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What is EA?
An enterprise architecture (EA) is a conceptual blueprint that defines the structure and operation of an organization. The intent of an enterprise architecture is to determine how an organization can most effectively achieve its current and future objectives. A single slide PowerPoint, size 36" x 48" has been produced for printing onto a poster board for display in the Pathways Internship showcase November 2018. This poster board presentation is a display of my internship from 2014 to present, to include my NIFS (NASA Interns, Fellowships, Scholarships) internship, ITSS (Information Technology Support Services) contract, and current Pathways internship. The presentation discusses my years producing deliverables for: the ISS (International Space Station) Anomaly Project in 2014, the FJET (Frangible Joint Empirical Test) database project in 2015, the CUI (Compact Unique Identifier) project in early 2016, and various Enterprise Architecture deliverables and contributions such as the Roadmap SQL (Structured Query Language) stored procedure and architectural views of Program data elements.
Human Research Program Requirements Document (Revision C)
The purpose of this document is to define, document, and allocate the Human Research Program (HRP) requirements to the HRP Program Elements. It establishes the flow-down of requirements from Exploration Systems Mission Directorate (ESMD) and Office of the Chief Health and Medical Officer (OCHMO) to the various Program Elements of the HRP to ensure that human research and technology countermeasure investments are made to insure the delivery of countermeasures and technologies that satisfy ESMD's and OCHMO's exploration mission requirements. Requirements driving the HRP work and deliverables are derived from the exploration architecture, as well as Agency standards regarding the maintenance of human health and performance. Agency human health and performance standards will define acceptable risk for each type and duration of exploration mission. It is critical to have the best available scientific and clinical evidence in setting and validating these standards. In addition, it is imperative that the best available evidence on preventing and mitigating human health and performance risks is incorporated into exploration mission and vehicle designs. These elements form the basis of the HRP research and technology development requirements and highlight the importance of HRP investments in enabling NASA's exploration missions. This PRD defines the requirements of the HRP which is comprised of the following major Program Elements: Behavioral Health and Performance (BHP), Exploration Medical Capability (ExMC), Human Health Countermeasures (HHC), ISS Medical Project (ISSMP), Space Human Factors and Habitability (SHFH), and Space Radiation (SR).
DCS: A Case Study of Identification of Knowledge and Disposition Gaps Using Principles of Continuous Risk Management
The Human Research Program (HRP) is formulated around the program architecture of Evidence-Risk-Gap-Task-Deliverable. Review of accumulated evidence forms the basis for identification of high priority risks to human health and performance in space exploration. Gaps in knowledge or disposition are identified for each risk, and a portfolio of research tasks is developed to fill them. Deliverables from the tasks inform the evidence base with the ultimate goal of defining the level of risk and reducing it to an acceptable level. A comprehensive framework for gap identification, focus, and metrics has been developed based on principles of continuous risk management and clinical care. Research towards knowledge gaps improves understanding of the likelihood, consequence or timeframe of the risk. Disposition gaps include development of standards or requirements for risk acceptance, development of countermeasures or technology to mitigate the risk, and yearly technology assessment related to watching developments related to the risk. Standard concepts from clinical care: prevention, diagnosis, treatment, monitoring, rehabilitation, and surveillance, can be used to focus gaps dealing with risk mitigation. The research plan for the new HRP Risk of Decompression Sickness (DCS) used the framework to identify one disposition gap related to establishment of a DCS standard for acceptable risk, two knowledge gaps related to DCS phenomenon and mission attributes, and three mitigation gaps focused on prediction, prevention, and new technology watch. These gaps were organized in this manner primarily based on target for closure and ease of organizing interim metrics so that gap status could be quantified. Additional considerations for the knowledge gaps were that one was highly design reference mission specific and the other gap was focused on DCS phenomenon.
Human Research Program: 2012 Fiscal Year Annual Report
Crew health and performance are critical to successful human exploration beyond low Earth orbit. Risks to health and performance include physiologic effects from radiation, hypogravity, and planetary environments, as well as unique challenges in medical treatment, human factors, and support of behavioral health. The scientists and engineers of the Human Research Program (HRP) investigate and reduce the greatest risks to human health and performance, and provide essential countermeasures and technologies for human space exploration. In its seventh year of operation, the HRP continued to refine its management architecture of evidence, risks, gaps, tasks, and deliverables. Experiments continued on the International Space Station (ISS), on the ground in analog environments that have features similar to those of spaceflight, and in laboratory environments. Data from these experiments furthered the understanding of how the space environment affects the human system. These research results contributed to scientific knowledge and technology developments that address the human health and performance risks. As shown in this report, HRP has made significant progress toward developing medical care and countermeasure systems for space exploration missions which will ultimately reduce risks to crew health and performance.
Overview of the Contributing Factor Map and How it can Support your Research
The Contributing Factor Map (CFM) is a visual representation of a taxonomy of factors influencing human health and performance in space. This presentation will give an overview of its development and its structure. It will describe various uses of the CFM that can support researchers working within the Human Research Program (HRP) Architecture of Evidence-Risk-Gap-Task-Deliverable. For example, during the Risk phase, the CFM can be used as a "menu" to help formulate a qualitative model of the factors contributing to specific consequences of concern. It provides a reference set of factors from across the operational, vehicle design, and human domains that otherwise might not be considered if approaching a risk from a specific domain perspective. Using the CFM as a reference can increase awareness of potential cross-disciplinary collaborations for overall risk mitigation. The CFM can also be used as a framework for identifying gaps in knowledge about a risk. This identification can support the subsequent development of gaps and tasks comprising the research plan aimed at risk mitigation. Examples of these types of applications of the CFM will be discussed and information on the support available to researchers in using it will be provided.
Mechanics Methodology for Textile Preform Composite Materials
NASA and its contractors have completed a program to develop a basic mechanics underpinning for textile composites. Three major deliverables were produced by the program: 1. A set of test methods for measuring material properties and design allowables; 2. Mechanics models to predict the effects of the fiber preform architecture and constituent properties on engineering moduli, strength, damage resistance, and fatigue life; and 3. An electronic data base of coupon type test data. This report describes these three deliverables.
The Habitable Worlds Observatory Technology Development Plan
The Habitable Worlds Observatory (HWO) is NASA’s next large space telescope, selected by the 2020 Decadal Survey in Astronomy and Astrophysics to search for and characterize habitable exoplanets while enabling a broad range of transformative astrophysics. In August 2024, the HWO Technology Maturation Project Office (TMPO) was formed to begin exploring the HWO science, technology, and mission architectures toward a Mission Concept Review (MCR) at the end of the decade. A primary deliverable of this effort is this technology development plan that identifies critical technologies that enable the mission, defines a process for assessing the readiness of those technologies, and outlines a strategy for developing those technologies to a Technology Readiness Level (TRL) of 5 before the MCR. This document covers technologies organized along three “tracks”: Coronagraph System technologies, Ultra-stable Telescope System technologies, and High-sensitivity Ultraviolet and Visible Instrumentation technologies. Additional emerging and enhancing technologies are also discussed.
The Habitable Worlds Observatory Technology Development Plan
The Habitable Worlds Observatory (HWO) is NASA’s next large space telescope, selected by the 2020 Decadal Survey in Astronomy and Astrophysics to search for and characterize habitable exoplanets while enabling a broad range of transformative astrophysics. In August 2024, the HWO Technology Maturation Project Office (TMPO) was formed to begin exploring the HWO science, technology, and mission architectures toward a Mission Concept Review (MCR) at the end of the decade. A primary deliverable of this effort is a technology development plan that identifies critical technologies that enable the mission, defines a process for assessing the readiness of those technologies, and outlines a strategy for developing those technologies to a Technology Readiness Level (TRL) of 5 before the MCR. In this paper we summarize the HWO technology development plan which comprises three “tracks”: Coronagraph System technologies, Ultra-stable Telescope System technologies, and High-sensitivity Ultraviolet and Visible Instrumentation technologies. Each track has completed an initial assessment of technology gaps, prioritized the gaps for investment, and laid out development roadmaps to mature candidate technologies to close those gaps.
Phase 1 Study for the Phobos L1 Operational Tether Experiment (PHLOTE)
NASA's Innovative Advanced Concepts (NIAC) program selected the PHLOTE mission proposal for a 2017 Phase 1 study. This PHLOTE study provides a credible example of an innovative mission architecture that can be used to enable many future missions throughout the solar system.One of the key Phase 1 deliverables identified in the PHLOTE proposal is this Study Report which is derived from the PHLOTE Concept of Operations (ConOps) Document developed during the study. The PHLOTE ConOps describes the PHLOTE mission and also provides a key systems engineering document to support future mission development.Since this report was produced as part of a NIAC feasibility study, it is intended to be publicly released at the completion of the NIAC study. Significant support was provided through the collaboration of NASA and PHLOTE team members from Space Technology And Research (STAR) Inc. and from the Clouds Architecture Office (Clouds AO). The NASA team was supported by summer and fall interns from five separate universities.
Extension of MBSE for Project Programmatics Management on the Asteroid Redirect Robotic Mission
Model-based Systems Engineering can be employed beyond management of the technical architecture development of a system to also manage the programmatics associated with Systems Engineering activities of a project. On NASA’s Asteroid Redirect Robotic Mission, MBSE has been successfully employed to manage, generate, and interact with the documentation-based deliverables associated with System Engineering activities. This has been involved in defining and tracking project document, milestone, and personnel metadata via the same modeling framework used for the technical architecture management. Additionally, it has focused on improving overall user experiences through linkage of documentation to technical content in the system model, automation of manually intensive tasks, and others stakeholderoriented features.
Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Translational controller results
The reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we also use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use two terms interchangeable to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS). This report is the deliverable D3 in our project activity and provides the test results of the fuzzy learning translational controller. This report is organized in six sections. Based on our experience and analysis with the attitude controller, we have modified the basic configuration of the reinforcement learning algorithm in ARIC as described in section 2. The shuttle translational controller and its implementation in fuzzy learning architecture is described in section 3. Two test cases that we have performed are described in section 4. Our results and conclusions are discussed in section 5, and section 6 provides future plans and summary for the project.
Quality of Service for Real-Time Applications Over Next Generation Data Networks
This project, which started on January 1, 2000, was funded by the NASA Glenn Research Center for duration of one year. The deliverables of the project included the following tasks: (1) Study of QoS mapping between the edge and core networks envisioned in the Next Generation networks will provide us with the QoS guarantees that can be obtained from next generation networks; (2) Buffer management techniques to provide strict guarantees to real-time end-to-end applications through preferential treatment to packets belonging to real-time applications. In particular, use of ECN to help reduce the loss on high bandwidth-delay product satellite networks needs to be studied; (3) Effect of Prioritized Packet Discard to increase goodput of the network and reduce the buffering requirements in the ATM switches; (4) Provision of new IP circuit emulation services over Satellite IP backbones using MPLS will be studied; and (5) Determine the architecture and requirements for internetworking ATN and the Next Generation Internet for real-time applications. The project has been completed on time. All the objectives and deliverables of the project have been completed. Research results obtained from this project have been published in a number of papers in journals, conferences, and technical reports, included in this document.
Managing Research in a Risk World
The Office of Chief Medical Officer (OCHMO) owns all human health and performance risks managed by the Human System Risk Board (HSRB). While the HSRB manages the risks, the Human Research Program (HRP) manages the research portion of the overall risk mitigation strategy for these risks. The HSRB manages risks according to a process that identifies and analyzes risks, plans risk mitigation and tracks and reviews the implementation of these strategies according to its decisions pertaining to the OCHMO risk posture. HRP manages risk research work using an architecture that describes evidence-based risks, gaps in our knowledge about characterizing or mitigating the risk, and the tasks needed to produce deliverables to fill the gaps and reduce the risk. A planning schedule reflecting expected research milestones is developed, and as deliverables and new evidence are generated, research progress is tracked via the Path to Risk Reduction (PRR) that reflects a risk's research plan for a design reference mission. HRP's risk research process closely interfaces with the HSRB risk management process. As research progresses, new deliverables and evidence are used by the HSRB in conjunction with other operational and non-research evidence to inform decisions pertaining to the likelihood and consequence of the risk and risk posture. Those decisions in turn guide forward work for research as it contributes to overall risk mitigation strategies. As HRP tracks its research work, it aligns its priorities by assessing the effectiveness of its contributions and maintaining specific core competencies that would be invaluable for future work for exploration missions.
How HRP Research Results Contribute to Human Space Exploration Risk Mitigation
In addition to the scientific value of publications derived from research, results from Human Research Program (HRP) research also support HRP’s goals of mitigating crew health and performance risks in space flight. Research results are used to build the evidence base characterizing crew health and performance risks, to support risk research plan development, to inform crew health and performance standards, and to provide technologies to programs for meeting those standards and optimizing crew health and performance in space. This talk will describe examples of how research results support these efforts. For example, HRP research results are used to revise or even create new standards for human space flight, which have been established to protect crew health and performance during flight, and prevent negative long-term health consequences due to space flight. These standards are based on the best available clinical and scientific evidence, as well as operational experience from previous space flight missions, and are reviewed as new evidence emerges. Research results are also used to update the HRP evidence base, which is comprised of a set of reports that provide a current record of the state of knowledge from research and operations for each of the defined human health and performance risks for future NASA exploration missions. A discussion of the role of evidence within the HRP architecture will also be presented. The scope of HRP research results extends well beyond publications, as they are used in several capacities to support HRP deliverables and, ultimately, the advancement of human space exploration beyond low-Earth orbit.
How HRP Research Results Contribute to Human Space Exploration Risk Mitigation
In addition to the scientific value of publications derived from research, results from Human Research Program (HRP) research also support HRP's goals of mitigating crew health and performance risks in space flight. Research results are used to build the evidence base characterizing crew health and performance risks, to support risk research plan development, to inform crew health and performance standards, and to provide technologies to programs for meeting those standards and optimizing crew health and performance in space. This talk will describe examples of how research results support these efforts. For example, HRP research results are used to revise or even create new standards for human space flight, which have been established to protect crew health and performance during flight, and prevent negative long-term health consequences due to space flight. These standards are based on the best available clinical and scientific evidence, as well as operational experience from previous space flight missions, and are reviewed as new evidence emerges. Research results are also used to update the HRP evidence base, which is comprised of a set of reports that provide a current record of the state of knowledge from research and operations for each of the defined human health and performance risks for future NASA exploration missions. A discussion of the role of evidence within the HRP architecture will also be presented. The scope of HRP research results extends well beyond publications, as they are used in several capacities to support HRP deliverables and, ultimately, the advancement of human space exploration beyond low-Earth orbit.
Analytical Assessment of the Reciprocating Feed System
A preliminary analysis tool has been created in Microsoft Excel to determine deliverable payload mass, total system mass, and performance of spacecraft systems using various types of propellant feed systems. These mass estimates are conducted by inserting into the user interface the basic mission parameters (e.g., thrust, burn time, specific impulse, mixture ratio, etc.), system architecture (e.g., propulsion system type and characteristics, propellants, pressurization system type, etc.), and design properties (e.g., material properties, safety factors, etc.). Different propellant feed and pressurization systems are available for comparison in the program. This gives the user the ability to compare conventional pressure fed, reciprocating feed system (RFS), autogenous pressurization thrust augmentation (APTA RFS), and turbopump systems with the deliverable payload, inert mass, and total system mass being the primary comparison metrics. Analyses of several types of missions and spacecraft were conducted and it was found that the RFS offers a performance improvement, especially in terms of delivered payload, over conventional pressure fed systems. Furthermore, it is competitive with a turbopump system at low to moderate chamber pressures, up to approximately 1,500 psi. Various example cases estimating the system mass and deliverable payload of several types of spacecraft are presented that illustrate the potential system performance advantages of the RFS. In addition, a reliability assessment of the RFS was conducted, comparing it to simplified conventional pressure fed and turbopump systems, based on MIL-STD 756B; these results showed that the RFS offers higher reliability, and thus substantially longer periods between system refurbishment, than turbopump systems, and is competitive with conventional pressure fed systems. This is primarily the result of the intrinsic RFS fail-operational capability with three run tanks, since the system can operate with just two run tanks.
Datascope to Enable Earth Independent Medical Operations (EIMO)
BACKGROUND: NASA has amassed sixty years of knowledge and experience relevant to maintenance of crew health and performance in low earth orbit. The Apollo Program introduced the importance of ensuring progressively autonomous operational capability. Earth Independent Medical Operations (EIMO) will require a gradual shift in the balance of medical responsibility, management, and authority from terrestrial to space-based assets. Terrestrial assets will continue to be essential for pre-mission screening and planning in addition to maintenance of crew health and performance. However, new capabilities are needed to enable EIMO and the amount of data required to support these systems, and mitigate the impacts of data transmission delays and reduced bandwidth coupled with lack of cloud-like resources and on-board computing capacity that is currently unclear or operationally insufficient. OVERVIEW: The overall goal of EIMO is to develop artificial intelligence (AI)-based solutions to analyze crew health and performance data utilizing a clinical decision support system (CDSS) to provide crew medical officers (CMO) with the equivalent of real-time, on-board medical consults. The EIMO ecosystem is envisioned as a “system of systems” where embedded reference databases and real-time data streams from multiple input vectors continuously and seamlessly assess crew health and performance. EIMO will be designed to make recommendations to the CMO using multi-modal AI-based natural language processing and machine learning methods with interoperability to push/pull data within and between multiple vehicle and habitat architectures. DISCUSSION: Data flows and storage/retrieval capacity are severely constrained during space missions and the challenges will become even greater during exploration missions. Just as each past program from Mercury to the International Space Station (ISS) required rethinking the interaction between ground-based controllers and space-based crew, so too will future missions to the Moon and Mars. While the NASA High-Performance Spaceflight Computing Processor project aims to increase computational capacity by 100 times over current spaceflight computers, the projected deliverable still lags considerably behind what will be needed to enable an AI-driven CDSS. Restrictions in processing speed and data storage capacity, coupled with transmission bottlenecks and delays, necessitate definition and optimization of an integrated data architecture to enable a progressively autonomous medical capability.