Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “File Systems”

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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

RAVEN User Manual, Revision 4

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Designing Track for Electrospinning Unit and Cost-Effective Laser Scanning System

Nanofibers are produced in the Targeted Systems Department (TSD) by applying a large voltage to the nanofiber fluid and the collection apparatus known as the electrospinner. The fiber is then shot out of nozzles and collected onto the electrospinner into a nanofiber mat. The problem is that since the nozzle heads are stationary, there is non-uniform deposition of nanofiber on the collection drum leading to variation in thickness of the produced nanofiber mat. To mitigate this issue, a reciprocating mechanism is designed to move the nozzle along the drum collector along with the ability to vary its stroke length. This design was realized using Siemens NX and several parts were printed using a 3D printer, but is, as of writing, untested. Second project involved providing an alternative method to scan a large sample that had undergone certain surface deformation by beam interaction and detect out of plane deformation such as micro-scale swelling and surface roughness. Systems to carry out this scanning already exist; however, they are expensive and produce many files that need to be stitched together and are cumbersome to deal with. The solution is to create a scanning system using an already purchased scanning laser and linear stage motor. The laser and stage motor were combined to produce length results that aligned with a digital microscope but differing height results.

Ruffolo, Leopoldo↗
DOE OSTI2024

Designing a track for an electrospinning unit and cost-effective laser scanning system

Nanofibers are produced in the Targeted Systems Department (TSD) by applying a large voltage to the nanofiber fluid and the collection apparatus known as the electrospinner. The fiber is then shot out of nozzles and collected onto the electrospinner into a nanofiber mat. The problem is that since the nozzle heads are stationary, there is non-uniform deposition of nanofiber on the collection drum leading to variation in thickness of the produced nanofiber mat. To mitigate this issue, a reciprocating mechanism is designed to move the nozzle along the drum collector along with the ability to vary its stroke length. This design was realized using Siemens NX and several parts were printed using a 3D printer, but is, as of writing, untested. Second project involved providing an alternative method to scan a large sample that had undergone certain surface deformation by beam interaction and detect out of plane deformation such as micro-scale swelling and surface roughness. Systems to carry out this scanning already exist; however, they are expensive and produce many files that need to be stitched together and are cumbersome to deal with. The solution is to create a scanning system using an already purchased scanning laser and linear stage motor. The laser and stage motor were combined to produce length results that aligned with a digital microscope but differing height results.

Ruffolo, Leopoldo↗
DOE OSTI2021

Analysis of the Innovation Trend in Cell-Free Synthetic Biology

Cell-free synthetic biology is a maturing field that aims to assemble biomolecular reactions outside cells for compelling applications in drug discovery, metabolic engineering, biomanufacturing, diagnostics, and education. Cell-free systems have several key features. They circumvent mechanisms that have evolved to facilitate species survival, bypass limitations on molecular transport across the cell wall, enable high-yielding and rapid synthesis of proteins without creating recombinant cells, and provide high tolerance towards toxic substrates or products. Here, we analyze ~750 published patents and ~2000 peer-reviewed manuscripts in the field of cell-free systems. Three hallmarks emerged. First, we found that both patent filings and manuscript publications per year are significantly increasing (five-fold and 1.5-fold over the last decade, respectively). Second, we observed that the innovation landscape has changed. Patent applications were dominated by Japan in the early 2000s before shifting to China and the USA in recent years. Finally, we discovered an increasing prevalence of biotechnology companies using cell-free systems. Our analysis has broad implications on the future development of cell-free synthetic biology for commercial and industrial applications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

RAVEN and Dynamic Probabilistic Risk Assessment: Software overview

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7 [], currently under development at the Idaho National Laboratory. Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism has been employed by providing Application Programming Interfaces (APIs). These interfaces are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable to investigate the system response, investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The paper presents an overview of the software capabilities and their implementation schemes followed by some application examples.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

SU-E-T-584: Commissioning of the MC2 Monte Carlo Dose Computation Engine

Purpose: An automated system, MC2, was developed to convert DICOM proton therapy treatment plans into a sequence MCNPX input files, and submit these to a computing cluster. MC2 converts the results into DICOM format, and any treatment planning system can import the data for comparison vs. conventional dose predictions. This work describes the data and the efforts made to validate the MC2 system against measured dose profiles and how the system was calibrated to predict the correct number of monitor units (MUs) to deliver the prescribed dose. Methods: A set of simulated lateral and longitudinal profiles was compared to data measured for commissioning purposes and during annual quality assurance efforts. Acceptance criteria were relative dose differences smaller than 3% and differences in range (in water) of less than 2 mm. For two out of three double scattering beam lines validation results were already published. Spot checks were performed to assure proper performance. For the small snout, all available measurements were used for validation vs. simulated data. To calibrate the dose per MU, the energy deposition per source proton at the center of the spread out Bragg peaks (SOBPs) was recorded for a set of SOBPs from each option. Subsequently these were then scaled to the results of dose per MU determination based on published methods. The simulations of the doses in the magnetically scanned beam line were also validated vs. measured longitudinal and lateral profiles. The source parameters were fine tuned to achieve maximum agreement with measured data. The dosimetric calibration was performed by scoring energy deposition per proton, and scaling the results to a standard dose measurement of a 10 x 10 x 10 cm3 volume irradiation using 100 MU. Results: All simulated data passed the acceptance criteria. Conclusion: MC2 is fully validated and ready for clinical application.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2017

Operational Plan for Underground Storage Tank 322 R2U2

This Operational Plan provides the operator of the tank system with guidelines relating to the safe and compliant operation and maintenance of the tank system. The tank system schematic and list of emergency contacts shall be posted near the tank so they are visible to tank personnel. This Operational Plan shall be kept on file by the Facility Supervisor. It should be understood when managing this tank system that it is used to store hazardous waste temporarily for 90 calendar days or less. The rinsewater handled in the tank system is considered hazardous and may exhibit the characteristic of toxicity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SU-C-BRD-03: Analysis of Accelerator Generated Text Logs for Preemptive Maintenance

Purpose: To develop a model to analyze medical accelerator generated parameter and performance data that will provide an early warning of performance degradation and impending component failure. Methods: A robust 6 MV VMAT quality assurance treatment delivery was used to test the constancy of accelerator performance. The generated text log files were decoded and analyzed using statistical process control (SPC) methodology. The text file data is a single snapshot of energy specific and overall systems parameters. A total of 36 system parameters were monitored which include RF generation, electron gun control, energy control, beam uniformity control, DC voltage generation, and cooling systems. The parameters were analyzed using Individual and Moving Range (I/MR) charts. The chart limits were calculated using a hybrid technique that included the use of the standard 3σ limits and the parameter/system specification. Synthetic errors/changes were introduced to determine the initial effectiveness of I/MR charts in detecting relevant changes in operating parameters. The magnitude of the synthetic errors/changes was based on: the value of 1 standard deviation from the mean operating parameter of 483 TB systems, a small fraction (≤ 5%) of the operating range, or a fraction of the minor fault deviation. Results: There were 34 parameters in which synthetic errors were introduced. There were 2 parameters (radial position steering coil, and positive 24V DC) in which the errors did not exceed the limit of the I/MR chart. The I chart limit was exceeded for all of the remaining parameters (94.2%). The MR chart limit was exceeded in 29 of the 32 parameters (85.3%) in which the I chart limit was exceeded. Conclusion: Statistical process control I/MR evaluation of text log file parameters may be effective in providing an early warning of performance degradation or component failure for digital medical accelerator systems. Research is Supported by Varian Medical Systems, Inc.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Code to map Xray reflectivity data from real space to reciprocal space

Code to map X-ray reflectivity data from real space to reciprocal space. The code is written as a Jupyter notebook. Example data are included as a separate file (example_data.csv). These data were collected on a Panalytical XRD system. As a first step, run all cells in the notebook. Opportunities are identified for users to make their own choices for choosing the boundaries of the region of interest to estimate background. A simple algorithm is used to estimate background from the outer regions of the data collection region, interpolating the result inside the region of interest. The background is removed and then the data mapped to Qx-Qz (reciprocal space) coordinates. The results are written as NumPy binary and Matlab formatted files.

Fitzsimmons, Michael [Oak Ridge National Lab. (ORN↗
DOE OSTI2021

Brady Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. These inputs and outputs were used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems at the Brady Hot Springs Geothermal Site. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Brady Hot Springs Geothermal Site.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Automatic and rapid calibration of urban building energy models by learning from energy performance database

Urban building energy modeling (UBEM) is attracting increasing attention in the energy modeling filed. Unlike modeling a single building using detailed building systems information, UBEM generally uses limited high-level building stock data to infer default assumptions about building characteristics and operations. Additionally, this practice inherently brings uncertainty to UBEM. This study introduced a novel method of automatic and rapid calibration of UBEM based on the annual electricity and natural gas energy use data by learning the correlations between crucial model input parameters and the building energy use from the reference building models. A case study was presented to calibrate 72 large office buildings built before 1978 in San Francisco. Seventeen model parameters were selected and Monte Carlo sampling was used to create 1000 samples that reasonably represent the parameter space. Then 1000 simulations were performed for the reference building model to create an energy performance database. The results showed that by learning from the energy performance database, it took less than four simulation runs on average to calibrate a building model. After the calibration, the distributions of each parameter were obtained to replace their single predefined default values. For example, the default lighting power density of 21.39 W/m 2 was calibrated to be 7.50 W/m 2 on average. The case study successfully demonstrated the effectiveness of the novel calibration method for UBEM in the mild climate. The method will be further tested in future for other climate zones and other building types.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A methodology for customizing implantation profiles of light ions using a single thin foil energy degrader

We report a method was developed to quantify the spatial distribution and implantation depth of energy-degraded light ions with a thin foil rotating energy degrader for use during multiple ion beam irradiation. The methodology covers three physical phenomena: ions passing through the thin foil, ions travelling through the vacuum to the target, and ion implantation into the target, and accounts for the distribution of ions both in depth and in plane. The processes of energy straggling and scattering were calculated using SRIM. The effects of raster-scanning, and the geometry of the system were implemented in scripts handling the SRIM output files. Elastic backscattering (EBS) using 2.38 MeV H+ protons was used to measure the helium depth profiles after implantation with and without thin foil energy degradation. Defect analysis with transmission electron microscopy confirmed the implantation profiles measured with EBS and calculated with SRIM.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform, Version 1.5 and 1.x.

EDGE bioinformatics was developed to help biologists process Next Generation Sequencing data (in the form of raw FASTQ files), even if they have little to no bioinformatics expertise. EDGE is a highly integrated and interactive web-based platform that is capable of running many of the standard analyses that biologists require for viral, bacterial/archaeal, and metagenomic samples. EDGE provides the following analytical workflows: quality trimming and host removal, assembly and annotation, comparisons against known references, taxonomy classification of reads and contigs, whole genome SNP-based phylogenetic analysis, and PCR analysis. EDGE provides an intuitive web-based interface for user input, allows users to visualize and interact with selected results (e.g. JBrowse genome browser), and generates a final detailed PDF report. Results in the form of tables, text files, graphic files, and PDFs can be downloaded. A user management system allows tracking of an individual’s EDGE runs, along with the ability to share, post publicly, delete, or archive their results.

Chain, Patrick↗
DOE OSTI2018

Multispecies Hypersonic Flow Simulator

MHYSA is a conservative finite-difference code to solve the 1D, 2D, and 3D multispecies Navier-Stokes equations for hypersonic flow applications. It supports Cartesian grids only and has the option of including immersed boundaries. - Solves 1D Euler and 2D and 3D Navier-Stokes equations. - Solves the PDEs over Cartesian grids. - Written entirely in C and uses the MPICH library. It also uses OpenMP threads but this is a work-inprogress. - Can be compiled with PETSc (http://www.mcs.anl.gov/petsc/), if available, where it can use PETSc's time integration module TS (http://www.mcs.anl.gov/petsc/petsc-current/src/ts/). - For 3-dimensional simulations, the immersed boundaries can be used to solve over non-Cartesian geometries. MHYSA has been developed to be scalable, and apart from the usual functionalities to solve a system of PDEs on distributed architectures, it provides scalable file I/O functions.

Ghosh, Debojyoti↗