Results for “framing” — Page 47

DOE OSTI2026

Design of a shipping fixture for a compact cryomodule hermetic assembly

In support of the development of a conduction-cooled 915MHz superconducting radio frequency (SRF) cryomodule, this study highlights the design of a shipping fixture for transporting the hermetic assembly 4500 km from Jefferson Lab to General Atomics in San Diego, California. The hermetic assembly consists of a 2-cell 915 MHz SRF cavity, a coaxial fundamental power coupler and warm-to-cold transition beam tubes. The two-part shipping assembly consists of an inner frame, providing direct mounting of the components, and an outer frame mounted to the ground transport vehicle. The inner frame is then connected to the outer frame by way of wire-rope isolators. Accelerometer data from ground transportation of previous projects at Jefferson Lab provides the baseline for the expected frequency and magnitude of vibrational and shock events during transit. Modal analyses were carried out in ANSYS on the inner frame assembly and critical components to identify an appropriate wire-rope isolator configuration such that peak loads are mitigated and the incurred frequencies do not correspond with the fundamental modes of the structures.

Accelerator Physics↗

DOE OSTI2021

Operating Hardware Impact on the Heat Transfer Properties of Windows

Despite significant advancements in fenestration technology in the last two decades, the thermal transmittance of fenestration products is still significantly higher than that of walls. This corresponds to 60% of the total energy loss of a modern building envelope through the windows. Hence, further development and improvements of fenestration products are necessary. Increasingly stringent codes and standards for fenestration stimulate industry to work on improved solutions. Thus, it is crucial that assessment techniques are able to account for innovations accurately. The thermal effects of non-continuous hardware in window frames are currently ignored by international rating procedures. A preliminary investigation conducted by our team showed significant performance degradation in two of the three out-opening casement profiles caused by the presence of operating hardware. Frames with the structure made of vinyl and fiberglass consist of many air cavities that are penetrated by operating hardware made of highly conductive materials. In these frames, in order to have an accurate assessment, it may be required to employ three-dimensional modeling due to the convective nature of heat transfer within the cavities. However, in this study, we demonstrate that the three-dimensional (3D) effects of non-continuous hardware can be approximated accurately with simpler two-dimensional (2D) simulations. We then develop a simplified model based on weighted average capable of replacing the time- and computation-intensive 3D simulations with 2D simulations and validate it against market available frames and their corresponding hardware. Validation results show that our approximation technique results in discrepancies lower than 0.05 W/(m2K), or 3% of the total thermal transmittance. Thus, we conclude that simplified 2D simulation models may be used for predicting hardware impact in window frames with reasonable accuracy. As windows and glazing structures are becoming ever better thermally insulated, it is becoming even more important to be able to model the impact of the operating hardware on the total thermal performance in order to design the best windows possible and not let the operating hardware ruin an otherwise well-proven design, which is hence addressed in this study.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Dynamically Reconfigurable Data Readout of Pixel Detectors for Automatic Synchronization with Data Acquisition Systems

Reconfigurable detectors with dynamically selectable sensing and readout modes are highly desirable for implementing edge computing as well as enabling advanced imaging techniques such as foveation. The concept of a camera system capable of simultaneous passive imaging and dynamic ranging in different regions of the detector is presented. Such an adaptive-autonomous detector with both spatial and temporal control requires programmable window of exposure (time frames), ability to switch between readout modes such as full-frame imaging and zero-suppressed data, modification of the number of pixel data bits and independent programmability for distinct detector regions. In this work, a method is presented for seamlessly changing time frames and readout modes without data corruption while still ensuring that the data acquisition system (DAQ) does not need to stop and resynchronize at each change of setting, thus avoiding significant dead time. Data throughput is maximized by using a minimum unique data format, rather than lengthy frame headers, to differentiate between consecutive frames. A data control and transmitter (DCT) synchronizes data transfer from the pixel to the periphery, reconfigures the data to transmit it serially off-chip, while providing optimized decision support based on a DAQ definable mode. Measurements on a test structure demonstrate that the DCT can operate at 1 GHz in a 65 nm LP CMOS process.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

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↗

DOE OSTI2023

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a collection of objects stored within an RDate file, stateWaterRightsHarmonized.RData. Each object either describes how one should interact with the other objects, or contains the harmonized water rights data. Here is each object described in detail: states: A character vector containing the state names for those states in which data was collected for. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the third index in this object, the data for California will also be in the third index for each accompanying list. rightsByState_ground: A list of data frames with the cleaned ground water rights collected from each state. The column headers for each data frame are: waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. basinNum - The alpha-numeric identifier of the WMA the record belongs to. priorityDate - The priority date associated with the right. allocatedFlow - The maximum flow of the allocation in cubic feet per second (ft3s-1). source - Whether the right is for surface water or groundwater. origWaterUse - The original stated water use from the state. waterUse - The water use category under the unified use categories established here. rightsByState_surface: A list of the cleaned surface water rights collected from each state. See rightsByState_ground for more details on how the data is formatted. fullRightsRecs: A list of the combined groundwater and surface water records for each state. Essentially, these lists are the merging of rightsByState_ground and rightsByState_surface by state. See rightsByState_ground for more details on how the data is formatted. projProj: The spatial projection used for map creation in the beginning of the project. Specifically, the World Geodetic System (WGS84) as a coordinate reference system (CRS) string in PROJ.4 format. wmaStateLabel: The name and/or abbreviation for what each state legally calls their WMAs. h2oUseByState: A list of spatial polygon data frames which contain the area(s) in which each water right is claimed to be used. It should be noted that not all water right records have a listed area(s) of use in this object. Currently, only Idaho and Washington provided valid data to be included in this object. h2oDivByState: A list of spatial points data frames which identifies the location of the Point of Diversion for the state's water rights. It should be noted that not all water right records have a listed Point of Diversion in this object. spatialWMAByState: A list of spatial polygon data frames which contain the spatial WMA boundaries for each state. The only data contained within the table are identifiers for each polygon. It is worth reiterating that Arizona is the only state in which the surface and groundwater WMA boundaries are not the same. wmaIDByState: A list which contains the unique ID values of the WMAs for each state. plottingDim: A character vector used to inform mapping functions for internal map making. Each state is classified as either "tall" or "wide", to maximize space on a typical 8x11 page.

Economics↗

DOE OSTI2024

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a series of various files organized by state sub-directories. The first two characters of each file name is the abbreviation for the state the in which the file contains data for. After the abbreviation is the text which describes the contents of the file. Here is each file type described in detail: XXFullHarmonizedRights.csv: A file of the combined groundwater and surface water records for each state. Essentially, this file is the merging of XXGroundwaterHarmonizedRights.csv and XXSurfaceWaterHarmonizedRights.csv by state. The column headers for each of this type of file are: state - The name of the state the data comes from. FIPS - The two-digit numeric state ID code. waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. priorityDate - The priority date associated with the right. origWaterUse - The original stated water use(s) from the state. waterUse - The water use category under the unified use categories established here. source - Whether the right is for surface water or groundwater. basinNum - The alpha-numeric identifier of the WMA the record belongs to. CFS - The maximum flow of the allocation in cubic feet per second (ft3s-1). Arizona is unique among the states, as its surface and groundwater resources are managed with two different sets of boundaries. So, for Arizona, the basinNum column is missing and instead there are two columns: surBasinNum - The alpha-numeric identifier of the surface water WMA the record belongs to. grdBasinNum - The alpha-numeric identifier of the groundwater WMA the record belongs to. XXStatePOD.shp: A shapefile which identifies the location of the Points of Diversion for the state's water rights. It should be noted that not all water right records in XXFullHarmonizedRights.csv have coordinates, and therefore may be missing from this file. XXStatePOU.shp: A shapefile which contains the area(s) in which each water right is claimed to be used. Currently, only Idaho and Washington provided valid data to include within this file. XXGroundwaterHarmonizedRights.csv: A file which contains only harmonized groundwater rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. XXSurfaceWaterHarmonizedRights.csv: A file which contains only harmonized surface water rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. Additionally, one file, stateWMALabels.csv, is not stored within a sub-directory. While we have referred to the spatial boundaries that each state uses to manage its water resources as WMAs, this term is not shared across all states. This file lists the proper name for each boundary set, by state. For those whom may be interested in exploring our code more in depth, we are also making available an internal data file for convenience. The file is in .RData format and contains everything described above as well as some minor additional objects used within the code calculating the cumulative curves. For completeness, here is a detailed description of the various objects which can be found within the .RData file: states: A character vector containing the state names for those states in which data was collected for. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the third index in this object, the data for California will also be in the third index for each accompanying list. rightsByState_ground: A list of data frames with the cleaned ground water rights collected from each state. This object holds the the data that is exported to created the xxGroundwaterHarmonizedRights.csv files. rightsByState_surface: A list of data frames with the cleaned surface water rights collected from each state. This object holds the the data that is exported to created the xxSurfaceWaterHarmonizedRights.csv files. fullRightsRecs: A list of the combined groundwater and surface water records for each state. This object holds the the data that is exported to created the xxFullHarmonizedRights.csv files. projProj: The spatial projection used for map creation in the beginning of the project. Specifically, the World Geodetic System (WGS84) as a coordinate reference system (CRS) string in PROJ.4 format. wmaStateLabel: The name and/or abbreviation for what each state legally calls their WMAs. h2oUseByState: A list of spatial polygon data frames which contain the area(s) in which each water right is claimed to be used. It should be noted that not all water right records have a listed area(s) of use in this object. Currently, only Idaho and Washington provided valid data to be included in this object. h2oDivByState: A list of spatial points data frames which identifies the location of the Point of Diversion for the state's water rights. It should be noted that not all water right records have a listed Point of Diversion in this object. spatialWMAByState: A list of spatial polygon data frames which contain the spatial WMA boundaries for each state. The only data contained within the table are identifiers for each polygon. It is worth reiterating that Arizona is the only state in which the surface and groundwater WMA boundaries are not the same. wmaIDByState: A list which contains the unique ID values of the WMAs for each state. plottingDim: A character vector used to inform mapping functions for internal map making. Each state is classified as either "tall" or "wide", to maximize space on a typical 8x11 page. The code related to the creation of this dataset can be viewed within HarDWR GitHub Repository/dataHarmonization.

Economics↗

DOE OSTI2026

AMVOS: Additive Manufacturing Video Object Segmentation Dataset

This dataset provides labeled video frames from four additive manufacturing (AM) processes for video object segmentation (VOS) tasks. It contains 90 video segments comprising 900 individually annotated frames across five AM datasets: laser hot-wire directed energy deposition (LHW-DED), tungsten inert gas wire arc additive manufacturing (TIG-WAAM), plasma arc welding (PAW), visible-light polymer extrusion (visPolymer), and near-infrared polymer extrusion (irPolymer). Each video segment consists of 10 contiguous frames with corresponding pixel-level object instance annotations. Depending on the process, two of four object classes are labeled per frame: Melt Pool, Feed Wire, Nozzle, or Material. Raw frames are provided as .jpg files and annotations as palettized .png files. The dataset follows the directory structure of established VOS benchmarks (DAVIS, YouTube-VOS, MOSE), enabling direct integration into VOS model training and evaluation pipelines for foundation model fine-tuning, domain adaptation, or zero-shot performance benchmarking. Data was collected at Oak Ridge National Laboratory's Manufacturing Demonstration Facility.

Wetzel, Jon [ORNL]↗

NASA NTRS1974

Method of laminating structural members

A laminate is obtained by providing a lightweight core material, such as a honeycombed plastic or metal, within the cavity defined by an annular mold cavity frame. Face sheets, which are to be bonded to the core material, are provided on opposite sides of the frame and extend over the frame, thus sealing the core material in the cavity. An adhesive is provided between the core material and the face sheets and the combined thickness of the core material and adhesive is a close fit within the opposed face sheets. A gas tight seal, such as an O-ring gasket, is provided between the frame and the face sheet members to form a gas tight cavity between the face sheet members and the frame. External heat and pressure are used to bond the face sheets to the core material. Gas pressure is introduced into the sealed cavity to minimize out-gasing of the adhesive.

Heier, W. C.↗

NASA NTRS1975

Anisotropy measurements of nearly 50 KeV solar protons

The Energetic Particles Experiment on IMP-7 measures the angular distribution of 50-200 keV solar protons in 16 sectors. The velocity of 50 keV protons may be less than 5 times that of the solar wind. A generalized nonlinear Compton-Getting point transformation into the co-moving frame that contains no assumptions as to the angular distribution of either the spectrum or intensity is presented. Nearly 50 keV proton data in the spacecraft frame exhibit an anisotropy ratio that is large (not less than 5) and radial throughout the October 29, 1972 event lasting more than 9 days at this energy. This anisotropy argues against impulsive injection and diffusive decay in the inner solar system. Application of the transformation to the data reveals a long lasting residual anisotropy in the co-moving frame with protons streaming from the sun. Differences between the co-moving frame and solar wind frame velocities suggest residual electric fields upstream from the bow shock.

Gold, R. E.↗

NASA NTRS1979

Feasibility study of silicon nitride protection of plastic encapsulated semiconductors

The application of low temperature silicon nitride protective layers on wire bonded integrated circuits mounted on lead frame assemblies is reported. An evaluation of the mechanical and electrical compatibility of both plasma nitride and photochemical silicon nitride (photonitride) passivations (parallel evaluations) of integrated circuits which were then encapsulated in plastic is described. Photonitride passivation is compatible with all wire bonded lead frame assemblies, with or without initial chip passivation. Plasma nitride passivation of lead frame assemblies is possible only if the chip is passivated before lead frame assembly. The survival rate after the environmental test sequence of devices with a coating of plasma nitride on the chip and a coating of either plasma nitride or photonitride over the assembled device is significantly greater than that of devices assembled with no nitride protective coating over either chip or lead frame.

Peters, J. W.↗

NASA NTRS1978

An example of branching in a variational problem

In an experiment in space it was found that when a cubical frame was slowly withdrawn from a soap solution, the wire frame retained practically a full cube of liquid. Removed from the frame (by shaking), the faces of the cube became progressively more concave, until adjacent faces became tangential. In the present paper a mathematical model describing the shape a liquid takes due to its surface tension while suspended on a wire frame in zero-g is solved by use of Lagrange multipliers. It is shown how the configuration of soap films so bounded is dependent upon the volume of liquid trapped in the films. A special case of the solution is a soap film naturally formed on a cubical wire frame.

Darbro, W.↗

NASA NTRS1980

Sweat collection capsule

A sweat collection capsule permitting quantitative collection of sweat is described. The device consists of a frame held immobile on the skin, a closure secured to the frame and absorbent material located next to the skin in a cavity formed by the frame and the closure. The absorbent material may be removed from the device by removing the closure from the frame while the frame is held immobile on the skin.

Greenleaf, J. E.↗

NASA NTRS1980

The spatial and logical organization of devices in an advanced industrial robot system

This paper describes the geometrical and device organization of a robot system which is based in part upon transformations of Cartesian frames and exchangeable device tree structures. It discusses coordinate frame transformations, geometrical device representation and solution degeneracy along with the data structures which support the exchangeable logical-physical device assignments. The system, which has been implemented in a minicomputer, supports vision, force, and other sensors. It allows tasks to be instantiated with logically equivalent devices and it allows tasks to be defined relative to appropriate frames. Since these frames are, in turn, defined relative other frames this organization provides a significant simplification in task specification and a high degree of system modularity.

Ruoff, C. F.↗

NASA NTRS1988

Efficient use of bit planes in the generation of motion stimuli

The production of animated motion sequences on computer-controlled display systems presents a technical problem because large images cannot be transferred from disk storage to image memory at conventional frame rates. A technique is described in which a single base image can be used to generate a broad class of motion stimuli without the need for such memory transfers. This technique was applied to the generation of drifting sine-wave gratings (and by extension, sine wave plaids). For each drifting grating, sine and cosine spatial phase components are first reduced to 1 bit/pixel using a digital halftoning technique. The resulting pairs of 1-bit images are then loaded into pairs of bit planes of the display memory. To animate the patterns, the display hardware's color lookup table is modified on a frame-by-frame basis; for each frame the lookup table is set to display a weighted sum of the spatial sine and cosine phase components. Because the contrasts and temporal frequencies of the various components are mutually independent in each frame, the sine and cosine components can be counterphase modulated in temporal quadrature, yielding a single drifting grating. Using additional bit planes, multiple drifting gratings can be combined to form sine-wave plaid patterns. A large number of resultant plaid motions can be produced from a single image file because the temporal frequencies of all the components can be varied independently. For a graphics device having 8 bits/pixel, up to four drifting gratings may be combined, each having independently variable contrast and speed.

Mulligan, Jeffrey B.↗

NASA NTRS1989

Spatial cognition and navigation

An experiment that provides data for the development of a cognitive model of pilot flight navigation is described. The experiment characterizes navigational awareness as the mental alignment of two frames of reference: (1) the ego centered reference frame that is established by the forward view out of the cockpit and (2) the world centered reference frame that is established by the aircraft's location on a map. The data support a model involving at least two components: (1) the perceptual encoding of the navigational landmarks and (2) the mental rotation of the map's world reference frame into alignment with the ego centered reference frame. The quantitative relationships of these two factors are provided as possible inputs for a computational model of spatial cognition during flight navigation.

Aretz, Anthony J.↗

NASA NTRS1990

Diagnosing multiple faults in SSM/PMAD

Multiple fault diagnosis for SSM/PMAD (space station module/power management and distribution) using the knowledge management design system as applied to the SSM/PMAD domain (KNOMAD-SSM/PMAD) is discussed. KNOMAD-SSM/PMAD provides a powerful facility for knowledge representation and reasoning which has been used to build the second generation of FRAMES (fault recovery and management expert system). FRAMES now handles the diagnosis of multiple faults and provides support for a more powerful interface for user interaction during autonomous operation. There are two types of multiple fault diagnosis handled in FRAMES. The first diagnoses hard faults, soft faults, and incipient faults simultaneously. The second diagnoses multiple hard faults which occur in close proximity in time to one another. Multiple fault diagnosis in FRAMES is performed using a rule-based approach. This rule-based approach, enabled by the KNOMAD-SSM/PMAD system, has proven to be powerful. Levels of autonomy are discussed, focusing on the approach taken in FRAMES for providing at least three levels of autonomy: complete autonomy, partial autonomy, and complete manual mode.

Riedesel, Joel↗

NASA NTRS1992

Compliant walker

A compliant walker is provided for humans having limited use of their legs and lower back. It includes an upright wheel frame which at least partially surrounds an upright user wearing a partial body harness. It is attached to the frame by means of cable compliant apparatus consisting of sets of cable segments and angle bracket members connected between opposite side members of the frame and adjacent side portions of the harness. Novelty is believed to exist in the combination of a wheeled frame including a side support structure, a body harness, and compliance means connecting the body harness to the side support structure for flexibility holding and supporting a person in a substantially upright position when the user sags in the frame when taking weight off the lower extremities.

Kerley, James J.↗

NASA NTRS1995

Precise Time-Tag Generator For A Local-Area-Network Monitor

Time-tag-generating circuit designed for use in LAN monitor, monitors frames of data transmitted among computers on local-area network (LAN). To each frame of data that LAN monitor receives from LAN, time-tag generator appends ancillary data on time of arrival of frame, precise to within 1 microsecond of centrally generated time signal. Inserts ancillary time data in place of already used frame-check data before frames of data stored in memory of LAN monitor.

Stauffer, David R.↗