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NASA NTRS2012

Large Scale Experiments on Spacecraft Fire Safety

Full scale fire testing complemented by computer modelling has provided significant know how about the risk, prevention and suppression of fire in terrestrial systems (cars, ships, planes, buildings, mines, and tunnels). In comparison, no such testing has been carried out for manned spacecraft due to the complexity, cost and risk associated with operating a long duration fire safety experiment of a relevant size in microgravity. Therefore, there is currently a gap in knowledge of fire behaviour in spacecraft. The entire body of low-gravity fire research has either been conducted in short duration ground-based microgravity facilities or has been limited to very small fuel samples. Still, the work conducted to date has shown that fire behaviour in low-gravity is very different from that in normal-gravity, with differences observed for flammability limits, ignition delay, flame spread behaviour, flame colour and flame structure. As a result, the prediction of the behaviour of fires in reduced gravity is at present not validated. To address this gap in knowledge, a collaborative international project, Spacecraft Fire Safety, has been established with its cornerstone being the development of an experiment (Fire Safety 1) to be conducted on an ISS resupply vehicle, such as the Automated Transfer Vehicle (ATV) or Orbital Cygnus after it leaves the ISS and before it enters the atmosphere. A computer modelling effort will complement the experimental effort. Although the experiment will need to meet rigorous safety requirements to ensure the carrier vehicle does not sustain damage, the absence of a crew removes the need for strict containment of combustion products. This will facilitate the possibility of examining fire behaviour on a scale that is relevant to spacecraft fire safety and will provide unique data for fire model validation. This unprecedented opportunity will expand the understanding of the fundamentals of fire behaviour in spacecraft. The experiment is being developed by an international topical team that is collaboratively defining the experiment requirements and performing supporting analysis, experimentation and technology development. This paper presents the objectives, status and concept of this project.

Urban, David L.↗
NASA NTRS2012

Large Scale Experiments on Spacecraft Fire Safety

Full scale fire testing complemented by computer modelling has provided significant knowhow about the risk, prevention and suppression of fire in terrestrial systems (cars, ships, planes, buildings, mines, and tunnels). In comparison, no such testing has been carried out for manned spacecraft due to the complexity, cost and risk associated with operating a long duration fire safety experiment of a relevant size in microgravity. Therefore, there is currently a gap in knowledge of fire behaviour in spacecraft. The entire body of low-gravity fire research has either been conducted in short duration ground-based microgravity facilities or has been limited to very small fuel samples. Still, the work conducted to date has shown that fire behaviour in low-gravity is very different from that in normal gravity, with differences observed for flammability limits, ignition delay, flame spread behaviour, flame colour and flame structure. As a result, the prediction of the behaviour of fires in reduced gravity is at present not validated. To address this gap in knowledge, a collaborative international project, Spacecraft Fire Safety, has been established with its cornerstone being the development of an experiment (Fire Safety 1) to be conducted on an ISS resupply vehicle, such as the Automated Transfer Vehicle (ATV) or Orbital Cygnus after it leaves the ISS and before it enters the atmosphere. A computer modelling effort will complement the experimental effort. Although the experiment will need to meet rigorous safety requirements to ensure the carrier vehicle does not sustain damage, the absence of a crew removes the need for strict containment of combustion products. This will facilitate the possibility of examining fire behaviour on a scale that is relevant to spacecraft fire safety and will provide unique data for fire model validation. This unprecedented opportunity will expand the understanding of the fundamentals of fire behaviour in spacecraft. The experiment is being developed by an international topical team that is collaboratively defining the experiment requirements and performing supporting analysis, experimentation and technology development. This paper presents the objectives, status and concept of this project.

Urban, David↗
NASA NTRS2018

Robust Exploration and Commercial Missions to the Moon Using Nuclear Thermal Rocket Propulsion and In Situ Propellants Derived from Lunar Polar Ice Deposits

The nuclear thermal rocket (NTR) has frequently been identified as a key space asset required for the human exploration of Mars. This proven technology can also provide the affordable “access through cislunar space” necessary for commercial development and sustained human presence on the Moon. It is a demonstrated technology capable of generating both high thrust and high specific impulse (I(sub sp) ~900 s)— twice that of today’s best chemical rockets. Nuclear lunar transfer vehicles—consisting of a propulsion stage using three ~16.5-klb(sub f) small nuclear rocket engines (SNREs), an in-line propellant tank, plus the payload—can enable a variety of reusable lunar missions. These include cargo delivery and crewed lunar landing missions. Even weeklong “tourism” missions carrying passengers into lunar orbit for a day of sightseeing and picture taking are possible. The NTR can play an important role in the next phase of lunar exploration and development by providing a robust in-space lunar transportation system (LTS) that can allow initial outposts to evolve into settlements supported by a variety of commercial activities such as in situ propellant production used to supply strategically located propellant depots and transportation nodes. The processing of lunar polar ice (LPI) deposits (estimated to be ~2 billion metric tons) for propellant production—specifically liquid oxygen (LO(sub 2)) and hydrogen (LH(sub 2))—can significantly reduce the launch mass requirements from Earth and can enable reusable, surface-based lunar landing vehicles (LLVs) using LO(sub 2)/LH(sub 2) chemical rocket engines. Afterwards, LO(sub 2)/LH(sub 2) propellant depots can be established in lunar polar and equatorial orbits to supply the LTS. At this point a modified version of the conventional NTR called the LO(sub 2)-augmented NTR, or LANTR, would be introduced into the LTS, allowing bipropellant operation and leveraging the mission benefits of refueling with lunar-derived propellants (LDPs) for Earth return. The bipropellant LANTR engine utilizes the large divergent section of its nozzle as an “afterburner” into which oxygen is injected and supersonically combusted with nuclear preheated hydrogen emerging from the engine’s choked sonic throat—essentially “scramjet propulsion in reverse.” By varying the oxygen-to-hydrogen mixture ratio, LANTR engines can operate over a range of thrust and I(sub sp) values while the reactor core power level remains relatively constant. A LANTR-based LTS offers unique mission capabilities including short transit time crewed cargo transports. Even a “commuter” shuttle service may be possible, allowing “one-way” trip times to and from the Moon on the order of 36 hr or less. If only 1% of the postulated trapped water ice were available for use in lunar orbit, such a supply could support routine commuter flights to the Moon for many thousands of years. This report outlines an evolving LTS architecture that uses propellants derived from LPI and examines a variety of mission types and transfer vehicle designs along with their operating characteristics and increasing demands on LDP production as mission complexity and velocity change ΔV requirements increase. A comparison of the LDP production and mining requirements using LPI and volcanic glass to produce lunar-derived liquid oxygen (LUNOX) via the hydrogen reduction process is included, and the synergy with an evolving helium-3 mining industry is also discussed.

Borowski, Stanley K.↗
NASA NTRS2013

Data Mining for Understanding and Improving Decision-making Affecting Ground Delay Programs

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions.

Weather↗
NASA NTRS2013

Data Mining for Understanding and Impriving Decision-Making Affecting Ground Delay Programs

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions. The work reported in this paper is focused on ground delay programs. Data mining algorithms have the potential to develop associations between weather patterns and the corresponding ground delay program responses. If successful, they can be used to improve and standardize TFM decision resulting in better predictability of traffic flows on days with reliable weather forecasts. The approach here seeks to develop a set of data mining and machine learning models and apply them to historical archives of weather observations and forecasts and TFM initiatives to determine the extent to which the theory can predict and explain the observed traffic flow behaviors.

data mining↗
NASA NTRS2007

Adaptive Modeling of the International Space Station Electrical Power System

Software simulations provide NASA engineers the ability to experiment with spacecraft systems in a computer-imitated environment. Engineers currently develop software models that encapsulate spacecraft system behavior. These models can be inaccurate due to invalid assumptions, erroneous operation, or system evolution. Increasing accuracy requires manual calibration and domain-specific knowledge. This thesis presents a method for automatically learning system models without any assumptions regarding system behavior. Data stream mining techniques are applied to learn models for critical portions of the International Space Station (ISS) Electrical Power System (EPS). We also explore a knowledge fusion approach that uses traditional engineered EPS models to supplement the learned models. We observed that these engineered EPS models provide useful background knowledge to reduce predictive error spikes when confronted with making predictions in situations that are quite different from the training scenarios used when learning the model. Evaluations using ISS sensor data and existing EPS models demonstrate the success of the adaptive approach. Our experimental results show that adaptive modeling provides reductions in model error anywhere from 80% to 96% over these existing models. Final discussions include impending use of adaptive modeling technology for ISS mission operations and the need for adaptive modeling in future NASA lunar and Martian exploration.

Thomas, Justin Ray↗
NASA NTRS2016

Design of an Excavation Robot: Regolith Advanced Surface Systems Operations Robot (RASSOR) 2.0

To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. Acquiring the water resources on Mars will require mining significant quantities of regolith and this is not possible with the state-of-the-art low mass excavation systems. Low gravity environments (Mars = 3/8 G) and launch mass restrictions limit the traction and the resulting reaction force of the vehicle, making current terrestrial techniques impractical. This project addressed this challenge by developing a completely new technology that can mine large quantities of regolith on Mars. Recent measurements by the “Curiosity” rover on Mars have found that the regolith contains ~ 2% water by weight globally, ~4% in Jezero Crater (Human Architecture Team’s reference landing site), and much more at the poles(Leshin et al, 2013). RASSOR 2.0 is a planetary excavator, which has a mass of 66 kg, with a 0.38 kg vehicle mass per kilogram, per hour of excavation rate and power usage of 4 W per kg of regolith excavation rate. A single RASSOR 2.0 can excavate a minimum of 2.7 metric tons of regolith per day.This is accomplished by using counteracting excavation forces on two opposing digging implements called bucket drums and an autonomous mining control system. This work has addressed several major research areas outlined in the NASA Technology Area (TA) 04 Robotics & Autonomous Systems and TA 07 Human Destination Systems roadmaps. This project started at Technology Readiness Level (TRL) 4 as a low fidelity “proof of concept” prototype which has successfully demonstrated basic regolith simulant excavation functionality in a lab-scale gravity off load test. The foundational technology described here was awarded US patent number: US 9027265 for a “Zero horizontal reaction force excavator” on May 12, 2015.

RASSOR↗
NASA NTRS2014

Clustering Days with Similar Airport Weather Conditions

On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport.

traffic flow management↗
NASA NTRS2014

Clustering Days with Similar Airport Weather Conditions

On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport.

weather↗
NASA NTRS2017

Understanding the International Space Station Crew Perspective following Long-Duration Missions through Data Analytics & Visualization of Crew Feedback

The International Space Station (ISS) first became a home and research laboratory for NASA and International Partner crewmembers over 16 years ago. Each ISS mission lasts approximately 6 months and consists of three to six crewmembers. After returning to Earth, most crewmembers participate in an extensive series of 30+ debriefs intended to further understand life onboard ISS and allow crews to reflect on their experiences. Examples of debrief data collected include ISS crew feedback about sleep, dining, payload science, scheduling and time planning, health & safety, and maintenance. The Flight Crew Integration (FCI) Operational Habitability (OpsHab) team, based at Johnson Space Center (JSC), is a small group of Human Factors engineers and one stenographer that has worked collaboratively with the NASA Astronaut office and ISS Program to collect, maintain, disseminate and analyze this data. The database provides an exceptional and unique resource for understanding the "crew perspective" on long duration space missions. Data is formatted and categorized to allow for ease of search, reporting, and ultimately trending, in order to understand lessons learned, recurring issues and efficiencies gained over time. Recently, the FCI OpsHab team began collaborating with the NASA JSC Knowledge Management team to provide analytical analysis and visualization of these over 75,000 crew comments in order to better ascertain the crew's perspective on long duration spaceflight and gain insight on changes over time. In this initial phase of study, a text mining framework was used to cluster similar comments and develop measures of similarity useful for identifying relevant topics affecting crew health or performance, locating similar comments when a particular issue or item of operational interest is identified, and providing search capabilities to identify information pertinent to future spaceflight systems and processes for things like procedure development and training. In addition, the comments were scored for sentiment using a polarity scoring algorithm to identify both positive and negative comments for particular groups and clusters, allowing the team to make analytically informed decisions regarding future hardware and operating procedures. The use of polarity scoring with time series analysis was used to provide insight into how crew health and habitability is changing throughout various spaceflight increments or the station lifecycle as a whole. Finally, a visualization framework was developed to address the needs of the end users to search for and analyze comments by user, category or mission. This paper will discuss how the use of an analytical framework in conjunction with the current human interface, improved the understanding of crew perspective and shortened the time for analysis allowing for more informed decisions and rapid development of improvements. These methods are significantly optimizing the way that this valuable data can be assessed and applied to current and future spaceflight design and development. This collaboration allows the FCI OpsHab team to effectively analyze and share data in a more automated and timely fashion. Trends are no longer derived manually and can be illustrated effectively and accurately with these evolving techniques to an ever growing group of human spaceflight end users.

Bryant, Cody↗
NASA NTRS1972

Lewis Research Center earth resources program

The Lewis Research Center earth resources program efforts are in the areas of: (1) monitoring and rapid evaluation of water quality; (2) determining ice-type and ice coverage distribution to aid operations in a possible extension of the Great Lakes ice navigation and shipping season; (3) monitoring spread of crop viruses; and (4) extent of damage to strip mined areas as well as success of efforts to rehabilitate such areas for agriculture.

Mark, H.↗
NASA NTRS2017

Enhanced Gravity Tractor Derived from the Asteroid Redirect Mission for Deflecting Hypothetical Asteroid 2017 PDC

The Asteroid Redirect Mission (ARM) concept would robotically visit a hazardous-size near-Earth asteroid (NEA) with a rendezvous spacecraft, collect a multi-ton boulder and regolith samples from its surface, demonstrate an innovative planetary defense technique known as the Enhanced Gravity Tractor (EGT), and return the asteroidal material to a stable orbit around the Moon, allowing astronauts to explore the returned material in the mid-2020s. Launch of the robotic vehicle to rendezvous with the ARM reference target, NEA (341843) 2008 EV5, would occur in late 2021 [1,2]. The robotic segment of the ARM concept uses a 40 kW Solar Electric Propulsion (SEP) system with a specific impulse (Isp) of 2600 s, and would provide the first ever demonstration of the EGT technique on a hazardous-size asteroid and validate one method of collecting mass in-situ. The power, propellant, and thrust capability of the ARM robotic spacecraft can be scaled from a 40 kW system to 150 kW and 300 kW, which represent a likely future power level progression. The gravity tractor technique uses the gravitational attraction of a station-keeping spacecraft with the asteroid to provide a velocity change and gradually alter the trajectory of the asteroid. EGT utilizes a spacecraft with a high-efficiency propulsion system, such as Solar Electric Propulsion (SEP), along with mass collected in-situ to augment the mass of the spacecraft, thereby increasing the gravitational force between the objects [3]. As long as the spacecraft has sufficient thrust and propellant capability, the EGT force is only limited by the amount of in-situ mass collected and can be increased several orders of magnitude compared to the traditional gravity tractor technique in which only the spacecraft mass is used to generate the gravitational attraction force. This increase in available force greatly reduces the required deflection time. The collected material can be a single boulder, multiple boulders, regolith, or a combination of different material types using a variety of collection techniques. The EGT concept assumes that the ability to efficiently collect asteroid mass in-situ from a wide variety of asteroid types and environments is a future capability that will be developed and perfected in the future by the asteroid mining community. Additionally, it is anticipated that the mass collection would likely be performed by a single or multiple separable spacecraft to allow the SEP spacecraft to operate at safe distance from the asteroid.

Mazanek, Daniel D.↗
NASA NTRS2018

Systems Development, Data Mining, and Knowledge Discovery

The primary role of the Technical Integration Office is to provide technical solutions and services to different branches at KSC (Kennedy Space Center) and NASA program customers. The Technical Integration Office helps support KSC's operational needs by providing services such as digital connectivity, data center services, modelling and simulation tools, and communication video services. To learn the necessary technology and processes for my internship, I am working on two projects: learning C# (C Sharp programming language) with SQL and developing requirements for a PX (Communication and Public Engagement) inventory management system. To learn how to efficiently program with C#, my mentor assigned me to complete a sports informatics application that would let users discover facts and rules about various sports. The sports informatics application comes with search capabilities, report generating features, rule lists that users can modify, and diagrams for various sport strategies. To further build upon this project, I also developed a sport simulation game with the application. Once I begin more SQL-based projects, I will have the opportunity to learn how to manage databases and link SQL servers with C# programs. To develop requirements for the inventory management system, I have met with PX representatives and toured their storage facilities to see how they organize and store their items and equipment. I will also be meeting with representatives from the budget office to find out what information must be in a system budget report. The main components the system must have are customer request management, a search feature for items and equipment, report generation capabilities, and automated system warnings when item quantities reach or go below administrator-specified threshold levels. I have drafted questions and shall statements that will ultimately become part of the inventory management system requirements document.

Espinosa, Gabriel↗
NASA NTRS2009

Ares V: Shifting the Payload Design Paradigm

NASA is designing the Ares V heavy-lift cargo launch vehicle to send more crew and cargo to more places on the lunar surface than the 1960s-era Saturn V and to provide ongoing support for a permanent lunar outpost. This uncrewed cargo vehicle is designed to operate together with the Ares I crew vehicle (Figure 1). In addition to this role, however, its unmatched mass and volume capability represent a national asset for exploration, science, and commerce. The Ares V also enables or significantly enhances a large class of space missions not thought possible by scientists and engineers since the Saturn V program ended over 30 years ago. Compared to current systems, it will offer approximately five times the mass and volume to most orbits and locations. This should allow prospective mission planners to build robust payloads with margins that are three to five times the industry norm. The space inside the planned payload shroud has enough usable volume to launch the volumetric equivalent of approximately 10 Apollo Lunar Modules or approximately five equivalent Hubble Space Telescopes. This mass and volume capability to low-Earth orbit (LEO) enables a host of new scientific and observation platforms, such as telescopes, satellites, planetary and solar missions, as well as being able to provide the lift for future large in-space infrastructure missions, such as space based solar power and mining, Earth asteroid defense, propellant depots, etc. In addition, payload designers may also have the option of simplifying their designs or employing Ares V s payload as dumb mass to reduce technical and operational risk. The Ares V team is engaging the potential payload community now, two to three years before System Requirements Review (SRR), in order to better understand the additional requirements from the payload community that could be accommodated in the Ares V design in its conceptual phase. This paper will discuss the Ares V reference mission and capability, as well as its potential to perform other missions in the future.

Sumrall, Phil↗
NASA NTRS1978

Application of LANDSAT data and digital image processing

The author has identified the following significant results. Based on LANDSAT 1 and 2 data, applications in the fields of coal mining, lignite exploration, and thematic mapping in geology are demonstrated. The hybrid image processing system, its software, and its utilization for educational purposes is described. A pre-operational European satellite is proposed.

Bodechtel, J.↗
NASA NTRS2001

Simple, Scalable, Script-Based Science Processor (S4P)

The development and deployment of data processing systems to process Earth Observing System (EOS) data has proven to be costly and prone to technical and schedule risk. Integration of science algorithms into a robust operational system has been difficult. The core processing system, based on commercial tools, has demonstrated limitations at the rates needed to produce the several terabytes per day for EOS, primarily due to job management overhead. This has motivated an evolution in the EOS Data Information System toward a more distributed one incorporating Science Investigator-led Processing Systems (SIPS). As part of this evolution, the Goddard Earth Sciences Distributed Active Archive Center (GES DAAC) has developed a simplified processing system to accommodate the increased load expected with the advent of reprocessing and launch of a second satellite. This system, the Simple, Scalable, Script-based Science Processor (S42) may also serve as a resource for future SIPS. The current EOSDIS Core System was designed to be general, resulting in a large, complex mix of commercial and custom software. In contrast, many simpler systems, such as the EROS Data Center AVHRR IKM system, rely on a simple directory structure to drive processing, with directories representing different stages of production. The system passes input data to a directory, and the output data is placed in a "downstream" directory. The GES DAAC's Simple Scalable Script-based Science Processing System is based on the latter concept, but with modifications to allow varied science algorithms and improve portability. It uses a factory assembly-line paradigm: when work orders arrive at a station, an executable is run, and output work orders are sent to downstream stations. The stations are implemented as UNIX directories, while work orders are simple ASCII files. The core S4P infrastructure consists of a Perl program called stationmaster, which detects newly arrived work orders and forks a job to run the appropriate executable (registered in a configuration file for that station). Although S4P is written in Perl, the executables associated with a station can be any program that can be run from the command line, i.e., non-interactively. An S4P instance is typically monitored using a simple Graphical User Interface. However, the reliance of S4P on UNIX files and directories also allows visibility into the state of stations and jobs using standard operating system commands, permitting remote monitor/control over low-bandwidth connections. S4P is being used as the foundation for several small- to medium-size systems for data mining, on-demand subsetting, processing of direct broadcast Moderate Resolution Imaging Spectroradiometer (MODIS) data, and Quick-Response MODIS processing. It has also been used to implement a large-scale system to process MODIS Level 1 and Level 2 Standard Products, which will ultimately process close to 2 TB/day.

Lynnes, Christopher↗
NASA NTRS1985

A new ultra high energy gamma ray telescope at Ohya mine

The search for ultra high energy gamma rays coming from point sources is one of the main experimental aims. A fast air shower timing system was constructed at ICRR for the study of the angular resolution of the system and operated approximately half a year. The characteristics of the surface array of Ohya air shower telescope is described.

Aoki, T.↗
NASA NTRS2015

Technology Alignment and Portfolio Prioritization (TAPP): Advanced Methods in Strategic Analysis, Technology Forecasting and Long Term Planning for Human Exploration and Operations, Advanced Exploration Systems and Advanced Concepts

The Advanced Concepts Office (ACO) at NASA, Marshall Space Flight Center is expanding its current technology assessment methodologies. ACO is developing a framework called TAPP that uses a variety of methods, such as association mining and rule learning from data mining, structure development using a Technological Innovation System (TIS), and social network modeling to measure structural relationships. The role of ACO is to 1) produce a broad spectrum of ideas and alternatives for a variety of NASA's missions, 2) determine mission architecture feasibility and appropriateness to NASA's strategic plans, and 3) define a project in enough detail to establish an initial baseline capable of meeting mission objectives ACO's role supports the decision­-making process associated with the maturation of concepts for traveling through, living in, and understanding space. ACO performs concept studies and technology assessments to determine the degree of alignment between mission objectives and new technologies. The first step in technology assessment is to identify the current technology maturity in terms of a technology readiness level (TRL). The second step is to determine the difficulty associated with advancing a technology from one state to the next state. NASA has used TRLs since 1970 and ACO formalized them in 1995. The DoD, ESA, Oil & Gas, and DoE have adopted TRLs as a means to assess technology maturity. However, "with the emergence of more complex systems and system of systems, it has been increasingly recognized that TRL assessments have limitations, especially when considering [the] integration of complex systems." When performing the second step in a technology assessment, NASA requires that an Advancement Degree of Difficulty (AD2) method be utilized. NASA has used and developed or used a variety of methods to perform this step: Expert Opinion or Delphi Approach, Value Engineering or Value Stream, Analytical Hierarchy Process (AHP), Technique for the Order of Prioritization by Similarity to Ideal Solution (TOPSIS), and other multi­‐criteria decision-making methods. These methods can be labor-intensive, often contain cognitive or parochial bias, and do not consider the competing prioritization between mission architectures. Strategic Decision-Making (SDM) processes cannot be properly understood unless the context of the technology is understood. This makes assessing technological change particularly challenging due to the relationships "between incumbent technology and the incumbent (innovation) system in relation to the emerging technology and the emerging innovation system." The central idea in technology dynamics is to consider all activities that contribute to the development, diffusion, and use of innovations as system functions. Bergek defines system functions within a TIS to address what is actually happening and has a direct influence on the ultimate performance of the system and technology development. ACO uses similar metrics and is expanding these metrics to account for the structure and context of the technology. At NASA technology and strategy is strongly interrelated. NASA's Strategic Space Technology Investment Plan (SSTIP) prioritizes those technologies essential to the pursuit of NASA's missions and national interests. The SSTIP is strongly coupled with NASA's Technology Roadmaps to provide investment guidance during the next four years, within a twenty-year horizon. This paper discusses the methods ACO is currently developing to better perform technology assessments while taking into consideration Strategic Alignment, Technology Forecasting, and Long Term Planning.

Funaro, Gregory V.↗