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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Spot and Departure Runway Advisor (SARDA)

Spot and Runway Departure Advisor (SARDA) is a decision support tool to assist airline ramp controllers and ATC tower controllers to manage traffic on the airport surface to significantly improve efficiency and predictability in surface operations. The core function of the tool is the runway scheduler which generates an optimal solution for runway sequence and schedule of departure aircraft, which would minimize system delay and maximize runway throughput. The presentation describes the concept of the SARDA tool and results from human-in-the-loop simulations conducted in 2012 for Dallas-Ft. Worth International Airport. The presentation also discusses the latest status of NASA's current surface research through a collaboration with an airline partner, where a tool is developed for airline ramp operators to assist departure pushback operations.

Departure Schedule↗
NASA NTRS2017

Magnetic Shield Design Modeling and Validation on SWOT Spacecraft Applied to Mars Flux Pinning Orbiting Sample Design

A modeling methodology was developed for use on the Surface Water and Ocean Topography (SWOT) mission for the computational modeling and design of a magnetic shield for a 63 A-sq m source. Shield options were modeled and tested across various design parameters and validated with measurement. Measurement results fell within 10% of simulation in most cases of concern with sources of error well understood. These methods results informed a subsequent modeling activity for a future Mars Sample Return (MSR) mission concept with stringent magnetic cleanliness requirements to retain geologic integrity of the samples. One option for capturing the sample from orbit requires rare earth magnets in close proximity to the Martian samples. Magnetic modeling with a finite-element method solver estimated the magnetic environment and established the need for shielding. Further modeling then determined the shielding necessary to meet magnetic requirements, followed by the design of a mass-optimized solution.

Gonzales, Edward↗
NASA NTRS2019

End to End Optimization of a Mars Hybrid Transportation Architecture

NASA’s Mars Study Capability Team (MSCT) is developing a reusable Mars hybrid transportation architecture in which both chemical and solar electric propulsion systems are used in a single vehicle design to send crew and cargo to Mars. This paper presents a new integrated framework that combines Earth departure/arrival, heliocentric trajectory, Mars orbit reorientation, and vehicle sizing into a single environment and solves the entire mission from beginning to end in an effort to find a globally optimized solution for the hybrid architecture.

Qu, Min↗
NASA NTRS2011

TPSAS-NF1676L-12321-DND

The operation of some networks, such as air transportation networks, can be complicated by congestion through a small subset of nodes. The congestion may be influenced by the connectivity of the network, or by the presence of constraints restricting the flow through particular nodes. This work investigates the effects of both connectivity and node flow constraints on the operation of a network. We develop the Minimax Node Load Problem (MNLP), a multicommodity flow model which minimizes the worst-case flow through any node in a given input network. The optimal solution to this problem provides us with the minimax node load, which we propose as a measure of network congestion. Keeping the number of nodes fixed, we first increase connectivity in a series of networks, and observe that topologies with more distributed connections result in a reduction in the minimax node load. However, when connectivity is increased further, the reductions diminish and are accompanied by solutions with undesirable qualities such as longer commodity paths. We then perform a second set of experiments over the same network, constraining flow through different subsets of nodes at different magnitudes of flow restriction, finding that (1) more constrained nodes lead to the largest increases in minimax node load and (2) constraints on the most connected nodes have the greatest effect on both congestion and commodity path length.

Douglas W Lee↗
NASA NTRS2011

TPSAS-NF1676L-12264-DND

The operation of some networks, such as air transportation networks, can be complicated by congestion through a small subset of nodes. The congestion may be influenced by the connectivity of the network, or by the presence of constraints restricting the flow through particular nodes. This work investigates the effects of both connectivity and node flow constraints on the operation of a network. We develop the Minimax Node Load Problem (MNLP), a multicommodity flow model which minimizes the worst-case flow through any node in a given input network. The optimal solution to this problem provides us with the minimax node load, which we propose as a measure of network congestion. Keeping the number of nodes fixed, we first increase connectivity in a series of networks, and observe that topologies with more distributed connections result in a reduction in the minimax node load. However, when connectivity is increased further, the reductions diminish and are accompanied by solutions with undesirable qualities such as longer commodity paths. We then perform a second set of experiments over the same network, constraining flow through different subsets of nodes at different magnitudes of flow restriction, finding that (1) more constrained nodes lead to the largest increases in minimax node load and (2) constraints on the most connected nodes have the greatest effect on both congestion and commodity path length.

Douglas Lee↗
NASA NTRS2021

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail.

mission design↗
NASA NTRSDate not supplied

Autonomous Spacecraft Attitude Control Using Deep Reinforcement Learning

While machine learning and spacecraft autonomy continue to gain research interest, significant work remains to be done in efficiently applying modern machine learning techniques to problems in space ight. This study presents a framework for deriving a discrete neural spacecraft attitude controller using reinforcement learning, a paradigm of machine learning, without the need for high-performance computing. The developed attitude controller is an approximately time-optimal solution to a highly constrained control problem, able to achieve well above industry-standard pointing accuracies. Control examples are also presented of the agent performing large-angle spacecraft slews in the developed simulation environment and future extensions of this work are discussed.

ATAP↗
NASA NTRS2004

Evolutionary computing for spacecraft power subsystem design search and optimization

Multi-objective optimization involves finding one or more optimal solutions when there is more than one conflicting objective. This means that a solution that is better in one objective compromises or trades-off, other objectives. Trade Studies are conducted by flight projects to create mission concepts with different trade-off solutions for mass, cost, performance and risk.

Hua, Hook↗
NASA NTRS2020

Scheduling Mission Reconfiguration for an Interferometry Synthetic Aperture Radar Using Deep Reinforcement Learning

This paper presents a method to intelligently adapt the baseline of a synthetic aperture radar based on Deep Rein- forcement Learning to help create plans for missions that use formation flight for Earth observation purposes. The main contribution of this paper is the initial results we have found from applying the tool to a toy mission: measuring the ver- tical structure of forests by using a synthetic aperture radar mounted on a formation of 7 satellites orbiting the Earth in a Sun Synchronous Orbit. We have found that with a reward function based on expected science return over time and fuel usage, the Deep Reinforcement Learning planner is able to create plans with positive scientific returns while minimizing fuel usage. We also find that fuel usage and collision avoid- ance planning is better done with traditional methods, as Deep Reinforcement Learning does not converge to optimal solutions.

Viros-i-Martin, Antoni↗
NASA NTRSDate not supplied

Simultaneous Aerosol and Ocean Polarimeter Products Using Coupled Atmosphere-Ocean Vector Radiative Transfer and Neural Networks: The PACE-MAPP Algorithm

We describe the PACE-MAPP algorithm that simultaneously retrieves aerosol and ocean optical parameters using multiangle and multi-channel polarimeter measurements from the SPEXone, Hyper-Angular Rainbow Polarimeter 2 (HARP2), and Ocean Color Instrument (OCI) instruments onboard the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observing system PACE-MAPP is adapted from the Research Scanning Polarimeter (RSP) Microphysical Aerosol Properties from Polarimetry (RSP-MAPP) algorithm. A key feature of the MAPP family of algorithms is the use of a coupled vector radiative transfer model such that the atmosphere and ocean are always considered together as one system. Consequently, conservation of energy ensures that negative water-leaving radiances do not occur. PACE-MAPP uses optimal estimation to simultaneously characterize the optical and microphysical properties of aerosol and ocean constituents, find the optimal solution, and reliably account for the uncertainties of each parameter. This coupled approach, together with multiangle, multi-channel polarimeter measurements, will enable retrievals of aerosol and water properties across the Earth’s oceans. The PACE-MAPP algorithm provides aerosol and ocean products for both the open ocean and coastal areas and is designed to be accurate, modular, and efficient by using fast neural networks that replace the time-consuming vector radiative transfer calculations. We provide an overview of the PACE-MAPP framework and also describe its modular components including its aerosol and hydrosol models, ocean bio-optical models, and thin cirrus model.

Snorre Stamnes↗
NASA NTRSDate not supplied

Enabling Thread Safety and Parallelism in the Program to Optimize Simulated Trajectories II

Development of the Program to Optimize Simulated Trajectories (POST) began in the 1970s. Since then, it has become widely utilized across NASA, industry, and academia to solve a variety of atmospheric ascent and entry problems. Its successor, POST2, has undergone many upgrades since its release in the 1990s. Recently, there has been an increasing desire to take advantage of the advances in parallel computing for both offline and online systems. Thus, modifications were made to allow POST2 to simulate multiple trajectories simultaneously without adversely affecting results. This capability is leveraged to calculate optimization solutions in parallel as opposed to sequentially. A demonstration of the benefits is presented using a small set of POST2 regression tests, as well as a project simulating a human-scale Lunar lander.

R. Anthony Williams↗
NASA NTRSDate not supplied

Enabling Thread Safety and Parallelism in the Program to Optimize Simulated Trajectories II

Development of the Program to Optimize Simulated Trajectories (POST) began in the 1970s. Since then, it has become widely utilized across NASA, industry, and academia to solve a variety of atmospheric ascent and entry problems. Its successor, POST2, has undergone many upgrades since its release in the 1990s. Recently, there has been an increasing desire to take advantage of the advances in parallel computing for both offline and online systems. Thus, modifications were made to allow POST2 to simulate multiple trajectories simultaneously without adversely affecting results. This capability is leveraged to calculate optimization solutions in parallel as opposed to sequentially. A demonstration of the benefits is presented using a small set of POST2 regression tests, as well as a project simulating a human-scale Lunar lander.

Anthony Williams↗
NASA NTRS2023

Analysis of Strategic Conflict Management Approaches as Applied to Simulated UAM Operations

This report presents the results of a comparison analysis of candidate Strategic Conflict Management (SCM) strategies as applied to Urban Air Mobility (UAM) operations. The SCM strategies investigated included: no SCM, resource scheduling (RS), resource flow rates (FR), area-based flow rates (AR), and conflict detection and resolution (CR). The study evaluated each strategy against the same 3 levels of flight demand in a representative airspace construct for the Dallas/Fort Worth region. The study also accounted for different levels of uncertainty in operational planning and equivalent levels of trajectory following error. In the analysis, we compared the SCM strategies’ effectiveness in reducing the need for the tactical conflict management layer to act and looked at the metrics of unmitigated losses-of-separation (LOS), flight delays imposed by strategic planning, and throughput of the overall airspace. The study results indicated that the CR strategy was the most effective at reducing LOS and percentage of flights with LOS, even in the presence of trajectory error when uncertainty is accounted for in planning. Thus, if the objective is to reduce the number of actions that the tactical conflict management layer will have to take, in terms of conflicts that may need to be resolved, CR is the strategy to use. The FR strategy was found to be the worst at reducing LOS and percentage of flights with LOS. Thus, it is not very effective at reducing the actions required by a tactical conflict management layer. In the airspace tested, the demand was high enough to produce unacceptable levels of flight delay and reductions of throughput when the SCM strategies were implemented. This was especially evident at the higher levels of trajectory uncertainty and error. The need to account for expected levels of uncertainty becomes more and more important as the demand level increases and as the level of trajectory error increases. This is because the likelihood of flights with LOS increases as the density of operations increases and as the level of trajectory error increases. Accounting for uncertainty in the SCM strategies improves the strategy effectiveness with respect to LOS but increases delays and reduces throughput. Thus, there is a tradeoff between scalability and allowable levels of uncertainty. That is, we can implement an air traffic management construct that allows high levels of uncertainty, but we can expect that same system to have limits on scalability that may be evident even at small demand levels, such as those used in this study. Therefore, the results in this study indicate that an air traffic system should attempt to implement mechanisms appropriate for reducing uncertainty where possible in order to increase the chances for scalability. And this increased level of predictability of operations needs to be balanced with mechanisms for ensuring flexibility when operational conditions and plans need to change, even though those types of changes should be the exception rather than the rule under normal conditions. The study also introduced a trade space that could serve as a mechanism for selecting the appropriate SCM strategy in a trade-off between uncertainty and error, mean flight delay, and the LOS metrics (which this study equates to the potential for tactical conflict management actions). The optimal solution for a given airspace, demand level, and other factors, could likely be a combination of SCM strategies, although this study only compared the use of a single SCM strategy at a time. The CR strategy appeared to have the best opportunity for scalability by limiting the number of potential tactical actions to nearly zero.

Strategic Conflict Management↗
NASA NTRS2022

Solar Polar Flux Redistribution Based on Observed Coronal Holes

We explore the use of observed polar coronal holes (CHs) to constrain the flux distribution within the polar regions of global solar magnetic field maps in the absence of reliable quality polar field observations. Global magnetic maps, generated by the Air Force Data Assimilative Photospheric flux Transport (ADAPT) model, are modified to enforce field unipolarity thresholds both within and outside observed CH boundaries. The polar modified and unmodified maps are used to drive Wang–Sheeley–Arge (WSA) models of the corona and solar wind (SW). The WSA-predicted CHs are compared with the observations, and SW predictions at the WIND and Ulysses spacecraft are also used to provide context for the new polar modified maps. We find that modifications of the polar flux never worsen and typically improve both the CH and SW predictions. We also confirm the importance of the choice of the domain over which WSA generates the coronal magnetic field solution but find that solutions optimized for one location in the heliosphere can worsen predictions at other locations. Finally, we investigate the importance of low-latitude (i.e., active region) magnetic fields in setting the boundary of polar CHs, determining that they have at least as much impact as the polar fields themselves.

Solar coronal holes↗
NASA NTRSDate not supplied

Avoiding Selection Bias in Generating Examples of Plans in the Presence of Heuristic Error

It is generally understood that heuristic error hurts the performance of search algorithms, measured in terms of search effort. Hence there is an interest in understanding how to reduce heuristic error. One way to do this is to learn a heuristic from a set of examples of plans generated offline, e.g. bootstrapping methods. In this paper, we consider how some methods for generating examples of plans may skew the training set in the presence of heuristic errors. Initial theoretical results show that duplicate detection is one source of selection bias in the canonical A* algorithm. We introduce a duplicate selection scheme for A* that avoids selection bias in generating cost-optimal examples, without compromising memory efficiency, and develop ideas in the satisficing setting. We evaluate our approach on n x m grids with multiple cost-optimal solutions and synthetic heuristic error. Finally, we attempt to extend these ideas to the problem of generating extreme examples of plans.

Alison S Paredes↗
NASA NTRS2021

Monte Carlo Tree Search Methods for the Earth-Observing Satellite Scheduling Problem

This work explores on-board planning for the single spacecraft, multiple ground station Earth-observing satellite scheduling problem through artificial neural network function approximation of state–action value estimates generated by Monte Carlo tree search (MCTS). An extensive hyperparameter search is conducted for MCTS on the basis of performance, safety, and downlink opportunity utilization to determine the best hyperparameter combination for data generation. A hyperparameter search is also conducted on neural network architectures. The learned behavior of each network is explored, and each network architecture’s robustness to orbits and epochs outside of the training distributions is investigated. Furthermore, each algorithm is compared with a genetic algorithm, which serves to provide a baseline for optimality. MCTS is shown to compute near-optimal solutions in comparison to the genetic algorithm. The state–action value networks are shown to match or exceed the performance of MCTS in six orders of magnitude less execution time, showing promise for execution on board spacecraft.

Adam P. Herrmann↗
NASA NTRSDate not supplied

An Automated Medical Inventory System (AMIS) to Enable Earth-Independent Medical Operations

BACKGROUND: Inventory of medical consumables and durables (medications, treatment aids, diagnostic equipment, etc.) aboard the International Space Station is a manual process whereby crewmembers reach out to their flight surgeon to relay when items are used. Performing a full medical system inventory is time intensive. However, as exploration progresses to long duration missions with little to no resupply or evacuation capabilities, maintaining an accurate account of inventory and location for medical systems across the mission will become increasingly critical. A new system must be developed for future exploration missions to meet the need for a crew-facing, real time method of managing medical inventory. OVERVIEW: NASA’s Exploration Medical Integrated Product Team (XMIPT) is funding the AMIS project to mature the technology readiness level and to conduct a flight demonstration of a medical inventory capability. AMIS will leverage lessons learned from a Medical Consumables Tracking project previously demonstrated aboard the ISS in 2016 and 2017. Key components of AMIS include a database, supporting hardware and software, and interfaces to power, communications, or other vehicle or medical systems. Some medical inventory capability may be provided by the vehicle inventory management system which relies upon RFID-based technology and can track larger items such as medical kits or medical hardware. AMIS will augment these capabilities to enable tracking of individual medical kit contents. Efforts are underway to characterize the optimal solution trade space by comparing system specifications (e.g. mass and volume, etc.) across maintenance and operational use cases (e.g. crew time saved, total inventory automated, etc). DISCUSSION: The contents of a Mars Medical System have not been fully defined which poses challenges to defining an inventory system and requires assumptions regarding medical kit contents and medical system design. Other important considerations include minimizing crew time required, avoiding access restrictions to medical inventory in the event of an emergency, and ensuring that data is accessible to other medical system elements to enable crew autonomy in provision of medical care.

Automated Medical Inventory System↗
NASA NTRSDate not supplied

Rolling Horizon with K-Position Search Method for Strategic Deconfliction of Package Delivery UAS

In this research, the strategic deconfliction of unmanned aircraft systems for an urban package delivery environment with two depots and multiple drop-off locations is studied. This research aims to formulate a mathematical model to compute both the departure sequence and scheduled time of departure for each unmanned aircraft system at a depot, considering temporal constraints at en-route crossing waypoints and depots for strategic deconfliction. However, the problem formulation results in an NP-hard mixed-integer nonlinear programming problem for the global optimal solution, so instead, a "rolling horizon with𝑘-position search"heuristic method is developed. The simulation studies show that an increase in the value of𝑘(the parameter used to determine the size of the local neighborhood) reduces the average ground delay at the cost of an increase in the computation time for a given problem size. The study also shows an order of magnitude increase in the maximum number of flights scheduled with the integration of rolling horizon (time decomposition) compared to those without the integration of rolling horizon in the heuristic algorithm for a given computation time cut off.

UTM↗