Search NASA⌕ Search

SEARCH · Search NASA

Results for “post processing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Deposition and post-processing techniques for transparent conductive films

In one embodiment, a method is provided for fabrication of a semitransparent conductive mesh. A first solution having conductive nanowires suspended therein and a second solution having nanoparticles suspended therein are sprayed toward a substrate, the spraying forming a mist. The mist is processed, while on the substrate, to provide a semitransparent conductive material in the form of a mesh having the conductive nanowires and nanoparticles. The nanoparticles are configured and arranged to direct light passing through the mesh. Connections between the nanowires provide conductivity through the mesh.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Deposition and post-processing techniques for transparent conductive films

In one embodiment, a method is provided for fabrication of a semitransparent conductive mesh. A first solution having conductive nanowires suspended therein and a second solution having nanoparticles suspended therein are sprayed toward a substrate, the spraying forming a mist. The mist is processed, while on the substrate, to provide a semitransparent conductive material in the form of a mesh having the conductive nanowires and nanoparticles. The nanoparticles are configured and arranged to direct light passing through the mesh. Connections between the nanowires provide conductivity through the mesh.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Studying effects of non-equilibrium radiative transfer via HPC

This report presents slides on Ph.D. Research Goals; Local Thermodynamic Equilibrium (LTE) Implications; Calculating an Opacity; Opacity: Pictographic Representation; Opacity: Pictographic Representation; Opacity: Pictographic Representation; Collisional Radiative Modeling; Radiative and Collisional Excitation; Photo and Electron Impact Ionization; Autoionization; The Rate Matrix; Example: Total Photoionization rate; The Rate Coefficients; inlinlte version 1.1; inlinlte: Verification; New capabilities: Rate Matrix – Flexibility; Memory Option Comparison; Improvements over previous DCA solver; Inter- and intra-node load balancing; Load Balance – Full Picture; Load Balance – Full Picture; Load Balance – Internode; Load Balance – Scaling; Description; Performance; xRAGE Simulation; Post-process @ 2hr; Post-process @ 4hr; Post-process @ 8hr; Takeaways; Performance for 1 realization; Motivation for QOI; Multigroup Er; Transport and NLTE large effects (1mm, 1keV); Transport large effect, NLTE lesser (1mm, 750eV); Blastwave Diagnostici – Description & Performance; Temperature Comparison; NLTE has effect on dynamics at wall; NLTE has lesser effect in the foam; Global Takeaways; The end.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

TH-CD-209-01: A Greedy Reassignment Algorithm for the PBS Minimum Monitor Unit Constraint

Purpose: To investigate a Greedy Reassignment algorithm in order to mitigate the effects of low weight spots in proton pencil beam scanning (PBS) treatment plans. Methods: To convert a plan from the treatment planning system’s (TPS) to a deliverable plan, post processing methods can be used to adjust the spot maps to meets the minimum MU constraint. Existing methods include: deleting low weight spots (Cut method), or rounding spots with weight above/below half the limit up/down to the limit/zero (Round method). An alternative method called Greedy Reassignment was developed in this work in which the lowest weight spot in the field was removed and its weight reassigned equally among its nearest neighbors. The process was repeated with the next lowest weight spot until all spots in the field were above the MU constraint. The algorithm performance was evaluated using plans collected from 190 patients (496 fields) treated at our facility. The evaluation criteria were the γ-index pass rate comparing the pre-processed and post-processed dose distributions. A planning metric was further developed to predict the impact of post-processing on treatment plans for various treatment planning, machine, and dose tolerance parameters. Results: For fields with a gamma pass rate of 90±1%, the metric has a standard deviation equal to 18% of the centroid value. This showed that the metric and γ-index pass rate are correlated for the Greedy Reassignment algorithm. Using a 3rd order polynomial fit to the data, the Greedy Reassignment method had 1.8 times better metric at 90% pass rate compared to other post-processing methods. Conclusion: We showed that the Greedy Reassignment method yields deliverable plans that are closest to the optimized-without-MU-constraint plan from the TPS. The metric developed in this work could help design the minimum MU threshold with the goal of keeping the γ-index pass rate above an acceptable value.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Terrestrial laser scanning data (Levels 0 and 1) for Pasoh, Malaysia, Sep 2024

This data package contains data from terrestrial laser scanning (TLS) at the Pasoh Forest Reserve, Malaysia. The Pasoh Forest Reserve is a facility of the Forest Research Institute Malaysia, and contains evergreen lowland dipterocarp forest. The Next-Generation Ecosystem Experiments Tropics (NGEE-Tropics) study areas at Pasoh were established to study how different species respond to climatic variation and soil water availability. Two study areas were chosen representing different topography and species. The TLS data archived here were collected to provide detailed, three-dimensional information about forest structure. Specifically, data were collected to allow tree-level characterization of woody structure and leaf area for 12 focal trees with FloraPulse and sap flux sensors, facilitating estimation of woody biomass and leaf area to allow upscaling of water content and transpiration data to the tree-level. Scan positions were not selected to provide consistent data for non-focal trees with the study areas. This data package contains the following data: - High-level files document further details of the campaign and data package: 1_CampaignSummary.csv provides details about the campaign and study site, 2_ScanAreasDetail.csv provides details about each separate scan area (groups of scans post-processed into a single point cloud), 3_TerrestrialLidarSensor.csv provides further technical details about the Riegl VZ-400i TLS sensor, TLS_CSV_dd.csv is a CSV Data Dictionary providing information about the fields in CSV files following the ESS-DIVE CSV File Formatting Guidelines Reporting Format, TLS_flmd.csv is a File Level Metadata file providing information about each file in the data package following the ESS-DIVE File Level Metadata Reporting Format, and README.txt is a text file describing the overall project and file structure. - Level 0 data are the raw data (.PROJ folders) as recorded by the Riegl VZ-400i TLS instrument before scan co-registration and post-processing with the Riegl's proprietary RiSCAN PRO software, which requires a license. - Level 1 data contain post-processed, co-registered data from each scan area. The "PointClouds" folder for each scan area contains a .las file with 1 cm resolution point cloud data exported from RiSCAN PRO. These are the main files likely to be of interest to most users and can be further processed with any software capable of manipulating .las files (e.g. Python, R CloudCompare). The "Project Information" folder contains log files from post-processing in RiSCAN PRO that may be of interest to users who want to see detailed records of post-processing, including all PDF reports generated by RiSCAN PRO. The "ScanPositions" folder contains information about the final position of all TLS scans, after post-processing, in multiple formats. The file ScanPositions_*.csv provides final geo-referenced scan positions, and the file SOP_backup_*.csv can be used in RiSCAN PRO to restore the co-registered scan positions if users wish to re-process raw data (Level 0 .PROJ folders) with RiSCAN PRO software (e.g., subsample to a different resolution, exclude a certain scan position, or apply different filters on reflectance or deviation values) without redoing time-consuming co-registration steps.

54 ENVIRONMENTAL SCIENCES↗