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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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NASA NTRS2020

Air Traffic Simulation Technology for High-Population Metroplexes

IAI's MetroSim optimizes air traffic by simulating departures, arrivals, and activity in air and onthe ground in busy metroplexes, where flights impact each other at a single airport and among traffic at nearby airports. MetroSim evolved out of several NASA SBIR/STTR Awards and has since been used by NASA for flight simulation analysis. MetroSim has also been integrated with FAA and DOT technology, has produced studies for the Port Authority of New York and New Jersey, and is under development to support the Navy

Cannon, Kimberly A.↗
NASA NTRS1991

Spatial and temporal variability of the extent of chemically processed stratospheric air

Simulations of the spatial and temporal variability of the extent of chemically processed air in the Arctic stratosphere have been carried out using a three-dimensional chemistry-transport model for the winters of 1979 and 1989. Chemically processed air is identified in the model as that in which the amounts of hydrogen chloride (HCl) calculated with parameterized loss for conditions appropriate to polar stratospheric cloud (PSC) formation are substantially smaller than those calculated in a model with gas phase chemistry only. It is seen that chemically processed air may be identified over much of the Arctic lower stratosphere from early January to late February, with HCl depletions being larger in 1989 than in 1979. Near the latitude of the Arctic circle, there is important spatial and temporal variability in the extent of chemically processed air. There is some evidence for transport to midlatitudes of processed air during these winters, but the HCl reductions are much smaller and more sporadic than those near the pole. At 62 and 42N, processed air is calculated to occur preferentially over the longitude regions from 60-120E and 270-330E.

Kaye, Jack A.↗
NASA NTRS2020

Air Traffic Management TestBed Traffic Viewer: Developer's Guide

The Air Traffic Management (ATM) TestBed is a Platform as a Service that is being developed by the National Aeronautics and Space Administration (NASA) to help design, configure, integrate, run, and monitor air traffic simulations. The platform is designed to provide cloud services including back-end, big-data analytics tools, on-demand computing resource management, data storage, and communication middleware. The ATM TestBed reduces the time to test concepts and technologies, supports interactions among various methods such as human-in-the-loop and automation-in-the-loop simulations, and enables collaborative simulations by sharing technologies and tools in the ATM community. The Traffic Viewer application provides a graphical user interface tool for visualizing real and simulated air traffic as well as airspace definition in two-dimensional space. This guide describes a high-level design and implementation of Traffic Viewer and provides information for a new developer or a user to add new capabilities by following the software design and leveraging existing capabilities.

Lai, Chok Fung↗
NASA NTRS1994

The analysis of a generic air-to-air missile simulation model

A generic missile model was developed to evaluate the benefits of using a dynamic missile fly-out simulation system versus a static missile launch envelope system for air-to-air combat simulation. This paper examines the performance of a launch envelope model and a missile fly-out model. The launch envelope model bases its probability of killing the target aircraft on the target aircraft's position at the launch time of the weapon. The benefits gained from a launch envelope model are the simplicity of implementation and the minimal computational overhead required. A missile fly-out model takes into account the physical characteristics of the missile as it simulates the guidance, propulsion, and movement of the missile. The missile's probability of kill is based on the missile miss distance (or the minimum distance between the missile and the target aircraft). The problems associated with this method of modeling are a larger computational overhead, the additional complexity required to determine the missile miss distance, and the additional complexity of determining the reason(s) the missile missed the target. This paper evaluates the two methods and compares the results of running each method on a comprehensive set of test conditions.

Kaplan, Joseph A.↗
NASA NTRS2006

Metallized Gelled Propellants Combustion Experiments in a Pulse Detonation Engine

A series of combustion tests were performed with metallized gelled JP 8/aluminum fuels in a Pulse Detonation Engine (PDE). Nanoparticles of aluminum were used in the 60 to 100 nanometer diameter. Gellants were also of a nanoparticulate type composed of hydrocarbon alkoxide materials. Using simulated air (a nitrogen-oxygen mixture), the ignition potential of metallized gelled fuels with nanoparticle aluminum was investigated. Ignition of the JP 8/aluminum was possible with less than or equal to a 23-wt% oxygen loading in the simulated air. JP 8 fuel alone was unable to ignite with less than 30 percent oxygen loaded simulated air. The tests were single shot tests of the metallized gelled fuel to demonstrate the capability of the fuel to improve fuel detonability. The tests were conducted at ambient temperatures and with maximal detonation pressures of 1340 psia.

Palaszewski, Bryan↗
NASA NTRSDate not supplied

Generic Urban Air Mobility Simulation

This research presents a simulation framework for autonomous research for a UAM vehicle using the NASA Revolutionary Vertical Lift Technology Lift+Cruise concept vehicle. Our research results were produced using the open-source, six degree of freedom, rigid-body, nonlinear generic urban air mobility (GUAM) simulation. The intent of this paper is to demonstrate the GUAM simulation and a series of Challenge Problems that our researchers have posed to the broader autonomous vehicle research community. Our team has developed the GUAM simulation for the express purpose of providing a high-fidelity transition vehicle dynamics model to foster collaboration and algorithm performance comparison across research teams. In this paper, we demonstrate some of the autonomous flight research challenges and some of our current approaches to tackling basic autonomous flight tasks (e.g., trajectory following, stationary and moving obstacle avoidance). Additionally, we propose some flight metrics to assess autonomous algorithm performance while accomplishing these basic autonomous tasks.

autonomous flight↗
NASA NTRSDate not supplied

A Data Analysis Approach for Simulations of Urban Air Mobility Operations

For the Urban Air Mobility (UAM) industry, NASA has defined a series of UAM Maturity Levels (UML) corresponding to increasingly more complex and operationally dense UAM operations. In support of the gradual progression towards higher UML levels, NASA is currently conducting a set of UAM air traffic simulations—collectively referred to as X4. This paper describes a set of system effectiveness measures, and their associated metrics, for data analysis of X4 simulations. The descriptions, rationales, and calculation procedures for two metrics to be used in data analysis of simulation results, the number of predicted demand-capacity imbalances and the pre-departure delays, are described. Results from data analysis of one set of simulation runs are presented to demonstrate how these metrics support the assessment of performance of the system architecture for X4 simulations and the verification of experiment requirements.

Urban Air Mobility↗
NASA NTRS1988

Rule-based air combat simulation

An improved version of the Adaptive Maneuvering Logic (AML) program for air-combat maneuvering is discussed. The modifications and improvements incorporated into the AML program are documented.

Burgin, G. H.↗
NASA NTRS2019

Accelerated Simulation of Air Pollution Using NVIDIA RAPIDS

Atmospheric chemistry models are a central tool to study and forecast the impact of air pollution on the environment, vegetation, and human health. However, the numerical simulation of chemical kinetics is computationally expensive due to the stiffness of the system of ordinary differential equations that describes atmospheric chemistry. Here we present an alternative approach to the computation of atmospheric chemistry based on machine learning. Our training data set is produced using the NASA Goddard Earth Observing System (GEOS) model with GEOS-Chem chemistry, run on the NASA Center for Climate Simulation (NCCS) Discover supercomputing cluster on 384 Intel Xeon Haswell cores. This model spends more than 50% of total run time on solving atmospheric chemistry. The data set contains as input features the air pollution concentrations before solving the differential equations, together with some key physical parameters such as temperature and sun intensity. As target variables we define the air pollution concentrations after solving the differential equations. Using Dask-cuDF and Dask-XGBoost on the NVIDIA RAPIDS platform on 8 Tesla V100 GPUs, we generate from this training set gradient boosted decision tree models that can reproduce the simulation of chemical kinetics. We do this on the NCCS Advanced Data Analytics Platform (ADAPT) science cloud environment. Our application takes full advantage of recent advances in Dask-XGBoost, such as multi-node and multi-GPU scaling for distributed training with large data sets. The increase in training data size enabled by this is critical to capture the full range of chemical environments encountered across the globe and all annual seasons.The boosted tree models offer good predictability and show many of the features of the full chemistry reference simulation. Further improvements can be achieved through mass balance considerations and by accounting for error correlations. We incorporate the boosted tree models into the GEOS reference model using XGBoost's C API. This enables a seamless integration of the GPU trained models into GEOS-Chem, which is written in Fortran and optimized for use in a massively parallel CPU environment. We show the benefits of this approach and discuss the potential speedup of this machine learning accelerated atmospheric chemistry model.

Keller, Christoph A.↗
NASA NTRSDate not supplied

Eye Glance Behaviors of Ground Control Station Operators in a Simulated Urban Air Mobility Environment

Research into concepts such as advanced air mobility (AAM) and urban air mobility (UAM) offers an opportunity for successfully and safely adding new classes of vehicles into the National Airspace System. However, a need exists for research into the human factors associated with these concepts. In this paper, we evaluate the gaze behaviors of three remote ground control station operators (GCSOs) conducting simulated UAM operations. The participants monitored and controlled an unmanned aircraft system (UAS) from pre-flight to landing using ground control station (GCS) software across nine scenarios within a remote UAS operations center at NASA Langley Research Center (LaRC). As this work was exploratory, descriptive statistics were calculated to provide some initial insight into GCSO gaze patterns associated with the GCS display. Scenarios that required the operator to directly interact with an airspace scheduling system off-screen resulted in fewer on-screen glances than scenarios that did not include direct interactions with the off-screen system. After investigating several areas of interest (AOIs) within the GCS display, participants primarily viewed three AOIs: the map, vehicle status, and operations checklist. The results yielded several GCS design and operational improvement recommendations to include: (a) adding altitude information to the vehicle icon, (b) adding additional traffic information, and (c) including additional GCS training, to reduce the need to scan an operations checklist, which would allow allocation of visual attention towards other AOIs.

Urban Air Mobility↗
NASA NTRSDate not supplied

Trajectory Simulation for Air Traffic Management Employing a Multirotor Urban Air Mobility Aircraft Model

This paper describes a point-mass model of a multirotor electric vertical takeoff and landing aircraft designed to carry up to five passengers and a pilot. The model employs thrust, thrust vector angle and bank angle as controls, which are computed in part using the acceleration, heading-angle rate, and flight-path-angle rate generated by control systems in response to speed, heading angle, and flight-path angle commands. The equations of motion are integrated forward in time using the controls needed for following the desired vertical climb/descent and speed profiles, and horizontal path and cruise speed in the presence of wind to generate the flight trajectory.

trajectory simulation, urban air mobility aircraft↗
NASA NTRS2013

Designing Scenarios for Controller-in-the-Loop Air Traffic Simulations

Within the Human Factors Division at NASA Ames Research Center the Airspace Operations Laboratory (AOL) is developing advanced automation concepts that help to transform the National Airspace System into NextGen, the Next Generation Air Transportation System. High-fidelity human-in-the-loop (HITL) simulations are used as a means to investigate and develop roles, responsibilities, support tools, and requirements for human operators and automation. This paper describes the traffic scenario design process and strategies as used by AOL researchers. Details are presented on building scenarios for specific simulation objectives using various design strategies. A focus is set on creating scenarios based on recorded real world traffic for terminal-area simulations.

scenario design↗
NASA NTRS1977

A simulation of air pollution model parameter estimation using data from a ground-based LIDAR remote sensor

One way to obtain estimates of the unknown parameters in a pollution dispersion model is to compare the model predictions with remotely sensed air quality data. A ground-based LIDAR sensor provides relative pollution concentration measurements as a function of space and time. The measured sensor data are compared with the dispersion model output through a numerical estimation procedure to yield parameter estimates which best fit the data. This overall process is tested in a computer simulation to study the effects of various measurement strategies. Such a simulation is useful prior to a field measurement exercise to maximize the information content in the collected data. Parametric studies of simulated data matched to a Gaussian plume dispersion model indicate the trade offs available between estimation accuracy and data acquisition strategy.

Kibler, J. F.↗
NASA NTRS2020

Local to Global Air Quality Simulations using the NASA GEOS Composition Forecast Model, GEOS-CF

We give an overview of the NASA Global Earth Observing System Composition Forecast model (GEOS-CF), a high-resolution (roughly 25-km) global composition model developed by the NASA Global Modeling and Assimilation Office (GMAO). This system combines the GEOS weather and aerosol model with the GEOS-Chem chemistry module to provide a holistic view of atmospheric composition that captures a wide range of air pollutants such as ozone,nitrogen oxides, volatile organic compounds, and fine particulate matter.Given the global extent of the model, GEOS-CF captures large-scale processes such as long-range transport of air pollutants, across ocean basins and continents.

Knowland, Emma K.↗