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NASA NTRS2017

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↗

NASA NTRS2017

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.↗

NASA NTRS2016

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↗

NASA NTRS2016

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↗

NASA NTRS2017

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

Thigpen, Eric B.↗

NASA NTRS2018

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↗

NASA NTRS2018

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.↗

NASA NTRS2016

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↗

NASA NTRS2018

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.↗

NASA NTRS2019

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↗

NASA NTRS2019

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↗

NASA NTRSDate not supplied

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↗

NASA NTRS2020

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↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

Vacuum Sealable Container (VSC) and Astronaut Lunar Drill (ALD) for Artemis

Introduction: NASA’s Artemis Program is under development to send first woman and next man to the Moon. Artemis will utilize a suite of new technology for Lunar exploration, including new space vehicles, new space suits, and new Astronaut Tools. Honeybee Robotics has been working with NASA JSC to develop a new Vacuum Sealable Container (VSC) and new Astronaut Lunar Drill (ALD) for the upcoming Artemis missions. Vacuum Sealable Container: Sample return continues to be the “Holy Grail” of space exploration, allowing for the analysis of materials using Earth-based laboratories instead of needing to miniaturize and ruggedize instrumentation for space. The Apollo missions to the Moon had several kinds of Sealable Containers which brought back Lunar samples for analysis [1]. These samples are still being analyzed, fifty years later. The VSC requirements are different from that for Apollo containers and as such, new development was required. One major difference between Artemis samples and those from Apollo is the desire to bring back volatiles which may be part of lunar regolith. The VSC is designed to withstand a high-pressure differential caused by sublimating volatiles. Because of the new, stricter sealing requirements, additional features have been added to the VSC. For example, the seal on the container is required to be more robust, thus required more force to actuate, and the seal must be locked in place with a secondary mechanism. Astronaut Lunar Drill: The ALD is designed to be a multi-functional platform for Lunar sample acquisition. The drill builds on lessons learned from the Apollo Lunar Surface Drill (ALSD), as well as Honeybee’s long history of mechanized sample acquisition devices for space [2]. The main functionality of the ALD is Deep Core Regolith Drilling. Additional functionality includes Surface Rock Coring (SRC), and GeoTech Tools (GTT). The ALD is a rotary-percussive drill designed with deep drilling in mind. The ALD is currently designed to have decoupled rotary and percussion subsystems to allow for maximum battery life and reduced fatigue on the crewmember. Honeybee drill technology will automatically engage the percussion when needed to drill at maximum efficiency. The mechanized drill stand helps improve drilling efficiency; the system utilizes advanced drilling algorithms which only require the crewmember to hold a single switch. Additionally, the stand aids in extraction of deep cores, something which was a problem on Apollo. The SRC functionality of the ALD utilizes Honeybee’s Eccentric Tube Core Breakoff technology to collect and retain rock core samples. This technology has also been infused into the Perseverance rover mission. The ALD is removable from the stand to allow crewmembers to collect samples from large boulders. Bringing back rock cores samples instead of full rocks allows for a wider variety of samples to be returned to Earth for study and puts them in a uniform form-factor for effective sealing and analysis. SRC bits will utilize the power of the drill’s percussion system to drill hard Lunar rocks and expedite sample acquisition. The mechanized stand on the ALD allows for additional attachments for taking geotechnical measurements with a Static Cone Penetrometer (SCP) and a Shear Vane (SV). With the stand, the ALD can take SCP measurements with the touch of a button, storing data for return to Earth. SV measurements utilize the ALD’s Rotary motor to spin the vanes in a controlled manner, getting clean data untampered by human error. References: [1] Bar Cohen and Zacny (2009), Drilling in Extreme Environments - Penetration and Sampling on Earth and Other Planets, Wiley. [2] Bar-Cohen and Zacny, Advances in Terrestrial and Extraterrestrial Drilling, CRC Press. [3] Myrick (2003), Core Break-off Mechanism. US Patent No. 6,550,549 Acknowledgements: This work has been supported by NASA via SBIR Phase 3.

Artemis↗

NASA NTRSDate not supplied

Streamlining GNC Architecture Development and FSW Integration for the Mars Ascent Vehicle

The Mars Ascent Vehicle (MAV) will be the first vehicle to perform an ascent from the surface of another atmospheric planetary body outside of the Earth-Moon system. Significant light-time delay requires complete autonomy of flight throughout ascent, and naturally a high level of reliability is desired 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 efficiency of algorithm integration onto the MAV flight processor, and to increase confidence that said integration is successful and without human error. An interface architecture is proposed for the GNC suite that allows both the guidance and navigation subsystems to provide code algorithms directly in C++, and the controls 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 from the GNC team to the FSW team. The GNC/FSW teams also worked together to develop a cFS-friendly wrapper which abstracts the integration of the GNC algorithm code into an interface-level API that is compatible with cFS. Several iterations of vehicle GNC code have been produced between the GNC/FSW team’s partnership, and this strong interface between these two teams have allowed the GNC/FSW teams to greatly increase confidence of efficient and error-free implementation of the GNC code onto MAV for a successful flight.

Engineering↗

NASA NTRSDate not supplied

Mars Ascent Vehicle GNC Targeting Routines with Considerations for Flight Software Development

The Mars Ascent Vehicle (MAV) will be the first vehicle to perform an ascent from the surface of another atmospheric planetary body outside of the Earth-Moon system. Significant light-time delay requires complete autonomy of flight throughout ascent, and naturally a high level of reliability is desired 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 efficiency of algorithm integration onto the MAV flight processor, and to increase confidence that said integration is successful and without human error. An interface architecture is proposed for the GNC suite that allows both the guidance and navigation subsystems to provide code algorithms directly in C++, and the controls 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 from the GNC team to the FSW team. The GNC/FSW teams also worked together to develop a cFS-friendly wrapper which abstracts the integration of the GNC algorithm code into an interface-level API that is compatible with cFS. Several iterations of vehicle GNC code have been produced between the GNC/FSW team’s partnership, and this strong interface between these two teams have allowed the GNC/FSW teams to greatly increase confidence of efficient and error-free implementation of the GNC code onto MAV for a successful flight.

Jason Everett↗