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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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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

SALT3-NIR: Taking the Open-source Type Ia Supernova Model to Longer Wavelengths for Next-generation Cosmological Measurements

Abstract A large fraction of Type Ia supernova (SN Ia) observations over the next decade will be in the near-infrared (NIR), at wavelengths beyond the reach of the current standard light-curve model for SN Ia cosmology, SALT3 (∼2800–8700 Å central filter wavelength). To harness this new SN Ia sample and reduce future light-curve standardization systematic uncertainties, we train SALT3 at NIR wavelengths (SALT3-NIR) up to 2 μ m with the open-source model-training software SALTshaker , which can easily accommodate future observations. Using simulated data, we show that the training process constrains the NIR model to ∼2%–3% across the phase range (−20 to 50 days). We find that Hubble residual (HR) scatter is smaller using the NIR alone or optical+NIR compared to optical alone, by up to ∼30% depending on filter choice (95% confidence). There is significant correlation between NIR light-curve stretch measurements and luminosity, with stretch and color corrections often improving HR scatter by up to ∼20%. For SN Ia observations expected from the Roman Space Telescope, SALT3-NIR increases the amount of usable data in the SALT framework by ∼20% at redshift z ≲ 0.4 and by ∼50% at z ≲ 0.15. The SALT3-NIR model is part of the open-source SNCosmo and SNANA SN Ia cosmology packages.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1970

Piezoelectric transducer

Transducer consists of a hybrid thin film and a piezoelectric transistor that acts as a stress-sensitive device with built-in gain. It provides a stress/strain transducer that incorporates a signal amplification stage and sensor in a single package.

Conragan, J.↗
NASA NTRS1972

Apollo experience report: Thermal design of Apollo lunar surface experiments package

The evolution of the thermal design of the Apollo lunar surface experiments package central station from the basic concept to the final flight hardware is discussed, including results of development, prototype, and qualification tests that were used to verify that the flight hardware would operate adequately on the lunar surface. In addition, brief discussions of the thermal design of experiments included in the experiments package are presented. The flight thermal performance is compared with analytical results and thermal-vacuum test results, and design modifications for future lunar surface experiment packages are presented.

Harris, R. S., Jr.↗
NASA NTRS1974

Room temperature stretch forming of scale space shuttle external tank dome gores. Volume 1: Technical

An account of activities and data gathered in the Room Temperature Stretch Forming of One-third Scale External Tank Bulkhead Gores for space shuttle study, and a tooling design and production cost study are reported. The following study phases are described: (1) the stretch forming of three approximately one-third scale external tank dome gores from single sheets of 2219-T37 aluminum alloy; (2) the designing of a full scale production die, including a determination of tooling requirements; and (3) the determination of cost per gore at the required production rates, including manufacturing, packaging, and shipping.

Blunck, R. D.↗
NASA NTRS1974

Tool for use in lifting pin supported objects

A tool for use in lifting a pin-supported, electronic package mounted in juxtaposition with the surface of an electronic circuit board is described. The tool is configured to be received beneath a pin-supported package and is characterized by a manually operable linkage, including an elongated, rigid link is supported for axial reciprocation and a pivotal link pinned to the body and supported for oscillation induced in response to axial motion imparted to the rigid link. A lifting plate is pivotally coupled to the distal end of the pivotal link so that oscillatory motion imparted to the pivotal link serves to move the plate vertically for elevating the plate into lifting engagement with the electronic package positioned thereabove.

Marzek, R. A.↗
NASA NTRS1975

Spoked wheels to deploy large surfaces in space-weight estimates for solar arrays

Extensible booms were used to deploy and support solar cell arrays of varying areas. Solar cell array systems were built with one or two booms to deploy and tension a blanket with attached cells and bussing. A segmented and hinged rim supported by spokes joined to a common hub is described. This structure can be compactly packaged and deployed.

Crawford, R. F.↗
NASA NTRS1976

Evaluation of advanced microelectronic fluxless solder-bump contacts for hybrid microcircuits

Technology for interconnecting monolithic integrated circuit chips with other components is investigated. The advantages and disadvantages of the current flip-chip approach as compared to other interconnection methods are outlined. A fluxless solder-bump contact technology is evaluated. Multiple solder-bump contacts were formed on silicon integrated circuit chips. The solder-bumps, comprised of a rigid nickel under layer and a compliant solder overlayer, were electroformed onto gold device pads with the aid of thick dry film photomasks. Different solder alloys and the use of conductive epoxy for bonding were explored. Fluxless solder-bump bond quality and reliability were evaluated by measuring the effects of centrifuge, thermal cycling, and high temperature storage on bond visual characteristics, bond electrical continuity, and bond shear tests. The applicability and suitability of this technology for hybrid microelectronic packaging is discussed.

Mandal, R. P.↗
NASA NTRS1981

The Vermont operational LANDSAT data analysis system

The implementation of the Office of Remote Sensing of Earth Resources (ORSER) software package from Pennsylvania State University onto the University of Vermont's IBM model 3031 computer is discussed. This version of ORSER was found to be much more user-friendly than remote access to the Penn State ORSER package.

Smith, G.↗
NASA NTRS1984

Effects of Shuttle bay environment on UV sensitive photographic film - Instrumentation for Get-Away-Special

An account is given of a Get-Away-Special experiment flown on Space Shuttles 7 and 8 investigating the effect of the space environment on Shumann emulsions. Shumann emulsions, having low gelatin content and no protective gelatin overcoating, are useful detectors of ultraviolet radiation shorter than 2200 angstroms but are extremely sensitive to environmental conditions and handling. The instrument required no interface with the Shuttle. It was turned on by an aneroid switch at an altitude of 50,000 feet. After that, its operation was controlled completely by a CMOS digital controller. Each hour, two temperatures and one voltage were read and stored in a CMOS programmable read only memory. At intervals, valves were opened and closed to expose SO 652 film strips of three sensitivities to the cargo bay environment for various time periods. The design and operation of the instrument package is described.

Kreplin, R. W.↗
NASA NTRS1990

Simulation of capacitor charging power supplies

By neglecting losses and other nonidealities, the authors were able to derive a simple single-loop equivalent circuit for each mode of operation of the series resonant converter. From these equivalent circuits, a good approximation of the current and voltages within the converter was obtained, despite the simplified approach taken. It is pointed out that designers of capacitor charging power supplies can utilize this approach in deciding what type of control strategy is needed to obtain a desired characteristic. Once the basic topology of the power supply has been determined, a detailed simulation could be implemented using one of the general-purpose software packages available.

Newton, S. R.↗
NASA NTRS1990

Implementation of efficient sensitivity analysis for optimization of large structures

The paper presents the theoretical bases and implementation techniques of sensitivity analyses for efficient structural optimization of large structures, based on finite element static and dynamic analysis methods. The sensitivity analyses have been implemented in conjunction with two methods for optimization, namely, the Mathematical Programming and Optimality Criteria methods. The paper discusses the implementation of the sensitivity analysis method into our in-house software package, AutoDesign.

Umaretiya, J. R.↗
NASA NTRS1995

Microelectronics in Japan

The purpose of this JTEC study is to evaluate Japan's electronic manufacturing and packaging capabilities within the context of global economic competition. To carry out this study, the JTEC panel evaluated the framework of the Japanese consumer electronics industry and various technological and organizational factors that are likely to determine who will win and lose in the marketplace. This study begins with a brief overview of the electronics industry, especially as it operates in Japan today. Succeeding chapters examine the electronics infrastructure in Japan and take an in-depth look at the central issues of product development in order to identify those parameters that will determine future directions for electronic packaging technologies.

Boulton, William R.↗
NASA NTRS1995

Building the electronic industry's roadmaps

JTEC panelists found a strong consistency among the electronics firms they visited: all the firms had clear visions or roadmaps for their research and development activities and had committed resources to ensure that they achieve targeted results. The overarching vision driving Japan's electronics industry is that of achieving market success through developing appealing, high-quality, low-cost consumer goods - ahead of the competition. Specifics of the vision include improving performance, quality, and portability of consumer electronics products. Such visions help Japanese companies define in detail the roadmaps they will follow to develop new and improved electronic packaging technologies.

Boulton, William R.↗
NASA NTRS1999

Accelerated Thermal Cycling and Failure Mechanisms

This paper reviews the accelerated thermal cycling test methods that are currently used by industry to characterize the interconnect reliability of commercial-off-the-shelf (COTS) ball grid array (BGA) and chip scale package (CSP) assemblies.

BGA CSP CBGA thermal cycle solder joint reliabilit↗
DOE OSTI2023

FUN-BioCROP model with litter decomposition parameters derived from the LIDET dataset

This repository contains the code and data necessary to run the FUN-BioCROP (Fixation and Uptake of Nitrogen-Bioenergy Carbon, Rhizosphere, Organisms, and Protection) model with litter decomposition parameters derived from a modified Monte Carlo simulation that used the Long-term Intersite Decomposition Experiment Team dataset. Related publication:Juice, S.M., Ridgeway, J.R., Hartman, M.D., Parton, W.J., Berardi, D.M., Sulman, B.N., Allen, K.E., & Brzostek, E.R. Reparameterizing litter decomposition using a simplified Monte Carlo method improves litter decay simulated by a microbial model and alters bioenergy soil carbon estimates. Description of Files: FUNBioCROP_LIDET Study.Rmd R code with FUN-BioCROP model that can be run with 10 different sets of parameters for litter decomposition (Baseline, LIDET, or eight other best parameter sets identified in the modified Monte Carlo simulation. CORPSE Functions_Bioenergy_V2.R Code with CORPSE model functions, called by FUNBioCROP_LIDET Study.Rmd Model Input Data: bulk.csv, bulk_till.csv, rhizo.csv, rhizo_till.csv, litter.csv Initial C and N (kg C or N/m2) pool values for each soil compartment, final values from spin up. All five files have the same columns: (Column - Description - Units) uFastC - Unprotected fast decomposing carbon - kg carbon/m2 uSlowC - Unprotected slow decomposing carbon - kg carbon/m2 uNecroC - Unprotected necromass carbon - kg carbon/m2 pFastC - Protected fast decomposing carbon - kg carbon/m2 pSlowC - Protected slow decomposing carbon - kg carbon/m2 pNecroC - Protected necromass carbon - kg carbon/m2 livingMicrobeC - Carbon in living microbial biomass - kg carbon/m2 uFastN - Unprotected fast decomposing nitrogen - kg nitrogen/m2 uSlowN - Unprotected slow decomposing nitrogen - kg nitrogen/m2 uNecroN - Unprotected necromass nitrogen - kg nitrogen/m2 pFastN - Protected fast decomposing nitrogen - kg nitrogen/m2 pSlowN - Protected slow decomposing nitrogen - kg nitrogen/m2 pNecroN - Protected necromass nitrogen - kg nitrogen/m2 inorganicN - Inorganic nitrogen - kg nitrogen/m2 CO2 - Carbon in carbon dioxide - kg carbon/m2 livingMicrobeN - Nitrogen in living microbial biomass - kg nitrogen/m2 Model Input Data: FluxTower_AvgSoilT.csv: Average daily soil temperature (oC) at 10 cm depth at University of Illinois Urbana-Champaign (UIUC) Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: FluxTower_AvgSoilVWC.csv: Average daily soil volumetric water content (VWC) at 10 cm depth at UIUC Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: input_CCS_LIDET Study.csv: This file has daily data to run FUN-BioCROP (Column - Description - Units): yr - calendar year - year doy - day of year (1 to 365) (no leap year) - day anpp - aboveground NPP (DayCent) - kg C/m2/day bnpp - belowground NPP (DayCent) - kg C/m2/day aglivc - live aboveground biomass carbon (DayCent) - kg C/m2 bglivcj - live juvenile fine root biomass carbon (DayCent) - kg C/m2 bglivcm - live mature fine root biomass carbon (DayCent) - kg C/m2 aglivn - live aboveground biomass nitrogen (DayCent) - kg N/m2 bglivnj - live juvenile fine root biomass nitrogen (DayCent) - kg N/m2 bglivnm - live mature fine root biomass nitrogen (DayCent) - kg N/m2 nyr - simulation year - year cult - indicates a cultivation event (0 or 1) crop - indicates a new crop (0 or 1) fert - indicates a fertilizer event (0 or 1) frst - indicates the first day of the growing season (0 or 1) harv - indicates a harvest event (0 or 1) last - indicates the end of the growing season (0 or 1) croptype - crop type (0=none; 1=alfalfa; 2=corn; 3=grass clover pasture; 4=soybean; 5=wheat) cropsrl - crop specific root length - mm/g root cultrhizmix - fraction of rhizosphere mixed with bulk soil during cultivation (0.0-1.0) - fraction cultlitmix - fraction of litter mixed with bulk soil during cultivation (0.0-1.0) - fraction harvremov - fraction of above ground biomass removed during harvest (0.0-1.0) - fraction fertamt - fertilization amount - g N/m2 lifehist - plant life history (0 = annual, 1 = perennial) froot_turnover_c - amount of C in fine root turnover - kg C/m2 froot_turnover_n - amount of N in fine root turnover - kg N/m2 agrd_turnover_c - amount of C in aboveground biomass turnover - kg C/m2 agrd_turnover_n - amount of N in aboveground biomass turnover - kg N/m2 leaf_litter_fastfrac - Fast decomposing fraction of leaf litter (0.0-1.0) - fraction root_litter_fastfrac - Fast decomposing fraction of root litter (0.0-1.0) - fraction root_diameter - root diameter - mm root_length - root length - mm root/m2 rhizo_frac - fraction of total soil volume that is rhizosphere (0.0 - 1.0) - fraction date - date in format YYYY-MM-DD Instructions: Save the model code ("FUN-BioCROP_LIDET Study.Rmd") and accompanything files (data streams and CORPSE function code) in the same folder. In model code "Chunk 3: Load CORPSE Data Streams" set the working directory (setwd) to the folder with the files saved in step #1. In "Chunk 5: Define LIDET parameter sets" select the litter decomposition parameter set to be used in the run, and comment out all other sets. If changing any parameter values, edit them in "Chunk 6: Load parameters." Run all chunks up to and including "Chunk 10: Prepare Data for Export." In "Chunk 11: Export Output Data" edit data frames for export and filenames, as necessary. "Chunk 12: Graph Total Soil C" makes a figure of C remaining over the model run period. Description of each model chunk (in file FUN-BioCROP_LIDET Study.Rmd): Chunk 1: Remove all functions, clear memory. Removes all functions from R environment, clears the memory. Chunk 2: Load Packages. Loads packages necessary to run the code. Chunk 3: Load CORPSE Data Streams. Sets the working directory and loads the data files necessary to run CORPSE. Chunk 4: Load CORPSE Functions. Loads the R script with CORPSE functions from the working directory, "CORPSE Functions_Bioenergy_V2.R". Chunk 5: Define LIDET parameter sets. Has ten different parameter sets for litter decomposition tested in this study: Baseline parameters, LIDET parameters, and the other 8 best performing parameter sets identified in the modified Monte Carlo. To run the model, all but one parameter set must be commented out. Chunk 6: Load Parameters. Loads all fixed parameters to run the model. Data frame with definitions of parameters is in the CORPSE function script "CORPSE Functions_Bioenergy_V2.R" Chunk 7: Prepare Data Streams. Takes data streams loaded in Chunk 3 and puts them in the format necessary to run the model. The model is coded to run at least two sites at a time, so if only one site is being run it must be run in duplicate. Individual data tables of daily values are created in this chunk from the input data file. Chunk 8: Set Initial Conditions. Creates data tables of soil C and N pools for each soil compartment (rhizo_till, rhizo, bulk_till, bulk, litter) and loads initial values into the data tables. Creates lists for each soil compartment to hold model output. Chunk 9: Load FUN Data and Set Up Matrices. Uses DayCent data to calculate FUN input data: root and leaf N demand, total N demand, plant CN, leaf N available for retranslocation, and litter production. Creates matrices for FUN model outputs. Chunk 10: Run Model. Runs the model. Chunk 11: Prepare Data for Export. Combines data from each day saved as lists into data frames for each soil compartment. Adds values from all soil compartments together to calculate total soil values, creates separate data frames for each soil C and N pool (e.g., protected slow C) for the total soil value. Adds different C and N pools together to calculate total soil C and N for all layers. Creates data frame of ratio of protected to unprotected SOC. Organizes FUN data for export. Chunk 12: Export Results. Exports CSV files of model results to the working directory. Chunk 13: Graph Total Soil C. Makes figure of C remaining over time. Related Links: Original FUN-BioCROP model: https://github.com/BrzostekEcologyLab/FUN-BioCROP LIDET dataset: https://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023

Juice, Stephanie↗