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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

New technology and vehicle operation on roadways

Some concepts towards the optimum movement of vehicles on highways are presented. It is stressed that in proper perspective, the private automobile is one element of several modes of transportation. The modal mix in the long run is determined by proper land use planning, population density, and historic growth patterns. Discussion investigates the two main interacting elements of the highway in the dynamic state: the roadway and the driver operated vehicle, noting that the inefficiencies of the system are caused by the uncoordinated and disassociated actions of the two elements. Also discussed are vehicle operations on streets, highways, and freeways, on board communication, control, and guidance, systems considerations and traffic management, and benefits of optimized vehicle operations on roadways.

Humphreys, D. W.↗
NASA NTRS1987

Fuel penalties and time flexibility of 4D flight profiles under mismodeled wind conditions

A parametric sensitivity study was conducted to evaluate time flexibility and fuel penalties associated with 4D operations in the presence of mismodeled wind. The final cruise and descent segments of a flight in an advanced time-metered air traffic control environment were considered. Optimal performance of a B-737-100 airplane in known, constant winds was determined. Performance in mismodeled wind was obtained by tracking no-wind reference profiles in the presence of actual winds. The results of the analysis are presented in terms of loss of time flexibility and fuel penalties compared to the optimum performance in modeled winds.

Williams, David H.↗
NASA NTRS1992

CTAS: Computer intelligence for air traffic control in the terminal area

A system for the automated management and control of arrival traffic, referred to as the Center-TRACON Automation System (CTAS), has been designed by the ATC research group at NASA Ames research center. In a cooperative program, NASA and the FAA have efforts underway to install and evaluate the system at the Denver and Dallas/Ft. Worth airports. CTAS consists of three types of integrated tools that provide computer-generated intelligence for both Center and TRACON controllers to guide them in managing and controlling arrival traffic efficiently. One tool, the Traffic Management Advisor (TMA), establishes optimized landing sequences and landing times for aircraft arriving in the center airspace several hundred miles from the airport. In TRACON, TMA frequencies missed approach aircraft and unanticipated arrivals. Another tool, the Descent Advisor (DA), generates clearances for the center controllers handling at crossing times provided by TMA. In the TRACON, the final approach spacing tool (FAST) provides heading and speed clearances that produce and accurately spaced flow of aircraft on the final approach course. A data base consisting of aircraft performance models, airline preferred operational procedures and real time wind measurements contribute to the effective operation of CTAS. Extensive simulator evaluations of CTAS have demonstrated controller acceptance, delay reductions, and fuel savings.

Erzberger, Heinz↗
NASA NTRS2003

Cost Effective Measures to Reduce CO2 Emissions in the Air Freight Sector

This paper presents cost effective measures to reduce CO2 emissions in the air freight sector. One door-to-door transport chain is studied in detail from a Scandinavian city to a city in southern Europe. The transport chain was selected by a group of representatives from the air freight sector in order to encompass general characteristics within the sector. Three different ways of shipping air cargo are studied, i.e., by air freighter, as belly freight (in passenger aircrafts) and trucking. CO2 emissions are calculated for each part of the transport chain and its relative importance towards the total amount CO2 emitted during the whole transport chain is shown. It is confirmed that the most CO2 emitting part of the transport chain is the actual flight and that it is in the take-off and climbing phases that most fuel are burned. It is also known that the technical development of aircraft implies a reduction in fuel consumption for each new generation of aircraft. Thus, the aircraft manufacturers have an important role in this development. Having confirmed these observations, this paper focuses on other factors that significantly affects the fuel consumption. Analyzed factors are, e.g., optimization of speed and altitude, traffic management, congestion on and around the airfields, tankering, "latest acceptance time" for goods and improving the load factor. The different factors relative contribution to the total emission levels for the transport chain has been estimated.

Blinge, Magnus↗
DOE OSTI2025

Optimizing on-ramp merging for connected and automated vehicles: A hierarchical approach using deep reinforcement learning and optimal control

On-ramp merging for Connected and Automated Vehicles (CAVs) presents significant challenges in dynamic traffic environments. Traditional methods and recent learning-based approaches often fail to simultaneously address decision-making complexity and execution precision under fluctuating conditions. This study introduces a novel hierarchical framework that combines: (1) a high-level Deep Reinforcement Learning (DRL) module that coordinates merging sequences through Virtual Traffic Signals (VTS) with Yield/Green phases and (2) a low-level optimal controller generating collision-free speed trajectories via pseudospectral convex optimization. A convolutional autoencoder compresses high-dimensional traffic states to enhance responsiveness. Extensive simulations demonstrate a 12.5% improvement in mainline throughput a 28% reduction in emergency braking events, and 31.66% lower fuel consumption compared to baseline methods. Furthermore, the framework’s effectiveness in coordinating CAV merges highlights its potential for real-world deployment. Future work will extend validation to multi-lane scenarios with mixed traffic and large-scale multiple merging points.

Connected and automated vehicles↗
NASA NTRS2011

Design and Evaluation of the Terminal Area Precision Scheduling and Spacing System

This paper describes the design, development and results from a high fidelity human-in-the-loop simulation of an integrated set of trajectory-based automation tools providing precision scheduling, sequencing and controller merging and spacing functions. These integrated functions are combined into a system called the Terminal Area Precision Scheduling and Spacing (TAPSS) system. It is a strategic and tactical planning tool that provides Traffic Management Coordinators, En Route and Terminal Radar Approach Control air traffic controllers the ability to efficiently optimize the arrival capacity of a demand-impacted airport while simultaneously enabling fuel-efficient descent procedures. The TAPSS system consists of four-dimensional trajectory prediction, arrival runway balancing, aircraft separation constraint-based scheduling, traffic flow visualization and trajectory-based advisories to assist controllers in efficient metering, sequencing and spacing. The TAPSS system was evaluated and compared to today's ATC operation through extensive series of human-in-the-loop simulations for arrival flows into the Los Angeles International Airport. The test conditions included the variation of aircraft demand from a baseline of today's capacity constrained periods through 5%, 10% and 20% increases. Performance data were collected for engineering and human factor analysis and compared with similar operations both with and without the TAPSS system. The engineering data indicate operations with the TAPSS show up to a 10% increase in airport throughput during capacity constrained periods while maintaining fuel-efficient aircraft descent profiles from cruise to landing.

Swenson, Harry N.↗
NASA NTRS2001

Civil Tiltrotor Feasibility Study for the New York and Washington Terminal Areas

NASA tasked LMI to assess the potential contributions of a yet-undeveloped Civil Tiltrotor aircraft (CTR) in improving capacity in the National Airspace System in all weather conditions. The CTRs studied have assumed operating parameters beyond current CTR capabilities. LMI analyzed CTRs three ways: in fast-time terminal area modeling simulations of New York and Washington to determine delay and throughput impacts; in the Integrated Noise Model, to determine local environmental impact; and with an economic model, to determine the price viability of a CTR. The fast-time models encompassed a 250 nmi range and included traffic interactions from local airports. Both the fast-time simulation and the noise model assessed impacts from traffic levels projected for 1999, 2007, and 2017. Results: CTRs can reduce terminal area delays due to concrete congestion in all time frames. The maximum effect, the ratio of CTRs to jets and turboprop aircraft at a subject airport should be optimized. The economic model considered US traffic only and forecasted CTR sales beginning in 2010.

Stouffer, Virginia↗
NASA NTRS2016

Deploying a Route Optimization EFB Application for Commercial Airline Operational Trials

The Traffic Aware Planner (TAP), developed for NASA Langley Research Center to support the Traffic Aware Strategic Aircrew Requests (TASAR) project, is a flight-efficiency software application developed for an Electronic Flight Bag (EFB). Tested in two flight trials and planned for operational testing by two commercial airlines, TAP is a real-time trajectory optimization application that leverages connectivity with onboard avionics and broadband Internet sources to compute and recommend route modifications to flight crews to improve fuel and time performance. The application utilizes a wide range of data, including Automatic Dependent Surveillance Broadcast (ADS-B) traffic, Flight Management System (FMS) guidance and intent, on-board sensors, published winds and weather, and Special Use Airspace (SUA) schedules. This paper discusses the challenges of developing and deploying TAP to various EFB platforms, our solutions to some of these challenges, and lessons learned, to assist commercial software developers and hardware manufacturers in their efforts to implement and extend TAP functionality in their environments. EFB applications (such as TAP) typically access avionics data via an ARINC 834 Simple Text Avionics Protocol (STAP) server hosted by an Aircraft Interface Device (AID) or other installed hardware. While the protocol is standardized, the data sources, content, and transmission rates can vary from aircraft to aircraft. Additionally, the method of communicating with the AID may vary depending on EFB hardware and/or the availability of onboard networking services, such as Ethernet, WIFI, Bluetooth, or other mechanisms. EFBs with portable and installed components can be implemented using a variety of operating systems, and cockpits are increasingly incorporating tablet-based technologies, further expanding the number of platforms the application may need to support. Supporting multiple EFB platforms, AIDs, avionics datasets, and user interfaces presents a challenge for software developers and the management of their code baselines. Maintaining multiple baselines to support all deployment targets can be extremely cumbersome and expensive. Certification also needs to be considered when developing the application. Regardless of whether the software is itself destined to be certified, data requirements in support of the application and user interface elements may introduce certification requirements for EFB manufacturers and the airlines. The example of TAP, the challenges faced, solutions implemented, and lessons learned will give EFB application and hardware developers insight into future potential requirements in deploying TAP or similar flight-deck EFB applications.

Roscoe, David A.↗
NASA NTRSDate not supplied

Fe3 Traffic Simulator Overview

NASA Ames developed a first of its kind air traffic simulation tool known as Flexible engine for Fast time evaluation of Flight environments (Fe3). The patent-pending technology is a robust simulation tool that provides the capability of statistically analyzing the high-density and low-altitude traffic system. With this capability, stakeholders can study the impacts of critical components (such as wind, surveillance, communication, collision, avoidance, traffic rules, energy consumption, etc.) in the low-altitude high-density traffic system, gain insights and help define requirements, policies, and protocols for a safe and efficient traffic system, and assess operational risks and optimize fight schedules.

Flexible engine for Fast time evaluation of Flight↗
DOE OSTI2025

Edge AI-Enhanced Traffic Monitoring and Anomaly Detection Using Multimodal Large Language Models

This paper addresses the challenge of traffic monitoring and incident detection in remote areas, utilizing multimodal large language models (LLMs) deployed on edge AI devices. The key novelty of the LLM is to convert real-time video streams into descriptive texts, enabling low-bandwidth transmissions and reliable detection of anomalies and incidents in environments of intermittent connectivity. The model is developed based on fine-tuning open-source LLMs and extending it with multi-modal capabilities to analyze video frames. Our work also involves deploying this model on edge devices such as Nvidia IGX Orin and is planned to be tested in realistic environments in future work. The methodology includes data set curation, iterative model fine-tuning and compression, and hardware-based optimization. This approach aims to enhance traffic safety and response speed in remote areas, marking a significant advancement in the application of AI for traffic monitoring and safety management.

Peruski, Ryan [University of Tennessee, Knoxville ↗
NASA NTRS1999

Advanced Interactive Display Formats for Terminal Area Traffic Control

This research project deals with an on-line dynamic method for automated viewing parameter management in perspective displays. Perspective images are optimized such that a human observer will perceive relevant spatial geometrical features with minimal errors. In order to compute the errors at which observers reconstruct spatial features from perspective images, a visual spatial-perception model was formulated. The model was employed as the basis of an optimization scheme aimed at seeking the optimal projection parameter setting. These ideas are implemented in the context of an air traffic control (ATC) application. A concept, referred to as an active display system, was developed. This system uses heuristic rules to identify relevant geometrical features of the three-dimensional air traffic situation. Agile, on-line optimization was achieved by a specially developed and custom-tailored genetic algorithm (GA), which was to deal with the multi-modal characteristics of the objective function and exploit its time-evolving nature.

Grunwald, Arthur J.↗
NASA NTRS1999

Advanced Interactive Display Formats for Terminal Area Traffic Control

This research project deals with an on-line dynamic method for automated viewing parameter management in perspective displays. Perspective images are optimized such that a human observer will perceive relevant spatial geometrical features with minimal errors. In order to compute the errors at which observers reconstruct spatial features from perspective images, a visual spatial-perception model was formulated. The model was employed as the basis of an optimization scheme aimed at seeking the optimal projection parameter setting. These ideas are implemented in the context of an air traffic control (ATC) application. A concept, referred to as an active display system, was developed. This system uses heuristic rules to identify relevant geometrical features of the three-dimensional air traffic situation. Agile, on-line optimization was achieved by a specially developed and custom-tailored genetic algorithm (GA), which was to deal with the multi-modal characteristics of the objective function and exploit its time-evolving nature.

Grunwald, Arthur J.↗
NASA NTRS1989

Data traffic reduction schemes for Cholesky factorization on asynchronous multiprocessor systems

Communication requirements of Cholesky factorization of dense and sparse symmetric, positive definite matrices are analyzed. The communication requirement is characterized by the data traffic generated on multiprocessor systems with local and shared memory. Lower bound proofs are given to show that when the load is uniformly distributed the data traffic associated with factoring an n x n dense matrix using n to the alpha power (alpha less than or equal 2) processors is omega(n to the 2 + alpha/2 power). For n x n sparse matrices representing a square root of n x square root of n regular grid graph the data traffic is shown to be omega(n to the 1 + alpha/2 power), alpha less than or equal 1. Partitioning schemes that are variations of block assignment scheme are described and it is shown that the data traffic generated by these schemes are asymptotically optimal. The schemes allow efficient use of up to O(n to the 2nd power) processors in the dense case and up to O(n) processors in the sparse case before the total data traffic reaches the maximum value of O(n to the 3rd power) and O(n to the 3/2 power), respectively. It is shown that the block based partitioning schemes allow a better utilization of the data accessed from shared memory and thus reduce the data traffic than those based on column-wise wrap around assignment schemes.

Naik, Vijay K.↗
NASA NTRS2011

Modeling the Environmental Impact of Air Traffic Operations

There is increased interest to understand and mitigate the impacts of air traffic on the climate, since greenhouse gases, nitrogen oxides, and contrails generated by air traffic can have adverse impacts on the climate. The models described in this presentation are useful for quantifying these impacts and for studying alternative environmentally aware operational concepts. These models have been developed by leveraging and building upon existing simulation and optimization techniques developed for the design of efficient traffic flow management strategies. Specific enhancements to the existing simulation and optimization techniques include new models that simulate aircraft fuel flow, emissions and contrails. To ensure that these new models are beneficial to the larger climate research community, the outputs of these new models are compatible with existing global climate modeling tools like the FAA's Aviation Environmental Design Tool.

Chen, Neil↗
NASA NTRS2016

Conflict Resolution for Wind-Optimal Aircraft Trajectories in North Atlantic Oceanic Airspace with Wind Uncertainties

Air traffic in the North Atlantic oceanic airspace (NAT) experiences very strong winds caused by jet streams. Flying wind-optimal trajectories increases individual flight efficiency, which is advantageous when operating in the NAT. However, as the NAT is highly congested during peak hours, a large number of potential conflicts between flights are detected for the sets of wind-optimal trajectories. Conflict resolution performed at the strategic level of flight planning can significantly reduce the airspace congestion. However, being completed far in advance, strategic planning can only use predicted environmental conditions that may significantly differ from the real conditions experienced further by aircraft. The forecast uncertainties result in uncertainties in conflict prediction, and thus, conflict resolution becomes less efficient. This work considers wind uncertainties in order to improve the robustness of conflict resolution in the NAT. First, the influence of wind uncertainties on conflict prediction is investigated. Then, conflict resolution methods accounting for wind uncertainties are proposed.

wind uncertainties↗
NASA NTRS1987

Data Bus Adapts to Changing Traffic Level

Access becomes timed when collisions threaten. Two-mode scheme used to grant terminals access to data bus. Causes bus to alternate between random accessibility and controlled accessibility to optimize performance and adapt to changing data-traffic conditions. Bus is part of 100-Mb/s optical-fiber packet data system.

Lew, Eugene↗
NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. It affects the efficiency of the National Airspace System (NAS) and both surface and airspace operations can benefit from better understanding future runway configurations. In this paper, we present a comprehensive implementation of predictive models for runway configuration estimation from large volumes of historical data. Specifically, operational data from two full years (2018 and 2019) is collected, analyzed, and fused together to build the data product used in this work. The data set differs from prior work in the field in terms of its scope, resolution, and variety of factors collected and considered. Meteorological data is collected from two different sources – current weather conditions from METAR (Meteorological Terminal Aviation Routine Weather Report) and forecast weather conditions from Localized Aviation MOS Program (LAMP). Operational data from the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) related to scheduled and actual number of arrivals and departures, average taxi times, etc. are collected. NASA’s Sherlock Data Warehouse is used to identify critical information such as go-arounds, and other events that might impact RCM decision-making. All data is collected and aggregated over 15-minute intervals throughout the two years. This provides a resolution like the timescales that might be necessary for runway configuration management decision-making. A variety of supervised learning algorithms are tested including Support Vector Machine, Random Forest, Gradient Boosting, etc. including tuning of the model hyperparameters. The modeling process is applied and presented on two representative U.S. airports – Charlotte Douglas International Airport (KCLT) and Denver International Airport (KDEN). The two airports present different levels of complexity in terms of the total number of configurations used and provide a balanced perspective on the generalizability of the developed approach to other airports in the NAS. Initial results are promising (F1 score of 0.91 at KCLT and 0.83 at KDEN) for data in the test set. The final paper will contain a comprehensive comparison between different models and model building strategies as well as further refined results. Most important predictors for each airport will be identified along with a discussion and recommendations on adapting the framework to other scenarios.

Tejas G Puranik↗