This data set contains raw GC-MS data files of neat ignitable liquids, substrates, and simulated fire debris samples. All samples were analyzed by conventional GC-MS and rapid GC-MS, a fast chromatographic screening technique. The raw data files contain chromatographic data (counts vs. retention time) for all samples analyzed. Gasoline and diesel fuel were chosen as ignitable liquids. Substrates (both unburned and burned without ignitable liquids) included carpet, wood, and primed wood.

In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. Using OpenFOAM, we simulated the extrusion of filaments and droplets into a moving bath. OpenFOAM is an open source computational fluid dynamics solver. This repository contains the following Python tools: - Tools for generating input files for OpenFOAM v1912 or OpenFOAM v8 tailored to a conical or cylindrical nozzle extruding a filament into a static support bath. - Tools for monitoring the status of OpenFOAM simulations and aborting them if they are too slow.

In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. Embedded 3D printing is particularly useful for bioprinting, wherein a cell-laden ink is extruded through the nozzle. Using OpenFOAM, we simulated the extrusion of filaments and droplets into a moving bath. This dataset focuses on the effect of common cell protection strategies on the extrusion of single lines and stresses imposed on cells. OpenFOAM is an open source computational fluid dynamics solver. This work used OpenFOAM 8 on a computing cluster.

To verify and validate the HR-VPTM technique, both synthetic images and gels with embedded particles undergoingcontrolled deformations were used to compared known and reconstructed deformations at assorted strain-ratesor frame-rates. We simulated the light-field representation of particles undergoing motion with ray tracing andinvestigated the sensitivity of the measurement technique to synthetic noise floor and various motion fields.

The following data files are provided in support of the 2022 Additive Manufacturing Benchmark test series (AM-Bench 2022) modeling challenges associated with laser powder bed fusion (LPBF) 3D builds of nickel-based superalloy IN718 test objects.  These AM builds performed on the NIST Additive Manufacturing Metrology Testbed (AMMT).  Description of the associated measurements and model are provided on the AMB2022-01 challenge description webpage (https://www.nist.gov/ambench), and information on the directory structure and file formats available in

The EDS spectra discussed and used to create figures in the MRS journal article "Simulating Electron-Excited Energy Dispersive X-Ray Spectra with the NIST DTSA-II Open-Source Software Platform"

A compilation of single fiber tensile tests for 6 different materials at 8 different gauge lengths. Fibers included are 3 different aramids, a liquid crystal polyester, regenerated cellulose, and nylon 6,6.

This data set includes (1) Experimental measurements, and (2) Simulation data. The experimental data set shows raw measurements and important calculated parameters. The simulation data are for the NIST vapor-compression system model, CYCLE_D-HX, and show the input data and output results. The simulation inputs are based on the experimental measurements.CYCLE_D-HX is a semi-theoretical model that simulates performance of a vapor-compression cycle with forced-convection heat exchangers for specified temperature profiles of the heat source and heat sink.

An icd (IED (Intelligent Electronic Device) Capability Description) file containing a model for an IEC 61850 profile for distributed energy resources supporting IEEE 1547.

The following data files are provided in support of the 2022 Additive Manufacturing Benchmark test series (AM-Bench 2022) modeling challenges associated with laser powder bed fusion (LPBF) 3D builds of nickel-based superalloy IN718 test objects using variety of custom scan strategies.  These AM builds were performed on the NIST Additive Manufacturing Metrology Testbed (AMMT, https://www.nist.gov/el/ammt-temps).  Note that these 3D builds are an extension of those for the AMB2022-01 challenges, and part geometry, materials data, and 'nomi