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Provide a high-level awareness of the Human Systems Integration(HSI) process and the human readiness complement of technology readiness level(TRL) to manage and maximizing system performance.
Since 2000, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) have used a local data integration system (LDIS) as part of their forecast and warning operations. Each has benefited from 3-dimensional analyses that are delivered to forecasters every 15 minutes across the peninsula of Florida. The intent is to generate products that enhance short-range weather forecasts issued in support of NWS MLB and SMG operational requirements within East Central Florida. The current LDIS uses the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS) package as its core, which integrates a wide variety of national, regional, and local observational data sets. It assimilates all available real-time data within its domain and is run at a finer spatial and temporal resolution than current national- or regional-scale analysis packages. As such, it provides local forecasters with a more comprehensive and complete understanding of evolving fine-scale weather features. Recent efforts have been undertaken to update the LDIS through the formal tasking process of NASA's Applied Meteorology Unit. The goals include upgrading LDIS with the latest version of ADAS, incorporating new sources of observational data, and making adjustments to shell scripts written to govern the system. A series of scripts run a complete modeling system consisting of the preprocessing step, the main model integration, and the post-processing step. The preprocessing step prepares the terrain, surface characteristics data sets, and the objective analysis for model initialization. Data ingested through ADAS include (but are not limited to) Level II Weather Surveillance Radar- 1988 Doppler (WSR-88D) data from six Florida radars, Geostationary Operational Environmental Satellites (GOES) visible and infrared satellite imagery, surface and upper air observations throughout Florida from NOAA's Earth System Research Laboratory/Global Systems Division/Meteorological Assimilation Data Ingest System (MADIS), as well as the Kennedy Space Center ICape Canaveral Air Force Station wind tower network. The scripts provide NWS MLB and SMG with several options for setting a desirable runtime configuration of the LDIS to account for adjustments in grid spacing, domain location, choice of observational data sources, and selection of background model fields, among others. The utility of an improved LDIS will be demonstrated through postanalysis warm and cool season case studies that compare high-resolution model output with and without the ADAS analyses. Operationally, these upgrades will result in more accurate depictions of the current local environment to help with short-range weather forecasting applications, while also offering an improved initialization for local versions of the Weather Research and Forecasting model.
NASA's Exploration Initiative will require development of many new elements to constitute a robust system of systems. New launch vehicles are needed to place cargo and crew in stable low earth orbit. This paper examines the systems integration processes NASA is utilizing to ensure integration and control of propulsion and non-propulsion elements within NASA's Crew Launch Vehicle (CLV). The objective of the CLV is to provide the transportation capabilities to meet the Constellation Program requirements for delivering a Crew Exploration Vehicle (CEV) or other payload to Low Earth Orbit (LEO) in support of the lunar and Mars missions. The CLV must successfully provide the capability within cost and schedule with an acceptable risk approach. This paper will describe in detail the systems engineering management processes that will be applied to assure CLV Project success through complete and efficient technical integration. Discussion of specific processes for requirements development and verification, integrated design and analysis, integrated simulation and testing and the integration of reliability, maintainability and supportability (RMS) into the design will also be included. The CLV Project is broken logically into elements by the major hardware groupings, and associated management, system engineering, and integration functions. The processes to be described herein are designed to integrate within these CLV elements and among the other Constellation projects. Launch vehicle stack integration (CLV to CEV, and Ground and Flight Operations integration) throughout the life cycle, including integrated vehicle performance through orbital insertion, recovery of the first stage, and reentry of the upper stage will also be discussed. The processes for decomposing requirements to the Elements and ensuring that requirements have been correctly validated, decomposed, allocated, and that the verification requirements are properly defined to ensure that the system design meets requirements will be discussed.
A sound basis to guide the community in the conception and implementation of ISHM (Integrated System Health Management) capability in operational systems was provided. The concept of "ISHM Model of a System" and a related architecture defined as a unique Data, Information, and Knowledge (DIaK) architecture were described. The ISHM architecture is independent of the typical system architecture, which is based on grouping physical elements that are assembled to make up a subsystem, and subsystems combine to form systems, etc. It was emphasized that ISHM capability needs to be implemented first at a low functional capability level (FCL), or limited ability to detect anomalies, diagnose, determine consequences, etc. As algorithms and tools to augment or improve the FCL are identified, they should be incorporated into the system. This means that the architecture, DIaK management, and software, must be modular and standards-based, in order to enable systematic augmentation of FCL (no ad-hoc modifications). A set of technologies (and tools) needed to implement ISHM were described. One essential tool is a software environment to create the ISHM Model. The software environment encapsulates DIaK, and an infrastructure to focus DIaK on determining health (detect anomalies, determine causes, determine effects, and provide integrated awareness of the system to the operator). The environment includes gateways to communicate in accordance to standards, specially the IEEE 1451.1 Standard for Smart Sensors and Actuators.
The Desert-Urban System Integrated Atmospheric Monsoon (DUSTIEAIM) campaign is a groundbreaking, high-impact scientific mission that will transform how we understand and respond to energy and water challenges in one of America’s fastest-growing and most heat-stressed urban regions: Phoenix, Arizona. Starting in April 2026, this 18-month field campaign harnesses the full power of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility and an interdisciplinary science team including national laboratories, universities, and agencies with a broad range of subject-matter expertise. With cutting-edge instruments, active and passive ground-based sensors, radars, and integrated modeling, DUSTIEAIM will deliver the most comprehensive environmental data set ever collected for a desert-urban-agricultural interface.
The purpose of this project was to create Human-System Integration (HSI) scorecard software, which could be utilized to validate that human factors have been considered early in hardware/system specifications and design. The HSI scorecard is partially based upon the revised Human Rating Requirements (HRR) intended for NASA's Constellation program. This software scorecard will allow for quick appraisal of HSI factors, by using visual aids to highlight low and rapidly changing scores. This project consisted of creating a user-friendly Visual Basic program that could be easily distributed and updated, to and by fellow colleagues. Updating the Microsoft Word version of the HSI scorecard to a computer application will allow for the addition of useful features, improved easy of use, and decreased completion time for user. One significant addition is the ability to create Microsoft Excel graphs automatically from scorecard data, to allow for clear presentation of problematic areas. The purpose of this paper is to describe the rational and benefits of creating the HSI scorecard software, the problems and goals of project, and future work that could be done.
NASA's Exploration Initiative will require development of many new elements to constitute a robust system of systems. New launch vehicles are needed to place cargo and crew in stable Low Earth Orbit (LEO). This paper examines the systems integration processes NASA is utilizing to ensure integration and control of propulsion and nonpropulsion elements within NASA's Crew Launch Vehicle (CLV), now known as the Ares I. The objective of the Ares I is to provide the transportation capabilities to meet the Constellation Program requirements for delivering a Crew Exploration Vehicle (CEV) or other payload to LEO in support of the lunar and Mars missions. The Ares I must successfully provide this capability within cost and schedule, and with an acceptable risk approach. This paper will describe the systems engineering management processes that will be applied to assure Ares I Project success through complete and efficient technical integration. Discussion of technical review and management processes for requirements development and verification, integrated design and analysis, integrated simulation and testing, and the integration of reliability, maintainability and supportability (RMS) into the design will also be included. The Ares I Project is logically divided into elements by the major hardware groupings, and associated management, system engineering, and integration functions. The processes to be described herein are designed to integrate within these Ares I elements and among the other Constellation projects. Also discussed is launch vehicle stack integration (Ares I to CEV, and Ground and Flight Operations integration) throughout the life cycle, including integrated vehicle performance through orbital insertion, recovery of the first stage, and reentry of the upper stage. The processes for decomposing requirements to the elements and ensuring that requirements have been correctly validated, decomposed, and allocated, and that the verification requirements are properly defined to ensure that the system design meets requirements, will be discussed.
This document serves as the final report for the SMAAART AFC-Enabled Vertical Tail System Integration Study. Included are the ground rule assumptions which have gone into the study, layouts of the baseline and AFC-enabled configurations, critical sizing information, system requirements and architectures, and assumed system properties that result in an NPV assessment of the two candidate AFC technologies.
A solar electric propulsion system integration technology study is discussed. Detailed analyses in support of the solar electric propulsion module were performed. The thrust subsystem functional description is presented. The space vehicle and the space mission to which the propulsion system is applied are analyzed.
A novel approach of integrated system identification and modal state estimation is proposed for control of linear dynamical systems including flexible space structures. There are four steps involved in this approach. First, the relation between a stochastic state space model of a dynamical system and the coefficients of its autoregressive model with exogenous input is derived. Second, an adaptive least-squares transversal predictor is used to estimate the coefficients of the model. Third, a state space model and a steady state Kalman filter gain of the dynamical system are then identified from the coefficients of the model by using the eigensystem realization algorithm. Fourth, a modal state estimator is constructed using the modal parameters of the identified model. On-line implementation of this algorithm can continually improve the modal parameters and the filter gain. It can also gradually update the system model when the system characteristics are slowly changing. A numerical example is used to illustrate the feasibility of the new approach.
The Ares I Crew Launch Vehicle (CLV) is being developed by the U.S. National Aeronautics and Space Administration (NASA) to provide crew access to the International Space Station (ISS) and, together with the Ares V Cargo Launch Vehicle (CaLV), serves as one component of a future launch capability for human exploration of the Moon. During the system requirements definition process and early design cycles, NASA defined and began implementing plans for integrated ground and flight testing necessary to achieve the first human launch of Ares I. The individual Ares I flight hardware elements: the first stage five segment booster (FSB), upper stage, and J-2X upper stage engine, will undergo extensive development, qualification, and certification testing prior to flight. Key integrated system tests include the Main Propulsion Test Article (MPTA), acceptance tests of the integrated upper stage and upper stage engine assembly, a full-scale integrated vehicle dynamic test (IVDT), aerodynamic testing to characterize vehicle performance, and integrated testing of the avionics and software components. The Ares I-X development flight test will provide flight data to validate engineering models for aerodynamic performance, stage separation, structural dynamic performance, and control system functionality. The Ares I-Y flight test will validate ascent performance of the first stage, stage separation functionality, and a highaltitude actuation of the launch abort system (LAS) following separation. The Orion-1 flight test will be conducted as a full, un-crewed, operational flight test through the entire ascent flight profile prior to the first crewed launch.
The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) Plum Brook Station (PBS) Space Propulsion Research Facility, commonly referred to as B-2, is NASA s third largest thermal vacuum facility. It is the largest designed to store and transfer large quantities of liquid hydrogen and liquid oxygen, and is perfectly suited to support developmental testing of chemical propulsion systems as well as fully integrated stages. The facility is also capable of providing thermal-vacuum simulation services to support testing of large lightweight structures, Cryogenic Fluid Management (CFM) systems, electric propulsion test programs, and other In-Space propulsion programs. A recently completed integrated system test demonstrated the refurbished thermal vacuum capabilities of the facility. The test used the modernized data acquisition and control system to monitor the facility during pump down of the vacuum chamber, operation of the liquid nitrogen heat sink (or cold wall) and the infrared lamp array. A vacuum level of 1.3x10(exp -4)Pa (1x10(exp -6)torr) was achieved. The heat sink provided a uniform temperature environment of approximately 77 K (140deg R) along the entire inner surface of the vacuum chamber. The recently rebuilt and modernized infrared lamp array produced a nominal heat flux of 1.4 kW/sq m at a chamber diameter of 6.7 m (22 ft) and along 11 m (36 ft) of the chamber s cylindrical vertical interior. With the lamp array and heat sink operating simultaneously, the thermal systems produced a heat flux pattern simulating radiation to space on one surface and solar exposure on the other surface. The data acquired matched pretest predictions and demonstrated system functionality.
The purpose of this document is to describe a simple method of integrating Inertial Navigation System (INS) information with Global Positioning System (GPS) information for an improved estimate of vehicle attitude and position. A simple two dimensional (2D) case is considered. The attitude estimates are derived from sensor data and used in the estimation of vehicle position and velocity through dead reckoning within the INS. The INS estimates are updated with GPS estimates using a Kalman filter. This tutorial is intended for the novice user with a focus on bringing the reader from raw sensor measurements to an integrated position and attitude estimate. An application is given using a remotely controlled ground vehicle operating in assumed 2D environment. The theory is developed first followed by an illustrative example.
The Implementation of Human Systems Integration (HSI) presents challenges within the acquisition community for two reasons. The first is that misconceptions of HSI still exist, with many Program Managers (PMs) and leadership uncertain of the value or where to begin. The second is due to an unbalanced approach to HSI in its own framework. These implementation challenges lead to barriers in the early prevention of mishaps. Understanding HSI practices and how they should be implemented in the Acquisition Product Life Cycle (PLC) has been a challenge across the government, leaving the value of HSI unknown and misunderstood with Program Managers. In the case for many acquisition programs, HSI is not implemented in early design, losing the perspective on human capabilities and limitations, creating impacts on human-centered design. Expectations in human performance are not clearly set and operations are baselined with no margin for changes in technology and processes that will affect system performance. The HSI framework addresses total system performance holistically using collaboration as the primary tool. The goal is to create a system with efficiencies while minimizing risk to the operators, maintainers, and support personnel, as well as any collateral personnel and systems. To accomplish this, HSI should be implemented as part of preemptive measures to minimize potential human error and mishaps during the operation phase. Investigative and assessment tools exist that consider events, issues, and other outside influences of a system that may not fall under the current construct of the HSI domains, leaving gaps in early HSI implementation and affecting the prevention of human errors and mishaps. This presentation will outline what NASA HSI is doing to support Early HSI implementation and Operational Performance shifts that affect human performance.
This is part 3 of the conference proceedings on rotorcraft technology. This volume is divided into areas on systems integration, research aircraft, and industry. Representative titles from each area are: system analysis in rotorcraft design, the past decade; rotorcraft flight research with emphasis on rotor systems; and an overview of key technology thrusts at Bell Helicopter Textron.
The document provides the Human System Integration(HSI) high-level functional C3 HSI requirements for the interface to the pilot. Description includes (1) the information required by the pilot to have knowledge C3 system status, and (2) the control capability needed by the pilot to obtain C3 information. Fundamentally, these requirements provide the candidate C3 technology concepts with the necessary human-related elements to make them compatible with human capabilities and limitations. The results of the analysis describe how C3 operations and functions should interface with the pilot to provide the necessary C3 functionality to the UA-pilot system. Requirements and guidelines for C3 are partitioned into three categories: (1) Pilot-Air Traffic Control (ATC) Voice Communications (2) Pilot-ATC Data Communications, and (3) command and control of the unmanned aircraft (UA). Each requirement is stated and is supported with a rationale and associated reference(s).
The New Millennium Program Space Technology 6 Project Autonomous Sciencecraft software implements an integrated system for autonomous planning and execution of scientific, engineering, and spacecraft-coordination actions. A prior version of this software was reported in "The TechSat 21 Autonomous Sciencecraft Experiment" (NPO-30784), NASA Tech Briefs, Vol. 28, No. 3 (March 2004), page 33. This software is now in continuous use aboard the Earth Orbiter 1 (EO-1) spacecraft mission and is being adapted for use in the Mars Odyssey and Mars Exploration Rovers missions. This software enables EO-1 to detect and respond to such events of scientific interest as volcanic activity, flooding, and freezing and thawing of water. It uses classification algorithms to analyze imagery onboard to detect changes, including events of scientific interest. Detection of such events triggers acquisition of follow-up imagery. The mission-planning component of the software develops a response plan that accounts for visibility of targets and operational constraints. The plan is then executed under control by a task-execution component of the software that is capable of responding to anomalies.
Integrated cryogenic storage system for space shuttle