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At least 289 records · Page 16

A Comparison of the SOCIT and DebriSat Experiments

This paper explores the differences between, and shares the lessons learned from, two hypervelocity impact experiments critical to the update of orbital debris environment models. The procedures and processes of the fourth Satellite Orbital Debris Characterization Impact Test (SOCIT) were analyzed and related to the ongoing DebriSat experiment. SOCIT was the first hypervelocity impact test designed specifically for satellites in Low Earth Orbit (LEO). It targeted a 1960's U.S. Navy satellite, from which data was obtained to update pre-existing NASA and DOD breakup models. DebriSat is a comprehensive update to these satellite breakup models- necessary since the material composition and design of satellites have evolved from the time of SOCIT. Specifically, DebriSat utilized carbon fiber, a composite not commonly used in satellites during the construction of the US Navy Transit satellite used in SOCIT. Although DebriSat is an ongoing activity, multiple points of difference are drawn between the two projects. Significantly, the hypervelocity tests were conducted with two distinct satellite models and test configurations, including projectile and chamber layout. While both hypervelocity tests utilized soft catch systems to minimize fragment damage to its post-impact shape, SOCIT only covered 65% of the projected area surrounding the satellite, whereas, DebriSat was completely surrounded cross-range and downrange by the foam panels to more completely collect fragments. Furthermore, utilizing lessons learned from SOCIT, DebriSat's post-impact processing varies in methodology (i.e., fragment collection, measurement, and characterization). For example, fragment sizes were manually determined during the SOCIT experiment, while DebriSat utilizes automated imaging systems for measuring fragments, maximizing repeatability while minimizing the potential for human error. In addition to exploring these variations in methodologies and processes, this paper also presents the challenges DebriSat has encountered thus far and how they were addressed. Accomplishing DebriSat's goal of collecting 90% of the debris, which constitutes well over 100,000 fragments, required addressing many challenges stemming from the very large number of fragments. One of these challenges arose in identifying the foam-embedded fragments. DebriSat addressed this by X-raying all of the panels once the loose debris were removed, and applying a detection algorithm developed in-house to automate the embedded fragment identification process. It is easy to see how the amount of data being compiled would be outstanding. Creating an efficient way to catalog each fragment, as well as archiving the data for reproducibility also posed a great challenge for DebriSat. Barcodes to label each fragment were introduced with the foresight that once the characterization process began, the datasheet for each fragment would have to be accessed again quickly and efficiently. The DebriSat experiment has benefited significantly by leveraging lessons learned from the SOCIT experiment along with the technological advancements that have occurred during the time between the experiments. The two experiments represent two ages of satellite technology and, together, demonstrate the continuous efforts to improve the experimental techniques for fragmentation debris characterization.

Ausay, Erick↗

Spaceport Command and Control System Automated Verification Software Development

For as long as we have walked the Earth, humans have always been explorers. We have visited our nearest celestial body and sent Voyager 1 beyond our solar system1 out into interstellar space. Now it is finally time for us to step beyond our home and onto another planet. The Spaceport Command and Control System (SCCS) is being developed along with the Space Launch System (SLS) to take us on a journey further than ever attempted. Within SCCS are separate subsystems and system level software, each of which have to be tested and verified. Testing is a long and tedious process, so automating it will be much more efficient and also helps to remove the possibility of human error from mission operations. I was part of a team of interns and full-time engineers who automated tests for the requirements on SCCS, and with that was able to help verify that the software systems are performing as expected.

Automation↗

Effects on Task Performance and Psychophysiological Measures of Performance During Normobaric Hypoxia Exposure

Human-autonomous systems have the potential to mitigate pilot cognitive impairment and improve aviation safety. A research team at NASA Langley conducted an experiment to study the impact of mild normobaric hypoxia induction on aircraft pilot performance and psychophysiological state. A within-subjects design involved non-hypoxic and hypoxic exposures while performing three 10-minute tasks. Results indicated the effect of 15,000 feet simulated altitude did not induce significant performance decrement but did produce increase in perceived workload. Analyses of psychophysiological responses evince the potential of biomarkers for hypoxia onset. This study represents on-going work at NASA intending to add to the current knowledge of psychophysiologically-based input to automation to increase aviation safety. Analyses involving coupling across physiological systems and wavelet transforms of cortical activity revealed patterns that can discern between the simulated altitude conditions. Specifically, multivariate entropy of ECG/Respiration components were found to be significant predictors (p< 0.02) of hypoxia. Furthermore, in EEG, there was a significant decrease in mid-level beta (15.19-18.37Hz) during the hypoxic condition in thirteen of sixteen sites across the scalp. Task performance was not appreciably impacted by the effect of 15,000 feet simulated altitude. Analyses of psychophysiological responses evince the potential of biomarkers for mild hypoxia onset.The potential for identifying shifts in underlying cortical and physiological systems could serve as a means to identify the onset of deteriorated cognitive state. Enabling such assessment in future flightdecks could permit increasingly autonomous systems-supported operations. Augmenting human operator through assessment of cognitive impairment has the potential to further improve operator performance and mitigate human error in safety critical contexts. This study represents ongoing work at NASA intending to add to the current knowledge of psychophysiologically-based input to automation to increase aviation safety.

Stephens, Chad↗

Optimizing Processes to Minimize Risk

NASA, like the other hazardous industries, has suffered very catastrophic losses. Human error will likely never be completely eliminated as a factor in our failures. When you can't eliminate risk, focus on mitigating the worst consequences and recovering operations. Bolstering processes to emphasize the role of integration and problem solving is key to success. Building an effective Safety Culture bolsters skill-based performance that minimizes risk and encourages successful engagement.

Loyd, David↗

Dynamic Positioning System (DPS) Risk Analysis Using Probabilistic Risk Assessment (PRA)

The National Aeronautics and Space Administration (NASA) Safety & Mission Assurance (S&MA) directorate at the Johnson Space Center (JSC) has applied its knowledge and experience with Probabilistic Risk Assessment (PRA) to projects in industries ranging from spacecraft to nuclear power plants. PRA is a comprehensive and structured process for analyzing risk in complex engineered systems and/or processes. The PRA process enables the user to identify potential risk contributors such as, hardware and software failure, human error, and external events. Recent developments in the oil and gas industry have presented opportunities for NASA to lend their PRA expertise to both ongoing and developmental projects within the industry. This paper provides an overview of the PRA process and demonstrates how this process was applied in estimating the probability that a Mobile Offshore Drilling Unit (MODU) operating in the Gulf of Mexico and equipped with a generically configured Dynamic Positioning System (DPS) loses location and needs to initiate an emergency disconnect. The PRA described in this paper is intended to be generic such that the vessel meets the general requirements of an International Maritime Organization (IMO) Maritime Safety Committee (MSC)/Circ. 645 Class 3 dynamically positioned vessel. The results of this analysis are not intended to be applied to any specific drilling vessel, although provisions were made to allow the analysis to be configured to a specific vessel if required.

Thigpen, Eric B.↗

Enhancing the Cassini Mission Through FP Applications After Launch

Although rigorous pre-emptive measures are taken to preclude failures and anomalous conditions from occurring in JPL spacecraft missions prior to launch, unforeseeable problems can still surface after liftoff. In the case of the Cassini/Huygens Mission-to-Saturn spacecraft, several problems were observed post-launch: 1) immediately after takeoff, the collected engineering/science data stored on the Solid State Recorders (SSR) contained a significantly higher number of corrupted bits than was expected (considerably over spec) due to human error in the memory mapping of these devices, 2) numerous Solid State Power Switches (SSPS) sporadically tripped off throughout the mission due to cosmic ray bombardment from the unique space environment, and 3) false assumptions in the pressure regulator design in combination with missing heritage test data led to inaccurate design conclusions, causing the issuance of two waivers for the regulator to close properly (a potentially mission catastrophic single-point failure which occurred 24 days after launch) - amongst other problems. For Cassini, some of these anomalies led to arduous work-arounds or required continuous monitoring of telemetry variables by the ground-based Spacecraft Operations Flight Support (SOFS) team in order to detect and fix fault occurrences as they happened. Fortunately, sufficient funding and schedule margin allowed several Fault Protection (FP) solutions to be implemented into post-launch Flight Software (FSW) uploads to help resolve these issues autonomously, reducing SOFS ground support efforts while improving anomaly recovery time in order to preserve maximum science capture. This paper details the FP applications used to resolve the above issues as well as to optimize solutions for several other problems experienced by the Cassini spacecraft during its fight, in order to enhance the spacecraft's overall mission success throughout the 18 years of its 20 year expedition to and within the Saturnian system.

fault protection↗

Automated Commanding of the SMAP Spacecraft Enables Efficient, Reliable, and Responsive Operations

The Soil Moisture Active Passive (SMAP) mission developed and deployed a system to autonomously handle most routine commanding of the observatory. This system of ground software is able to build commands, validate them, and radiate the commands to the spacecraft, all without human interaction. In the case of an off -nominal scenario, the system will abort gracefully and notify the mission operations team of the problem. The system was phased into operations during the first three months of the SMAP mission and handles over 90% of the weekly commanding of the vehicle. The gradual introduction of the automation in flight, along with an extensive test campaign, was instrumental in the success of the software. The automation has enabled substantial efficiencies in operations team staffing and has improved reliability by removing the potential for human error. The system also allows the SMAP project to be more responsive which has shown significant benefits in areas of data latency and science accuracy.

automation↗

Dynamic Positioning System (DPS) Risk Analysis Using Probabilistic Risk Assessment (PRA)

The National Aeronautics and Space Administration (NASA) Safety & Mission Assurance (S&MA) directorate at the Johnson Space Center (JSC) has applied its knowledge and experience with Probabilistic Risk Assessment (PRA) to projects in industries ranging from spacecraft to nuclear power plants. PRA is a comprehensive and structured process for analyzing risk in complex engineered systems and/or processes. The PRA process enables the user to identify potential risk contributors such as, hardware and software failure, human error, and external events. Recent developments in the oil and gas industry have presented opportunities for NASA to lend their PRA expertise to both ongoing and developmental projects within the industry. This paper provides an overview of the PRA process and demonstrates how this process was applied in estimating the probability that a Mobile Offshore Drilling Unit (MODU) operating in the Gulf of Mexico and equipped with a generically configured Dynamic Positioning System (DPS) loses location and needs to initiate an emergency disconnect. The PRA described in this paper is intended to be generic such that the vessel meets the general requirements of an International Maritime Organization (IMO) Maritime Safety Committee (MSC)/Circ. 645 Class 3 dynamically positioned vessel. The results of this analysis are not intended to be applied to any specific drilling vessel, although provisions were made to allow the analysis to be configured to a specific vessel if required.

Thigpen, Eric B.↗

GPM Mission's Best Practices: PERP

Similar to other missions, the Global Precipitation Measurement (GPM) Core Observatory's Command and Data Handling (C&DH) subsystem is critical for operations of the spacecraft. The onboard C&DH system comprises of two fully redundant boxes - a primary and a cold backup. Within each box, amongst other components, is a Single Board Computer (SBC) that hosts the flight software (FSW) system. In the event of an SBC reset, the Flight Operations Team (FOT) is poised with a lengthy task of restoring the SBC to nominal configuration. Due to the complexity of the C&DH system, this may take many days at a time to complete. The spacecraft's FSW applications are located in Electronically Erasable Programmable Read-Only Memory (EEPROM) and are copied into Random Access Memory (RAM) upon SBC initialization/reset. Each SBC has two banks of EEPROM, with each bank containing a copy of the FSW. Since launch, there have been many configuration changes to tables and applications that have been loaded into just RAM. Unfortunately, these changes are vulnerable to being wiped during a SBC initialization/reset, when the RAM is overwritten by the EEPROM. Although the EEPROM loads the default FSW configurations, the process to command non-default individual table and application changes is very cumbersome and time consuming. This consequentially increases the time until the spacecraft is back into nominal Mission Science Mode (MSM) drastically. The GPM Power-On Reset (POR) Expedited Recovery Process (PERP) Design introduces a method of consolidating commands into a single file load which the SBC can process independently of the ground - decreasing recovery time, the level of TDRS support reliance, and human error. This tested design can be implemented across many other missions that utilize a similar core Flight Executive (cFE) platform; hence providing an easy-to-follow, safe, and efficient process that can be applied across the board.

recovery↗

Innovative Development of a Cross-Center Timeline Planning Tool

The Payload Operations Integration Center (POIC) at Marshall Space Flight Center (MSFC) is the United States focal point to support operations controllers and payload developers conducting payload science operations for the National Aeronautics and Space Administration (NASA) aboard the International Space Station (ISS). Some of the key functions are planning, coordination and scheduling of science activities. This effort occurs in coordination with other NASA centers, international partners, and payload developers. The ability to efficiently plan and re-plan in response to change is critical to the flight planning teams. Additionally, in Fall 2017, NASA will increase its ability to perform payload science operations aboard the ISS with a fourth crew member. In order to support this, there will be an increasing need to quickly plan and schedule more activities. In the past, it was cumbersome and time-consuming to consolidate copious amounts of planning and change request data from various sources. Planners would summarize information from the Johnson Space Center (JSC) Operations Planning Timeline Integration System (OPTIMIS) and manually integrate it with other data in order to produce a Timeline Planning Summary (TPS). This lengthy process of updating static documents while planning and re-planning was cumbersome, introduced human error, and was inflexible to last minute changes. There was a need for a dynamic, more efficient, less erroneous, and more concise way of building a report that could be readily updated as fast as payload science plans change.

Pedoto, Ramon W.↗

International Space Station (ISS) Payload Autonomous Operations Past, Present and Future

Draper Laboratorys Timeliner is a scripting and automation system that runs onboard computers in the International Space Station (ISS). Timeliner is fully integrated with ISS and can be used to automate ISS operations tasks. Some of the most challenging aspects of operating a payload in low earth orbit are communication delays, ground equipment failures, and human errors. How does a Payload Developer (PD) know their equipment is operating nominally and collecting science in the most efficient way possible or even powered at any given time? During a ground Loss of Signal (LOS) data outage, PDs have no insight into their experiments state, and benefit greatly from Timeliner scripts executing on ISS to perform telemetry monitoring and commanding operations. This paper will discuss current software designs, and new operational uses for Timeliner. Existing Timeliner capabilities discussed will include: autonomous EXPRESS Rack activation and deactivation; autonomous science data downlinks; Minus Eighty Degree Freezer (MELFI) Dewar autonomous safing; JAXA and ESA module autonomous payload facility safing; as well as many others. New operational concepts discussed will include allowing Timeliner on the payload computer to issue core commands (Thermal, Power, Fire Detection), creation of new ground tools that will monitor the current status of Payload Racks as well as all the messaging from autonomous scripts executing, decreasing approval time for Timeliner bundles, and opening up the Payload MDM Enhanced Processor Integrated Communications Card (EPIC) interface. The EPIC interface could provide a new crew interface for PL Timeliner execution. The new interface could operate on either a Payload Computer System (PCS) or a Station Support Computer (SSC) that is plugged into either the Payload LAN or the Operations LAN which will make communicating to the PL MDM more flexible and greatly increase band width for communication.

Space Mission Automation↗

Cassini Spacecraft Attitude Control System: Flight Performance and Lessons Learned, 1997-2017

A sophisticated interplanetary spacecraft, Cassini/Huygens was launched on October 15, 1997. Since achieving orbit at Saturn in 2004, Cassini has collected science data throughout its four-year prime mission (2004–08), and has since been approved for first and second extended missions through September 2017. The Cassini Attitude and Articulation Control Subsystem (AACS) is perhaps the spacecraft subsystem that must satisfy the most mission and science pointing requirements. Since launch, the performance of the Cassini AACS design has been superb. All key mission and science requirements are met with significant margins. An overview of the flight performance of the Cassini attitude control system as well as AACS mission operation-centric lessons learned, from launch to 2017, are described by topics. Many of these lessons learned should be applicable to the safe operations of other interplanetary missions. Processes taken by the AACS operation team to guard against “human” errors are also outlined in this paper.

Lee, Allan Y.↗

Novel Classifier of Neural Impairment

The proposed research aligns with Ames Core Competency within the area of Intelligent/Adaptive Systems under Human Systems Integration. This will serve as a robust, efficient, and user-friendly human performance assessment tool that could provide immediate neural status of crewmembers in low-orbit and deep space missions. The human operator is a critical element of crew safety and should be evaluated consistently to mitigate potential off-nominal situations propagated from human error. Flagged deficits in oculomotor performance could act as an alert signal for crewmembers to opt out of cognitively demanding tasks or to prevent irreversible compromise in crewmember health.

impairment↗

Understanding Machine Learning in Earth Science: A Natural Language Processing Approach

Machine learning (ML) is being increasingly utilized in Earth science research. Benefits of ML include efficiency, reduction of human error, and ability to extract hidden patterns within data. However, the mutual lack of each other’s domain knowledge by ML and Earth science stands as a barrier to timely and effective implementation. Earth science, in particular, faces challenges in generating sample data, compared to those of traditional ML problems such as face recognition or stock predictions, where data is abundant and not lacking in ground truth, which is necessary for labeling. Earth science data are more varying in formats, such as HDF5 and image resolutions, and are not standardized across instruments, even within a given Earth science discipline. Previous studies have been done to outline the specific challenges that Earth science faces with ML, while others have focused on using existing publications to mine information efficiently. Other resources such as Scikit-Learn have developed decision trees for choosing appropriate machine learning algorithms, but application within Earth science subjects becomes much more complex. For the current study, we propose a methodology and tool that aids in implementation of ML in Earth science using natural language processing (NLP). Our work comprises three main parts: (1) analyzing existing publications related to ML and Earth science, using natural language processing: (2) extracting from the publications information on ML models subjects in Earth Science: and (3) visualizing the extracted relationships as a network graph. The resulting network graph should aid the Earth science communities in applying optimal ML algorithms and guiding data preparation through visualization of similar studies. The network graph and analysis of document similarity will be the basis of our next step, which is to develop a decision tree for selecting optimal machine learning methodologies for specified Earth science applications.

Zheng, Laura↗

Studying Microbial Adaptation in the Laboratory: Sensor & Control Upgrades for an Experimental Evolution Biofluidics System

Experimental evolution (EE) involves iteratively exposing a microbial community to specific stressors to study its response to changes in environment over time. EE work is commonly done manually in the laboratory, but, when there are many environmental variables to measure and adjust, it is highly labor intensive, prone to human error, and challenging to scale. Single-purpose automated continuous culturing chambers exist, but implement only limited stressor types. A more general-purpose design is desirable. The BeING Lab at Ames Research Center created the prototype Automated Adaptive Directed Evolution Chamber (AADEC) to address these problems, beginning with Escherichia coli tolerance of short-wave ultraviolet (UV-C) radiation and of temperature. In newer versions, AADEC monitors microbial activity and can adjust the UV-C and temperature levels automatically. An optical density measurement is used to determine how many cells are present in the growth medium—over time, this corresponds to how many survive and reproduce. Oxidation-reduction potential provides information on consumed metabolic energy, and pH and electrical conductivity on metabolic products. Dissolved oxygen content is used to determine aerobic vs anaerobic growth. A Raspberry Pi computer processes all this data to set the UV-C stressor level. AADEC’s auxiliary systems include peristaltic pumps to change media and agitation to counteract cell settling. These actuators can also act as additional stressors. With the Raspberry Pi monitoring sensors and adjusting actuators in real time, AADEC takes measurements and controls the environment much more accurately than can be done with a manual EE implementation. The third and latest AADEC iteration is the first to simplify design and usage with circuits on PCBs and the ability to pre-program experimental protocols. Still planned is expansion to a multi-well design for the study of varying cell cultures in parallel, which will enable researchers to retain and re-inoculate cultures exhibiting the desired trait most strongly while flushing out others. AADEC’s special capabilities make it a valuable tool for studying life under multiple stressors, enabling scientists to replicate changes in climate on microbes for study in a lab setting.

Microbial Adaptation↗

Conducted Susceptibility Data Adaptation Tool

The purpose of this research project is to help NASA scientists increase effectivity in testing by reducing the amount of time required for data conversion from initial measurements to the analysis stage. This project resulted in a data conversion tool developed in python using the openpyxl library. When lab personnel perform the Conducted Susceptibility 101 (CS101) and Conducted Susceptibility 02 (CS02) tests in the Semi-anechoic Electromagnetic Compatibility Test Facility at NASA Langley Research Center, the driving code produces a text file under the .DAT extension.The data must be manually converted the .DAT file into an Excel file line by line for data analysis.The Conducted Susceptibility Data Adaptation Tool (CSDAT) uses python code to convert the .DAT files to spreadsheets automatically which saves lab personnel time which they could use for faster analysis as well as prevents possible human error such as leaving out a line of data in the manual conversion process.

Gabriel Johnson↗

Automating the Study of Microbial Adaptation Dynamics on and off the ISS

The International Space Station (ISS) not only serves as a unique environment for humans, but also the microorganisms that join alongside. Many microbes present on the spacecraft arrive via humans, and as they interact with different surfaces they begin to inhabit those locations. Much like how human health has shown to be impacted by these extreme environments, microbial viability and response to stress also changes. Experimental evolution (EE) can aid in studying how microbes’ growth and activity changes within the ISS environments by applying controlled stressors to microbial cultures and monitoring their response over generations. EE studies are commonly done manually in laboratories, but, with multiple environmental variables to measure and adjust, it becomes highly labor-intensive, prone to human error, and challenging to scale. A multipurpose automated EE system named the AADEC has been developed to address these problems. This system integrates multiple sensors into a single fluidic chamber using UV-C flux, temperature, and media composition as stressors. AADEC contains five sensors: oxidation-reduction potential, electrical conductivity, pH, dissolved oxygen, and optical density. On their own, each is able to provide certain information on growth rate or metabolism; together, they show in detail how stressors affect life. AADEC studies can be conducted on Earth and repeated aboard the ISS to see how behavior changes when exposed to space mission stressors such as microgravity and radiation. AADEC’s auxiliary systems include peristaltic pumps for media exchange, magnetic rods for agitation, and a Raspberry Pi microprocessor to monitor, store, and adjust stressor levels real-time. This allows researchers to gather information within rapid generations, data and accuracy which is challenging to achieve through manual studies. With further miniaturization and automation, such as a more robust single-piece fluidics card, AADEC has the potential to be developed as a spacecraft payload. Support: NASA Ames CIF Award

Automating↗

Streamlining GNC Architecture Development and FSW Integration forthe Mars Ascent Vehicle

The Mars Ascent Vehicle (MAV) will be the first vehicle to perform an ascent from the surface ofanother atmospheric planetary body outside of the Earth-Moon system. Significant light-time delayrequires complete autonomy of flight throughout ascent, and naturally a high level of reliability isdesired in both MAV’s hardware and software subsystems. The MAV Guidance, Navigation and Controls(GNC) team and the MAV Flight Software (FSW) team have partnered together to improve the efficiencyof algorithm integration onto the MAV flight processor, and to increase confidence that said integrationis successful and without human error. An interface architecture is proposed for the GNC suite thatallows both the guidance and navigation subsystems to provide code algorithms directly in C++, and thecontrols subsystem to provide MATLAB Simulink auto-coded algorithms. Several continuous integration/deployment (CI/CD) methodologies have been considered for ease of transition of algorithm code fromthe GNC team to the FSW team. The GNC/FSW teams also worked together to develop a cFS-friendlywrapper which abstracts the integration of the GNC algorithm code into an interface-level API that iscompatible with cFS. Several iterations of vehicle GNC code have been produced between the GNC/FSWteam’s partnership, and this strong interface between these two teams have allowed the GNC/FSWteams to greatly increase confidence of efficient and error-free implementation of the GNC code ontoMAV for a successful flight.

GNC↗