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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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Water vapor transmission rate measurement for moisture barriers using infrared imaging

Here, in this work, we demonstrate a spatially resolved imaging methodology for water vapor transmission rate (WVTR) testing that relies on quantified infrared characterization at water absorption bands. This technique is validated using a moisture barrier on a polymer substrate via calibration of the infrared image intensity with moisture content in the polymer substrate from images taken at different times. The method is compared to existing state of the art techniques such as membrane permeation measurement with coulometric phosphorous pentoxide sensor and optical calcium film testing. This fast, non-destructive and in-situ method enables defect visualization and shows the WVTR with a sensitivity limit of 5·10 –5 g/m 2 /day.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Assessing chromophoric dissolved organic matter (CDOM) distribution, stocks, and fluxes in Apalachicola Bay using combined field, VIIRS ocean color, and model observations

Understanding the role of estuarine-carbon fluxes is essential to improve estimates of the global carbon budget. Dissolved organic matter (DOM) plays an important role in aquatic carbon cycling. Here, the chromophoric fraction of DOM (CDOM) can be readily detected via in situ and remotely-sensed optical measurements. DOM properties, including CDOM absorption coefficient at 412 nm (a g 412) and dissolved organic carbon (DOC) concentrations were examined in Apalachicola Bay, a national estuarine research reserve located in the northeast Gulf of Mexico, using in situ and satellite observations during the spring and fall of 2015. Synoptic and accurate representation of estuarine-scale processes using satellite ocean color imagery necessitates the removal of atmospheric contribu- tion (~90%) to signals received by satellite sensors to successfully link to in situ observations. Three atmospheric correction schemes (e.g., Standard NIR correction, Iterative NIR correction, and SWIR correction) were tested first to find a suitable correction scheme for the VIIRS imagery in low to moderately turbid Apalachicola Bay. The iterative NIR correction performed well, and validation showed high correlation (R 2 = 0.95, N = 25) against in situ light measurements. A VIIRS-based CDOM algorithm was developed (R 2 = 0.87, N = 9) and validated (R 2 = 0.76, N = 20, RMSE = 0.29 m -1 ) against in situ observations. Subsequently, ag412 was used as a proxy of DOC in March (DOC = 1.08 + 0.94 × a g 412,R 2 =0.88, N = 13)and in November (DOC= 1.61 + 1.33 × a g 412, R 2 = 0.83, N = 24) to derive DOC maps that provided synoptic views of DOC distribution, sources, and their transport to the coastal waters during the wet and dry seasons. The estimated DOC stocks were ~3.71 × 10 6 kg C in March and ~4.07 × 10 6 kg C in November over an area of ~560 km2. Volume flux (out of the bay) almost doubled for March 24 (735 m 3 s -1 ) relative to November 4 (378 m 3 s -1 ). However, estimates of DOC fluxes exported out of the bay from model-derived currents and satellite-derived DOC were only marginally greater in March (0.163 × 10 6 kg C d -1 ) than in November (0.124 × 10 6 kg C d -1 ) and reflected greater DOC stocks in the fall. Finally, the combination of satellite-, field-, and model-based observations revealed the strong linkage between the Apalachicola River plume, a major source of DOM, and the overall hydrodynamic forcing that controlled distributions of CDOM abundance, DOC concentration, stocks, and fluxes in the bay.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Collection of low-grade waste heat for enhanced energy harvesting

Enhanced energy harvesting through the collection of low-grade waste heat is experimentally demonstrated. A structural optimization technique is exploited in the design of a thermal-composite substrate to guide and gather the heat emanating from multiple sources to a predetermined location. A thermoelectric generator is then applied at the selected focusing region to convert the resulting low-grade waste heat to electrical power. The thermal characteristics of the device are experimentally verified by direct temperature measurements of the system and numerically validated via heat conduction simulations. Electrical performance under natural and forced convection is measured, and in both cases, the device with optimized heat flow control plus energy harvesting demonstrates increased power generation when compared with a baseline waste heat recovery system. Electronics applications include energy scavenging for autonomously powered sensor networks or self-actuated devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Detection of small earthquakes with dense array data: example from the San Jacinto fault zone, southern California

We present a technique to detect small earthquakes not included in standard catalogues using data from a dense seismic array. The technique is illustrated with continuous waveforms recorded in a test day by 1108 vertical geophones in a tight array on the San Jacinto fault zone. Waveforms are first stacked without time-shift in nine non-overlapping subarrays to increase the signal-to-noise ratio. The nine envelope functions of the stacked records are then multiplied with each other to suppress signals associated with sources affecting only some of the nine subarrays. Running a short-term moving average/long-term moving average (STA/LTA) detection algorithm on the product leads to 723 triggers in the test day. Using a local P-wave velocity model derived for the surface layer from Betsy gunshot data, 5 s long waveforms of all sensors around each STA/LTA trigger are beamformed for various incident directions. Of the 723 triggers, 220 are found to have localized energy sources and 103 of these are confirmed as earthquakes by verifying their observation at 4 or more stations of the regional seismic network. This demonstrates the general validity of the method and allows processing further the validated events using standard techniques. The number of validated events in the test day is >5 times larger than that in the standard catalogue. Using these events as templates can lead to additional detections of many more earthquakes.

58 GEOSCIENCES↗
DOE OSTI2019

Extreme background rejection techniques for the ELROI satellite license plate

The extremely low-resource optical identifier (ELROI) is a concept for an autonomous, low-power optical “license plate” that can be attached to anything that goes into space. ELROI uses short, omnidirectional flashes of laser light to encode a unique ID number that can be read by a small ground telescope using a photon-counting sensor and innovative extreme background-rejection techniques. ELROI is smaller and lighter than a typical radio beacon, low-power enough to run on its own small solar cell, and can safely operate for the entire orbital lifetime of a satellite or debris object. The concept has been validated in ground tests, and orbital prototypes are scheduled for launch in 2018 and beyond. In this paper, we focus on the details of the encoding scheme and data analysis that allow a milliwatt optical signal to be read from orbit. We describe the techniques of extreme background-rejection needed to achieve this, including spectral filtering and temporal filtering using a period- and phase-recovery algorithm, and discuss the requirements for an error-correcting code to encode the ID number. Worked examples with both simulated and experimental (long-range ground test) data illustrate the methods used. Finally, we present these techniques to describe a new optical communication concept, and to encourage others to consider observing and analyzing our upcoming test flights.

42 ENGINEERING↗
DOE OSTI2020

A Sheaf Theoretical Approach to Uncertainty Quantification of Heterogeneous Geolocation Information

Integration of multiple, heterogeneous sensors is a challenging problem across a range of applications. Prominent among these are multi-target tracking, where one must combine observations from different sensor types in a meaningful and efficient way to track multiple targets. Because different sensors have differing error models, we seek a theoretically justified quantification of the agreement among ensembles of sensors, both overall for a sensor collection, and also at a fine-grained level specifying pairwise and multi-way interactions among sensors. We demonstrate that the theory of mathematical sheaves provides a unified answer to this need, supporting both quantitative and qualitative data. Furthermore, the theory provides algorithms to globalize data across the network of deployed sensors, and to diagnose issues when the data do not globalize cleanly. We demonstrate and illustrate the utility of sheaf-based tracking models based on experimental data of a wild population of black bears in Asheville, North Carolina. A measurement model involving four sensors deployed among the bears and the team of scientists charged with tracking their location is deployed. This provides a sheaf-based integration model which is small enough to fully interpret, but of sufficient complexity to demonstrate the sheaf’s ability to recover a holistic picture of the locations and behaviors of both individual bears and the bear-human tracking system. A statistical approach was developed in parallel for comparison, a dynamic linear model which was estimated using a Kalman filter. This approach also recovered bear and human locations and sensor accuracies. When the observations are normalized into a common coordinate system, the structure of the dynamic linear observation model recapitulates the structure of the sheaf model, demonstrating the canonicity of the sheaf-based approach. However, when the observations are not so normalized, the sheaf model still remains valid.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Technology for the Future: In-Space Technology Experiments Program, part 1

The purpose of the Office of Aeronautics and Space Technology (OAST) In-Space Technology Experiment Program (In-STEP) 1988 Workshop was to identify and prioritize technologies that are critical for future national space programs and require validation in the space environment, and review current NASA (In-Reach) and industry/university (Out-Reach) experiments. A prioritized list of the critical technology needs was developed for the following eight disciplines: structures; environmental effects; power systems and thermal management; fluid management and propulsion systems; automation and robotics; sensors and information systems; in-space systems; and humans in space. This is part one of two parts and is the executive summary and experiment description. The executive summary portion contains keynote addresses, strategic planning information, and the critical technology needs summaries for each theme. The experiment description portion contains brief overviews of the objectives, technology needs and backgrounds, descriptions, and development schedules for current industry, university, and NASA space flight technology experiments.

Breckenridge, Roger A.↗
NASA NTRS1998

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model: Aggregation - 2

This is a follow up on the preceding presentation by Crosson. The grid size for remote microwave measurements is much coarser than the hydrological model computational grids. To validate the hydrological models with measurements we propose mechanisms to aggregate the hydrological model outputs for soil moisture to allow comparison with measurements. Weighted neighborhood averaging methods are proposed to facilitate the comparison. We will also discuss such complications as misalignment, rotation and other distortions introduced by a generalized sensor image.

Schamschula, Marius↗
NASA NTRS2000

Wireless Telemetry and Command (T and C) Program

The Wireless Telemetry and Command (T&C) program is to investigate methods of using commercial telecommunications service providers to support command and telemetry services between a remote user and a base station. While the initial development is based on ground networks, the development is being done with an eye towards future space communications needs. Both NASA and the Air Force have indicated a plan to consider the use of commercial telecommunications providers to support their space missions. To do this, there will need to be an understanding of the requirements and limitations of interfacing with the commercial providers. The eventual payoff will be the reduced operations cost and the ability to tap into commercial services being developed by the commercial networks. This should enable easier realization of EP services to the end points, commercial routing of data, and quicker integration of new services into the space mission operations. Therefore, the ultimate goal of this program is not just to provide wireless radio communications for T&C services but to enhance those services through wireless networking and provider enhancements that come with the networks. In the following chapters, the detailed technical procedure will be showed step by step. Chapter 2 will talk about the general idea of simulation as well as the implementation of data acquisition including sensor array data and GPS data. Chapter 3 will talk about how to use LabVEEW and Component Works to do wireless communication simulation and how to distribute the real-time information over the Internet by using Visual Basic and ActiveX controls. Also talk about the test configuration and validation. Chapter 4 will show the test results both from In-Lab test and Networking Test. Chapter 5 will summarize the whole procedure and give the perspective for the future consideration.

Jiang, Hui↗
NASA NTRS2002

Ship Radar Observations of a Developing Hurricane in the East Pacific

During the recently completed East Pacific Investigation of Climate Processes in the Coupled Ocean-Atmosphere System (EPIC) field program, the NOAA research vessel Ronald H. Brown (RHB) was deployed in the east Pacific Inter Tropical Convergence Zone (ITCZ) for approximately 3 weeks near 10 deg. N, 95 deg. W. One of the principal objectives of the EPIC-ITCZ program was to observe the modulation of convection by synoptic-scale easterly waves and the air-sea coupling process in this poorly sampled region of the east Pacific. Data from the experiment will be used as validation to improve forecast models. The RHB carried a variety of platforms during EPIC to sample atmospheric and oceanic phenomena, including a scanning C-band Doppler radar, radiation flux instrumentation, air-sea flux system, Doppler lidar, 35 GHz cloud radar, UHF wind profiler, sea surface temperature (SST) sensors, as well as standard surface meteorological instrumentation and a suite of rain gauges. This presentation will focus on the analysis of C-band radar data that was collected on 10 September, 2001 as the ship passed through an easterly wave which later developed into hurricane Ivo. The ship captured approximately 12 hours of convection associated with the tropical disturbance. During this period, the domain sampled by the radar (approximately 71,000 sq km) contained a significant number of echo features. Specifically, the fraction of the domain containing radar echo above 10 dBZ reached 80% for over 2 hours and remained near 60% for a continuous six hour period. Animation of radar images showed distinct rotation in echo features associated with the easterly wave passage. Despite an approximate 4 C drop in surface air temperature and sustained winds approaching 20 m/ s, the SST remained nearly constant throughout the observation period (approx. 29.5 C). Peak values of latent and sensible heat flux exceeded 400 and 100 W /sq m, respectively. The radar documented the change in precipitation vertical structure as the ship passed through regions of significant convection with echo tops approaching 14 km and 30 dBZ echo tops extending to near 9 km embedded in regions that were predominantly stratiform in nature. Single Doppler retrievals (Extended Velocity Azimuth Display) were conducted continuously at 10-minute resolution for approximately 4 hours in the latter part of the observation period, and documented the transition from convection (low-level convergence, upper level divergence) to stratiform (mid-level convergence sandwiched between upper and lower level divergence) kinematic structure.

Cifelli, Robert↗
NASA NTRS2008

An Overview of the NASA Spring/Summer 2008 Arctic Campaign - ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites)

ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) is a major NASA led airborne field campaign being performed in the spring and summer of 2008 at high latitudes (http://cloud1.arc.nasa.gov/arctas/). ARCTAS is a part of the International Polar Year program and its activities are closely coordinated with multiple U. S. (NOAA, DOE), Canadian, and European partners. Observational data from an ensemble of aircraft, surface, and satellite sensors are closely integrated with models of atmospheric chemistry and transport in this experiment. Principal NASA airborne platforms include a DC-8 for detailed atmospheric composition studies, a P-3 that focuses on aerosols and radiation, and a B-200 that is dedicated to remote sensing of aerosols. Satellite validation is a central activity in all these platforms and is mainly focused on CALIPSO, Aura, and Aqua satellites. Major ARCTAS themes are: (1) Long-range transport of pollution to the Arctic including arctic haze, tropospheric ozone, and persistent pollutants such as mercury; (2) Boreal forest fires and their implications for atmospheric composition and climate; (3) Aerosol radiative forcing from arctic haze, boreal fires, surface-deposited black carbon, and other perturbations; and (4) Chemical processes with focus on ozone, aerosols, mercury, and halogens. The spring deployment (April) is presently underway and is targeting plumes of anthropogenic and biomass burning pollution and dust from Asia and North America, arctic haze, stratosphere-troposphere exchange, and ozone photochemistry involving HOx and halogen radicals. The summer deployment (July) will target boreal forest fires and summertime photochemistry. The ARCTAS mission is providing a critical link to enhance the value of NASA satellite observations for Earth science. In this talk we will discuss the implementation of this campaign and some preliminary results.

Jacob, Daniel J.↗
NASA NTRS2010

The Global Precipitation Measurement (GPM) Project

The Global Precipitation Measurement (GP!v1) mission is an international cooperative effort to advance the understanding of the physics of the Earth's water and energy cycle. Accurate and timely knowledge of global precipitation is essential for understanding the weather/climate/ecological system, for improving our ability to manage freshwater resources, and for predicting high-impact natural hazard events including floods, droughts, extreme weather events, and landslides. The GPM Core Observatory will be a reference standard to uniformly calibrate data from a constellation of spacecraft with passive microwave sensors. GPM is being developed under a partnership between the United States (US) National Aeronautics and Space Administration (NASA) and the Japanese Aerospace and Exploration Agency (JAXA). NASA's Goddard Space Flight Center (GSFC), in Greenbelt, MD is developing the Core Observatory, two GPM Microwave Imager (GMI) instruments, Ground Validation System and Precipitation Processing System for the GPM mission. JAXA will provide a Dual-frequency Precipitation Radar (DPR) for installation on the Core satellite and launch services for the Core Observatory. The second GMI instrument will be flown on a partner-provided spacecraft. Other US agencies and international partners contribute to the GPM mission by providing precipitation measurements obtained from their own spacecraft and/or providing ground-based precipitation measurements to support ground validation activities. The Precipitation Processing System will provide standard data products for the mission.

Azarbarzin, Ardeshir↗
DOE OSTI2015

Ultrafast image-based dynamic light scattering for nanoparticle sizing

An ultrafast sizing method for nanoparticles is proposed, called as UIDLS (Ultrafast Image-based Dynamic Light Scattering). This method makes use of the intensity fluctuation of scattered light from nanoparticles in Brownian motion, which is similar to the conventional DLS method. The difference in the experimental system is that the scattered light by nanoparticles is received by an image sensor instead of a photomultiplier tube. A novel data processing algorithm is proposed to directly get correlation coefficient between two images at a certain time interval (from microseconds to milliseconds) by employing a two-dimensional image correlation algorithm. This coefficient has been proved to be a monotonic function of the particle diameter. Samples of standard latex particles (79/100/352/482/948 nm) were measured for validation of the proposed method. The measurement accuracy of higher than 90% was found with standard deviations less than 3%. A sample of nanosilver particle with nominal size of 20 ± 2 nm and a sample of polymethyl methacrylate emulsion with unknown size were also tested using UIDLS method. The measured results were 23.2 ± 3.0 nm and 246.1 ± 6.3 nm, respectively, which is substantially consistent with the transmission electron microscope results. Since the time for acquisition of two successive images has been reduced to less than 1 ms and the data processing time in about 10 ms, the total measuring time can be dramatically reduced from hundreds seconds to tens of milliseconds, which provides the potential for real-time and in situ nanoparticle sizing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2019

Validation of ICESat-2 ATLAS Bathymetry and Analysis of ATLAS’s Bathymetric Mapping Performance

NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in September, 2018. The satellite carries a single instrument, ATLAS (Advanced Topographic Laser Altimeter System), a green wavelength, photon-counting lidar, enabling global measurement and monitoring of elevation with a primary focus on the cryosphere. Although bathymetric mapping was not one of the design goals for ATLAS, pre-launch work by our research team showed the potential to map bathymetry with ICESat-2, using data from MABEL (Multiple Altimeter Beam Experimental Lidar), NASA’s high-altitude airborne ATLAS emulator, and adapting the laser-radar equation for ATLAS specific parameters. However, many of the sensor variables were only approximations, which limited a full assessment of the bathymetric mapping capabilities of ICESat-2 during pre-launch studies. Following the successful launch, preliminary analyses of the geolocated photon returns have been conducted for a number of coastal sites, revealing several salient examples of seafloor detection in water depths of up to ~40 m. The geolocated seafloor photon returns cannot be taken as bathymetric measurements, however, since the algorithm used to generate them is not designed to account for the refraction that occurs at the air–water interface or the corresponding change in the speed of light in the water column. This paper presents the first early on-orbit validation of ICESat-2 bathymetry and quantification of the bathymetric mapping performance of ATLAS using data acquired over St. Thomas, U.S. Virgin Islands. A refraction correction, developed and tested in this work, is applied, after which the ICESat-2 bathymetry is compared against high-accuracy airborne topo-bathymetric lidar reference data collected by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). The results show agreement to within 0.43—0.60 m root mean square error (RMSE) over 1 m grid resolution for these early on-orbit data. Refraction-corrected bottom return photons are then inspected for four coastal locations around the globe in relation to Visible Infrared Imaging Radiometer Suite (VIIRS) K(d)(490) data to empirically determine the maximum depth mapping capability of ATLAS as a function of water clarity. It is demonstrated that ATLAS has a maximum depth mapping capability of nearly 1 Secchi in depth for water depths up to 38 m and K(d)(490) in the range of 0.05–0.12 per m. Collectively, these results indicate the great potential for bathymetric mapping with ICESat-2, offering a promising new tool to assist in filling the global void in nearshore bathymetry.

ICESat-2↗
NASA NTRS2011

Pad A Main Flame Deflector Sensor Data and Evaluation

Space shuttle launch pads use flame deflectors beneath the vehicle to channel hot gases away from the vehicle. Pad 39 A at the Kennedy Space Center uses a steel structure coated with refractory concrete. The solid rocket booster plume is comprised of gas and molten alumina oxide particles that erodes the refractory concrete. During the beginning of the shuttle program the loads for this system were never validated with a high level of confidence. This paper presents a representation of the instrumentation data collected and follow on materials science evaluation of the materials exposed to the SRB plume. Data collected during STS-133 and STS-134 will be presented that support the evaluation of the components exposed to the SRB plume.

Parlier, Christopher R.↗
DOE OSTI2022

Profile Images and Annotations for Vehicle Re-identification Algorithms (PRIMAVERA)

This dataset contains 636,246 profile images of vehicles representing 13,963 unique vehicles. The data was collected by a set of roadside sensors over the course of three years. Each time a vehicle passed by one of the sensors, a series of images was collected. The images were processed to detect and localize each vehicle, and a license plate reader collocated with the sensor was used to provide a unique ID for the vehicle. Actual license plate numbers have been obfuscated by replacing with an arbitrary numerical ID for each vehicle. After localizing the vehicle in each image, the original RGB image was rotated, scaled, and shifted to produce a new RGB image of size 234x234 pixels such that the outermost two wheels are located at predetermined pixel locations in the image. In this way, all vehicle images are aligned to one another. This registration process occasionally results in a portion of certain vehicles being cutoff at the edges of the image. The dataset has been partitioned into two sets called training and validation. The two partitions no common vehicles, i.e., a vehicle present in one partition is guaranteed not to be present in the other. In this way, an algorithm can be validated against a set of new vehicles that were not seen during the training process. The training set contains 543,926 images from 64,440 vehicle passes representing 11,918 unique vehicles, while the validation set contains 92,320 images from 10,991 vehicle passes representing 2,045 unique vehicles. Vehicle images are organized by directories corresponding to unique vehicles. The file naming scheme is as follows: veh_{vehID}_tr_{passID}_{frameID}_{elevation}_{timeofday}.jpg where {vehID} is the vehicle ID (unique across the entire dataset), {passID} is an identifier for each tracked vehicle pass (unique across the entire dataset), {frameID} is the index of the frame within the given vehicle pass starting at 0, {elevation} is a two-letter string indicating whether the sensor was elevated (el) or at ground-level (gl), and {timeofday} is a two-letter string indicating whether the image was captured during daytime (dt) or nighttime (nt).

image↗
NASA NTRS2018

Active and Passive Radiative Transfer Modeling of the Olympic Mountains Experiment

Sensor forward models are an important tool for interpreting remote sensing observations of geophysical phenomena. By implementing a three-dimensional framework, we can simulate and analyze observations from various sensors on disparate platforms. To demonstrate our model framework, we simulate observations from the Olympic Mountains Experiment (OLYMPEX). The use of cloud model simulations allows us to understand sensor response to cloud ice, falling snow, and other processes and features, and the application of model tools to observations allows us to quantify precipitation.MIIST 3D Forward ModelThe Multi-Instrument Inverse Solver Testbed(MIIST) uses the Atmospheric Radiative TransferSimulator (ARTS) for solving the vector radiativetransfer (RT) equation in up to three spatialdimensions within a spherical geometry• Gas absorptiono Line-by-line calculationso Fast transmittance tables• Hydrometeor scattering solverso Discrete ordinateo RT4 (Evans, 1D)o Radar Single Scattering (1D or 3D)o Monte Carlo (3D)Scattering TablesHigh-fidelity hydrometeor scatteringtables are necessary for accurateand consistent forward modeling ofmulti-frequency observations• Requires full Stokes matriceso And absorption vector• Randomly oriented particleso Discrete Dipole Approximationo Characteristic Basis Function Method(coming soon)• Horizontally-oriented plateso Invariant Imbedding T-matrix MethodCloud Resolving SimulationsCloud resolving simulations (e.g.,NU-WRF) supply output consistentwith ARTS needs• Atmospheric Informationo Temperatureo Pressure / heighto Water vapor• Hydrometeor Profileso ARTS architecture ripe for explicit binmicrophysics• Examples use Morrison 2M schemeThe Olympic Mountains Experiment (OLYMPEX)Validation for GPM of mid-latitudefrontal systems approaching nearcoastalmountains from the ocean• Large collection of ground-based andairborne sensorso Radarso Radiometerso In situ• Contemporaneous with RADEXo Two sets of radar at same frequenciesRadiometer Simulation (3 km NUWRF, 20151203, 15:00)2018.12.14 7Simulate 166 GHz polarizationdifference• Corresponds to the presence of aligned icecrystals• Look at trends for both simulations andobservations• Simulations can tolerate lower resolutiono Larger domainSimulations from Observations: OLYMPEXSimulate sensor response usinggeophysical retrievals as input• Single frequency radar retrievals• Multiple scattering enhancementapparent at W band• Spatially dependent phenomenonModeling Application: 1D Retrievals03 December 2015• DC-8 and ER-2 flightso Focus on APR-3 (DC-8)• Citationo Stacked microphysics legso Qualitative comparisonso Range of frozen habitso Presence of supercooledliquid cloudsResults• Retrievals match probeso Good qualitative match• Bands of increasedreflectivity correspond tolarge Dm and highaggregate fraction• Significant amounts ofsupercooled liquid water

Adams, Ian S.↗
DOE OSTI2022

A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures

Abstract Part quality manufactured by the laser powder bed fusion process is significantly affected by porosity. Existing works of process–property relationships for porosity prediction require many experiments or computationally expensive simulations without considering environmental variations. While efforts that adopt real-time monitoring sensors can only detect porosity after its occurrence rather than predicting it ahead of time. In this study, a novel porosity detection-prediction framework is proposed based on deep learning that predicts porosity in the next layer based on thermal signatures of the previous layers. The proposed framework is validated in terms of its ability to accurately predict lack of fusion porosity using computerized tomography (CT) scans, which achieves a F1-score of 0.75. The framework presented in this work can be effectively applied to quality control in additive manufacturing. As a function of the predicted porosity positions, laser process parameters in the next layer can be adjusted to avoid more part porosity in the future or the existing porosity could be filled. If the predicted part porosity is not acceptable regardless of laser parameters, the building process can be stopped to minimize the loss.

42 ENGINEERING↗