Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Reading”

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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Conversion of Environment Files from ORDEM 3 & MEM 3 to STENVI

To perform Micrometeoroid and Orbital Debris (MMOD) analyses, NASA describes the orbital debris particle environment using the ORDEM 3 tool and the micrometeoroid environment using the MEM 3 tool. Both output files that detail the flux of particles as a function of several parameters. European MMOD analyses use different tools to perform similar functions, including the Meteoroid And Space debris Terrestrial Environment Reference (MASTER) environment model, which outputs flux data in the Standard Environment Interface (STENVI) format. The NASA Johnson Space Center Hypervelocity Impact Technology Group was asked to create a converter to translate flux data from the output file formats used by ORDEM 3.x and MEM 3 into the STENVI file format. Validation was performed to ensure that the converter was reproducing the input data correctly. The Bumper 3 MMOD risk assessment tool has a capability for reading ORDEM 3.x, MEM 3, and STENVI environment file formats and was used to compare MMOD risk results generated using the converter’s output STENVI files to the risk results generated using the input ORDEM 3.x and MEM 3 igloo files. Specific point comparisons validated that the converter translated the environment data correctly, producing the intended output. However, realistic risk assessments showed that the bin methodology employed by Bumper for determining particle flux as a function of particle size when reading files in STENVI format produces much higher MMOD risk than the point-interpolation methodology used in Bumper risk assessments using ORDEM and MEM. Modifications to how Bumper 3 reads STENVI files during analyses are considered to improve Bumper’s ability to perform useful analyses with environment files in this format.

STENVI↗
NASA NTRS2023

Conversion of ORDEM 3.X & MEM 3 Into STENVI File Format

To perform Micrometeoroid and Orbital Debris (MMOD) analyses, NASA describes the orbital debris particle environment using the ORDEM 3 tool and the micrometeoroid environment using the MEM 3 tool. Both output files that detail the flux of particles as a function of several parameters. European MMOD analyses use different tools to perform similar functions, including the Meteoroid And Space debris Terrestrial Environment Reference (MASTER) environment model, which outputs flux data in the Standard Environment Interface (STENVI) format. The NASA Johnson Space Center Hypervelocity Impact Technology Group was asked to create a converter to translate flux data from the output file formats used by ORDEM 3.x and MEM 3 into the STENVI file format. Validation was performed to ensure that the converter was reproducing the input data correctly. The Bumper 3 MMOD risk assessment tool has a capability for reading ORDEM 3.x, MEM 3, and STENVI environment file formats and was used to compare MMOD risk results generated using the converter’s output STENVI files to the risk results generated using the input ORDEM 3.x and MEM 3 igloo files. Specific point comparisons validated that the converter translated the environment data correctly, producing the intended output. However, realistic risk assessments showed that the bin methodology employed by Bumper for determining particle flux as a function of particle size when reading files in STENVI format produces much higher MMOD risk than the point-interpolation methodology used in Bumper risk assessments using ORDEM and MEM. Modifications to how Bumper 3 reads STENVI files during analyses are considered to improve Bumper’s ability to perform useful analyses with environment files in this format.

STENVI↗
NASA NTRS2024

Celebrating 10 Years of the Sub-Seasonal to Seasonal Prediction Project and Looking to the Future

The conference clearly demonstrated the increasing interest and growth of the scientific community working on the development and application of sub-seasonal to seasonal prediction since the start of the World Weather Research Programme (WWRP)/World Climate Research Programme (WCRP) sub-seasonal to seasonal (S2S) prediction project in 2013. The conference, which was held at the University of Reading (United Kingdom), was organized into three main themes as briefly summarized below, with eleven invited talks, 74 oral contributed talks, and 101 posters. The conference also included a two-hour breakout session, wherein eight groups discussed the current state and prospect for S2S prediction, and an early career researcher event. A summary of these discussions and recommendations is presented below. The conference web page (https://research.reading.ac.uk/s2s-summit2023/) is archived at the University of Reading. Introductory comments by representatives of the World Meteorological Organization (WMO) WWRP and WCRP emphasized the importance of the weather–climate linkage, targeted by S2S forecasts (from 2 weeks to a season ahead), addressing the challenges of creating “end-to-end” forecasts that encompass the entire climate-services chain from the prediction science and forecast, to the development and issuing of forecast products tailored to informing user-decisions. They also emphasized the efficacy of multi-model ensemble efforts and databases to foster collaborations internationally and between operational centres and academia. Although the WWRP/WCRP S2S project comes to an end in 2023, S2S prediction will remain an important focus for WWRP and WCRP. In WWRP, a new project called SAGE (Sub-seasonal to seasonal predictions for Agriculture and Environment) will start in 2024. Another important legacy of the S2S project will be the maintenance of the S2S database (Vitart et al. 2017) and the establishment of a WMO Lead Center for sub-seasonal prediction multi-model ensemble (LC-SSPMME) which will provide real-time multi-model S2S climate information. In two keynote presentations, Prof. Brian Hoskins (University of Reading) and Dr. Gilbert Brunet (Australian Bureau of Meteorology) discussed the potential of S2S predictability and the ongoing journey for understanding and improving these predictions. This conference was a sequel to the International Conference on Sub-seasonal to Seasonal Prediction (Robertson et al., 2014) which took place in College Park (Maryland, USA) in February 2014 to celebrate the start of the WWRP/WCRP S2S project, and to WCRP and WWRP conferences in Boulder, USA, in 2018 (Merryfield et al., 2020). A significant development compared to the previous S2S conferences was the large number of presentations on research to operation (R2O) and S2S applications and on the use of artificial intelligence and machine learning (AI/ML) methods for S2S prediction. Some of these methods provide empirical S2S forecasts which are competitive with state-of-the-art dynamical models. Other presentations demonstrated that AI/ML can provide alternative calibration of dynamical model outputs to traditional methods. Several talks and posters highlighted the increasing use of AI/ML, including deep learning, in S2S forecast post-processing and using AI to identify higher flow-dependent skill. Finally, some presentations demonstrated the value of AI/ML methods for a better understanding of S2S sources of predictability and attribution of extreme events.

S. J. Woolnough↗
NASA NTRSDate not supplied

Spectral Measurement Errors due to CCD Serial Pixel-to-Pixel Readout Interference in the Ocean Color Instrument of the NASA PACE Mission

The Ocean Color Instrument on NASA’s PACE mission is a hyperspectral imager with a spatial resolution of 1km x 1km and spectral resolution of 5nm in 2.5nm steps over 320-890nm. The detection system is based on two Charge-Coupled Devices (CCDs) operating in Time Delay Integration (TDI) mode to achieve high signal-to-noise ratio. The front-end optical imager is a rotating mirror-based system that images the ground-scene onto a slit with a field of view of 16km x 1km. The slit-image is re-imaged and wavelength dispersed on the CCDs. As the ground-scene moves through the slit, it moves along the CCD columns as charge is moved along with it. The accumulated charge at the end of each column is collected in a serial pixel. Each of the 16 CCD outputs read out 32 columns of the same ground scene spaced 0.625nm apart. The 32-pixel serial register is swiftly read out before the next TDI cycle. The telescope is spinning at 5.77Hz to achieve the required spatial resolution. This results in a serial pixel readout speed of 8.5MHz. Each serial read-cycle goes through a reset and video period that are each sampled to create a low-noise correlated double sample value. This only allows 59ns for the reset and video to be asserted and settled before sampling. Due to the short time period, the response of the CCD exhibits serial pixel-to-pixel readout interference as the reset and video signals do not have time to fully settle before sampling. Each serial pixel value therefore has a dependence on the value of the preceding pixel value. This leads to a spectral measurement error of up to 0.3%. We explain the operation of the detection system, the behavior of the interference and the resulting wavelength error with results from ground testing and on-orbit characterization.

Ulrik B Gliese↗
NASA NTRSDate not supplied

Benchmarking Computational Tools for Calling SNPs and Indels in Complex Microbial Populations

The NASA BioNutrients missions seek to understand the suitability of microorganisms for bioproduction during space flight. One topic of interest is the stability of microbial genomes during long-term ambient storage and subsequent rehydration and growth. To address these questions, samples from 8 species were flown to ISS for 5 years of desiccated storage at ambient temperature (Stasis Packs) and 2 species were packaged along with powdered media inside a bioreactor system to allow hydration and growth in microgravity (Production Packs). For both systems, Whole Genome Sequencing (WGS) of the DNA extracted from the returned samples and paired ground controls will be conducted to identify changes in genome stability due to time, storage conditions and growth in space. Across the technical replicates, ground controls, 10 timepoints, and multiple experimental conditions, ~300 samples have been selected for initial analysis with WGS sequencing to 100x coverage. A flexible and resource efficient mutation calling pipeline is needed to process this large dataset and allow for comparisons between species. Many bioinformatics tools for calling Indels and Single Nucleotide Variants (SNVs) are designed for use with pure isolates, where true variations from the reference genome are expected to dominate the reads aligning to the location of mutation. In contrast, DNA from the Stasis Pack (SP) samples was collected directly after recovery from desiccated storage and the Production Pack (PP) samples were collected after fermentation. In this context, reads with mutations are expected to be less frequent than reads that align with the reference genome, as each sample will include multiple lines of cells. Thus, BioNutrients samples are expected to be similar to samples from cancer cell or “pooled” sequencing approaches. In preparation for the analysis of the BioNutrients samples, we have tested three mutation calling tools (GATK for Microbes, BreSeq and DiscoSNP) designed for complex samples. A challenge of validating mutation identification pipelines is a lack of “Ground Truth” datasets, especially for complex samples. To compare these three tools, we sought to identify mutations in pre-existing WGS data collected from populations of Chlamydomonas reinhardtii that were exposed to UV mutagenesis and growth in LEO as part of the Space Algae-1 mission. Here we present a summary of these tools against the analysis originally conducted using the CRISP tool. Critical metrics are compared such as runtime, the number of SNPs, the number and size of Indels, and patterns of transversion and transitions identified by each tool are reported. By sharing these benchmarking results collected in support of the BioNutrients mission, we aim to guide others seeking to identify SNVs in similarly complex microbial samples.

Biology↗
NASA NTRSDate not supplied

Improving Satellite-Based Hotspot Detection Through Deep Learning-Enabled Smoke Recognition

While geostationary satellites, such as the GOES-R series, provide wildland fire hotspot readings at a high temporal resolution, they are prone to false negative readings and decreased confidence. One cause of decreased hotspot confidence is cloud contamination. Smoke produced from wildfire is often misinterpreted as cloud contamination, resulting in inaccurate and unsure sensor readings. To this end, we built a deep learning image segmentation model to identify smoke and cloud in true color satellite images. The model is pre-trained using self-supervised learning on over 10,000 GOES-R images to learn the underlying structure of satellite imagery. Then, the model is fine-tuned on a set of 130 labeled documents using supervised learning. The resulting model performs multi-class image segmentation with 85% accuracy and runs in under a minute on a standard personal computer. When paired alongside hotspot data, the model’s outputs can help increase confidence in wildfire location by identifying cases of cloud contamination that are due to smoke. The resulting model can be deployed in a stand-alone application or bundled in an Open Data Integration for wildland fire management (ODIN) application.

Earth observation↗
NASA NTRSDate not supplied

Toward Large Field-of-view X-ray Spectrometers with Magnetic Microcalorimeters

A future X-ray telescope mission like Lynx, a X-ray flagship mission concept, requires a large field of view, high energy resolution, and high spatial resolution. The Magnetic Microcalorimeter (MMC) detectors we are developing for such a mission feature a 5-arcminute field of view, approximately 100,000 pixels, each spanning 0.5–1 arcsecond. These detectors require the readout of 8,000 sensors. Three different array types have been designed to meet scientific goals, two of which utilize thermal multiplexing to reduce the number of readout channels in a scheme called “Hydra.” We are currently fabricating detectors on a recently completed buried wiring wafer with connections to every sensor. The sensors are read out using a microwave multiplexer (μMUX) with flux ramp modulation. A single μMUX can read out approximately 1,000 sensors with only two feed lines. We will report on the test of the first two-dimensional μMUX chip, consisting of 78 resonators. μMUX chips that pass screening will be directly bonded to MMC array chips using indium bump bonding. In parallel, we have tested a kinetic inductance traveling wave parametric amplifier (KI-TWPA) to amplify signals at low temperatures. The KI-TWPA exhibits extremely low internal noise, approaching the standard quantum limit. Since the energy resolution of an MMC is primarily limited by the readout system, the KI-TWPA is an ideal amplifier for MMCs with μMUX readout. We recently measured the readout noise of a μMUX with a KI-TWPA, demonstrating its capability for reading out MMC arrays. We present the design and fabrication of the latest MMC detectors, along with its readout performance.

Magntic Microcalorimeters↗
DOE OSTI2014

Cache directory lookup reader set encoding for partial cache line speculation support

In a multiprocessor system, with conflict checking implemented in a directory lookup of a shared cache memory, a reader set encoding permits dynamic recordation of read accesses. The reader set encoding includes an indication of a portion of a line read, for instance by indicating boundaries of read accesses. Different encodings may apply to different types of speculative execution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Enabling Graph Appliance for Genome Assembly

In recent years, there has been a huge growth in the amount of genomic data available as reads generated from various genome sequencers. The number of reads generated can be huge, ranging from hundreds to billions of nucleotide, each varying in size. Assembling such large amounts of data is one of the challenging computational problems for both biomedical and data scientists. Most of the genome assemblers developed have used de Bruijn graph techniques. A de Bruijn graph represents a collection of read sequences by billions of vertices and edges, which require large amounts of memory and computational power to store and process. This is the major drawback to de Bruijn graph assembly. Massively parallel, multi-threaded, shared memory systems can be leveraged to overcome some of these issues. The objective of our research is to investigate the feasibility and scalability issues of de Bruijn graph assembly on Cray s Urika-GD system; Urika-GD is a high performance graph appliance with a large shared memory and massively multithreaded custom processor designed for executing SPARQL queries over large-scale RDF data sets. However, to the best of our knowledge, there is no research on representing a de Bruijn graph as an RDF graph or finding Eulerian paths in RDF graphs using SPARQL for potential genome discovery. In this paper, we address the issues involved in representing a de Bruin graphs as RDF graphs and propose an iterative querying approach for finding Eulerian paths in large RDF graphs. We evaluate the performance of our implementation on real world ebola genome datasets and illustrate how genome assembly can be accomplished with Urika-GD using iterative SPARQL queries.

Big data↗
DOE OSTI2015

Detecting and correcting hard errors in a memory array

Hard errors in the memory array can be detected and corrected in real-time using reusable entries in an error status buffer. Data may be rewritten to a portion of a memory array and a register in response to a first error in data read from the portion of the memory array. The rewritten data may then be written from the register to an entry of an error status buffer in response to the rewritten data read from the register differing from the rewritten data read from the portion of the memory array.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Using the Model Perennial Grass Brachypodium sylvaticum to Engineer Resistance to Multiple Abiotic Stresses

We are using the perennial model grass Brachypodium sylvaticum to identify combinations of transgenes that enhance tolerance to multiple, simultaneous abiotic stresses. The most successful transgene combinations will ultimately be used to create improved switchgrass (Panicum virgatum L.) cultivars. To further develop B. sylvaticum as a perennial model grass, and facilitate our planned transcriptional profiling, we are sequencing and annotating the genome. We have generated ~40x genome coverage using PacBio sequencing of the largest possible size selected libraries (18, 22, 25 kb). Our initial assembly using only long-read sequence contained 320 Mb of sequence with an N50 contig length of 315 kb and an N95 contig length of 40 kb. This assembly consists of 2,430 contigs, the largest of which was 1.6 Mb. The estimated genome size based on c-values is 340 Mb indicating that about 20 Mb of presumably repetitive DNA remains yet unassembled. Significantly, this assembly is far superior to an assembly created from paired-end short-read sequence, ~100x genome coverage. The short-read-only assembly contained only 226 Mb of sequence in 19k contigs. To aid the assembly of the scaffolds into chromosome-scale assemblies we produced an F2 mapping population and have genotyped 480 individuals using a genotype by sequence approach. One of the reasons for using B. sylvaticum as a model system is to determine if the transgenes adversely affect perenniality and winter hardiness. Toward this goal, we examined the freezing tolerance of wild type B. sylvaticum lines to determine the optimal conditions for testing the freezing tolerance of the transgenics. A survey of seven accessions noted significant natural variation in freezing tolerance. Seedling or adult Ain-1 plants, the line used for transformation, survived an 8 hour challenge down to -6 oC and 50% survived a challenge down to -9 oC. Thus, we will be able to easily determine if the transgenes compromise freezing tolerance. In the effort to develop biotechnological tools for perennial grass improvement, we have completed the transformation of B. sylvaticum with constructs containing 20 genes shown to be associated with enhanced abiotic stress tolerance in monocots. In addition, we have transformed plants with constructs containing a combination of genes (i.e. SARK::IPT- Ubi::HSR1::Ubi::NHX1) in order to simultaneously overexpress genes associated with drought + heat tolerance + salt tolerance. We generated single copy insert T1 lines for all constructs and the generation and bulking of homozygous T2 lines is well underway. In addition to our B. sylvaticum transgenics, we transformed B. distachyon with many of the same genes. Some of the transgenic B. distachyon plants subjected to a combined stress of both drought and salinity were able to produced higher yields than wild type plants. Our results indicate a great potential for the development of grasses with improved performance and yield in water-limited areas.

Gordon, Sean↗
DOE OSTI2016

Checkpoint triggering in a computer system

According to an aspect, a method for triggering creation of a checkpoint in a computer system includes executing a task in a processing node of the computer system and determining whether it is time to read a monitor associated with a metric of the task. The monitor is read to determine a value of the metric based on determining that it is time to read the monitor. A threshold for triggering creation of the checkpoint is determined based on the value of the metric. Based on determining that the value of the metric has crossed the threshold, the checkpoint including state data of the task is created to enable restarting execution of the task upon a restart operation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Apparatus and method for implementing power saving techniques when processing floating point values

An apparatus and method are described for reducing power when reading and writing graphics data. For example, one embodiment of an apparatus comprises: a graphics processor unit (GPU) to process graphics data including floating point data; a set of registers, at least one of the registers of the set partitioned to store the floating point data; and encode/decode logic to reduce a number of binary 1 values being read from the at least one register by causing a specified set of bit positions within the floating point data to be read out as 0s rather than 1s.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

The Fermilab Test Beam Facility Data Acquisition System Based on $otsdaq$

The Real-Time Systems Engineering Department of the Scientific Computing Division at Fermilab has deployed set of customizations to our Off-The-Shelf Data Acquisition solution (otsdaq) at the Fermilab Test Beam Facility (FTBF) to read out the beamline instrumentation in support of FTBF users. In addition to reading out several detectors which use various detection technologies and readout hardware, the FTBF Data Acquisition system (DAQ) can perform basic track reconstruction through the facility in real time and provide data to facility users. An advanced prototype smart coincidence module, known as the NIM+, performs trigger distribution and throttling, allowing the beamline instrumentation to be read out at different rates. Spill data are saved to disk for studies of the facility performance, and hit data are also made available on the FTBF network for experiments' use. A web-based run control and configuration GUI are provided, and the online monitoring snapshots created after each beam spill are viewable from any computer connected to the Fermilab network. The integrated DAQ system for the facility provides users with high-precision tracking data along the beamline and a single location for obtaining data from the facility detectors, which set the baseline for testing their own detectors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Bulk force in a battery pack and its application to state of charge estimation

There is disclosed an electrical device including a battery pack, a pressure sensor for measuring a volume change of the battery pack, a voltage sensor in electrical communication with a positive terminal and a negative terminal of the battery pack, a temperature sensor positioned in a cell of the battery pack, and a battery management system. The battery management system includes a controller in electrical communication with the pressure sensor, the voltage sensor, and the temperature sensor, the controller being configured to execute a program stored in the controller to determine a state of charge percentage of the battery pack based on a pressure reading from the pressure sensor, a terminal voltage reading from the voltage sensor, and a temperature reading from the temperature sensor.

Stefanopoulou, Anna G.↗
DOE OSTI2018

Checkpoint triggering in a computer system

According to an aspect, a method for triggering creation of a checkpoint in a computer system includes executing a task in a processing node and determining whether it is time to read a monitor associated with a metric of the task. The monitor is read to determine a value of the metric based on determining that it is time to read the monitor. A threshold for triggering creation of the checkpoint is determined based on the metric. A monitoring block size is determined for the checkpoint. A checkpoint interval is determined based on the monitoring block size, a checkpoint bandwidth, and a failure rate of the computer system. Based on determining that the value of the metric has crossed the threshold and the checkpoint interval has elapsed, the checkpoint including state data of the task is created to enable restarting execution of the task upon a restart operation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Using redundant transactions to verify the correctness of program code execution

In the described embodiments, a processor core (e.g., a GPU core) receives a section of program code to be executed in a transaction from another entity in a computing device. The processor core sends the section of program code to one or more compute units in the processor core to be executed in a first transaction and concurrently executed in a second transaction, thereby creating a “redundant transaction pair.” When the first transaction and the second transaction are completed, the processor core compares a read-set of the first transaction to a read-set of the second transaction and compares a write-set of the first transaction to a write-set of the second transaction. When the read-sets and the write-sets match and no transactional error condition has occurred, the processor core allows results from the first transaction to be committed to an architectural state of the computing device.

Gurumurthi, Sudhanva↗
DOE OSTI2016

Error-correction coding for hot-swapping semiconductor devices

A memory read operation is directed at a group of semiconductor devices from which a first semiconductor device has been removed. An error in data for the memory read operation is detected based on error-correction coding (ECC). The error is caused at least in part by the first semiconductor device having been removed. ECC is used to determine corrected data for the memory read operation.

42 ENGINEERING↗