Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “network operational objectives”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Results from the ASTERIA CubeSat Extended Mission Experiments

Over the past two years, JPL has used the ASTERIA (Arcsecond Space Telescope Enabling Research In Astrophysics) CubeSat as an in-flight test platform during extended missions. ASTERIA successfully completed its prime mission in early 2018, and continued to operate in low Earth orbit (LEO) for an additional twenty months. This paper describes demonstrations that were performed on the spacecraft and on the ground-based testbed during the extended mission. These demonstrations fall into three categories: Autonomy technology maturation, hardware characterization, and science discovery. Autonomy technology maturation supported three development efforts. The first shifted the spacecraft commanding paradigm from time-based sequences to Task Networks (tasknets), which allow simpler commanding and more robust onboard execution. The second demonstrated onboard orbit determination in Low Earth Orbit (LEO) without GPS. This activity used a fully-independent means of spacecraft orbit determination for Earth orbiters using only passive imaging. The third technology provided in situ hardware health state estimation using a model-based reasoning technique. These three technologies were demonstrated either in flight or on the testbed individually, and then were combined to demonstrate the capability to perform autonomous navigation on board without ground intervention, even in the presence of anomalies. Hardware characterization involved both onboard and ground-based activities. On board, nonstandard attitude control modes were commanded to characterize the spacecraft pointing jitter as a function of target brightness, reaction wheel speed, controller gain, and the number of guide stars. The results provide insights into the contribution of jitter to the ASTERIA photometry and inform the feasibility of future astrophysics small satellite missions for which jitter control is an enabling technology. On the ground, the ASTERIA Operations Team coordinated with Amazon Web Services (AWS) to configure their new ground stations to communicate with ASTERIA to prove out their viability. ASTERIA used AWS ground stations for nominal operations for the last four months of the mission. Finally, ASTERIA continued to perform exoplanet science as the spacecraft was well-suited to execute long-term monitoring of stars such as alpha Centauri to search for small transiting planets. The science team also imaged a number of interesting objects including a comet, an asteroid, cities at night, and the moon, and coordinated with other projects on Targets of Opportunity for follow-up confirmations and co-observations. Throughout the prime and the extended missions, the ASTERIA spacecraft proved to be a mighty platform that “will go into history as an innovative milestone.”[1 - Zurbuchen]

Doran, Patrick↗
NASA NTRS2007

Telerobotic Surgery: An Intelligent Systems Approach to Mitigate the Adverse Effects of Communication Delay

An extremely innovative approach has been presented, which is to have the surgeon operate through a simulator running in real-time enhanced with an intelligent controller component to enhance the safety and efficiency of a remotely conducted operation. The use of a simulator enables the surgeon to operate in a virtual environment free from the impediments of telecommunication delay. The simulator functions as a predictor and periodically the simulator state is corrected with truth data. Three major research areas must be explored in order to ensure achieving the objectives. They are: simulator as predictor, image processing, and intelligent control. Each is equally necessary for success of the project and each of these involves a significant intelligent component in it. These are diverse, interdisciplinary areas of investigation, thereby requiring a highly coordinated effort by all the members of our team, to ensure an integrated system. The following is a brief discussion of those areas. Simulator as a predictor: The delays encountered in remote robotic surgery will be greater than any encountered in human-machine systems analysis, with the possible exception of remote operations in space. Therefore, novel compensation techniques will be developed. Included will be the development of the real-time simulator, which is at the heart of our approach. The simulator will present real-time, stereoscopic images and artificial haptic stimuli to the surgeon. Image processing: Because of the delay and the possibility of insufficient bandwidth a high level of novel image processing is necessary. This image processing will include several innovative aspects, including image interpretation, video to graphical conversion, texture extraction, geometric processing, image compression and image generation at the surgeon station. Intelligent control: Since the approach we propose is in a sense predictor based, albeit a very sophisticated predictor, a controller, which not only optimizes end effector trajectory but also avoids error, is essential. We propose to investigate two different approaches to the controller design. One approach employs an optimal controller based on modern control theory; the other one involves soft computing techniques, i.e. fuzzy logic, neural networks, genetic algorithms and hybrids of these.

Cardullo, Frank M.↗
NASA NTRS2022

The Mars 2020 Ground Data System Architecture

The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.

Lopez-Roig, Reynaldo↗
NASA NTRS1996

Geodata Modeling and Query in Geographic Information Systems

Geographic information systems (GIS) deal with collecting, modeling, man- aging, analyzing, and integrating spatial (locational) and non-spatial (attribute) data required for geographic applications. Examples of spatial data are digital maps, administrative boundaries, road networks, and those of non-spatial data are census counts, land elevations and soil characteristics. GIS shares common areas with a number of other disciplines such as computer- aided design, computer cartography, database management, and remote sensing. None of these disciplines however, can by themselves fully meet the requirements of a GIS application. Examples of such requirements include: the ability to use locational data to produce high quality plots, perform complex operations such as network analysis, enable spatial searching and overlay operations, support spatial analysis and modeling, and provide data management functions such as efficient storage, retrieval, and modification of large datasets; independence, integrity, and security of data; and concurrent access to multiple users. It is on the data management issues that we devote our discussions in this monograph. Traditionally, database management technology have been developed for business applications. Such applications require, among other things, capturing the data requirements of high-level business functions and developing machine- level implementations; supporting multiple views of data and yet providing integration that would minimize redundancy and maintain data integrity and security; providing a high-level language for data definition and manipulation; allowing concurrent access to multiple users; and processing user transactions in an efficient manner. The demands on database management systems have been for speed, reliability, efficiency, cost effectiveness, and user-friendliness. Significant progress have been made in all of these areas over the last two decades to the point that many generalized database platforms are now available for developing data intensive applications that run in real-time. While continuous improvement is still being made at a very fast-paced and competitive rate, new application areas such as computer aided design, image processing, VLSI design, and GIS have been identified by many as the next generation of database applications. These new application areas pose serious challenges to the currently available database technology. At the core of these challenges is the nature of data that is manipulated. In traditional database applications, the database objects do not have any spatial dimension, and as such, can be thought of as point data in a multi-dimensional space. For example, each instance of an entity EMPLOYEE will have a unique value corresponding to every attribute such as employee id, employee name, employee address and so on. Thus, every Employee instance can be thought of as a point in a multi-dimensional space where each dimension is represented by an attribute. Furthermore, all operations on such data are one-dimensional. Thus, users may retrieve all entities satisfying one or more constraints. Examples of such constraints include employees with addresses in a certain area code, or salaries within a certain range. Even though constraints can be specified on multiple attributes (dimensions), the search for such data is essentially orthogonal across these dimensions.

Adam, Nabil↗
NASA NTRS1993

A broad-band microseismometer for planetary operations

There has recently been renewed interest in the development of instrumentation for making measurements on the surface of Mars. This is due to the Mars Environmental Survey (MESUR) Mission, for which approximately 16 small, long-lived (2-10 years), relatively inexpensive surface stations will be deployed in a planet-wide network. This will allow the investigation of processes (such as seismology and meteorology) which require the simultaneous measurement of phenomena at many widely spaced locations on the surface over a considerable length of time. Due to the large number of vehicles involved, the mass, power, and cost of the payload will be severely constrained. A seismometer has been identified as one of the highest priority instruments in the MESUR straw-man payload. The requirements for an effective seismic experiment on Mars place a number of constraints on any viable sensor design. First, a large number of sensors must be deployed in a long-lived global network in order to be able to locate many events reliably, provide good spatial sampling of the interior, and increase the probability of seismic detection in the event of localized seismicity and/or high attenuation. From a practical standpoint, this means that individual surface stations will necessarily be constrained in terms of cost, mass, and power. Landing and thermal control systems will probably be simple, in order to minimize cost, resulting in large impact accelerations and wide daily and seasonal thermal swings. The level of seismic noise will determine the maximum usable sensitivity for seismometer. Unfortunately, the ambient seismic noise level for Mars is not well known. However lunar seismic noise levels are several orders of magnitude below that of the Earth. Sensitivities on the order of 10(exp -11)g over a bandwidth of .04 to 20 Hz are thought to be necessary to fulfill the science objectives for a seimometer placed on the Martian surface. Silicon micromachined sensor technology offers techniques for the fabrication of monolithic, robust, compact, lower power and mass accelerometers. Conventional micro-machined accelerometers have been developed and are commercially available for high frequency and large acceleration measurements. The new seismometer we are developing incorporates certain principles of conventional silicon micromachined accelerometer technology. However, currently available silicon micromachined sensors offer inadequate sensitivity and bandwidth for the Mars seismometer application. Our implementation of an advanced silicon micromachined seismometer is based on principles recently developed at JPL for high-sensitivity position sensor technology.

Banerdt, W. B.↗
NASA NTRS2019

Deep Learning Method for Detecting Precursors to Adverse Events

With the recent advancements in Deep Learning methods, the ability to model large complex heterogeneous data sets are fundamentally changing industry and research. Coupled with hardware improvements, and ease of implementation, a wide variety of deep neural network architectures can quickly be developed to solve a sweeping range of problems such as: object detection in images, automatic healthcare diagnosis using heterogenous data sources, real time language translating and sentence prediction, upscaling low resolution images, and forecasting of multivariate timeseries. Generally, many of these architectures outperform classical machine learning approaches in their respective tasks, however, this typically comes at a cost of interpretability. These black box algorithms generally suffer from lack of transparency in both model complexity as well as the rationale behind the prediction. This lack of comprehension, is driving an emerging area of interest in “Explainable AI”. An algorithm called: “Deep Temporal Multiple Instance Learning”1 was a recently developed to identify precursors to adverse events and has been applied in the aviation domain. The deep learning architecture is designed to capture the evolution of the probability of the outcome over the time preceding the adverse event using a multiple instance learning approach as illustrated in Figure 1. Precursors are defined when the probability of the event has exceeded a threshold at some point in the timeseries, at which point, a sensitivity analysis is performed to determine contributing factors. The contributing factors are used to explain and define the precursor during the periods where the probability score is high. The identified contributing factors are then presented to subject matter experts to provide objective insights into the leading factors associated with the particular adverse event. The algorithm has been tested on flight data from a commercial airline and has the ability to discover precursors to known adverse events that take the form of safety critical operations, such as unstable approach events on final approach. Apart from detecting precursors to adverse events, the converse can also be leveraged to discover corrective actions. These positive actions manifest themselves as periods in the timeseries when the precursor score has been lowered from an elevated state; meaning that if the system had been left uncorrected, it would have eventually reached the adverse event state. Characterizing these state changes can help identify successful interventions that may not have been known before. Policy makers and procedure designers can use this additional knowledge to craft more safety and efficient resilient procedures for future operations and therefore improve the overall performance of the National Airspace.

Matthews, Bryan L.↗
NASA NTRS2016

Internship at the Office of the Chief Scientist

The NASA Ames Research Center (ARC) Office of the Chief Scientist (OCS) is part of the Center Management. The OCS adds value to the organization by encouraging and facilitating innovation and value to the Agency, Nation, and the scientific community through its well-developed, diverse, and strategically formed charter. The OCS provides the Center with the oversight and advice by facilitating and evaluating the Scientific Innovation Fund (SIF) and promoting science-based research partnerships. Additionally, the Office contributes to to the Agency by organizing annual Summer Series Colloquia, Workshops, and support to the Early Career Network, which allows for international and inter-agency collaborations. Working at the OCS allowed me to further learn about the working of the Office and how the Office engages and provides guidance with rest of the Center and the Agency. As part of my internship, I am working on developing the initial infrastructure required for the organization of the Standardized Distributed Workshop and further developing the layout for the Hibernation Workshop. The Standardized Distributed System workshop will investigate the scope of developing and using standardized distributed systems for a wide range of applications including satellite systems, aerospace-oriented systems, water-based systems, automobiles, and various other robotics applications. The goal of this workshop is to determine the feasibility of developing a standardized distributed system that can be customized for different technological applications, which will enable resource sharing, openness, robustness, and fault tolerance. This workshop will bring together experts from different fields, including satellite operations, unmanned aerial vehicles, aircraft operation and management, and artificial intelligence to provide a global evaluation of the aspects associated with the development and adoption of distributed systems. The workshop will also guide the participants through state of the art distributed systems, discuss some of the challenges in designing control systems with a distributed structure, and consider the future of distributed system based technologies for future applications. Additionally, I am also working on finalizing the details and documentation for the proposed Hibernation Workshop. This workshop will address the phenomenon of hibernation through three major aspects: hibernation on Earth, hibernation in space, and knowledge gaps in the approaches to future deep space exploration. The primary objective of the workshop is to explore the current state of science in the area of hibernation, and as a scientific community, explore the gaps and challenges preventing this scientific field from advancing to the next level. This workshop will consist of presentations, panel discussions, and breakout sessions focused on addressing this scientific field, and bringing experts from educational and commercial sectors together. As a part of my internship report, I will be document the process of planning these workshops and further provide a brief summary of my experience with the 2016 NASA Ames Summer Series and various professional visits.

ISU↗
NASA NTRS2001

Challenges and Lessons Learned in the Application of Autonomy to Space Operations

NASA's Space Operations Management Office (SOMO) is working toward a goal of providing an integrated infrastructure of mission and data services for space missions undertaken by NASA enterprises. A significant portion of this effort is focused on reducing the cost of these services. We are interested in the potential of autonomy to reduce operations costs. SOMO services support space missions, but are not part of the mission objectives; therefore the level of acceptable risk is very low. In fact, SOMO could be effective ly prevented from applying autonomy if customers merely perceive it as adding risk to their mission(s). We are interested in this workshop from the standpoint of understanding what can be done to realize the potential cost savings due to autonomy while maintaining acceptable risk and serving the needs of our customers. We would like to present our lessons learned so far in adopting autonomy and automation, which we think will contribute to clarifying the challenges facing the use of such technology. SOMO provides services to a diverse and ambitious set of mission customers. Many of these missions are groundbreaking missions for which communications, data, and other operations requirements sometimes cannot be clearly articulated early in the program. This motivates a need for systems that are robust in the face of unanticipated situations so that customer missions are not unreasonably constrained or impacted by "shortcomings" in SOMO services. One of SOMO's primary goals is to realize a paradigm in which SOMO acts as a service provider to organizations that fly space missions for NASA, other government agencies, and even the commercial sector. These organizations purchase SOMO services "by the pound" as customers. We have to provide systems that are not experiments themselves, but rather stable bases from which to do bold experiments. To this end, SOMO also seeks to work closely with industry to see that robust autonomy technology gets infused into products and services for the space industry and beyond. The potential for application of these technologies spans space-based communications networks (e.g. TDRSS) and ground-based assets including communication and tracking antenna systems, data networks, and control centers. There are several problems that are candidates for the application of autonomy, if it can be made reliable enough, including: antenna control, antenna scheduling, communication link scheduling and operation, navigation, attitude determination, fault detection, isolation, and reconfiguration (for spacecraft or ground assets), and mission-level planning and scheduling. Some attempts have been made to apply autonomy and automation in these areas in the past with varying degrees of success. We will present relevant case histories and the lessons inferred from them. Combining this past experience with anticipated future needs, we can clarify the challenges that must be met in order to realize the benefits of autonomy.

Forrest, David J.↗
NASA NTRS2004

Objective Lightning Forecasting at Kennedy Space Center/Cape Canaveral Air Force Station using Cloud-to-Ground Lightning Surveillance System Data

The 45th Weather Squadron (45 WS) forecasters at Cape Canaveral Air Force Station (CCAFS) in Florida include a probability of thunderstorm occurrence in their daily morning briefings. This information is used by personnel involved in determining the possibility of violating Launch Commit Criteria, evaluating Flight Rules for the Space Shuttle, and daily planning for ground operation activities on Kennedy Space Center (KSC)/CCAFS. Much of the current lightning probability forecast is based on a subjective analysis of model and observational data. The forecasters requested that a lightning probability forecast tool based on statistical analysis of historical warm-season (May - September) data be developed in order to increase the objectivity of the daily thunderstorm probability forecast. The tool is a set of statistical lightning forecast equations that provide a lightning occurrence probability for the day by 1100 UTC (0700 EDT) during the warm season. This study used 15 years (1989-2003) of warm season data to develop the objective forecast equations. The local CCAFS 1000 UTC sounding was used to calculate stability parameters for equation predictors. The Cloud-to-Ground Lightning Surveillance System (CGLSS) data were used to determine lightning occurrence for each day. The CGLSS data have been found to be more reliable indicators of lightning in the area than surface observations through local informal analyses. This work was based on the results from two earlier research projects. Everitt (1999) used surface observations and rawinsonde data to develop logistic regression equations that forecast the daily thunderstorm probability at CCAFS. The Everitt (1999) equations showed an improvement in skill over the Neumann-Pfeffer thunderstorm index (Neumann 1971), which uses multiple linear regression, and also persistence and climatology forecasts. Lericos et al. (2002) developed lightning distributions over the Florida peninsula based on specific flow regimes. The flow regimes were inferred from the average wind direction in the 1000-700 mb layer at Miami (MIA), Tampa (TBW), and Jacksonville (JAX), Florida, and the lightning data were from the National Lightning Detection Network. The results suggested that the daily flow regime may be an important predictor of lightning occurrence on KSC/CCAFS.

Lambert, Winifred↗
NASA NTRS1992

A Backscatter Moessbauer Spectrometer (BaMS) for use on Mars

The use of Moessbauer spectroscopy for in situ analysis on the surface of Mars was proposed and the design and implementation of a backscatter Moessbauer spectrometer (BaMS) instrument suitable for planetary missions to the surfaces of Mars (MESUR), the Moon (Artemis and lunar outpost), asteroids, or other solid solar system objects is discussed. The BaMS instrument is designed to be capable of analysis of a sample for the mineralogy of its iron-bearing phases without any sample preparation. A requirement of lander missions to Mars is instrumentation for in situ mineralogical analyses. Such analyses provide data needed for primary characterization as to the type of surface materials present and by inference the processes that formed and subsequently modified them. For purposes of providing diagnostic information about naturally occurring materials, the element iron is particularly important because it is abundant and multivalent (primarily 0, +2, and +3 oxidation states). Knowledge of the oxidation state of iron and its distribution among iron-bearing mineralogies tightly constrains the types of materials present. The pivotal role of iron was already recognized in 1978 by COMPLEX, who recommended development of flight instruments that would identify mineralogy and the oxidation state of iron in planetary surface materials. The near-term U.S. strategy for the exploration of Mars is the MESUR (Mars Environmental SURvey) program, which entails emplacement of a network of small, long-lived surface landers. For the Moon, BaMS was recommended as part of a three-instrument landed payload for the Artemis missions, targeted for 1997. BaMS would prospect for ilmenite, an oxygen resource material, and provide data to assess the maturity of lunar soil. Because instrumental characteristics are low mass, low volume, and low power consumption, BaMS is suitable for implementation on even small landers and rovers, as are being envisioned in MESUR and Artemis concepts. In addition to providing highly diagnostic data, a BaMS analyzer is inherently simple and, as is highly desirable for remote operation, no sample preparation is required.

Agresti, D. G.↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
NASA NTRS2006

Real Time On-line Space Research Laboratory Environment Monitoring with Off-line Trend and Prediction Analysis

One of the responsibilities of the NASA Glenn Principal Investigator Microgravity Services is to support NASA sponsored investigators in the area of reduced-gravity acceleration data analysis, interpretation and the monitoring of the reduced-gravity environment on-board various carriers. With the International Space Station currently operational, a significant amount of acceleration data is being down-linked and processed on ground for both the space station onboard environment characterization (and verification) and scientific experiments. Therefore, to help principal investigator teams monitor the acceleration level on-board the International Space Station to avoid undesirable impact on their experiment, when possible, the NASA Glenn Principal Investigator Microgravity Services developed an artificial intelligence monitoring system, which detects in near real time any change in the environment susceptible to affect onboard experiments. The main objective of the monitoring system is to help research teams identify the vibratory disturbances that are active at any instant of time onboard the International Space Station that might impact the environment in which their experiment is being conducted. The monitoring system allows any space research scientist, at any location and at any time, to see the current acceleration level on-board the Space Station via the World Wide Web. From the NASA Glenn s Exploration Systems Division web site, research scientists can see in near real time the active disturbances, such as pumps, fans, compressor, crew exercise, re-boost, extra-vehicular activity, etc., and decide whether or not to continue operating or stopping (or making note of such activity for later correlation with science results) their experiments based on the g-level associated with that specific event. A dynamic graphical display accessible via the World Wide Web shows the status of all the vibratory disturbance activities with their degree of confidence as well as their g-level contribution to the environment. The system can detect both known and unknown vibratory disturbance activities. It can also perform trend analysis and prediction by analyzing past data over many Increments of the space station for selected disturbance activities. This feature can be used to monitor the health of onboard mechanical systems to detect and prevent potential system failure as well as for use by research scientists during their science results analysis. Examples of both real time on-line vibratory disturbance detection and off-line trend analysis are presented in this paper. Several soft computing techniques such as Kohonen s Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.

Jules, Kenol↗
NASA NTRS2019

Design of Experiment for the Measurement of Aerosol Droplet Size Distribution of Temperature-Controlled Thermally Atomized Printed Electronic Inks

In-Space Manufacturing (ISM) centers around NASA’s growing need and ability to produce space technologies on demand in space. As the future of long term presence in space and deep space exploration approach, fundamental questions of our dependence on earth resupply to Low Earth Orbit (LEO) remain unanswered. ISM is leading various effort to evaluate the feasibility of producing essential spares and redundant parts on demand to enable a sustainable space-based supply chain model for part resupply. Among the parts and systems being considered, Avionics form the neural network of modern day aircraft and space vehicles providing a wealth of information ranging from Guidance, Navigation, and Control (GN&C) systems to on board Environmental Control and Life Support (ECLS) systems. Recent advances in the use of Aerosol Jet Technology to print Avionics components ranging from electrical traces on a circuit board to complex transistors and sensors raise the possibility of using such technology to reproduce or recreate electronic parts on demand with the help of custom electronics 3D printers. The challenge herein lies within the ability of such printers to generate and deposit an aerosol of electronic material utilizing processes independent of or enhanced by gravity to ensure controllably identical or improved behavior of the aerosol in an International Space Station (ISS) laboratory and on the ground. The behavior as well as the hazards and properties associated with such aerosols in a microgravity environment must be understood well in order to merit a feasible approach to utilizing them for manufacturing in space. In this report, we outline the experimental setup of a modified conventional vaping device to be used as the ideal gravity independent thermal atomization mechanism to generate aerosol. Our objective is to identify the ideal mass, density, and volume of our aerosolized droplets of ink to conclude that there exist a threshold of aerosolized ink droplet sizes that are indeed independent of the effects of gravity and remain stable after atomization. We use a Malvern Spraytec® Spray Particle Size Analyzer to perform real-time laser diffraction measurement of our ink droplets during atomization. The droplet size between conductive ink, dielectric ink and vegetable glycerin have been measured and contrasted. Furthermore, the mechanism of thermal atomization versus traditional pneumatic and ultrasonic atomization for operation in microgravity have been explored.

Wu, Chih-Hao↗
NASA NTRSDate not supplied

Flux Comparison of Master-8 and Ordem 3.1 Modelled Space Debris Population

With ESA’s Meteoroid And Space debris Terrestrial Environment Reference (MASTER-8) model and NASA’s Orbital Debris Engineering Model(ORDEM) 3.1, the two premier orbital debris engineering models have been officially released. The two models come with significant enhancements and now represent state-of-the-art orbital debris modelling for their respective agencies. Both models provide the community with estimates of the space debris environment from low Earth orbit (LEO) up to at least geostationary altitude. The MASTER population is an event-based simulation of all known events that generate debris and objects that are part of the U.S. Space Surveillance Network (SSN) catalog, which provides coverage of objects with diameters down to approximately 10cm in LEO and 1 m in geosynchronous Earth orbit (GEO). Different models are used to simulate the artificial objects and their orbit evolution over time. These models are called “sources” since they assign an origin to each individual object and consist of fragments, solid rocket motor (SRM) remainders, sodium-potassium(NaK) droplets, paintflakes, ejecta, and multi-layer insulation (MLI) fragments. The objects from each source are characterized by having individual release mechanisms, as well as orbital distributions, material composition, size, and mass distributions. Dedicated radar and telescope observation data is used to calibrate the model for objects larger than 1 cm in LEO and larger than 10 cm in GEO. For calibrating the small-sized objects, below 1 cm, impact data from returned surfaces are analyzed. Because the >1cm object population is dominated by fragments, the fragmentation event database was updated to include new events, as well as re-evaluate past events. Special attention was drawn to re-evaluating theFengyun-1C anti-satellite test from 2007 and Cosmos-Iridium collision event from 2009 since these events shape the fragment population because of their severity. After 2009, the two largest fragmentations in terms of number of tracked debris are the Briz-M explosion in 2012 and the NOAA-16explosion in 2015. In total, there are 261 confirmed fragmentations in the database up to November 2016.The baseline population for ORDEM 3.1 is based on the U.S. SSN catalog, and observational datasets from radar, in situ, and optical sources provide a foundation from which the model populations are statistically extrapolated to smaller size regions. These regions are not well-covered by the SSN catalog yet may pose the greatest threat to operational spacecraft. The NASA Standard Satellite Breakup Model is used to generate fragments greater than 1mm from collisions and explosions, and these fragment populations are scaled using ground-based radar data. Specific major debris-producing events, including the Fengyun-1C, Iridium 33, and Cosmos2251 debris clouds, and unique populations, such as NaK droplets, were re-examined, modelled, and added to the ORDEM environment separately. Optical measurement data is used to model the GEO population down to 10 cm. The debris environment is propagated using NASA’s LEO-to-GEO Environment Debris model, and future explosions of intact objects and collisions involving objects greater than 10 cm are assessed statistically. The environment from a few millimetres down to 10 𝜇m is modelled using a special degradation model where small particles are generated from intact spacecraft and rocket bodies, then the populations are scaled to fit in situ cratering data from Space Shuttle returned surfaces. Fragments smaller than 10 cm are differentiated based on material density categories, i.e., high-, medium-, and low-density, to better characterize the potential debris risk posed to upper stages and spacecraft. This paper will discuss the MASTER and ORDEM approaches for modelling populations and compare fluxes for specific orbits, including sun-synchronous, ISS-altitude, geosynchronous transfer, and GEO. In the end, a conclusion is drawn towards the importance of having multiple fundamentally different, yet validated, models to estimate the space debris population.

Andre Horstmann↗
NASA NTRS2022

National Campaign (NC)-1 Strategic Conflict Management Simulation (X4) Community Based Rules

Projected demand for transportation services in the urban environment has led to the development of several Concepts of Operation for Urban Air Mobility, or UAM. UAM is a concept for the transportation of people and goods in the metropolitan environment using small, efficient aircraft over short distances as part of an expanding multimodal transportation network. UAM will leverage emerging technologies including electric Vertical Takeoff and Landing (eVTOL) aircraft, increasing levels of automation and a new operational paradigm in dense airspace where a set of agreed-upon rules govern the procedures and interactions defining a cooperative environment in which operators are entrusted with a range of functions typically conducted by Air Traffic Control (ATC). These rules, proposed in the FAA NextGen Office’s UAM Concept of Operations [1], were originally termed Community Based Rules or Community Business Rules (CBRs), and will in the future termed Cooperative Operating Practices (COPs); this document uses the original term, CBR. CBRs are a set of rules, developed by the UAM community and (where necessary) approved by the FAA that govern the interactions between UAM entities and limit the need for ATC services including, but not limited to, separation control by ATC, addressing a fundamental challenge to scaling UAM operations. UAM community development of CBRs is anticipated to accelerate the adoption of new practices while retaining the regulatory authority of the FAA within required domains (e.g., NAS safety, security and equal access). However, there currently exists no agreed industry forum or defined procedures for CBR development. Investigation of best practices for the development of UAM CBRs was identified by NASA and the FAA NextGen Office as a research need. In collaboration with seven industry partners, NASA participated in a series of simulations that investigated elements of the envisioned UAM operations, with a primary focus on Strategic Conflict Management (SCM). The development and conduct of cooperative UAM simulations with seven industry partners provided a unique opportunity to investigate CBR development practices. Development of CBRs for the UAM SCM simulations was conducted in parallel with simulation capability development and was closely related to requirements definition for the simulations. As such, the CBR development effort presented herein had two objectives: explore CBR development practices in collaboration with the industry partners and develop an initial set of UAM CBRs to support simulation requirements definition and development. Consensus was achieved among NASA and the industry partners on 24 CBRs that were developed to support the cooperative simulation operations across five topic areas: General (related to test requirements), Operational Intent, Conformance Monitoring, Demand Capacity Balancing, and Airspace Constraint Management. Additional topic areas and CBRs were discussed but were deemed outside the scope of the simulation; these are included in the appendices. A collaborative, iterative process was employed for developing the CBRs engaging both NASA and Industry; because CBR development is envisioned to be community-driven, opportunities were sought that provided industry partners leadership roles in developing CBRs. The following key observations and recommendations may aid the UAM industry in future CBR development efforts: - The lack of a defined process proved challenging initially. Stakeholder engagement in the early stages of CBR development was intermittent and may have been due to the lack of a clear definition of roles and responsibilities of those involved in the effort. - Industry leadership of CBR topic areas proved successful. Discussions in these topic areas were engaging, with alternate viewpoints freely discussed and detailed CBRs resulting. This points to the importance of identifying the best-suited leadership in technical areas for CBR development. - Discussions within a CBR topic area were typically dominated by only a few participants. Whereas all industry partners contributed to CBR development, within each topic area, technical leadership was evident even when not formally established. This observation may indicate that smaller, focused groups may be more effective in initial CBR development than an open forum or large standards development effort (although both maybe required prior to FAA review and approval for some CBRs). - Identifying suitable forums for initial UAM CBR development and identifying the most effective industry participants and leadership will be crucial for successful CBR development. Although the operational need for UAM CBRs may not be immediate, establishing the forums and leadership to define the processes for CBR development is a prudent early step to UAM realization.

Community Based Rules↗
NASA NTRS2000

Autonomy Architectures for a Constellation of Spacecraft

Until the past few years, missions typically involved fairly large expensive spacecraft. Such missions have primarily favored using older proven technologies over more recently developed ones, and humans controlled spacecraft by manually generating detailed command sequences with low-level tools and then transmitting the sequences for subsequent execution on a spacecraft controller. This approach toward controlling a spacecraft has worked spectacularly on previous missions, but it has limitations deriving from communications restrictions - scheduling time to communicate with a particular spacecraft involves competing with other projects due to the limited number of deep space network antennae. This implies that a spacecraft can spend a long time just waiting whenever a command sequence fails. This is one reason why the New Millennium program has an objective to migrate parts of mission control tasks onboard a spacecraft to reduce wait time by making spacecraft more robust. The migrated software is called a "remote agent" and has 4 components: a mission manager to generate the high level goals, a planner/scheduler to turn goals into activities while reasoning about future expected situations, an executive/diagnostics engine to initiate and maintain activities while interpreting sensed events by reasoning about past and present situations, and a conventional real-time subsystem to interface with the spacecraft to implement an activity's primitive actions. In addition to needing remote planning and execution for isolated spacecraft, a trend toward multiple-spacecraft missions points to the need for remote distributed planning and execution. The past few years have seen missions with growing numbers of probes. Pathfinder has its rover (Sojourner), Cassini has its lander (Huygens), and the New Millenium Deep Space 3 (DS3) proposal involves a constellation of 3 spacecraft for interferometric mapping. This trend is expected to continue to progressively larger fleets. For example, one mission proposed to succeed DS3 would have 18 spacecraft flying in formation in order to detect earth-sized planets orbiting other stars. A proposed magnetospheric constellation would involve 5 to 500 spacecraft in Earth orbit to measure global phenomena within the magnetosphere. This work describes and compares three autonomy architectures for a system that continuously plans to control a fleet of spacecraft using collective mission goals instead of goals or command sequences for each spacecraft. A fleet of self-commanding spacecraft would autonomously coordinate itself to satisfy high level science and engineering goals in a changing partially-understood environment making feasible the operation of tens or even a hundred spacecraft (such as for interferometry or plasma physics missions). The easiest way to adapt autonomous spacecraft research to controlling constellations involves treating the constellation as a single spacecraft. Here one spacecraft directly controls the others as if they were connected. The controlling "master" spacecraft performs all autonomy reasoning, and the slaves only have real-time subsystems to execute the master's commands and transmit local telemetry/observations. The executive/diagnostics module starts actions and the master's real-time subsystem controls the action either locally or remotely through a slave. While the master/slave approach benefits from conceptual simplicity, it relies on an assumption that the master spacecraft's executive can continuously monitor the slaves' real-time subsystems, and this relies on high-bandwidth highly-reliable communications. Since unintended results occur fairly rarely, one way to relax the bandwidth requirements involves only monitoring unexpected events in spacecraft. Unfortunately, this disables the ability to monitor for unexpected events between spacecraft and leads to a host of coordination problems among the slaves. Also, failures in the communications system can result in losing slaves. The other two architectures improve robustness while reducing communications by progressively distributing more of the other three remote agent components across the constellation. In a teamwork architecture, all spacecraft have executives and real-time subsystems - only the leader has the planner/scheduler and mission manager. Finally, distributing all remote agent components leads to a peer-to-peer approach toward constellation control.

Barrett, Anthony↗
NASA NTRSDate not supplied

Deep Space navigation for the BioSentinel spacecraft science orbit

BioSentinel is an astrobiology small spacecraft mission. The payload consists of two parts, the first has optical and microfluidics sensors, and the second is a Linear Energy Transfer spectrometer that has the objective to measure deep space radiation from events such as coronal mass ejections. The goal of the mission is to observe potential DNA damage due to the radiation in heliocentric space on the living organism Saccharomyces cerevisiae, which is a budding yeast. Two types of this living organism are included in the payload. The first is a natural type that is more radiation tolerant, while the second is a mutant strain that has a deficiency in a gene that allows DNA repair once damage occurs. The impact caused by the radiation on the DNA is compared to an identical sample aboard the International Space Station, as well as another identical sample at a laboratory on the ground. The BioSentinel mission consists of a 6U CubeSat currently ,as of January 2024, active in heliocentric orbit. The spacecraft was launched aboard the first SLS flight as part of the Artemis-I campaign in November 2022. After successful deployment from the launch vehicle, it performed a lunar flyby with an altitude of 406 km. The delta-V imparted by the flyby provided the necessary energy to achieve a heliocentric orbit, in an Earth-trailing pattern. The navigation analysis consisted of a Kalman-filter that utilized data from the Deep Space Network and the ESA Estrack network. All those antennas were needed since the Artemis-1 campaign included the deployment of several other cubesats, therefore the scheduling process required more antenna assets than usual due to simultaneous demands from various missions. The processed tracking data was later also refined with a smoother in order to obtain a more accurate solution. The type of tracking data included TCP, Sequential Range, Doppler and Range formats. The solar radiation pressure coefficient, as well as the delta-V from the deployment and the flyby were modeled to obtain suitable solutions that could decrease the position and velocity uncertainties at several steps along the mission concept of operations. The final product each time resulted in updated ephemeris files that were used by the mission and the antenna networks as the mission progressed. Once in the final science orbit, the utilized antennas are only from the DSN network and the data format is bounded to just TCP. Regular orbit determination is performed, every two weeks. The spacecraft is in a nominal well-known orbit, performing regular operations. This paper includes an analysis of the final science orbit, the techniques and procedures utilized to perform orbit determination and a description of the overall navigation campaign produced during the mission and, more specifically, during the final science operations in Deep Space.

BioSentinel↗
NASA NTRS2024

Launch Complex 39A, SWMU 008 2023 Long-Term Monitoring Report Kennedy Space Center, Florida

The 2023 Long-Term Monitoring (LTM) Report (LTMR) presents the annual and semi-annual groundwater monitoring results for Launch Complex 39A (LC39A), Solid Waste Management Unit (SWMU) 008, at Kennedy Space Center (KSC), Florida. The reporting period covered under this LTMR is from January to December 2023, and represents the first year of site-wide monitoring following Air Sparge (AS) system shutdown at the end of 2022. AS system operation at LC39A began operation in 2015. The AS system consisted of a total of 173 AS wells ranging in depth from 11 to 37 feet below land surface. The overall remedial objective for the AS Interim Measure was to actively decrease concentrations of COCs to less than their respective Florida Department of Environmental Protection Natural Attenuation Default Concentrations, so that LC39A can transition into an LTM program. This objective was achieved in 2022, with 2023 marking Year 1 of the LTM program to monitor the residual groundwater plume. VOC results indicate that COC concentrations have continued to decrease or stabilize, with no rebound observed since AS system shutdown. Vinyl chloride was the only COC detected above its GCTL, with the highest concentration detected at 8.0 μg/L. PFAS results detected 15 different PFAS compounds (out of 32 analyzed) between the six monitoring wells sampled. Two PFAS compounds, perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA), currently have FDEP Provisional GCTLs of 70 nanograms per liter (ng/L). PFOS was detected in one of the six samples above the FDEP Provisional GCTL at a concentration of 200 ng/L. PFOA was not detected above the FDEP Provisional GCTL in any of the six samples. PFOS and PFOA also currently have United States Environmental Protection Agency (USEPA) Maximum Contaminant Levels (MCLs) of 4 ng/L. PFOS was detected in all six samples above the USEPA MCL. PFOA was detected in three of the six samples above the USEPA MCL. Three other PFAS compounds, perfluorohexanesulfonic acid (PFHxS), perfluoro-n-nonanoic acid (PFNA), and hexafluoropropylene oxide dimer acid (GenX), currently have USEPA MCLs of 10 ng/L. PFHxS, PFNA, and GenX were not detected at concentrations greater than their respective USEPA MCLs in any of the six wells. Additional PFAS sampling will occur as part of a PFAS Site Assessment to be conducted in the future. Based on Year 1 LC39A LTM results, recommendations for 2024 are to continue with the second year of LTM in 2024 with the same monitoring well network as Year 1 (18 wells). The three wells that were on a semi-annual schedule will transition to an annual schedule to align with the remaining wells. The next annual event is scheduled for May 2024. The three wells that were found to be inadvertently destroyed will be properly abandoned and a replacement well in the vicinity of former monitoring well 21ST-MW0030I will be installed. This new well will be added to the LTM sampling plan upon installation.

Deborah M. Wilson↗