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DTS: Building custom, intelligent schedulers
DTS is a decision-theoretic scheduler, built on top of a flexible toolkit -- this paper focuses on how the toolkit might be reused in future NASA mission schedulers. The toolkit includes a user-customizable scheduling interface, and a 'Just-For-You' optimization engine. The customizable interface is built on two metaphors: objects and dynamic graphs. Objects help to structure problem specifications and related data, while dynamic graphs simplify the specification of graphical schedule editors (such as Gantt charts). The interface can be used with any 'back-end' scheduler, through dynamically-loaded code, interprocess communication, or a shared database. The 'Just-For-You' optimization engine includes user-specific utility functions, automatically compiled heuristic evaluations, and a postprocessing facility for enforcing scheduling policies. The optimization engine is based on BPS, the Bayesian Problem-Solver (1,2), which introduced a similar approach to solving single-agent and adversarial graph search problems.
Set covering algorithm, a subprogram of the scheduling algorithm for mission planning and logistic evaluation
A computer program using Lemke, Salkin and Spielberg's Set Covering Algorithm (SCA) to optimize a traffic model problem in the Scheduling Algorithm for Mission Planning and Logistics Evaluation (SAMPLE) was documented. SCA forms a submodule of SAMPLE and provides for input and output, subroutines, and an interactive feature for performing the optimization and arranging the results in a readily understandable form for output.
Traffic Flow Management Wrap-Up
Traffic Flow Management involves the scheduling and routing of air traffic subject to airport and airspace capacity constraints, and the efficient use of available airspace. Significant challenges in this area include: (1) weather integration and forecasting, (2) accounting for user preferences in the Traffic Flow Management decision making process, and (3) understanding and mitigating the environmental impacts of air traffic on the environment. To address these challenges, researchers in the Traffic Flow Management area are developing modeling, simulation and optimization techniques to route and schedule air traffic flights and flows while accommodating user preferences, accounting for system uncertainties and considering the environmental impacts of aviation. This presentation will highlight some of the major challenges facing researchers in this domain, while also showcasing recent innovations designed to address these challenges.
A study of interactive control scheduling and economic assessment for robotic systems
A class of interactive control systems is derived by generalizing interactive manipulator control systems. Tasks of interactive control systems can be represented as a network of a finite set of actions which have specific operational characteristics and specific resource requirements, and which are of limited duration. This has enabled the decomposition of the overall control algorithm simultaneously and asynchronously. The performance benefits of sensor referenced and computer-aided control of manipulators in a complex environment is evaluated. The first phase of the CURV arm control system software development and the basic features of the control algorithms and their software implementation are presented. An optimal solution for a production scheduling problem that will be easy to implement in practical situations is investigated.
SURE (Science User Resource Expert): A science planning and scheduling assistant for a resource based environment
SURE (Science User Resource Expert) is one of three components that compose the SURPASS (Science User Resource Planning and Scheduling System). This system is a planning and scheduling tool which supports distributed planning and scheduling, based on resource allocation and optimization. Currently SURE is being used within the SURPASS by the UARS (Upper Atmospheric Research Satellite) SOLSTICE instrument to build a daily science plan and activity schedule and in a prototyping effort with NASA GSFC to demonstrate distributed planning and scheduling for the SOLSTICE II instrument on the EOS platform. For the SOLSTICE application the SURE utilizes a rule-based system. Development of a rule-based program using Ada CLIPS as opposed to using conventional programming, allows for capture of the science planning and scheduling heuristics in rules and provides flexibility in inserting or removing rules as the scientific objectives and mission constraints change. The SURE system's role as a component in the SURPASS, the purpose of the SURE planning and scheduling tool, the SURE knowledge base, and the software architecture of the SURE component are described.
Fast Optimization for Aircraft Descent and Approach Trajectory
We address problem of on-line scheduling of the aircraft descent and approach trajectory. We formulate a general multiphase optimal control problem for optimization of the descent trajectory and review available methods of its solution. We develop a fast algorithm for solution of this problem using two key components: (i) fast inference of the dynamical and control variables of the descending trajectory from the low dimensional flight profile data and (ii) efficient local search for the resulting reduced dimensionality non-linear optimization problem. We compare the performance of the proposed algorithm with numerical solution obtained using optimal control toolbox General Pseudospectral Optimal Control Software. We present results of the solution of the scheduling problem for aircraft descent using novel fast algorithm and discuss its future applications.
Design of 9.271-pressure-ratio 5-stage core compressor and overall performance for first 3 stages
Overall aerodynamic design information is given for all five stages of an axial flow core compressor (74A) having a 9.271 pressure ratio and 29.710 kg/sec flow. For the inlet stage group (first three stages), detailed blade element design information and experimental overall performance are given. At rotor 1 inlet tip speed was 430.291 m/sec, and hub to tip radius ratio was 0.488. A low number of blades per row was achieved by the use of low-aspect-ratio blading of moderate solidity. The high reaction stages have about equal energy addition. Radial energy varied to give constant total pressure at the rotor exit. The blade element profile and shock losses and the incidence and deviation angles were based on relevant experimental data. Blade shapes are mostly double circular arc. Analysis by a three-dimensional Euler code verified the experimentally measured high flow at design speed and IGV-stator setting angles. An optimization code gave an optimal IGV-stator reset schedule for higher measured efficiency at all speeds.
System-Availability And Resource-Allocation Program
ACARA analyzes availability, life-cycle cost, and scheduling of resources. Uses statistical Monte Carlo method to simulate capacity states of system as well as failure and repair of components. Failures of components modeled mathematically by use of combination of exponential and Weibull probability distributions. Schedules replacement of components to optimize performance of system. Made to comply with any constraints on production of components, capacities of resupply vehicles, spares kept on site, crews, and/or equipment. Written in APL2.
Stress In A Fiber During Curing Of Surrounding Matrix Resin
Experiments run to determine variation in tensile stress in single fiber during curing of matrix resin surrounding fiber. Study part of effort to understand physical mechanisms affecting residual stresses in matrix/fiber composites, with view toward optimizing curing cycles (in particular, optimizing temperature-vs.-time schedules of final cooldowns to ambient temperature) to minimize residual stresses. Results signify primary mechanisms affecting residual stress in fibers are thermal expansion and contraction and cure shrinkage of matrix material.
Prediction of Pushback Times and Ramp Taxi Times for Departures at Charlotte Airport
When optimizing the takeoff sequence and schedule for departures at busy airports, it is important to accurately predict the taxi times from gate to runway because those are used to calculate the earliest possible takeoff times. Several airports like Charlotte Douglas International Airport show relatively long taxi times inside the ramp area with large variations, with respect to the travel times in the airport movement area. Also, the pushback process times have not been accurately modeled so far mainly due to the lack of accurate data. The recent deployment of the integrated arrival, departure, and surface traffic management system at Charlotte airport by NASA enables more accurate flight data in the airport surface operations to be obtained. Taking advantage of this system, actual pushback times and ramp taxi times from historical flight data at this airport are analyzed. Based on the analysis, a simple, data-driven prediction model is introduced for estimating pushback times and ramp transit times of individual departure flights. To evaluate the performance of this prediction model, several machine learning techniques are also applied to the same dataset. The prediction results show that the data-driven prediction model is as good as the machine learning algorithms when comparing various prediction performance metrics.
Prediction of Pushback Times and Ramp Taxi Times for Departures at Charlotte Airport
When optimizing the takeoff sequence and schedule for departures at busy airports, it is important to accurately predict the taxi times from gate to runway because those are used to calculate the earliest possible takeoff times. Several airports like Charlotte Douglas International Airport show relatively long taxi times inside the ramp area with large variations, with respect to the travel times in the airport movement area. Also, the pushback process times have not been accurately modeled so far mainly due to the lack of accurate data. The recent deployment of the integrated arrival, departure, and surface traffic management system at Charlotte airport by NASA enables more accurate flight data in the airport surface operations to be obtained. Taking advantage of this system, actual pushback times and ramp taxi times from historical flight data at this airport are analyzed. Based on the analysis, a simple, data-driven prediction model is introduced for estimating pushback times and ramp transit times of individual departure flights. To evaluate the performance of this prediction model, several machine learning techniques are also applied to the same dataset. The prediction results show that the data-driven prediction model is as good as the machine learning algorithms when comparing various prediction performance metrics.
Merging Analytic Collaborative Frameworks with New Observing Strategies Toward a Digital Twin: Earth – Episodic Pulse Event Impacts on Ocean Carbon Cycle as an Example
Virtual representations of the Earth will allow us to address some of the most critical environmental issues of our time. Here, we show the first steps toward representation of riverine, estuarine, and coastal carbon processes to enable scenario driven “what-if” analyses of the carbon system and human footprint. Excess sediment and nutrient runoff from land-based human activities impact water quality and can pose serious threats to coastal and marine ecosystems. Episodic pulse events, such as extreme precipitation events, can increase the amount of nutrients entering estuaries and coastal regions, potentially leading to large phytoplankton blooms followed by anoxic conditions. Consequences of coastal runoff are predicted to increase with the higher intensity and frequency of extreme events. Beyond the threat to coastal ecosystems, recent findings suggest these episodic pulses might play a significant role for biological production influencing regional and global carbon fluxes and budgets. An improved understanding of these events through optimal, dynamic observing strategies will increase our knowledge of the land-ocean continuum and how regional events and nutrient fluxes affect the carbon cycle and ocean ecosystem. This conceptual framework enables focused science investigations by pairing data analytics and artificial intelligence tools (otherwise termed an Analytic Center Framework, ACF) with targeted measurement acquisition through distributed sensing and intelligent asset tasking (or New Observing Strategies, NOS). This NOS and ACF iterative approach acquires and integrates complementary and coincident satellite, in-situ and model data to build a more complete and in-depth picture of science phenomena. Specifically, Apache Science Data Analytic Platform (SDAP) is extended to incorporate relevant datasets for data access, harmonized analysis, and anomaly detection. When conditions are met for a likely pulse event, NASA’s D-SHIELD (Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions) tool is triggered to optimize asset overpass frequency and schedule observations for persistent monitoring. Targeted data is ingested by SDAP for enhanced investigation via iterative analysis until the trigger criteria is no longer met - steps toward a digital twin.
High Data Rate Architecture (HiDRA)
One of the greatest challenges in developing new space technology is in navigating the transition from ground based laboratory demonstration at Technology Readiness Level 6 (TRL-6) to conducting a prototype demonstration in space (TRL-7). This challenge is com- pounded by the relatively low availability of new spacecraft missions when compared with aeronautical craft to bridge this gap, leading to the general adoption of a low-risk stance by mission management to accept new, unproven technologies into the system. Also in consideration of risk, the limited selection and availability of proven space-grade components imparts a severe limitation on achieving high performance systems by current terrestrial technology standards. Finally from a space communications point of view the long duration characteristic of most missions imparts a major constraint on the entire space and ground network architecture, since any new technologies introduced into the system would have to be compliant with the duration of the currently deployed operational technologies, and in some cases may be limited by surrounding legacy capabilities. Beyond ensuring that the new technology is verified to function correctly and validated to meet the needs of the end users the formidable challenge then grows to additionally include: carefully timing the maturity path of the new technology to coincide with a feasible and accepting future mission so it flies before its relevancy has passed, utilizing a limited catalog of available components to their maximum potential to create meaningful and unprecedented new capabilities, designing and ensuring interoperability with aging space and ground infrastructures while simultaneously providing a growth path to the future. The International Space Station (ISS) is approaching 20 years of age. To keep the ISS relevant, technology upgrades are continuously taking place. Regarding communications, the state-of-the-art communication system upgrades underway include high-rate laser terminals. These must interface with the existing, aging data infrastructure. The High Data Rate Architecture (HiDRA) project is designed to provide networked store, carry, and forward capability to optimize data flow through both the existing radio frequency (RF) and new laser communications terminal. The networking capability is realized through the Delay Tolerant Networking (DTN) protocol, and is used for scheduling data movement as well as optimizing the performance of existing RF channels. HiDRA is realized as a distributed FPGA memory and interface controller that is itself controlled by a local computer running DTN software. Thus HiDRA is applicable to other arenas seeking to employ next-generation communications technologies, e.g. deep space. In this paper, we describe HiDRA and its far-reaching research implications.
FASTER - A tool for DSN forecasting and scheduling
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 -- A Tool for DSN Forecasting and Scheduling
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.
Computational Approaches to Simulation and Optimization of Global Aircraft Trajectories
This study examines three possible approaches to improving the speed in generating wind-optimal routes for air traffic at the national or global level. They are: (a) using the resources of a supercomputer, (b) running the computations on multiple commercially available computers and (c) implementing those same algorithms into NASAs Future ATM Concepts Evaluation Tool (FACET) and compares those to a standard implementation run on a single CPU. Wind-optimal aircraft trajectories are computed using global air traffic schedules. The run time and wait time on the supercomputer for trajectory optimization using various numbers of CPUs ranging from 80 to 10,240 units are compared with the total computational time for running the same computation on a single desktop computer and on multiple commercially available computers for potential computational enhancement through parallel processing on the computer clusters. This study also re-implements the trajectory optimization algorithm for further reduction of computational time through algorithm modifications and integrates that with FACET to facilitate the use of the new features which calculate time-optimal routes between worldwide airport pairs in a wind field for use with existing FACET applications. The implementations of trajectory optimization algorithms use MATLAB, Python, and Java programming languages. The performance evaluations are done by comparing their computational efficiencies and based on the potential application of optimized trajectories. The paper shows that in the absence of special privileges on a supercomputer, a cluster of commercially available computers provides a feasible approach for national and global air traffic system studies.