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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Requirements Development Issues for Advanced Life Support Systems: Solid Waste Management

Long duration missions pose substantial new challenges for solid waste management in Advanced Life Support (ALS) systems. These possibly include storing large volumes of waste material in a safe manner, rendering wastes stable or sterilized for extended periods of time, and/or processing wastes for recovery of vital resources. This is further complicated because future missions remain ill-defined with respect to waste stream quantity, composition and generation schedule. Without definitive knowledge of this information, development of requirements is hampered. Additionally, even if waste streams were well characterized, other operational and processing needs require clarification (e.g. resource recovery requirements, planetary protection constraints). Therefore, the development of solid waste management (SWM) subsystem requirements for long duration space missions is an inherently uncertain, complex and iterative process. The intent of this paper is to address some of the difficulties in writing requirements for missions that are not completely defined. This paper discusses an approach and motivation for ALS SWM requirements development, the characteristics of effective requirements, and the presence of those characteristics in requirements that are developed for uncertain missions. Associated drivers for life support system technological capability are also presented. A general means of requirements forecasting is discussed, including successive modification of requirements and the need to consider requirements integration among subsystems.

Levri, Julie A.↗
NASA NTRS1996

Airplane takeoff and landing performance monitoring system

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V.sub.R) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane acceleration and engine-performance anomalies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a continually predicted nominal performance based upon given conditions, performance deficiencies are detected by the system and conveyed to pilot in form of both elemental information and integrated information.

Middleton, David B.↗
NASA NTRS1991

Airplane takeoff and landing performance monitoring system

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V.sub.R) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane and engine performance deficiencies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a predicted nominal performance based upon given conditions, performance deficiencies are detected by the system.

Middleton, David B.↗
NASA NTRS2020

MEXEC: An Onboard Integrated Planning and Execution Approach for Spacecraft Commanding

The traditional form of spacecraft commanding is with sequences that specify when commands should execute based on a schedule generated on the ground. Some sequences have control logic and event driven responses to increase flexibility, but it is limited. An approach to increase autonomy is to use goal-based planning and commanding. Using this paradigm, intention and behavior is modeled on board the spacecraft. In this paper we describe MEXEC (Multi-mission EXECutive), a multi-mission, task-based, onboard planning and execution software designed specifically to be used as flight software. As a path to infusion for future flight projects, we describe two experiments performed on the ASTERIA CubeSat and testbed that demonstrate that MEXEC can be integrated and used for spacecraft operations and increase robustness and science return compared to the standard sequences that were being used.

Campuzano, Brian↗
DOE OSTI2022

A Comparison of Machine Learning Methods for Frequency Nadir Estimation in Power Systems: Preprint

An increasing penetration level of inverter-based renewable energy resources changes the inertia of power systems, posing challenges for maintaining the desired system frequency stability. An accurate frequency nadir estimation is crucial for power system operators to prepare preventive actions against large frequency excursions. In this paper, five machine learning methods - linear regression, gradient boosting, support vector regression, an artificial neural network, and XGBoost - are applied to two different sets of preprocess data for the prediction of the frequency nadir in the Western Electricity Coordinating Council 240-bus system with high renewable penetration levels. The training and testing data sets are collected by extensive generation scheduling simulations on the Multi-timescale Integrated Dynamic and Scheduling (MIDAS) toolbox. Numerical results show that all five machine learning methods can achieve high performance accuracy for power system nadir frequency estimation. Among them, the gradient boosting and the XGBoost are clear winners by providing the best prediction accuracy.

data driven↗
DOE OSTI2023

Impact of Wildfires on Solar Resource Availability in California in a Changing Climate

Wildfires can emit large amounts of atmospheric particulate matters and influence not only air quality but also availability of photovoltaic (PV) generation due to scattering and absorption of solar radiation. Under anthropogenic changing climate, wildfire activity is projected to increase over western North America due to drier and warmer climate, implying increasing impact on solar resource and larger uncertainty in solar generation especially in regions with faster PV penetration. This study focuses on quantifying the impact of wildfires on aerosol optical depth (AOD) and thus solar resource over California using National Solar Radiation Database (NSRDB), developed by the National Renewable Energy Laboratory (NREL). This assessment includes historical analysis and estimation of solar resource under wildfire scenarios (2020 wildfire and an enhanced wildfire scenario based on 2020 wildfires). Historical analysis for the period of July-October 2019/2020 (low/high fire activity period) shows that the averaged global horizontal irradiance (GHI) and direct normal irradiance (DNI) are reduced by about 30 and 90 W m-2 (5.6 and 15.3%), respectively, during high fire activity period. To create AOD dataset for enhanced fire scenario, 165% increase in burn area (in around 2050) is selected based on comprehensive literature review, which is further applied to the Fire INventory from NCAR (FINN) fire emission. WRF-Chem model with the enhanced FINN emissions is used to simulate and represent a preserving spatial distribution of burned areas and wildfire-emitted aerosols. Our initial analysis suggests that the"enhanced 2020 wildfire" AOD can increase by a factor of 1.3 - 2, which can significantly reduce solar irradiance and increase uncertainty in generation and reliability of power system in extreme wildfire events under a high solar penetration scenario. Overall, this study provides an estimate of the impacts of wildfires on solar resource to make informed decisions on reserve planning, generation scheduling, and reliability investments.

aerosol optical depth↗
NASA NTRS1994

Automatic generation of efficient orderings of events for scheduling applications

In scheduling a set of tasks, it is often not known with certainty how long a given event will take. We call this duration uncertainty. Duration uncertainty is a primary obstacle to the successful completion of a schedule. If a duration of one task is longer than expected, the remaining tasks are delayed. The delay may result in the abandonment of the schedule itself, a phenomenon known as schedule breakage. One response to schedule breakage is on-line, dynamic rescheduling. A more recent alternative is called proactive rescheduling. This method uses statistical data about the durations of events in order to anticipate the locations in the schedule where breakage is likely prior to the execution of the schedule. It generates alternative schedules at such sensitive points, which can be then applied by the scheduler at execution time, without the delay incurred by dynamic rescheduling. This paper proposes a technique for making proactive error management more effective. The technique is based on applying a similarity-based method of clustering to the problem of identifying similar events in a set of events.

Morris, Robert A.↗
NASA NTRS2011

Scheduling Results for the THEMIS Observation Scheduling Tool

We describe a scheduling system intended to assist in the development of instrument data acquisitions for the THEMIS instrument, onboard the Mars Odyssey spacecraft, and compare results from multiple scheduling algorithms. This tool creates observations of both (a) targeted geographical regions of interest and (b) general mapping observations, while respecting spacecraft constraints such as data volume, observation timing, visibility, lighting, season, and science priorities. This tool therefore must address both geometric and state/timing/resource constraints. We describe a tool that maps geometric polygon overlap constraints to set covering constraints using a grid-based approach. These set covering constraints are then incorporated into a greedy optimization scheduling algorithm incorporating operations constraints to generate feasible schedules. The resultant tool generates schedules of hundreds of observations per week out of potential thousands of observations. This tool is currently under evaluation by the THEMIS observation planning team at Arizona State University.

Thermal Emission Imaging System (THEMIS)↗
NASA NTRS1996

ASTER Scheduling Prioritization Function

ASTER schedules are generated by an automated scheduling system. This scheduler will generate psuedo-optimal schedules based on a priority scheme. This priority scheme is controlled by the Science Team.

scheduling ASTER↗
NASA NTRS1990

PLAN-IT-2: The next generation planning and scheduling tool

PLAN-IT is a scheduling program which has been demonstrated and evaluated in a variety of scheduling domains. The capability enhancements being made for the next generation of PLAN-IT, called PLAN-IT-2 is discussed. PLAN-IT-2 represents a complete rewrite of the original PLAN-IT incorporating major changes as suggested by the application experiences with the original PLAN-IT. A few of the enhancements described are additional types of constraints, such as states and resettable-depletables (batteries), dependencies between constraints, multiple levels of activity planning during the scheduling process, pattern constraint searching for opportunities as opposed to just minimizing the amount of conflicts, additional customization construction features for display and handling of diverse multiple time systems, and reduction in both the size and the complexity for creating the knowledge-base to address the different problem domains.

Eggemeyer, William C.↗
NASA NTRS1999

Stochastic Modeling of Airlines' Scheduled Services Revenue

Airlines' revenue generated from scheduled services account for the major share in the total revenue. As such, predicting airlines' total scheduled services revenue is of great importance both to the governments (in case of national airlines) and private airlines. This importance stems from the need to formulate future airline strategic management policies, determine government subsidy levels, and formulate governmental air transportation policies. The prediction of the airlines' total scheduled services revenue is dealt with in this paper. Four key components of airline's scheduled services are considered. These include revenues generated from passenger, cargo, mail, and excess baggage. By addressing the revenue generated from each schedule service separately, air transportation planners and designers are able to enhance their ability to formulate specific strategies for each component. Estimation results clearly indicate that the four stochastic processes (scheduled services components) are represented by different Box-Jenkins ARIMA models. The results demonstrate the appropriateness of the developed models and their ability to provide air transportation planners with future information vital to the planning and design processes.

Hamed, M. M.↗
NASA NTRS1999

Stochastic Modeling of Airlines' Scheduled Services Revenue

Airlines' revenue generated from scheduled services account for the major share in the total revenue. As such, predicting airlines' total scheduled services revenue is of great importance both to the governments (in case of national airlines) and private airlines. This importance stems from the need to formulate future airline strategic management policies, determine government subsidy levels, and formulate governmental air transportation policies. The prediction of the airlines' total scheduled services revenue is dealt with in this paper. Four key components of airline's scheduled services are considered. These include revenues generated from passenger, cargo, mail, and excess baggage. By addressing the revenue generated from each schedule service separately, air transportation planners and designers arc able to enhance their ability to formulate specific strategies for each component. Estimation results clearly indicate that the four stochastic processes (scheduled services components) are represented by different Box-Jenkins ARIMA models. The results demonstrate the appropriateness of the developed models and their ability to provide air transportation planners with future information vital to the planning and design processes.

Hamed, M. M.↗
NASA NTRS2006

Projecting Future Scheduled Airline Demand, Schedules and NGATS Benefits Using TSAM

The Transportation Systems Analysis Model (TSAM) developed by Virginia Tech s Air Transportation Systems Lab and NASA Langley can provide detailed analysis of the effects on the demand for air travel of a full range of NASA and FAA aviation projects. TSAM has been used to project the passenger demand for very light jet (VLJ) air taxi service, scheduled airline demand growth and future schedules, Next Generation Air Transportation System (NGATS) benefits, and future passenger revenues for the Airport and Airway Trust Fund. TSAM can project the resulting demand when new vehicles and/or technology is inserted into the long distance (100 or more miles one-way) transportation system, as well as, changes in demand as a result of fare yield increases or decreases, airport transit times, scheduled flight times, ticket taxes, reductions or increases in flight delays, and so on. TSAM models all long distance travel in the contiguous U.S. and determines the mode choice of the traveler based on detailed trip costs, travel time, schedule frequency, purpose of the trip (business or non-business), and household income level of the traveler. Demand is modeled at the county level, with an airport choice module providing up to three airports as part of the mode choice. Future enplanements at airports can be projected for different scenarios. A Fratar algorithm and a schedule generator are applied to generate future flight schedules. This paper presents the application of TSAM to modeling future scheduled air passenger demand and resulting airline schedules, the impact of NGATS goals and objectives on passenger demand, along with projections for passenger fee receipts for several scenarios for the FAA Airport and Airway Trust Fund.

Dollyhigh, Samuel↗
NASA NTRS2022

SatNet: A Benchmark for Satellite Scheduling Optimization

Satellites provide essential services such as networking and weather tracking, and the number of near-earth and deep space satellites are expected to grow rapidly in the coming years. Communications with terrestrial ground stations is one of the critical functionalities of any space mission. Satellite scheduling is a problem that has been scientifically investigated since the 1970s. A central aspect of this problem is the need to consider resource contention and satellite visibility constraints as they require line of sight. Due to the combinatorial nature of the problem, prior solutions such as linear programs and evolutionary algorithms require extensive compute capabilities to output a feasible schedule for each scenario. Machine learning based scheduling can provide an alternative solution by training a model with historical data and generating a schedule quickly with model inference. We present SatNet, a benchmark for satellite scheduling optimization based on historical data from the NASA Deep Space Network. We propose formulation of the satellite scheduling problem as a Markov Decision Process and use reinforcement learning (RL) policies to generate schedules. The nature of constraints imposed by SatNet differ from other combinatorial optimization problems such as vehicle routing studied in prior literature. Our initial results indicate that RL is an alternative optimization approach that can generate candidate solutions of comparable quality to existing state-of-the-practice results. However, we also find that RL policies overfit to the training dataset and do not generalize well to new data, thereby necessitating continued research on reusable and generalizable agents.

Wilson, Brian↗
NASA NTRS1992

Generating effective project scheduling heuristics by abstraction and reconstitution

A project scheduling problem consists of a finite set of jobs, each with fixed integer duration, requiring one or more resources such as personnel or equipment, and each subject to a set of precedence relations, which specify allowable job orderings, and a set of mutual exclusion relations, which specify jobs that cannot overlap. No job can be interrupted once started. The objective is to minimize project duration. This objective arises in nearly every large construction project--from software to hardware to buildings. Because such project scheduling problems are NP-hard, they are typically solved by branch-and-bound algorithms. In these algorithms, lower-bound duration estimates (admissible heuristics) are used to improve efficiency. One way to obtain an admissible heuristic is to remove (abstract) all resources and mutual exclusion constraints and then obtain the minimal project duration for the abstracted problem; this minimal duration is the admissible heuristic. Although such abstracted problems can be solved efficiently, they yield inaccurate admissible heuristics precisely because those constraints that are central to solving the original problem are abstracted. This paper describes a method to reconstitute the abstracted constraints back into the solution to the abstracted problem while maintaining efficiency, thereby generating better admissible heuristics. Our results suggest that reconstitution can make good admissible heuristics even better.

Janakiraman, Bhaskar↗
NASA NTRS1997

Automatic Generation of Heuristics for Scheduling

This paper presents a technique, called GenH, that automatically generates search heuristics for scheduling problems. The impetus for developing this technique is the growing consensus that heuristics encode advice that is, at best, useful in solving most, or typical, problem instances, and, at worst, useful in solving only a narrowly defined set of instances. In either case, heuristic problem solvers, to be broadly applicable, should have a means of automatically adjusting to the idiosyncrasies of each problem instance. GenH generates a search heuristic for a given problem instance by hill-climbing in the space of possible multi-attribute heuristics, where the evaluation of a candidate heuristic is based on the quality of the solution found under its guidance. We present empirical results obtained by applying GenH to the real world problem of telescope observation scheduling. These results demonstrate that GenH is a simple and effective way of improving the performance of an heuristic scheduler.

Morris, Robert A.↗
NASA NTRS1994

Advanced timeline systems

The Mission Planning Division of the Mission Operations Laboratory at NASA's Marshall Space Flight Center is responsible for scheduling experiment activities for space missions controlled at MSFC. In order to draw statistically relevant conclusions, all experiments must be scheduled at least once and may have repeated performances during the mission. An experiment consists of a series of steps which, when performed, provide results pertinent to the experiment's functional objective. Since these experiments require a set of resources such as crew and power, the task of creating a timeline of experiment activities for the mission is one of resource constrained scheduling. For each experiment, a computer model with detailed information of the steps involved in running the experiment, including crew requirements, processing times, and resource requirements is created. These models are then loaded into the Experiment Scheduling Program (ESP) which attempts to create a schedule which satisfies all resource constraints. ESP uses a depth-first search technique to place each experiment into a time interval, and a scoring function to evaluate the schedule. The mission planners generate several schedules and choose one with a high value of the scoring function to send through the approval process. The process of approving a mission timeline can take several months. Each timeline must meet the requirements of the scientists, the crew, and various engineering departments as well as enforce all resource restrictions. No single objective is considered in creating a timeline. The experiment scheduling problem is: given a set of experiments, place each experiment along the mission timeline so that all resource requirements and temporal constraints are met and the timeline is acceptable to all who must approve it. Much work has been done on multicriteria decision making (MCDM). When there are two criteria, schedules which perform well with respect to one criterion will often perform poorly with respect to the other. One schedule dominates another if it performs strictly better on one criterion, and no worse on the other. Clearly, dominated schedules are undesireable. A nondominated schedule can be generated by some sort of optimization problem. Generally there are two approaches: the first is a hierarchical approach while the second requires optimizing a weighting or scoring function.

Bulfin, R. L.↗
NASA NTRS1994

Robust telescope scheduling

This paper presents a technique for building robust telescope schedules that tend not to break. The technique is called Just-In-Case (JIC) scheduling and it implements the common sense idea of being prepared for likely errors, just in case they should occur. The JIC algorithm analyzes a given schedule, determines where it is likely to break, reinvokes a scheduler to generate a contingent schedule for each highly probable break case, and produces a 'multiply contingent' schedule. The technique was developed for an automatic telescope scheduling problem, and the paper presents empirical results showing that Just-In-Case scheduling performs extremely well for this problem.

Swanson, Keith↗