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Project cost risk is the uncertainty in final project cost, contingent on initial budget, requirements and schedule. For a proposed mission, a dynamic simulation model relying for some of its input on a simple risk elicitation is used to identify and quantify systemic cost risk.
LaGuardia Airport (LGA) in New York has many unique challenges that create excess taxi-out delays. The purpose of this paper is to investigate the potential benefit that could be gained by tactically adjusting the Terminal Sequencing and Spacing (TSS) schedule to precisely manage inter-arrival spacing to maximize the number of departures per arrival pair. Three strategies for dynamically adjusting arrival schedules are proposed in this paper: Delay Control, Delay and Advance, and No Slack Capacity. The benefits of these strategies were examined on actual traffic data at LGA. The results showed that by applying these strategies, a 10 to 60 increase in departures and a reduction in un-utilized departure capacity (gaps) could be achieved during the airports busiest six-hour period. Significant increases in departure throughput would improve air traffic operations by reducing departure delay time. Furthermore, the concept could be used to resolve temporal mismatches between departure capacity and demand which also cause excessive departure delays.
The Photogrammetric Appendage Structural Dynamics Experiment (PASDE) is a Hitchhiker payload scheduled to fly as part of the International Space Station (ISS) Phase-1 flight program to the Russian Space Station Mir. The objective of the first flight of PASDE on STS-74 is to obtain video images of the Mir Kvant-2 solar array response to various structural dynamic excitation events. This experiment will demonstrate the use of photogrammetric techniques for on-orbit structural dynamics measurements. Photogrammetric measurements will provide a low cost alternative to appendage mounted accelerometers to the ISS program. The PASDE experiment hardware consists of three instruments each containing two video cameras, two video tape recorders, a modified video signal time inserter, and associated avionics boxes. The instruments were designed and built at the NASA Langley Research Center, and are integrated into standard Hitchhiker canisters at the NASA Goddard Space Flight Center. The Hitchhiker canisters are then installed into the Space Shuttle cargo bay in locations selected to achieve good video coverage and photogrammetric geometry. The measurement resolution of the instruments is expected to be on the order of 0.25 cm (0.1 in.).
Multiprocessors used in life-critical real-time systems must recover quickly from failure. Part of this recovery consists of switching to a new task schedule which ensures that hard deadlines for critical tasks continue to be met. A dynamic programming algorithm is presented that ensures that backup, or contingency, schedules can be efficiently embedded within the original, 'primary' schedule to ensure that hard deadlines continue to be met in the face of up to a given maximum number of processor failures. Several illustrative examples are included.
This report describes the analysis performed and the findings of a study of the software development cost and schedule estimation models used by the Flight Dynamics Division (FDD), Goddard Space Flight Center. The study analyzes typical FDD projects, focusing primarily on those developed since 1982. The study reconfirms the standard SEL effort estimation model that is based on size adjusted for reuse; however, guidelines for the productivity and growth parameters in the baseline effort model have been updated. The study also produced a schedule prediction model based on empirical data that varies depending on application type. Models for the distribution of effort and schedule by life-cycle phase are also presented. Finally, this report explains how to use these models to plan SEL projects.
The development of a dynamic autonomous system (DYASS) of resources for the mission support of near-Earth NASA spacecraft is discussed and the current NASA space data system is described from a functional perspective. The future (late 80's and early 90's) NASA space data system is discussed. The DYASS concept, the autonomous process control, and the NASA space data system are introduced. Scheduling and related disciplines are surveyed. DYASS as a scheduling problem is also discussed. Artificial intelligence and knowledge representation is considered as well as the NUDGE system and the I-Space system.
FASTER (Forecasting And Scheduling Tool for Earth-based Resources) is a suite of tools designed for forecasting and scheduling JPL's Deep Space Network (DSN). The DSN is a set of antennas and other associated resources that must be scheduled for satellite communications, astronomy, maintenance, and testing. FASTER consists of MS-Windows based programs that replace two existing programs (RALPH and PC4CAST). FASTER was designed to be more flexible, maintainable, and user friendly. FASTER makes heavy use of commercial software to allow for customization by users. FASTER implements scheduling as a two pass process: the first pass calculates a predictive profile of resource utilization; the second pass uses this information to calculate a cost function used in a dynamic programming optimization step. This information allows the scheduler to 'look ahead' at activities that are not as yet scheduled. FASTER has succeeded in allowing wider access to data and tools, reducing the amount of effort expended and increasing the quality of analysis.
FASTER (Forecasting And Scheduling Tool for Earth-based Resources) is a suite of tools designed for forecasting and scheduling of JPL's DSN. The DSN is a set of antennas and other associated resources that must be scheduled for satellite communications, astronomy, maintenance, and testing. FASTER is a MS-Windows based program that replaces two existing programs (RALPH and PC4CAST). FASTER was designed to be more flexible, maintainable, and user friendly. FASTER makes heavy use of commercial software to allow for customization by users. FASTER implements scheduling as a two pass process: the first pass calculates a predictive profile of resource utilization; the second pass uses this information to calculate a cost function used in a dynamic programming optimization step. This information allows the scheduler to "look ahead" at activities that are not as yet scheduled. FASTER has succeeded in allowing wider access to data and tools, reducing the amount of effort expended and increasing the quality of analysis.
In hard real-time systems, tasks have to be performed not only correctly, but also in a timely fashion. If timing constraints are not met, there might be severe consequences. Task scheduling is the most important problem in designing a hard real-time system, because the scheduling algorithm ensures that tasks meet their deadlines. However, the inherent nature of uncertainty in dynamic hard real-time systems increases the problems inherent in scheduling. In an effort to alleviate these problems, we have developed a fuzzy scheduler to facilitate searching for a feasible schedule. A set of fuzzy rules are proposed to guide the search. The situation we are trying to address is the performance of the system when no feasible solution can be found, and therefore, certain tasks will not be executed. We wish to limit the number of important tasks that are not scheduled.
Mathematical-analytical methods as used in Operations Research approaches are often insufficient for scheduling problems. This is due to three reasons: the combinatorial complexity of the search space, conflicting objectives for production optimization, and the uncertainty in the production process. Knowledge-based techniques, especially approximate reasoning and constraint relaxation, are promising ways to overcome these problems. A case study from an industrial CIM environment, namely high-grade steel production, is presented to demonstrate how knowledge-based scheduling with the desired capabilities could work. By using fuzzy set theory, the applied knowledge representation technique covers the uncertainty inherent in the problem domain. Based on this knowledge representation, a classification of jobs according to their importance is defined which is then used for the straightforward generation of a schedule. A control strategy which comprises organizational, spatial, temporal, and chemical constraints is introduced. The strategy supports the dynamic relaxation of conflicting constraints in order to improve tentative schedules.
In this paper, we summarize current research at Carnegie Mellon University aimed at development of high performance techniques and tools for space mission scheduling. Similar to prior research in opportunistic scheduling, our approach assumes the use of dynamic analysis of problem constraints as a basis for heuristic focusing of problem solving search. This methodology, however, is grounded in representational assumptions more akin to those adopted in recent temporal planning research, and in a problem solving framework which similarly emphasizes constraint posting in an explicitly maintained solution constraint network. These more general representational assumptions are necessitated by the predominance of state-dependent constraints in space mission planning domains, and the consequent need to integrate resource allocation and plan synthesis processes. First, we review the space mission problems we have considered to date and indicate the results obtained in these application domains. Next, we summarize recent work in constraint posting scheduling procedures, which offer the promise of better future solutions to this class of problems.
The purpose of dynamic modeling and simulation of Advanced Life Support (ALS) systems is to help design them. Static steady state systems analysis provides basic information and is necessary to guide dynamic modeling, but static analysis is not sufficient to design and compare systems. ALS systems must respond to external input variations and internal off-nominal behavior. Buffer sizing, resupply scheduling, failure response, and control system design are aspects of dynamic system design. We develop two dynamic mass flow models and use them in simulations to evaluate systems issues, optimize designs, and make system design trades. One model is of nitrogen leakage in the space station, the other is of a waste processor failure in a regenerative life support system. Most systems analyses are concerned with optimizing the cost/benefit of a system at its nominal steady-state operating point. ALS analysis must go beyond the static steady state to include dynamic system design. All life support systems exhibit behavior that varies over time. ALS systems must respond to equipment operating cycles, repair schedules, and occasional off-nominal behavior or malfunctions. Biological components, such as bioreactors, composters, and food plant growth chambers, usually have operating cycles or other complex time behavior. Buffer sizes, material stocks, and resupply rates determine dynamic system behavior and directly affect system mass and cost. Dynamic simulation is needed to avoid the extremes of costly over-design of buffers and material reserves or system failure due to insufficient buffers and lack of stored material.
Dynamic-inversion-based flight control laws present an attractive alternative to conventional gain-scheduled designs for high angle-of-attack maneuvering, where nonlinearities dominate the dynamics. Dynamic inversion is easily applied to the aircraft dynamics requiring a knowledge of the nonlinear equations of motion alone, rather than an extensive set of linearizations. However, the robustness properties of the dynamic inversion are questionable especially when considering the uncertainties involved with the aerodynamic database during post-stall flight. This paper presents a simple analysis and some preliminary results of simulations with a perturbed database. It is shown that incorporating integrators into the control loops helps to improve the performance in the presence of these perturbations.
Simulation models of acquisition processes range in scope from isolated applications to the 'Big Picture' captured by SNAP technology. SNAP integrates a family of models to portray the full scope of acquisition planning and management activities, including budgeting, scheduling, testing and risk analysis. SNAP replicates the dynamic management processes that underlie design, production and life-cycle support. SNAP provides the unique 'Big Picture' capability needed to simulate the entire acquisition process and explore the 'what-if' tradeoffs and consequences of alternative policies and decisions. Comparison of cost, schedule and performance tradeoffs help managers choose the lowest-risk, highest payoff at each step in the acquisition process.
The purpose of this grant was to support NASA in modeling efforts to predict the transient dynamic and thermodynamic response of the space station solar dynamic power generation system. In order to meet the initial schedule requirement of providing results in time to support installation of the system as part of the initial phase of space station, early efforts were executed with alacrity and often in parallel. Initially, methods to predict the transient response of a Rankine as well as a Brayton cycle were developed. Review of preliminary design concepts led NASA to select a regenerative gas-turbine cycle using a helium-xenon mixture as the working fluid and, from that point forward, the modeling effort focused exclusively on that system. Although initial project planning called for a three year period of performance, revised NASA schedules moved system installation to later and later phases of station deployment. Eventually, NASA selected to halt development of the solar dynamic power generation system for space station and to reduce support for this project to two-thirds of the original level.
NASA is interested in developing technology that leads to more routine, safe, and affordable access to space. Access to space using airbreathing propulsion systems has potential to meet these objectives based on Airbreathing Access to Space (AAS) system studies. To this end, the NASA Fundamental Aeronautics Program (FAP) Hypersonic Project is conducting fundamental research on a Turbine Based Combined Cycle (TBCC) propulsion system. The TBCC being studied considers a dual flow-path inlet system. One flow-path includes variable geometry to regulate airflow to a turbine engine cycle. The turbine cycle provides propulsion from take-off to supersonic flight. The second flow-path supports a dual-mode scramjet (DMSJ) cycle which would be initiated at supersonic speed to further accelerate the vehicle to hypersonic speed. For a TBCC propulsion system to accelerate a vehicle from supersonic to hypersonic speed, a critical enabling technology is the ability to safely and effectively transition from the turbine to the DMSJ-referred to as mode transition. To experimentally test methods of mode transition, a Combined Cycle Engine (CCE) Large-scale Inlet testbed was designed with two flow paths-a low speed flow-path sized for a turbine cycle and a high speed flow-path designed for a DMSJ. This testbed system is identified as the CCE Large-Scale Inlet for Mode Transition studies (CCE-LIMX). The test plan for the CCE-LIMX in the NASA Glenn Research Center (GRC) 10- by 10-ft Supersonic Wind Tunnel (10x10 SWT) is segmented into multiple phases. The first phase is a matrix of inlet characterization (IC) tests to evaluate the inlet performance and establish the mode transition schedule. The second phase is a matrix of dynamic system identification (SysID) experiments designed to support closed-loop control development at mode transition schedule operating points for the CCE-LIMX. The third phase includes a direct demonstration of controlled mode transition using a closed loop control system developed with the data obtained from the first two phases. Plans for a fourth phase include mode transition experiments with a turbine engine. This paper, focusing on the first two phases of experiments, presents developed operational and analysis tools for streamlined testing and data reduction procedures.
As part of the NASA Langley Research Center's effort to develop technology for VTOL operation in the air transportation system in the late 1980's and beyond, research has been conducted aimed at developing digital controller design procedures. This paper describes the verification of one design procedure by the flight evaluation of an advanced digital control algorithm. The control algorithm, operating at 10 iterations per second, follows step guidance commands with zero steady state error and thus provides an autotrim capability for the nonlinear vehicle. Changes in vehicle dynamics are accounted for using a gain scheduling technique. This control algorithm is combined with sensor filters, a trajectory generator, and a closed loop guidance algorithm to form a VTOL autoland system. A CH-47 tandem rotor helicopter which contains a set of sensors, onboard digital flight computers and electro-hydraulic actuators is used in the evaluation. All software, except input-output routines, is coded in FORTRAN using floating point arithmetic and executed in the flight computer. This autoland system is exercised by automatically flying straight-in descending decelerating trajectories typical of VFR manual approaches to a predetermined landing pad.
Computer programs for degaussing, magnetic field calculation, low speed wing flap systems aerodynamics, structural panel analysis, dynamic stress/strain data acquisition, allocation and network scheduling, and digital filters are discussed.