Singh, Manmeet (ORCID:0000000233747149)↗