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NASA NTRS1990

Neural Network Solves "Traveling-Salesman" Problem

Experimental electronic neural network solves "traveling-salesman" problem. Plans round trip of minimum distance among N cities, visiting every city once and only once (without backtracking). This problem is paradigm of many problems of global optimization (e.g., routing or allocation of resources) occuring in industry, business, and government. Applied to large number of cities (or resources), circuits of this kind expected to solve problem faster and more cheaply.

Thakoor, Anilkumar P.↗
NASA NTRS2010

Optimizing Flight Departure Delay and Route Selection Under En Route Convective Weather

This paper presents a linear Integer Programming model for managing air traffic flow in the United States. The decision variables in the model are departure delays and predeparture reroutes of aircraft whose trajectories are predicted to cross weather-impacted regions of the National Airspace System. The model assigns delays to a set of flights while ensuring their trajectories are free of any conflicts with weather. In a deterministic setting, there is no airborne holding due to unexpected weather incursion in a flight s path. The model is applied to solve a large-scale traffic flow management problem with realistic weather data and flight schedules. Experimental results indicate that allowing rerouting can reduce departure delays by nearly 57%, but it is associated with an increase in total airborne time due to longer routes flown by aircraft. The computation times to solve this problem were significantly lower than those reported in the earlier studies.

Mukherjee, Avijit↗
NASA NTRS2014

Agent Based Modeling of Air Carrier Behavior for Evaluation of Technology Equipage and Adoption

As part of ongoing research, the National Aeronautics and Space Administration (NASA) and LMI developed a research framework to assist policymakers in identifying impacts on the U.S. air transportation system (ATS) of potential policies and technology related to the implementation of the Next Generation Air Transportation System (NextGen). This framework, called the Air Transportation System Evolutionary Simulation (ATS-EVOS), integrates multiple models into a single process flow to best simulate responses by U.S. commercial airlines and other ATS stakeholders to NextGen-related policies, and in turn, how those responses impact the ATS. Development of this framework required NASA and LMI to create an agent-based model of airline and passenger behavior. This Airline Evolutionary Simulation (AIRLINE-EVOS) models airline decisions about tactical airfare and schedule adjustments, and strategic decisions related to fleet assignments, market prices, and equipage. AIRLINE-EVOS models its own heterogeneous population of passenger agents that interact with airlines; this interaction allows the model to simulate the cycle of action-reaction as airlines compete with each other and engage passengers. We validated a baseline configuration of AIRLINE-EVOS against Airline Origin and Destination Survey (DB1B) data and subject matter expert opinion, and we verified the ATS-EVOS framework and agent behavior logic through scenario-based experiments. These experiments demonstrated AIRLINE-EVOS's capabilities in responding to an input price shock in fuel prices, and to equipage challenges in a series of analyses based on potential incentive policies for best equipped best served, optimal-wind routing, and traffic management initiative exemption concepts..

Horio, Brant M.↗
NASA NTRS2010

Design and Evaluation of a Dynamic Programming Flight Routing Algorithm Using the Convective Weather Avoidance Model

The optimization of traffic flows in congested airspace with varying convective weather is a challenging problem. One approach is to generate shortest routes between origins and destinations while meeting airspace capacity constraint in the presence of uncertainties, such as weather and airspace demand. This study focuses on development of an optimal flight path search algorithm that optimizes national airspace system throughput and efficiency in the presence of uncertainties. The algorithm is based on dynamic programming and utilizes the predicted probability that an aircraft will deviate around convective weather. It is shown that the running time of the algorithm increases linearly with the total number of links between all stages. The optimal routes minimize a combination of fuel cost and expected cost of route deviation due to convective weather. They are considered as alternatives to the set of coded departure routes which are predefined by FAA to reroute pre-departure flights around weather or air traffic constraints. A formula, which calculates predicted probability of deviation from a given flight path, is also derived. The predicted probability of deviation is calculated for all path candidates. Routes with the best probability are selected as optimal. The predicted probability of deviation serves as a computable measure of reliability in pre-departure rerouting. The algorithm can also be extended to automatically adjust its design parameters to satisfy the desired level of reliability.

Ng, Hok K.↗
NASA NTRS2011

Cross-Polar Aircraft Trajectory Optimization and Potential Climate Impact

Cross-Polar routes offer new opportunities for air travel markets. Transpolar flights reduce travel times, fuel burns, and associated environmental emissions by flying direct paths between many North American and Asian cities. This study evaluates the potential benefits of flying wind-optimal polar routes and assessed their potential impact on climate change. An optimization algorithm is developed for transpolar flights to generate wind-optimal trajectories that minimize climate impact of aircraft, in terms of global warming potentials (relative to warming by one kg of CO2) of several types of emissions, while avoiding regions of airspace that facilitate persistent contrail formation. Estimations of global warming potential are incorporated into the objective function of the optimization algorithm to assess the climate impact of aircraft emissions discharged at a given location and altitude. The regions of airspace with very low ambient temperature and areas favorable to persistent contrail formation are modeled as undesirable regions that aircraft should avoid and are formulated as soft state constraints. The fuel burn and climate impact of cross-polar air traffic flying various types of trajectory including flightplan, great circle, wind-optimal, and contrail-avoidance are computed for 15 origin-destination pairs between major international airports in the U.S. and Asia. Wind-optimal routes reduce average fuel burn of flight plan routes by 4.4% on December 4, 2010 and 8.0% on August 7, 2010, respectively. The tradeoff between persistent contrail formation and additional global warming potential of aircraft emissions is investigated with and without altitude optimization. Without altitude optimization, the reduction in contrail travel times is gradual with increase in total fuel consumption. When altitude is optimized, a one percent increase in additional global warming potential, a climate impact equivalent to that of 4070kg and 4220kg CO2 emission, reduces 135 and 105 minutes persistent contrail formation per flight during a day with medium and high contrail formation, respectively.

Sridhar, Banavar↗
NASA NTRS2011

Cross-Polar Aircraft Trajectory Optimization and the Potential Climate Impact

Cross-Polar routes offer new opportunities for air travel markets. Transpolar flights reduce travel times, fuel burns, and associated environmental emissions by flying direct paths between many North American and Asian cities. This study evaluates the potential benefits of flying wind-optimal polar routes and assessed their potential impact on climate change. An optimization algorithm is developed for transpolar flights to generate wind-optimal trajectories that minimize climate impact of aircraft, in terms of global warming potentials (relative to warming by one kg of CO2) of several types of emissions, while avoiding regions of airspace that facilitate persistent contrail formation. Estimations of global warming potential are incorporated into the objective function of the optimization algorithm to assess the climate impact of aircraft emissions discharged at a given location and altitude. The regions of airspace with very low ambient temperature and areas favorable to persistent contrail formation are modeled as undesirable regions that aircraft should avoid and are formulated as soft state constraints. The fuel burn and climate impact of cross-polar air traffic flying various types of trajectory including flight plan, great circle, wind-optimal, and contrail-avoidance are computed for 15 origin-destination pairs between major international airports in the U.S. and Asia. Wind-optimal routes reduce average fuel burn of flight plan routes by 4.4% on December 4, 2010 and 8.0% on August 7, 2010, respectively. The tradeoff between persistent contrail formation and additional global warming potential of aircraft emissions is investigated with and without altitude optimization. Without altitude optimization, the reduction in contrail travel times is gradual with increase in total fuel consumption. When altitude is optimized, a one percent increase in additional global warming potential, a climate impact equivalent to that of 4070kg and 4220kg CO2 emission, reduces 135 and 105 minutes persistent contrail formation per flight during a day with medium and high contrail formation, respectively.

Ng, Hok K.↗
NASA NTRS2020

Thermal system and environmental testing of the Mars Helicopter

The Mars Helicopter will be a technology demonstration conducted during the Mars 2020 mission. The primary mission objective is to achieve several 90-second flights demonstrating the feasibility of heavier than air flight on Mars and capture visible light images via forward and nadir mounted cameras. These flights could possibly provide reconnaissance data for sampling site selection for other Mars surface missions. A solar array and batteries for flight operations, imaging, communications, and survival heating power the Helicopter. The thermal design is driven by minimizing survival heater energy while maintaining compliance with allowable flight temperatures in a variable thermal environment. Due to the small size of the Helicopter and its complex geometries, along with the fact that it operates with very low power and small margins in the extreme Mars environment, additional care had to be paid while planning thermal tests and designing the thermal system. The first section of the paper describes the evolution of the thermal system of the Mars Helicopter. After the first thermal vacuum test of the engineering model, the thermal team has conducted a partial effect analysis on the thermal design components that had a major impact on the system performance. Several design choices derived by analysis and test have been made to meet the energy allocation and the temperature requirements. These changes included increased gas gaps to reduce gas conduction, low emissivity coatings for internal components, blanket implementation, optimization of wire routing and fine-tuning of surface operations to optimize waste heat recovery. The second part of the paper describes the flight model thermal vacuum test and the subsequent thermal model correlation necessary to confirm the fidelity of the analysis results.

Pauken, Michael↗
NASA NTRSDate not supplied

Multigraph-based Routing in Delay Tolerant Networks: An Alternative to Contact Graph Routing

Satellites are leaving the realms of niche use, extending our day-to-day networked infrastructure to space – thereby forcing a generalization of network architectures. The Delay Tolerant Networking (DTN) protocol is being developed to give rise to this new Solar System Internet. Predominantly, DTNs in space use globally-distributed contact tables to compute routes. In this paper, we propose and analyze a novel optimized approach for route computations that improves upon traditional approaches. As the general DTN will always include some scheduled links, our new algorithm enables greater scalability and practicality of DTN routing. These contact tables include windows when two nodes can communicate and were classically organized into a contact graph, where the vertices represent contact opportunities. Because the complexity of a contact graph grows with the number of contacts, pathfinding on it does not scale. A new structure using multigraphs with the same data is proposed. We show that a multigraph-based approach, which we call contact multigraph routing, exhibits performance superior to routing based on contact graphs, allowing greater scaling to schedule-based routing. In this paper, the multigraph-based algorithm is detailed and a proof is included showing it outperforms the previous algorithm given the same input. Pseudocode is included, as are simulation results. We conclude with suggested future work.

Delay Tolerant Networking↗
NASA NTRS1990

Depth-first search approach for fault-tolerant routing in hypercube multicomputers

Using depth-first search, the authors develop and analyze the performance of a routing scheme for hypercube multicomputers in the presence of an arbitrary number of faulty components. They derive an exact expression for the probability of routing messages by way of optimal paths (of length equal to the Hamming distance between the corresponding pair of nodes) from the source node to an obstructed node. The obstructed node is defined as the first node encountered by the message that finds no optimal path to the destination node. It is noted that the probability of routing messages over an optimal path between any two nodes is a special case of the present results and can be obtained by replacing the obstructed node with the destination node. Numerical examples are given to illustrate the results, and they show that, in the presence of component failures, depth-first search routing can route a message to its destination by means of an optimal path with a very high probability.

Chen, Ming-Syan↗
NASA NTRSDate not supplied

Toward Time Synchronization in Delay Tolerant Network based Solar System Internetworking

The expanding presence in space will place an increased dependency on networked communications– a scalable communications infrastructure; that is, the Solar System Internet (SSI). Upcoming developments towards a SSI include NASA’s upcoming LunaNet, or lunar Internet, which provides multi-hop multi-path communications using Delay Tolerant Networking (DTN). DTN has been an active area of research and development, particularly in routing, security, and optimization. DTNs are marked by mobility, disconnection, and a wide variance of latencies (propagation and processing delays). In this paper, we outline progress towards a theory of time synchronization across such a network. An underlying assumption of DTN is that the network is time synchronized already, rather than synchronization being provided as a service. While this is necessary for schedule-based routing, which is necessarily prevalent in DTNs, it is so deeply ingrained as to be built into the primary unit of data in DTNs– the bundle. Indeed, a bundle’s creation timestamp and its time to live (called the lifetime) are based on time, and there are special recommendations for systems that lack accurate clocks. The assumption of time synchronization makes sense when limiting considerations to smaller-scale and more traditional space communication. However, just as end-to-end connectivity cannot be guaranteed in DTNs, neither can access to a reference or authoritative clock. In this more general case, it might be necessary to synchronize over time-varying meshes, and perhaps even to consider relativistic effects. Moreover, by imposing synchronization restrictions in order to sustain a network, the effectiveness of the network to achieve scalability will be necessarily muted. To work towards a time synchronization theory for DTNs, we build upon past successes in modeling DTNs using time-varying graphs and sheaves. This includes error and limitation estimation, which allows one to define domains over which schedule-based routing is possible, up to some threshold sensitivity. Despite the theoretical nature of these results, the approaches taken are also algorithmic, and hence lend themselves to practical implementations. The paper concludes with comparisons of the various methods along with suggestions for future work.

Delay Tolerant Networking↗
NASA NTRS2011

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.

Grabbe, Shon↗
NASA NTRSDate not supplied

Fine Optimization of TES Design for the X-IFU Instrument on ATHENA

The X-ray Integral Field Unit (X-IFU) instrument on the Advanced Telescope for High ENergy Astrophysics (ATHENA) is baselined to have 3168 transition-edge sensor (TES) microcalorimeter pixels. These pixels will be DC biased and readout using time division multiplexing. We recently reported a demonstration of the required pixel and readout performance in 252 pixels from a 1 kilo-pixel array (e.g. 2.16 eV resolution for 6.9 keV X-rays). Having achieved the required performance, the 50um square TES design in this demonstration array is considered the baseline pixel design for X-IFU. However, by making small adjustments to the TES design, we may be able to make small but consequential improvements to the instrument performance, while staying within the many constraints of the X-IFU requirements. In this presentation, we will show results from newly fabricated devices with subtle changes from the baseline TES design to further optimize the performance for X-IFU. We will discuss optimizing the TES sheet resistance, normal metal features, X-ray absorber attachment points, TES aspect ratio, and heat capacity. These minor design changes can have significant effects on thermo-electric time constants, thermal fluctuation noise, resistive transition shape and uniformity, and non-linearity of X-ray event pulses. In addition, full characterization of these new TES designs allows greater understanding of the relevant physics within the TES and gives routes for further optimization.

Nick Wakeham↗
NASA NTRS2010

A Mixed Integer Linear Program for Solving a Multiple Route Taxi Scheduling Problem

Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.

Montoya, Justin Vincent↗
NASA NTRS1993

Analog Processor To Solve Optimization Problems

Proposed analog processor solves "traveling-salesman" problem, considered paradigm of global-optimization problems involving routing or allocation of resources. Includes electronic neural network and auxiliary circuitry based partly on concepts described in "Neural-Network Processor Would Allocate Resources" (NPO-17781) and "Neural Network Solves 'Traveling-Salesman' Problem" (NPO-17807). Processor based on highly parallel computing solves problem in significantly less time.

Duong, Tuan A.↗
NASA NTRS2016

Proof-of-Concept Demonstrations of a Flight Adjustment Logging and Communication Network

The National Airspace System is a highly complex system of systems within which a number of participants with widely varying business and operating models exist. From the airspace user's perspective, a means by which to operate flights in a more flexible and efficient manner is highly desired to meet their business objectives. From the air navigation service provider's viewpoint, there is a need for increasing the capacity of the airspace, while maintaining or increasing the levels of efficiency and safety that currently exist in order to meet the charter under which they operate. Enhancing the communication between airspace operators and users is essential in order to meet these demands. In the spring of 2015, a prototype system that implemented an airborne tool to optimize en-route flight paths for fuel and time savings was designed and tested. The system utilized in-flight Internet as a high-bandwidth data link to facilitate collaborative decision making between the flight deck and an airline dispatcher. The system was tested and demonstrated in a laboratory environment, as well as in-situ. Initial results from these tests indicate that this system is not only feasible, but could also serve as a growth path and testbed for future air traffic management concepts that rely on shared situational awareness through data exchange and electronic negotiation between multiple entities operating within the National Airspace System.

Underwood, Matthew C.↗
NASA NTRS2012

HURON (HUman and Robotic Optimization Network) Multi-Agent Temporal Activity Planner/Scheduler

HURON solves the problem of how to optimize a plan and schedule for assigning multiple agents to a temporal sequence of actions (e.g., science tasks). Developed as a generic planning and scheduling tool, HURON has been used to optimize space mission surface operations. The tool has also been used to analyze lunar architectures for a variety of surface operational scenarios in order to maximize return on investment and productivity. These scenarios include numerous science activities performed by a diverse set of agents: humans, teleoperated rovers, and autonomous rovers. Once given a set of agents, activities, resources, resource constraints, temporal constraints, and de pendencies, HURON computes an optimal schedule that meets a specified goal (e.g., maximum productivity or minimum time), subject to the constraints. HURON performs planning and scheduling optimization as a graph search in state-space with forward progression. Each node in the graph contains a state instance. Starting with the initial node, a graph is automatically constructed with new successive nodes of each new state to explore. The optimization uses a set of pre-conditions and post-conditions to create the children states. The Python language was adopted to not only enable more agile development, but to also allow the domain experts to easily define their optimization models. A graphical user interface was also developed to facilitate real-time search information feedback and interaction by the operator in the search optimization process. The HURON package has many potential uses in the fields of Operations Research and Management Science where this technology applies to many commercial domains requiring optimization to reduce costs. For example, optimizing a fleet of transportation truck routes, aircraft flight scheduling, and other route-planning scenarios involving multiple agent task optimization would all benefit by using HURON.

Hua, Hook↗
NASA NTRS2024

Optimizing Air Traffic - Integrating Artificial Intelligence and Machine Learning in Flight Path Planning and 3D Airspace Visualization for Air Traffic Control

Air Traffic Control (ATC) systems are vital components of the National Airspace System (NAS). ATC, Airport Traffic Control Towers (ATCT), and Terminal Radar Approach Control (TRACON) are responsible for directing all flights departing from and arriving at airports, managing our nation’s airspace, preventing potential accidents, and ensuring that every flight is accounted for. However, these systems often face challenges in effectively monitoring the skies. Issues such as poor communication between operators, difficulty in performing operations, and the constant need for vigilance frequently burden ATC operators. Additionally, the projected increase in air traffic in the coming years will only exacerbate the stress associated with this role. To address these issues, we propose a system that assists ATC operators in situations such as handovers, emergencies, and routing aircraft to avoid weather hazards. Our solution includes an Artificial Intelligence (AI) and Machine Learning (ML)-based Flight Pathways Planning System (FPPS) designed to find the fastest and most optimal routes for aircraft, taking into account weather conditions, restricted terrain, and Extended-Range Twin-Engine Operational Performance Standards (ETOPS) ratings. The proposed Predictive Weather Planning Model, included in FPPS, adjusts routes based on real-time and forecasted weather conditions. Additionally, our NVIDIA Omniverse 3D Visualization System offers a highly interactive environment for better visualization and a clear view of the airspace. By incorporating these systems, the roles of ATC, ATCT, and TRACON operators will become more manageable and less stressful, equipping them to efficiently handle the growing density of airspace.

Regina Ayoubi↗
NASA NTRS2010

Concepts and Challenges for Environmentally Friendly En Route Operations

A flight trajectory optimization algorithm with fuel and contrails models, which develops alternative flight paths, provides policy makers the necessary data to make tradeoffs between persistent contrails mitigation and aircraft fuel consumption. This study develops an algorithm that calculates wind-optimal trajectories for cruising aircraft while avoiding the regions of airspace prone to persistent contrails formation. The optimal trajectory is derived using Singular Perturbation Method. The regions of airspace favorable to persistent contrails formation are modeled as high-risk areas that aircraft should avoid and are adjustable. The tradeoffs between persistent contrails formation and additional travel time are investigated for wind-optimal trajectories and various contrails-avoidance trajectories at 10 different cruising altitudes for flights departing from Chicago and San Diego to New York. The additional travel times required for avoiding 100% persistent contrails formation at various flight altitudes ranged from approximately 0% to 4.3% for flights from Chicago to New York. For flights between San Diego and New York, additional traveling times vary between 1.3% and 5% depending on the cruise altitude and the percentage of contrail avoidance. Talk will present the results of aircraft fuel consumptions that are proportional to the travel time for cruising aircraft.

Sridhar, Banavar↗