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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Dual-Use Aspects of System Health Management

System Health Management functionality is an essential part of any space launch system. Health management functionality is an integral part of mission reliability, since it is needed to verify the reliability before the mission starts. Health Management is also a key factor in life cycle cost reduction and in increasing system availability. The degree of coverage needed by the system and the degree of coverage made available at a reasonable cost are critical parameters of a successful design. These problems are not unique to the launch vehicle world. In particular, the Intelligent Vehicle Highway System, commercial aircraft systems, train systems, and many types of industrial production facilities require various degrees of system health management. In all of these applications, too, the designers must balance the benefits and costs of health management in order to optimize costs. The importance of an integrated system is emphasized. That is, we present the case for considering health management as an integral part of system design, rather than functionality to be added on at the end of the design process. The importance of maintaining the system viewpoint is discussed in making hardware and software tradeoffs and in arriving at design decisions. We describe an approach to determine the parameters to be monitored in any system health management application. This approach is based on Design of Experiments (DOE), prototyping, failure modes and effects analyses, cost modeling and discrete event simulation. The various computer-based tools that facilitate the approach are discussed. The approach described originally was used to develop a fault tolerant avionics architecture for launch vehicles that incorporated health management as an integral part of the system. Finally, we discuss generalizing the technique to apply it to other domains. Several illustrations are presented.

Owens, P. R.↗
NASA NTRS1998

Aircraft/Air Traffic Management Functional Analysis Model: Technical Description

The Aircraft/Air Traffic Management Functional Analysis Model, Version 2.0 (FAM 2.0), is a discrete event simulation model designed to support analysis of alternative concepts in air traffic management and control. FAM 2.0 was developed by the Logistics Management Institute (LMI) under a National Aeronautics and Space Administration (NASA) contract. This document provides a technical description of FAM 2.0 and its computer files to enable the modeler and programmer to make enhancements or modifications to the model. Those interested in a guide for using the model in analysis should consult the companion document, Aircraft/Air Traffic Management Functional Analysis Model, Version 2.0 Users Manual.

Etheridge, Melvin↗
NASA NTRS1998

Aircraft/Air Traffic Management Functional Analysis Model: User's Guide - Version 2.0

The Aircraft/Air Traffic Management Functional Analysis Model, Version 2.0 (FAM 2.0), is a discrete event simulation model designed to support analysis of alternative concepts in air traffic management and control. FAM 2.0 was developed by the Logistics Management Institute (LMI) a National Aeronautics and Space Administration (NASA) contract. This document provides a guide for using the model in analysis. Those interested in making enhancements or modification to the model should consult the companion document, Aircraft/Air Traffic Management Functional Analysis Model, Version 2.0 Technical Description.

Etheridge, Melvin↗
NASA NTRS1998

An Analysis of an Improved Bus-Based Multiprocessor Architecture

This paper analyses the effectiveness of a hybrid multiprocessing/multicomputing architecture that is based upon a single-board-computer multiprocessor (SBCM) architecture. Based upon empirical analysis using discrete event simulations and Monte Carlo techniques, this hybrid architecture, called the enhanced single-board-computer multiprocessor (ESBCM), is shown to have improved performance and scalability characteristics over current SBCM designs.

Ricks, Kenneth G.↗
NASA NTRS2003

An Advanced Hierarchical Hybrid Environment for Reliability and Performance Modeling

The key issue we intended to address in our proposed research project was the ability to model and study logical and probabilistic aspects of large computer systems. In particular, we wanted to focus mostly on automatic solution algorithms based on a state-space exploration as their first step, in addition to the more traditional discrete-event simulation approaches commonly employed in industry. One explicitly-stated goal was to extend by several orders of magnitude the size of models that can be solved exactly, using a combination of techniques: 1) Efficient exploration and storage of the state space using new data structures that require an amount of memory sublinear in the number states; and 2) Exploitation of the existing symmetries in the matrices describing the system behavior using Kronecker operators. Not only we have been successful in achieving the above goals, but we exceeded them in many respects.

Ciardo, Gianfranco↗
NASA NTRS2003

Weight and the Future of Space Flight Hardware Cost Modeling

Weight has been used as the primary input variable for cost estimating almost as long as there have been parametric cost models. While there are good reasons for using weight, serious limitations exist. These limitations have been addressed by multi-variable equations and trend analysis in models such as NAFCOM, PRICE, and SEER; however, these models have not be able to address the significant time lags that can occur between the development of similar space flight hardware systems. These time lags make the cost analyst's job difficult because insufficient data exists to perform trend analysis, and the current set of parametric models are not well suited to accommodating process improvements in space flight hardware design, development, build and test. As a result, people of good faith can have serious disagreement over the cost for new systems. To address these shortcomings, new cost modeling approaches are needed. The most promising approach is process based (sometimes called activity) costing. Developing process based models will require a detailed understanding of the functions required to produce space flight hardware combined with innovative approaches to estimating the necessary resources. Particularly challenging will be the lack of data at the process level. One method for developing a model is to combine notional algorithms with a discrete event simulation and model changes to the total cost as perturbations to the program are introduced. Despite these challenges, the potential benefits are such that efforts should be focused on developing process based cost models.

Prince, Frank A.↗
NASA NTRS2004

Turnaround Time Modeling for Conceptual Rocket Engines

Recent years have brought about a paradigm shift within NASA and the Space Launch Community regarding the performance of conceptual design. Reliability, maintainability, supportability, and operability are no longer effects of design; they have moved to the forefront and are affecting design. A primary focus of this shift has been a planned decrease in vehicle turnaround time. Potentials for instituting this decrease include attacking the issues of removing, refurbishing, and replacing the engines after each flight. less, it is important to understand the operational affects of an engine on turnaround time, ground support personnel and equipment. One tool for visualizing this relationship involves the creation of a Discrete Event Simulation (DES). A DES model can be used to run a series of trade studies to determine if the engine is meeting its requirements, and, if not, what can be altered to bring it into compliance. Using DES, it is possible to look at the ways in which labor requirements, parallel maintenance versus serial maintenance, and maintenance scheduling affect the overall turnaround time. A detailed DES model of the Space Shuttle Main Engines (SSME) has been developed. Trades may be performed using the SSME Processing Model to see where maintenance bottlenecks occur, what the benefits (if any) are of increasing the numbers of personnel, or the number and location of facilities, in addition to trades previously mentioned, all with the goal of optimizing the operational turnaround time and minimizing operational cost. The SSME Processing Model was developed in such a way that it can easily be used as a foundation for developing DES models of other operational or developmental reusable engines. Performing a DES on a developmental engine during the conceptual phase makes it easier to affect the design and make changes to bring about a decrease in turnaround time and costs.

Nix, Michael↗
NASA NTRS2004

Learning to Control Advanced Life Support Systems

Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges. In particular, advanced life support systems are nonlinear coupled dynamical systems and it is difficult for humans to take all interactions into account to design an effective control strategy. In this project. we developed several reinforcement learning controllers that actively explore the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated these controllers using a discrete event simulation of an advanced life support system. This simulation, called BioSim, designed by Nasa scientists David Kortenkamp and Scott Bell has multiple, interacting life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. They are implemented in a consumer/producer relationship in which certain modules produce resources that are consumed by other modules. Stores hold resources between modules. Control of this simulation is via adjusting flows of resources between modules and into/out of stores. We developed adaptive algorithms that control the flow of resources in BioSim. Our learning algorithms discovered several ingenious strategies for maximizing mission length by controlling the air and water recycling systems as well as crop planting schedules. By exploiting non-linearities in the overall system dynamics, the learned controllers easily out- performed controllers written by human experts. In sum, we accomplished three goals. We (1) developed foundations for learning models of coupled dynamical systems by active exploration of the state space, (2) developed and tested algorithms that learn to efficiently control air and water recycling processes as well as crop scheduling in Biosim, and (3) developed an understanding of the role machine learning in designing control systems for advanced life support.

Subramanian, Devika↗
NASA NTRS2007

Surveillance and Control of Malaria Transmission in Thailand using Remotely Sensed Meteorological and Environmental Parameters

These slides address the use of remote sensing in a public health application. Specifically, this discussion focuses on the of remote sensing to detect larval habitats to predict current and future endemicity and identify key factors that sustain or promote transmission of malaria in a targeted geographic area (Thailand). In the Malaria Modeling and Surveillance Project, which is part of the NASA Applied Sciences Public Health Applications Program, we have been developing techniques to enhance public health's decision capability for malaria risk assessments and controls. The main objectives are: 1) identification of the potential breeding sites for major vector species; 2) implementation of a risk algorithm to predict the occurrence of malaria and its transmission intensity; 3) implementation of a dynamic transmission model to identify the key factors that sustain or intensify malaria transmission. The potential benefits are: 1) increased warning time for public health organizations to respond to malaria outbreaks; 2) optimized utilization of pesticide and chemoprophylaxis; 3) reduced likelihood of pesticide and drug resistance; and 4) reduced damage to environment. !> Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. The NASA Earth science data sets that have been used for malaria surveillance and risk assessment include AVHRR Pathfinder, TRMM, MODIS, NSIPP, and SIESIP. Textural-contextual classifications are used to identify small larval habitats. Neural network methods are used to model malaria cases as a function of the remotely sensed parameters. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Discrete event simulations are used for modeling the detailed interactions among the vector life cycle, sporogonic cycle and human infection cycle, under the explicit influences of selected extrinsic and intrinsic factors. The output of the model includes the individual infection status and the quantities normally observed in field studies, such as mosquito biting rates, sporozoite infection rates, gametocyte prevalence and incidence. Results are in good agreement with mosquito vector and human malaria data acquired by Coleman et al. over 4.5 years in Kong Mong Tha, a remote village in western Thailand. Application of our models is not restricted to the Greater Mekong Subregion. Our models have been applied to malaria in Indonesia, Korea, and other regions in the world with similar success.

Kiang, Richard K.↗
NASA NTRS2007

Modeling Malaria Transmission in Thailand and Indonesia

Malaria Modeling and Surveillance is a project in the NASA Applied Sciences Public Health Applications Program. The main objectives of this project are: 1) identification of the potential breeding sites for major vector species: 2) implementation of a malaria transmission model to identify they key factors that sustain or intensify malaria transmission; and 3) implementation of a risk algorithm to predict the occurrence of malaria and its transmission intensity. Remote sensing and GIs are the essential elements of this project. The NASA Earth science data sets used in this project include AVHRR Pathfinder, TRMM, MODIS, NSIPP and SIESIP. Textural-contextual classifications are used to identify small larval habitats. Neural network methods are used to model malaria cases as a function of precipitation, temperatures, humidity and vegetation. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Examples for spatio-temporal modeling of malaria transmissions in Southeast Asia are given. Discrete event simulations were used for modeling the detailed interactions among the vector life cycle, sporogonic cycle and human infection cycle, under the explicit influences of selected extrinsic and intrinsic factors. The output of the model includes the individual infection status and the quantities normally observed in field studies, such as mosquito biting rates, sporozoite infection rates, gametocyte prevalence and incidence. Results are in good agreement with mosquito vector and human malaria data acquired by Coleman et al. over 4.5 years in Kong Mong Tha, a remote village in western Thailand. Application of our models is not restricted to Southeast Asia. The model and techniques are equally applicable to other regions of the world, when appropriate epidemiological and vector ecological parameters are used as input.

Kiang, Richard↗
NASA NTRS2009

Launch Order, Launch Separation, and Loiter in the Constellation 1 1/2-Launch Solution

The NASA Constellation Program (CxP) is developing a two-element Earth-to-Orbit launch system to enable human exploration of the Moon. The first element, Ares I, is a human-rated system that consists of a first stage based on the Space Shuttle Program's solid rocket booster (SRB) and an upper stage that consists of a four-crew Orion capsule, a service module, and a Launch Escape System. The second element, Ares V, is a Saturn V-plus category launch system that consists of the core stage with a cluster of six RS-68B engines and augmented with two 5.5-segment SRBs, a Saturn-derived J-2X engine powering an Earth Departure Stage (EDS), and the lunar-lander vehicle payload, Altair. Initial plans called for the Ares V to be launched first, followed the next day by the Ares I. After the EDS performs the final portion of ascent and subsequent orbit circularization, the Orion spacecraft then performs a rendezvous and docks with the EDS and its Altair payload. Following checkout, the integrated stack loiters in low Earth orbit (LEO) until the appropriate Trans-Lunar Injection (TLI) window opportunity opens, at which time the EDS propels the integrated Orion Altair to the Moon. Successful completion of this 1 1/2-launch solution carries risks related to both the orbital lifetime of the assets and the probability of achieving the launch of the second vehicle within the orbital lifetime of the first. These risks, which are significant in terms of overall system design choices and probability of mission success, dictated a thorough reevaluation of the launch strategy, including the order of vehicle launch and the planned time period between launches. The goal of the effort described in this paper was to select a launch strategy that would result in the greatest possible expected system performance, while accounting for launch risks and the cost of increased orbital lifetime. Discrete Event Simulation (DES) model of the launch strategies was created to determine the probability of a second launch not occurring in a timely fashion (i.e., before the assets waiting in LEO expire). An overview of the launch strategy evaluation process is presented, along with results of specific cases that were analyzed. A high-level comparison of options is then presented, along with the conclusion derived from the analysis.

Stromgren, Chel↗
NASA NTRS2010

Validating Human Performance Models of the Future Orion Crew Exploration Vehicle

NASA's Orion Crew Exploration Vehicle (CEV) will provide transportation for crew and cargo to and from destinations in support of the Constellation Architecture Design Reference Missions. Discrete Event Simulation (DES) is one of the design methods NASA employs for crew performance of the CEV. During the early development of the CEV, NASA and its prime Orion contractor Lockheed Martin (LM) strived to seek an effective low-cost method for developing and validating human performance DES models. This paper focuses on the method developed while creating a DES model for the CEV Rendezvous, Proximity Operations, and Docking (RPOD) task to the International Space Station. Our approach to validation was to attack the problem from several fronts. First, we began the development of the model early in the CEV design stage. Second, we adhered strictly to M&S development standards. Third, we involved the stakeholders, NASA astronauts, subject matter experts, and NASA's modeling and simulation development community throughout. Fourth, we applied standard and easy-to-conduct methods to ensure the model's accuracy. Lastly, we reviewed the data from an earlier human-in-the-loop RPOD simulation that had different objectives, which provided us an additional means to estimate the model's confidence level. The results revealed that a majority of the DES model was a reasonable representation of the current CEV design.

Wong, Douglas T.↗
NASA NTRS2011

Comparative Assessment and Decision Support System for Strategic Military Airlift Capability

The Lockheed Martin Aeronautics Company has been awarded several programs to modernize the aging C-5 military transport fleet. In order to ensure its continuation amidst budget cuts, it was important to engage the decision makers by providing an environment to analyze the benefits of the modernization program. This paper describes an interface that allows the user to change inputs such as the scenario airfields, take-off conditions, and reliability characteristics. The underlying logistics surrogate model was generated using data from a discrete-event simulation. Various visualizations such as intercontinental flight paths illustrated in 3D, have been created to aid the user in analyzing scenarios and performing comparative assessments for various output logistics metrics. The capability to rapidly and dynamically evaluate and compare scenarios was developed enabling real time strategy exploration and trade-offs.

Salmon, John↗
NASA NTRS2011

Kennedy Space Center Orion Processing Team Planning for Ground Operations

Topics in this presentation are: Constellation Ares I/Orion/Ground Ops Elements Orion Ground Operations Flow Orion Operations Planning Process and Toolset Overview, including: 1 Orion Concept of Operations by Phase 2 Ops Analysis Capabilities Overview 3 Operations Planning Evolution 4 Functional Flow Block Diagrams 5 Operations Timeline Development 6 Discrete Event Simulation (DES) Modeling 7 Ground Operations Planning Document Database (GOPDb) Using Operations Planning Tools for Operability Improvements includes: 1 Kaizen/Lean Events 2 Mockups 3 Human Factors Analysis

Letchworth, Gary↗
NASA NTRS2011

NASA Planning for Orion Multi-Purpose Crew Vehicle Ground Operations

The NASA Orion Ground Processing Team was originally formed by the Kennedy Space Center (KSC) Constellation (Cx) Project Office's Orion Division to define, refine and mature pre-launch and post-landing ground operations for the Orion human spacecraft. The multidisciplined KSC Orion team consisted of KSC civil servant, SAIC, Productivity Apex, Inc. and Boeing-CAPPS engineers, project managers and safety engineers, as well as engineers from Constellation's Orion Project and Lockheed Martin Orion Prime contractor. The team evaluated the Orion design configurations as the spacecraft concept matured between Systems Design Review (SDR), Systems Requirement Review (SRR) and Preliminary Design Review (PDR). The team functionally decomposed prelaunch and post-landing steps at three levels' of detail, or tiers, beginning with functional flow block diagrams (FFBDs). The third tier FFBDs were used to build logic networks and nominal timelines. Orion ground support equipment (GSE) was identified and mapped to each step. This information was subsequently used in developing lower level operations steps in a Ground Operations Planning Document PDR product. Subject matter experts for each spacecraft and GSE subsystem were used to define 5th - 95th percentile processing times for each FFBD step, using the Delphi Method. Discrete event simulations used this information and the logic network to provide processing timeline confidence intervals for launch rate assessments. The team also used the capabilities of the KSC Visualization Lab, the FFBDs and knowledge of the spacecraft, GSE and facilities to build visualizations of Orion pre-launch and postlanding processing at KSC. Visualizations were a powerful tool for communicating planned operations within the KSC community (i.e., Ground Systems design team), and externally to the Orion Project, Lockheed Martin spacecraft designers and other Constellation Program stakeholders during the SRR to PDR timeframe. Other operations planning tools included Kaizen/Lean events, mockups and human factors analysis. The majority of products developed by this team are applicable as KSC prepares 21st Century Ground Systems for the Orion Multi-Purpose Crew Vehicle and Space Launch System.

Letchworth, Gary↗
NASA NTRS2012

Launch and Assembly Reliability Analysis for Human Space Exploration Missions

NASA's future human space exploration strategy includes single and multi-launch missions to various destinations including cis-lunar space, near Earth objects such as asteroids, and ultimately Mars. Each campaign is being defined by Design Reference Missions (DRMs). Many of these missions are complex, requiring multiple launches and assembly of vehicles in orbit. Certain missions also have constrained departure windows to the destination. These factors raise concerns regarding the reliability of launching and assembling all required elements in time to support planned departure. This paper describes an integrated methodology for analyzing launch and assembly reliability in any single DRM or set of DRMs starting with flight hardware manufacturing and ending with final departure to the destination. A discrete event simulation is built for each DRM that includes the pertinent risk factors including, but not limited to: manufacturing completion; ground transportation; ground processing; launch countdown; ascent; rendezvous and docking, assembly, and orbital operations leading up to trans-destination-injection. Each reliability factor can be selectively activated or deactivated so that the most critical risk factors can be identified. This enables NASA to prioritize mitigation actions so as to improve mission success.

Cates, Grant↗
NASA NTRS2006

Low Earth Orbit Rendezvous Strategy for Lunar Missions

On January 14, 2004 President George W. Bush announced a new Vision for Space Exploration calling for NASA to return humans to the moon. In 2005 NASA decided to use a Low Earth Orbit (LEO) rendezvous strategy for the lunar missions. A Discrete Event Simulation (DES) based model of this strategy was constructed. Results of the model were then used for subsequent analysis to explore the ramifications of the LEO rendezvous strategy.

Cates, Grant R.↗