Results for “Collective behavior” — Page 24

NASA NTRS1987

Analysis of implicit local linearization techniques for upwind and TVD algorithms

An attempt is made to investigate local time linearization techniques for implicit flux-difference splitting and flux-vector splitting schemes in the simplest settings (i.e., first-order spatial schemes and one-dimensional Euler flows). It is noted that first-order spatial schemes provide the simplest examples of schemes which are collective extensions of scalar TVD schemes. Simple analytical results concerning the local linearizations are highlighted and subsequently verified using a numerical fixed-point analysis on selected problems. It is noted that while primary emphasis is on asymptotic behavior, many of the results have implications for time-accurate calculations as well.

Barth, Timothy J.↗

NASA NTRS2000

A national survey of policies on disclosure of conflicts of interest in biomedical research

BACKGROUND: Conflicts of interest pose a threat to the integrity of scientific research. The current regulations of the U.S. Public Health Service and the National Science Foundation require that medical schools and other research institutions report the existence of conflicts of interest to the funding agency but allow the institutions to manage conflicts internally. The regulations do not specify how to do so. METHODS: We surveyed all medical schools (127) and other research institutions (170) that received more than $5 million in total grants annually from the National Institutes of Health or the National Science Foundation; 48 journals in basic science and clinical medicine; and 17 federal agencies in order to analyze their policies on conflicts of interest. RESULTS: Of the 297 institutions, 250 (84 percent) responded by March 2000, as did 47 of the 48 journals and 16 of the 17 federal agencies. Fifteen of the 250 institutions (6 percent)--5 medical schools and 10 other research institutions--reported that they had no policy on conflicts of interest. Among the institutions that had policies, there was marked variation in the definition and management of conflicts. Ninety-one percent had policies that adhered to the federal threshold for disclosure ($10,000 in annual income or equity in a relevant company or 5 percent ownership), and 9 percent had policies that exceeded the federal guidelines. Only 8 percent had policies requiring disclosure to funding agencies, only 7 percent had such policies regarding journals, and only 1 percent had policies requiring the disclosure of information to the relevant institutional review boards or to research subjects. Twenty journals (43 percent) reported that they had policies requiring disclosure of conflicts of interest. Only four federal agencies had policies that explicitly addressed conflicts of interest in extramural research, and all but one of the agencies relied primarily on institutional discretion. CONCLUSIONS: There is substantial variation among policies on conflicts of interest at medical schools and other research institutions. This variation, combined with the fact that many scientific journals and funding agencies do not require disclosure of conflicts of interest, suggests that the current standards may not be adequate to maintain a high level of scientific integrity.

Human↗

NASA NTRSDate not supplied

Multidisciplinary Dynamic Testing Challenges in Validating the NASA Artemis Architecture

NASA is in the midst of bold and exciting next steps in human exploration and spaceflight. The designs of the new Space Launch System (SLS), the Orion spacecraft and the Exploration Ground Systems (EGS) for vehicle processing and launch are essentially complete and there has been significant progress in manufacturing and assembly of specific hardware for the Artemis I and Artemis II missions. Equally as important, the program level and integrated system level testing and analyses are also well underway to support integrated verification, validation, and certificate of flight readiness (CoFR) for the first Artemis mission. Testing and analysis are key to addressing technical challenges that the Artemis missions offer. Building block approaches are required that provide the right balance between component, system, and/or element level testing that satisfies verification and validation objectives and where, uncertainties are quantified and minimized. Artemis I is a system of systems that requires a fusion of test and analysis that adeptly characterizes critical interfaces between major program elements. NASA is implementing new in-situ testing that fuse traditional aerospace structures with civil structures, such as the Integrated Modal Test for the Artemis I vehicle where the Mobile Launcher and Crawler Transporter serve as a support structure whose dynamics couple with that of the Artemis I vehicle. This new paradigm requires a closer inspection of structural behavior of the Crawler Transporter and the Mobile Launcher as they now serve multiple purposes. This requires a paradigm shift to look beyond experimental modal techniques and incorporates operational modal analysis techniques to validate dynamic models from data collected during rollout to the launch pad. A further complicating factor is the Crawler Transporter generated ground forces have numerous harmonics making extracting dynamic responses of the Artemis I, Mobile Launcher, and Crawler Transporter coupled system challenging. This discussion explores all these challenges with and attempts to understand how we best build confidence in systems and system-of-systems performance capabilities and margins and understand uncertainties.

Joel W Sills↗

NASA NTRSDate not supplied

Expanding Repository Data Available For Sharing and Knowledge Discovery

Some of the hardest space biology and space health challenges require data-intensive, bioinformatic, meta-analytical, and computer-assisted research approaches. These challenges include examining interdisciplinary space life science research across experiments and across interacting spaceflight hazards (radiation, altered gravity, confinement, hostile-closed environments, distance-duration from Earth). The approaches to confront these challenges involve mining multiple datasets simultaneously from various hierarchical organizations of biological complexity, all while concurrently evaluating how experimental design factors affect endpoints of standard assays. To enable this field, it is essential that principal investigators (PIs) submit data in a structure so it can be maximally re-used. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make publicly available all non-human space-relevant biological data. ALSDA must also ensure data are open-access, and maximally findable, accessible, interoperable, and reusable (FAIR). The scope of ALSDA data collected and submitted by PIs include subject and study design metadata, assay metadata parameters, raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). ALSDA recently integrated into a collaborative group of Open Science projects to facilitate a suite of new tools and workflows that will improve data submission, accessibility, and reusability by implementing digital data submission agreements, and adopting the data management system originally developed by NASA GeneLab. ALSDA intends to bring current biological repository data and all future collected data into this new scientific data reuse reality. This new suite of tools will enable ALSDA to deploy a science curation system using scientific assay configurations for the data submission portal. It will capture essential assay parameters according to established standards in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. Data submissions can be brought into cutting-edge informatic analysis portals to enable mining of physiological, behavioral, biochemical, and imaging datasets in conjunction with ‘omics-level datasets. As ALSDA datasets are submitted, curated, and published (e.g., micro-computed tomography, histology, pulse oximetry, serum metabolites, magnetic resonance imaging, intraocular pressure, novel object recognition, etc.), the merging together of spaceflight data along this multi-hierarchical complexity of biology will enable informatics and data-intensive approaches resulting in knowledge discoveries across missions, space hazards, and biological disciplines.

Biology↗

NASA NTRSDate not supplied

Expanding Repository Data Available For Sharing And Knowledge Discovery

Some of the hardest space biology and space health challenges require data-intensive, bioinformatic, meta-analytical, and computer-assisted research approaches. These challenges include examining interdisciplinary space life science research across experiments and across interacting spaceflight hazards (radiation, altered gravity, confinement, hostile-closed environments, distance-duration from Earth). The approaches to confront these challenges involve mining multiple datasets simultaneously from various hierarchical organizations of biological complexity, all while concurrently evaluating how experimental design factors affect endpoints of standard assays. To enable this field, it is essential that principal investigators (PIs) submit data in a structure so it can be maximally re-used. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make publicly available all non-human space-relevant biological data. ALSDA must also ensure data are open-access, and maximally findable, accessible, interoperable, and reusable (FAIR). The scope of ALSDA data collected and submitted by PIs include subject and study design metadata, assay metadata parameters, raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). ALSDA recently integrated into a collaborative group of Open Science projects to facilitate a suite of new tools and workflows that will improve data submission, accessibility, and reusability by implementing digital data submission agreements, and adopting the data management system originally developed by NASA GeneLab. ALSDA intends to bring current biological repository data and all future collected data into this new scientific data reuse reality. This new suite of tools will enable ALSDA to deploy a science curation system using scientific assay configurations for the data submission portal. It will capture essential assay parameters according to established standards in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. Data submissions can be brought into cutting-edge informatic analysis portals to enable mining of physiological, behavioral, biochemical, and imaging datasets in conjunction with ‘omics-level datasets. As ALSDA datasets are submitted, curated, and published (e.g., micro-computed tomography, histology, pulse oximetry, serum metabolites, magnetic resonance imaging, intraocular pressure, novel object recognition, etc.), the merging together of spaceflight data along this multi-hierarchical complexity of biology will enable informatics and data-intensive approaches resulting in knowledge discoveries across missions, space hazards, and biological disciplines.

life science↗

NASA NTRS1987

The formation process of flight crews

A study which uses Hackman's Normative Model (1986) for group effectiveness to see if there are any differences between the behaviors of effective and less effective captains at building and maintaining their crews is presented. Captains were selected using crew evaluations, creating a final pool of six effective crew managers and four captains less proficient as crew leaders. Data collection began at crew briefings, and continued through two trips, with intense data gathering during critical incidents for both task and process events. It was found that a predetermined set of interactions that can occur between crew members exists for the forming crew. It is concluded that effective captains expand the set of interactions, decreasing the limitations on how the group will work together.

Ginnett, Robert C.↗

NASA NTRS1991

Development of a calibrated software reliability model for flight and supporting ground software for avionic systems

The object of this project was to develop and calibrate quantitative models for predicting the quality of software. Reliable flight and supporting ground software is a highly important factor in the successful operation of the space shuttle program. The models used in the present study consisted of SMERFS (Statistical Modeling and Estimation of Reliability Functions for Software). There are ten models in SMERFS. For a first run, the results obtained in modeling the cumulative number of failures versus execution time showed fairly good results for our data. Plots of cumulative software failures versus calendar weeks were made and the model results were compared with the historical data on the same graph. If the model agrees with actual historical behavior for a set of data then there is confidence in future predictions for this data. Considering the quality of the data, the models have given some significant results, even at this early stage. With better care in data collection, data analysis, recording of the fixing of failures and CPU execution times, the models should prove extremely helpful in making predictions regarding the future pattern of failures, including an estimate of the number of errors remaining in the software and the additional testing time required for the software quality to reach acceptable levels. It appears that there is no one 'best' model for all cases. It is for this reason that the aim of this project was to test several models. One of the recommendations resulting from this study is that great care must be taken in the collection of data. When using a model, the data should satisfy the model assumptions.

Lawrence, Stella↗

NASA NTRS2014

Characteristics of Extreme Auroral Charging Events

The highest level spacecraft charging observed in low Earth orbit (LEO) occurs when spacecraft are exposed to energetic auroral electrons. Since auroral charging has been identified as a mechanism responsible for on-orbit anomalies and even possible satellite failures it is important to consider extreme auroral charging events as design and test environments for spacecraft to be used in high inclination LEO orbits. This paper will report on studies of extreme auroral charging events using data from the SSJ/4 and SSJ/5 precipitating electron and ion sensors on the Defense Meteorology Satellite Program (DMSP) satellites. Early studies of DMSP charging to negative potentials ≥100 V focused on statistics of the electron environment responsible for charging. Later statistical studies of auroral charging have generally focused on solar cycle dependence of charging behavior and magnitude of the maximum potential and duration of the charging events. We extend these studies to focus on more detailed investigations of extreme charging event characteristics that are required to evaluate potential threats to spacecraft systems. A collection of example auroral charging events is assembled from the DMSP data set using the criteria that "extreme auroral charging" is defined as periods with spacecraft negative potentials ≥400 V. Specific characteristics to be treated include (but are not limited to) maximum and mean potentials, time history of spacecraft potentials through the events, total charging duration and the time potentials exceed voltage thresholds, frame charging/discharging rates, and information on geographic and geomagnetic latitudes at which the events are observed. Finally, we will comment on the implications of these studies for potential auroral charging risks to the International Space Station.

Minow, Joseph I.↗

NASA NTRS2022

Development and Testing of Femtosecond Laser Surface Processing (FLSP) Treated Guide Vanes for Application to Advanced Cryogenic Liquid Acquisition Devices

Liquid Acquisition Devices (LADs) are used inside propellant tanks in microgravity to collect liquid propellant to move towards the sump or outlet of a tank during expulsion or transfer. In microgravity, vehicle thrusters can produce necessary phase separation, but in the absence of thrust, surface tension is the driver of fluid behavior. Liquid in microgravity will tend to form a sphere to minimize surface area. If a LAD can be made more “attractive” to a propellant (omniphilic), then it should perform its function more effectively. This paper presents preliminary results on using femtosecond laser surface processing (FLSP) to change the wetting properties of a vane LAD. FLSP can make metals superhydrophilic or omniphilic; by controlling the applied laser parameters, the resulting micro and nanoscale surface features can be tailored to control and optimize hydrophilic/omniphilic properties. Two LAD galleries with guide vanes were recently fabricated, a control sample and an FLSP-treated sample. The LADs were ground tested and also implemented in a parabolic flight experiment. Based on contact angle and roll-off angle measurements of the surface-treated vane, FLSP appears to be a promising technique to improve efficiency of LADs

Liquid Acquisition Devices↗

NASA NTRSDate not supplied

Petrologic Comparison of High- and Low-Titanium Basalt Clasts Derived From ANGSA Core 73001

Volatile elements and compounds significantly influence the properties and behavior of magma, including ascent and eruptive processes. Basalts record a complicated history of the volatile species inherent to their parental magma, and the processes, such as degassing, that change these volatile inventories. On the Moon, outstanding questions remain concerning the behavior of magmatic volatiles and their roles in the evolution of lunar magmas and the formation of mare basalts. As part of the Apollo Next Generation Sample Analysis (ANGSA) program, we are investigating the petrogenesis of two basalt clasts collected from the recently processed Apollo 17 drive tube, 73001. Our team is studying the petrology of the two basalt clasts in 2D and 3D. Recent work has highlighted the utility of coupling traditional 2D methods with 3D measurements to better understand the crystallization and degassing histories of lunar lava flows. We are also investigating the volatile inventory of the samples through in situ studies of volatile-bearing phases, like apatite, to understand the eruptive signatures and degassing histories of low-titanium and high-titanium lunar basalts. At the upcoming conference, we will present the first detailed study of the 2D and 3D mineralogy, textures, 3D vesiculation, and chemistry of these basalt clasts to shed light on their magmatic, volcanic, and post-eruptive histories.

Basalt↗

NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗

NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗

NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗

NASA NTRS2008

Visualization Component of Vehicle Health Decision Support System

The visualization front-end of a Decision Support System (DSS) also includes an analysis engine linked to vehicle telemetry, and a database of learned models for known behaviors. Because the display is graphical rather than text-based, the summarization it provides has a greater information density on one screen for evaluation by a flight controller.This tool provides a system-level visualization of the state of a vehicle, and drill-down capability for more details and interfaces to separate analysis algorithms and sensor data streams. The system-level view is a 3D rendering of the vehicle, with sensors represented as icons, tied to appropriate positions within the vehicle body and colored to indicate sensor state (e.g., normal, warning, anomalous state, etc.). The sensor data is received via an Information Sharing Protocol (ISP) client that connects to an external server for real-time telemetry. Users can interactively pan, zoom, and rotate this 3D view, as well as select sensors for a detail plot of the associated time series data. Subsets of the plotted data can be selected and sent to an external analysis engine to either search for a similar time series in an historical database, or to detect anomalous events. The system overview and plotting capabilities are completely general in that they can be applied to any vehicle instrumented with a collection of sensors. This visualization component can interface with the ISP for data streams used by NASA s Mission Control Center at Johnson Space Center. In addition, it can connect to, and display results from, separate analysis engine components that identify anomalies or that search for past instances of similar behavior. This software supports NASA's Software, Intelligent Systems, and Modeling element in the Exploration Systems Research and Technology Program by augmenting the capability of human flight controllers to make correct decisions, thus increasing safety and reliability. It was designed specifically as a tool for NASA's flight controllers to monitor the International Space Station and a future Crew Exploration Vehicle.

Jacob, Joseph↗

NASA NTRS1977

Thermal and Albedo Mapping of Mars During the Viking Primary Mission

The behavior of Mars as observed by the Viking infrared thermal mapper (IRTM) is considered. The IRTM is a 28-channel, 4-telescope radiometer that operated in six spectral bands. The studies considered include observations from the interplanetary phase through data collection on November 7, 1976. During this interval, thermal mapping of the whole Martian surface has been possible. Attention is given to polar temperatures, global albedos, predawn temperatures, a thermal inertia contour map, geometry considerations, clouds, aspects of predawn warming, and observations of earth.

Kieffer, H. H.↗

NASA NTRS1993

Aerodynamic control of NASP-type vehicles through vortex manipulation, volume 4

Forebody Vortex Control (FVC) is an emerging technology that has received widespread and concentrated attention by many researchers for application on fighter aircraft to enhance aerodynamic controllability at high angles of attack. This research explores potential application of FVC to a NASP-type configuration. The configuration investigated is characterized by a slender, circular cross-section forebody and a 78 deg swept delta wing. A man-in-the-loop, six-degress-of-freedom, high-fidelity simulation was developed that demonstrates the implementation and advantages of pneumatic forebody vortex control. Static wind tunnel tests were used as the basis for the aerodynamic characteristics modeled in the simulation. Dynamic free-to-roll wind tunnel tests were analyzed and the wing rock motion investigated. A non-linear model of the dynamic effects of the bare airframe and the forebody vortex control system were developed that closely represented the observed behavior. Multiple state-of-the-art digital flight control systems were developed that included different utilizations of pneumatic vortex control. These were evaluated through manned simulation. Design parameters for a pneumatic forebody vortex control system were based on data collected regarding the use of blowing and the mass flow required during realistic flight maneuvers.

Smith, Brooke C.↗

NASA NTRS2000

Measurements and Modeling of Aerosol Absorption and Single Scattering Albedo at Ambient Relative Hum

Uncertainties in the aerosol single scattering albedo have been identified to be an important source of errors in current large-scale model estimates of the direct aerosol radiative forcing of climate. A number of investigators have obtained estimates of the single scattering albedo from a variety of remote sensing and in situ measurements during aerosol field experiments. During the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX, 1996) for example, estimates of the aerosol single scattering albedo were obtained (1) as a best-fit parameter in comparing radiative flux changes measured by airborne pyranometer to those computed from independently measured aerosol properties; (2) from estimates of the aerosol complex index of refraction derived using a combination of airborne sunphotometer, lidar backscatter and in situ size distribution measurements; and (3) from airborne measurements of aerosol scattering and absorption using nephelometers and absorption photometers. In this paper, we briefly compare the results of the latter two methods for two TARFOX case studies, since those techniques provide height-resolved information about the aerosol single scattering albedo. Estimates of the aerosol single scattering albedo from nephelometer and absorption photometer measurements require knowledge of the scattering and absorption humidification (i.e., the increase in these properties in response to an increase in ambient relative humidity), since both measurements are usually carried out at a relative humidity different from the ambient atmosphere. In principle, the scattering humidification factor can be measured, but there is currently no technique widely available to measure the absorption of an aerosol sample as a function of relative humidity. Frequently, for lack of better knowledge, the absorption humidification is assumed to be unity (meaning that there is no change in aerosol absorption due to an increase in ambient relative humidity). This assumption then enters the estimate of the single scattering albedo at ambient relative humidity. To investigate the validity of this assumption we have carried out modeling studies of the absorption humidification factor, assuming that the aerosols contain an insoluble soot core and a coating which determines its hygroscopic growth behavior. The aerosol optical properties are then computed on the basis of the shell/core particle morphology using a Mie-code for concentric shells. From basic physical principles, it is conceivable that aerosol absorption increases when an atmospheric aerosol particle collects a non-absorbing shell, since the soot core is then exposed to an increased (focused) electric field strength. Indeed, our preliminary modeling studies show that the absorption of an atmospheric aerosol particle composed of a soot core and an aqueous sulfuric acid shell may increase by a factor of 50% due to a change in ambient relative humidity from 30 to 95%. We will show how this increased absorption is a function of the initial particle size and soot mass fraction.

Redemann, J.↗

NASA NTRS2017

On-Orbit Structural Dynamics Performance of the Roll-Out Solar Array

The Roll-Out Solar Array (ROSA) flight experiment was launched to the International Space Station (ISS) on June 3rd, 2017. ROSA is an innovative, lightweight solar array with a flexible substrate that makes use of the stored strain energy in its composite structural members to provide deployment without the use of motors. This paper will discuss the results of various structural dynamics experiments conducted on the ISS during the weeks following launch. Data gathered from instrumentation on the solar array wing during the experiments are compared with pre-flight predictions from two different finite element modeling efforts. Two distinct methods were used to reconstruct the modal characteristics of ROSA from the data collected on orbit. Of particular interest in this effort are the first few system modes and mode shapes of the array, the amount of structural damping present, and degree of structural thermal interaction seen during eclipse exit. Discrepancies between the behavior predicted by the models and that observed on orbit are identified and discussed. The goal in this effort was to better understand the performance of ROSA and to improve modeling efforts for future designs of similar solar arrays.

Chamberlain, Matthew K.↗