Results for “Planning Scheduling Analysis” — Page 8

NASA NTRS1991

A method for interference mitigation in space communications scheduling

Increases in the number of user spacecraft and data rates supported by NASA's Tracking and Data Relay Satellite System (TDRSS) in the S and Ku bands could result in communications conflicts due to mutual interference. A method to mitigate interference while minimizing unnecessary scheduling restrictions on both TDRSS network and user resources, based on consideration of all relevant communications parameters, was developed. The steps of this method calculate required separation angles at TDRS and produce interference intervals, which can be used in the production of schedules free of unacceptable interference. The method can also be used as a basis for analysis, evaluation, and optimization of user schedules with respect to communications performance. Described here are the proposed method and its potential application to scheduling in space communications. Test cases relative to planned missions, including the Earth Observing System, the Space Station Manned Base, and the Space Shuttle are discussed.

Wong, Yen F.↗

NASA NTRS1991

A method for interference mitigation in space communications scheduling

Increases in the number of user spacecraft and data rates supported by NASA's Tracking and Data Relay Satellite System (TDRSS) in the S and Ku bands could result in communications conflicts due to mutual interference. A method to mitigate interference while minimizing unnecessary scheduling restrictions on both TDRSS network and user resources, based on consideration of all relevant communications parameters, was developed. The steps of this method calculate required separation angles at TDRSS and produce interference intervals, which can be used in the production of schedules free of unacceptable interference. The method also can be used as the basis for analysis, evaluation, and optimization of user schedules with respect to communications performance. Described here are the proposed method and its potential application to scheduling in space communications. Test cases relative to planned missions, including the Earth Observing System, the Space Station Manned Base, and the Space Shuttle are discussed.

Wong, Yen F.↗

NASA NTRS1994

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments.

Source record↗

NASA NTRS2018

Integrated System Health Management (ISHM) and Autonomy

Systems capabilities on ISHM (Integrated System Health Management) and autonomy have traditionally been addressed separately. This means that ISHM functions, such as anomaly detection, diagnostics, prognostics, and comprehensive system awareness have not been considered traditionally in the context of autonomy functions such as planning, scheduling, and mission execution. One key reason is that although they address systems capabilities, both ISHM and autonomy have traditionally individually been approached as independent strategies and models for analysis. Additionally, to some degree, a unified paradigm for ISHM and autonomy has been difficult to implement due to limitations of hardware and software. This paper explores a unified treatment of ISHM and autonomy in the context of distributed hierarchical autonomous operations.

Figueroa, Fernando↗

NASA NTRS2017

Lost in Translation: the Case for Integrated Testing

The building of a spacecraft is complex and often involves multiple suppliers and companies that have their own designs and processes. Standards have been developed across the industries to reduce the chances for critical flight errors at the system level, but the spacecraft is still vulnerable to the introduction of critical errors during integration of these systems. Critical errors can occur at any time during the process and in many cases, human reliability analysis (HRA) identifies human error as a risk driver. Most programs have a test plan in place that is intended to catch these errors, but it is not uncommon for schedule and cost stress to result in less testing than initially planned. Therefore, integrated testing, or "testing as you fly," is essential as a final check on the design and assembly to catch any errors prior to the mission. This presentation will outline the unique benefits of integrated testing by catching critical flight errors that can otherwise go undetected, discuss HRA methods that are used to identify opportunities for human error, lessons learned and challenges over ownership of testing will be discussed.

Young, Aaron↗

NASA NTRS2021

Scheduling and Operations of the ECOSTRESS Mission

This paper describes the development and use of an automated scheduling system for the National Aeronautics and Space Administration’s (NASA) ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission. Key to the success of the ECOSTRESS mission has been the use of automated scheduling in mission analysis pre-launch, and in successful operations where automated scheduling was deployed to address several operational challenges. ECOSTRESS uses an adaptation of the Compressed Large-scale Activity Scheduling and Planning (CLASP) system to automatically select science observations respecting area and point target priorities as well as visibility, illumination, onboard storage, and radiation constraints to satisfy high-level prioritized science campaigns. The ECOSTRESS scheduler was used pre-launch to predict the effectiveness of alternative formulations of science campaign definitions accounting for the impact of data volume, keepout, and orbit/illumination/visibility constraints to derive the initial operational science campaign definitions and priorities. The scheduler was then used after instrument checkout for operations. ECOSTRESS has faced multiple operational challenges relating to instrument firmware and hardware, and the scheduler has been updated several times to address these challenges. The instrument Mass Storage Units (MSUs) had operational issues, requiring the scheduler to plan for and schedule commands to handle intricacies of data management. After many months of operations, both MSUs on the instrument became non-functioning and the firmware of the instrument was updated to bypass the MSUs. A further update to the ECOSTRESS scheduler enabled the scheduler to operate in this new operations mode. The ECOSTRESS scheduler has also been updated to improve handling of along-track uncertainty inherent in International Space Station operations. The flexibility and ease of updating of the automated scheduler has been a significant contributor to successful operations of the ECOSTRESS mission.

Padams, Jordan↗

NASA NTRS2005

National Research Council Dialogue to Assess Progress on NASA's Advanced Modeling, Simulation and Analysis Capability and Systems Engineering Capability Roadmap Development

Contents include the following: General Background and Introduction of Capability Roadmaps. Agency Objective. Strategic Planning Transformation. Advanced Planning Organizational Roles. Public Involvement in Strategic Planning. Strategic Roadmaps and Schedule. Capability Roadmaps and Schedule. Purpose of NRC Review. Capability Roadmap Development (Progress to Date).

Aikins, Jan↗

NASA NTRS1980

Coal gasification systems engineering and analysis. Volume 1: Executive summary

Feasibility analyses and systems engineering studies for a 20,000 tons per day medium Btu (MBG) coal gasification plant to be built by TVA in Northern Alabama were conducted. Major objectives were as follows: (1) provide design and cost data to support the selection of a gasifier technology and other major plant design parameters, (2) provide design and cost data to support alternate product evaluation, (3) prepare a technology development plan to address areas of high technical risk, and (4) develop schedules, PERT charts, and a work breakdown structure to aid in preliminary project planning. Volume one contains a summary of gasification system characterizations. Five gasification technologies were selected for evaluation: Koppers-Totzek, Texaco, Lurgi Dry Ash, Slagging Lurgi, and Babcock and Wilcox. A summary of the trade studies and cost sensitivity analysis is included.

Source record↗

NASA NTRS2011

Solar Dynamics Observatory On-Orbit Jitter Testing, Analysis, and Mitigation Plans

The Solar Dynamics Observatory (SDO) was designed to understand the Sun and the Sun s influence on Earth. SDO was launched on February 11, 2010 carrying three scientific instruments: the Atmospheric Imaging Assembly (AIA), the Helioseismic and Magnetic Imager (HMI), and the Extreme Ultraviolet Variability Experiment (EVE). Both AIA and HMI are sensitive to high frequency pointing perturbations and have sub-arcsecond level line-of-sight (LOS) jitter requirements. Extensive modeling and analysis efforts were directed in estimating the amount of jitter disturbing the science instruments. To verify the disturbance models and to validate the jitter performance prior to launch, many jitter-critical components and subassemblies were tested either by the mechanism vendors or at the NASA Goddard Space Flight Center (GSFC). Although detailed analysis and assembly level tests were performed to obtain good jitter predictions, there were still several sources of uncertainties in the system. The structural finite element model did not have all the modes correlated to test data at high frequencies (greater than 50 Hz). The performance of the instrument stabilization system was not known exactly but was expected to be close to the analytical model. A true disturbance-to-LOS observatory level test was not available due to the tight schedule of the flight spacecraft, the cost in time and manpower, difficulties in creating gravity negation systems, and risks of damaging flight hardware. To protect the observatory jitter performance against model uncertainties, the SDO jitter team devised several on-orbit jitter reduction plans in addition to reserve margins on analysis results. Since some of these plans severely restricted the capabilities of several spacecraft components (e.g. wheels and High Gain Antennas), the SDO team performed on-orbit jitter tests to determine which jitter reduction plans, if any, were necessary to satisfy science LOS jitter requirements. The SDO on-orbit jitter tests were designed to satisfy the following four objectives: 1. Determine the acceptable reaction wheel operational speed range during Science Mode. 2. Determine HGA algorithm jitter parameters (number of stagger steps and enable/disable no-steprequests). 3. Determine acceptable EVE instrument filter wheels spin rates. 4. Determine if AIA instrument filter wheels excite the first AIA telescope structural mode. This paper provides detailed information on the SDO wheel jitter test plan, shows on-orbit jitter measurements and how ground predictions compare to those measurements, and describes the final jitter mitigation plan executed on SDO.

Liu, Kuo-Chia (Alice)↗

NASA NTRS2006

NAS Demand Predictions, Transportation Systems Analysis Model (TSAM) Compared with Other Forecasts

The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These resulting demand scenarios can be incorporated into future flight schedules, therefore providing a quantifiable demand for flights in the NAS for a range of futures. In addition, new future airline business scenarios are investigated that illustrate when direct flights can replace connecting flights and larger aircraft can be substituted, only when justified by demand.

Viken, Jeff↗

NASA NTRS2006

Utilizing Traveler Demand Modeling to Predict Future Commercial Flight Schedules in the NAS

The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These resulting demand scenarios can be incorporated into future flight schedules, therefore providing a quantifiable demand for flights in the NAS for a range of futures. In addition, new future airline business scenarios are investigated that illustrate when direct flights can replace connecting flights and larger aircraft can be substituted, only when justified by demand.

Viken, Jeff↗

NASA NTRS1984

Practical considerations in aeroelastic design

The structural design process for large transport aircraft is described. Critical loads must be determined from a large number of load cases within the flight maneuver envelope. The structural design is also constrained by considerations of producibility, reliability, maintainability, durability, and damage tolerance, as well as impact dynamics and multiple constraints due to flutter and aeroelasticity. Aircraft aeroelastic design considerations in three distinct areas of product development (preliminary design, advanced design, and detailed design) are presented and contrasted. The present state of the art is challenged to solve the practical difficulties associated with design, analysis, and redesign within cost and schedule constraints. The current practice consists of largely independent engineering disciplines operating with unorganized data interfaces. The need is then demonstrated for a well-planned computerized aeroelastic structural design optimization system operating with a common interdisciplinary data base. This system must incorporate automated interfaces between modular programs. In each phase of the design process, a common finite-element model for static and dynamic optimization is required to reduce errors due to modeling discrepancies. As the design proceeds from the simple models in preliminary design to the more complex models in advanced and detailed design, a means of retrieving design data from the previous models must be established.

Rommel, B. A.↗

NASA NTRS1976

Final definition and preliminary design study for the initial atmospheric cloud physics laboratory, a Spacelab mission payload

The following areas related to the final definition and preliminary design study of the initial atmospheric cloud physics laboratory (ACPL) were covered: (1) proposal organization, personnel, schedule, and project management, (2) proposed configurations, (3) study objectives, (4) ACPL experiment program listing and description, (5) mission/flight flexibility and modularity/commonality, (6) study plan, and (7) description of following tasks: requirement analysis and definition task flow, systems analysis and trade studies, subsystem analysis and trade studies, specifications and interface control documents, preliminary design task flow, work breakdown structure, programmatic analysis and planning, and project costs. Finally, an overview of the scientific requirements was presented.

Source record↗

NASA NTRS2020

Space Launch System Mobile Launcher Modal Pretest Analysis

NASA is developing an expendable heavy lift launch vehicle capability, the Space Launch System, to support lunar and deep space exploration. To support this capability, an updated ground infrastructure is required including modifying an existing Mobile Launcher system. The Mobile Launcher is a very large heavy beam/truss steel structure designed to support the Space Launch System during its buildup and integration in the Vehicle Assembly Building, transportation from the Vehicle Assembly Building out to the launch pad, and provides the launch platform at the launch pad. The previous Saturn/Apollo and Space Shuttle programs had integrated vehicle ground vibration tests of their integrated launch vehicles performed with simulated free-free boundary conditions to experimentally anchor and validate structural and flight controls analysis models. For the Space Launch System program, the Mobile Launcher will be used as the modal test fixture for the ground vibration test of the first Space Launch System flight vehicle, Artemis 1, programmatically referred to as the integrated vehicle modal test. The integrated vehicle modal test of the Artemis 1 integrated launch vehicle will have its core and second stages unfueled while mounted to the Mobile Launcher while inside the Vehicle Assembly Building, which is currently scheduled for the summer of 2020. The Space Launch System program has implemented a building block approach for dynamic model validation. The modal test of the Mobile Launcher is an important part of this building block approach in supporting the integrated vehicle modal test since the Mobile Launcher will serve as a structurally dynamic test fixture whose modes will couple with the modes of the Artemis 1 integrated vehicle. The Mobile Launcher modal test will further support understanding the structural dynamics of the Mobile Launcher and Space Launch System during rollout to the launch pad, which will play a key role in better understanding and prediction of the rollout forces acting on the launch vehicle. The Mobile Launcher modal test is currently scheduled for the summer of 2019. Due to a very tight modal testing schedule, this independent Mobile Launcher modal pretest analysis has been performed to ensure there is a high likelihood of successfully completing the modal test (i.e. identify the primary target modes) using the planned instrumentation, shakers, and excitation types. This paper will discuss this Mobile Launcher modal pretest analysis for its three test configurations and the unique challenges faced due to the Mobile Launcherâ€™s size and weight, which are typically not faced when modal testing aerospace structures.

Akers, James C.↗

NASA NTRSDate not supplied

SLS Payload Launch Loads Analysis Using the NTRC Method

Norton-Thevenin Receptance Coupling (NTRC), as described in several NASA Engineering and Safety Center (NESC) papers shows promise in enabling loads development for payloads with less computational cost and analyst time as compared to a full integrated vehicle coupled loads analysis (CLA). NTRC allows the free vehicle responses and impedance (accelerance) at the payload to vehicle interface (derived from integrated vehicle CLA) to be used with a payload model in a payload CLA. Due to the NTRC damping differing from the full integrated vehicle damping, results are slightly different, so coverage factors were developed to ensure NTRC results enveloped results from a traditional CLA. NTRC results with coverage factors were very close to full CLA results. The Space Launch System (SLS) coupled loads team has developed an implementation of the NTRC method to enable support of co-manifested payloads with analysis needs that do not fit the primary SLS load cycle schedule. The NTRC method was successfully used to support the European System Providing Refueling, Infrastructure, and Communications (ESPRIT) module for Gateway planned to fly on Artemis V and has been adopted as the SLS approach for supporting payload CLA requests that do not align with vehicle load cycles.

SLS↗

NASA NTRSDate not supplied

Application of Norton-Thevenin Receptance Coupling (NTRC) to Space Launch System (SLS) Payload Coupled Loads Analysis (CLA)

Norton-Thevenin Receptance Coupling (NTRC), as described in several NASA Engineering and Safety Center (NESC) papers shows promise in enabling loads development for payloads with less computational cost and analyst time as compared to a full integrated vehicle coupled loads analysis (CLA). NTRC allows the free vehicle responses and impedance (accelerance) at the payload to vehicle interface (derived from integrated vehicle CLA) to be used with a payload model in a payload CLA. Due to the NTRC damping differing from the full integrated vehicle damping, results are slightly different, so coverage factors were developed to ensure NTRC results enveloped results from a traditional CLA. NTRC results with coverage factors were very close to full CLA results. The Space Launch System (SLS) coupled loads team has developed an implementation of the NTRC method to enable support of co-manifested payloads with analysis needs that do not fit the primary SLS load cycle schedule. The NTRC method was successfully used to support the European System Providing Refueling, Infrastructure, and Communications (ESPRIT) module for Gateway planned to fly on Artemis V and has been adopted as the SLS approach for supporting payload CLA requests that do not align with vehicle load cycles.

SLS↗

NASA NTRS2000

A Comparison of Center/TRACON Automation System and Airline Time of Arrival Predictions

Benefits from information sharing between an air traffic service provider and a major air carrier are evaluated. Aircraft arrival time schedules generated by the NASA/FAA Center/TRACON Automation System (CTAS) were provided to the American Airlines System Operations Control Center in Fort Worth, Texas, during a field trial of a specialized CTAS display. A statistical analysis indicates that the CTAS schedules, based on aircraft trajectories predicted from real-time radar and weather data, are substantially more accurate than the traditional airline arrival time estimates, constructed from flight plans and en route crew updates. The improvement offered by CTAS is especially advantageous during periods of heavy traffic and substantial terminal area delay, allowing the airline to avoid large predictive errors with serious impact on the efficiency and profitability of flight operations.

Heere, Karen R.↗

NASA NTRS2004

Planning for rover opportunistic science

The Mars Exploration Rover Spirit recently set a record for the furthest distance traveled in a single sol on Mars. Future planetary exploration missions are expected to use even longer drives to position rovers in areas of high scientific interest. This increase provides the potential for a large rise in the number of new science collection opportunities as the rover traverses the Martian surface. In this paper, we describe the OASIS system, which provides autonomous capabilities for dynamically identifying and pursuing these science opportunities during longrange traverses. OASIS uses machine learning and planning and scheduling techniques to address this goal. Machine learning techniques are applied to analyze data as it is collected and quickly determine new science gods and priorities on these goals. Planning and scheduling techniques are used to alter the behavior of the rover so that new science measurements can be performed while still obeying resource and other mission constraints. We will introduce OASIS and describe how planning and scheduling algorithms support opportunistic science.

artificial intelligence↗