Development of Complexity Science and Technology Tools for NextGen Airspace Research and Applications

The objective of this research by NextGen AeroSciences, LLC is twofold: 1) to deliver an initial "toolbox" of algorithms, agent-based structures, and method descriptions for introducing trajectory agency as a methodology for simulating and analyzing airspace states, including bulk properties of large numbers of heterogeneous 4D aircraft trajectories in a test airspace -- while maintaining or increasing system safety; and 2) to use these tools in a test airspace to identify possible phase transition structure to predict when an airspace will approach the limits of its capacity. These 4D trajectories continuously replan their paths in the presence of noise and uncertainty while optimizing performance measures and performing conflict detection and resolution. In this approach, trajectories are represented as extended objects endowed with pseudopotential, maintaining time and fuel-efficient paths by bending just enough to accommodate separation while remaining inside of performance envelopes. This trajectory-centric approach differs from previous aircraft-centric distributed approaches to deconfliction. The results of this project are the following: 1) we delivered a toolbox of algorithms, agent-based structures and method descriptions as pseudocode; and 2) we corroborated the existence of phase transition structure in simulation with the addition of "early warning" detected prior to "full" airspace. This research suggests that airspace "fullness" can be anticipated and remedied before the airspace becomes unsafe

In Pursuit of Aviation Cybersecurity: Experiences and Lessons From a Competitive Approach

The passive and independent localization of aircraft has been the subject of much cyberphysical security research. We designed a multistage open competition focusing on the offline batch localization problem using opportunistic data sources. We discuss setup, results, and lessons learned

Surveying Underwater Shipwrecks with Probabilistic Roadmaps

Almost two thirds of the Earth\u27s surface is covered in ocean, and yet, only about 5% of it is mapped. There are an unknown amount of sunken ships, planes, and other artifacts hidden below the sea. Extensive search via boat and a sonar tow fish following a standard lawnmower pattern is used to identify sites of interest. Then, if a site has been determined to potentially be historically significant, the most common next step is a survey by either a human dive team or remotely operated vehicle. These are time consuming, error prone, and potentially dangerous options, but autonomous underwater vehicles (AUVs) are a possible solution. This thesis introduces a system for automatically generating paths for AUVs to survey and map shipwrecks. Most AUVs include software to set a lawnmower path for a given region of ocean, and individualized paths can be set via specifying GPS encoded nodes for the AUV to pass through. This thesis presents an algorithm for generating an individualized path that permits the AUV, equipped with a camera to see all sides of a region of interest (i.e. a shipwreck). This allows the region of interest to be completely documented. Photogrammetry can then be used to reconstruct a three-dimensional model, but a path is needed to do so. Paths are generated by a probabilistic roadmap algorithm that uses a rapidly-exploring random tree to quickly cover the volume of exploration space and generate small maps with good coverage. The roadmap is constructed out of nodes, each having its own weight. The weight of a given node is calculated using an objective function which measures an approximate view coverage by casting rays from the virtual view and intersecting them with the region of interest. In addition, the weight of a node is increased if this node allows the AUV to see a new side of the region of interest. In each iteration of the algorithm, a node to expand off of is selected based off its location in space or its high weight, a new node with a given amount of freedom is generated, and then added to the roadmap. The algorithm has degrees of freedom in position, pitch, and yaw as well as the objective function to encourage the path to see all sides of the region of interest. Once all sides of the region of interest have been viewed, a path is determined to be complete. The algorithm was tested in a virtual world where the virtual camera acted as the AUV. All of the images collected from our automatically generated path were used to create 3D models and point clouds using photogrammetry. To measure the effectiveness of our paths versus the pre-packaged lawnmower paths, the 3D models and point clouds created from our algorithm were compared to those generated from running a standard lawnmower pattern. The paths generated by our algorithm captured images that could be used in a 3D reconstruction which were more detailed and showed better coverage of the region of interest than those from the lawnmower pattern

Applying the ADS-B Out to Facilitate Flight Data Analysis for General Aviation

The International Civil Aviation Organization (ICAO) and major airlines believe that flight data analysis is an effective approach to mitigate the risk of aviation accidents (International Civil Aviation Organization, 2010; International Air Transport Association, 2016). In the United States, flight data analysis is encouraged by the Federal Aviation Administration (FAA) through the flight operational quality assurance (FOQA) program. Among all aviation activities, general aviation (GA) has the highest accident rate (National Transportation Safety Board, 2014). However, implementation of flight data analysis for GA not only requires expensive investment on flight data recording devices, but also increases long-term labor cost due to regular data collection and data analysis. Automatic Dependent Surveillance Broadcast Out (ADS-B Out) is a precise satellite-based surveillance system that periodically broadcasts flight data retrieved from satellites and onboard avionics of the ADS-B Out capable aircraft. Based on the standard technical provisions of the ADS-B Out, the use of ADS-B data is expected to be a possible approach to facilitate the flight data analysis for general aviation. This research explored the use of ADS-B data to facilitate flight data analysis for general aviation. Researchers started the current study phase from analyzing the structure and content of the ADSB message by referring to the ICAO technical provisions (2008) and the operational performance standard of ADS-B from the Radio Technical Commission for Aeronautics (RTCA) (2009). Based upon the findings of the ADS-B data structure and content, a set of retrievable aircraft parameters was identified, and additional aircraft parameters were derived from the basic ADS-B information. Furthermore, sets of flight metrics were developed using the aircraft parameters broadcasted by ADS-B Out. The development of flight metrics was expected to be essential for measuring flight operational performance to support flight data analysis. In addition, exceedance detection was adopted to analyze the flight metrics in flight data analysis. ADS-B data were collected using an ADS-B receiver, and 40 sets of ADS-B data were selected to detect five operational exceedances of the Cirrus SR-20 aircraft of the Purdue Fleet. Exceedances were detected from the 40 sets of data. However, researchers noticed that the sparse ADS-B data caused by the low reception rate might affect the exceedance detection. Therefore, a preliminary analysis was conducted to investigate the difference of exceedance detection using ADS-B data with different reception rates. The results of analysis indicated that sparse ADS-B data could affect the detection of exceedances, but some exceedances might be less sensitive to the sparse data. Based on the findings of this research, recommendations were proposed for future studies

Applying the ADS-B Out to Facilitate Flight Data Analysis for General Aviation

The International Civil Aviation Organization (ICAO) and major airlines believe that flight data analysis is an effective approach to mitigate the risk of aviation accidents (International Civil Aviation Organization, 2010; International Air Transport Association, 2016). In the United States, flight data analysis is encouraged by the Federal Aviation Administration (FAA) through the flight operational quality assurance (FOQA) program. Among all aviation activities, general aviation (GA) has the highest accident rate (National Transportation Safety Board, 2014). However, implementation of flight data analysis for GA not only requires expensive investment on flight data recording devices, but also increases long-term labor cost due to regular data collection and data analysis. Automatic Dependent Surveillance Broadcast Out (ADS-B Out) is a precise satellite-based surveillance system that periodically broadcasts flight data retrieved from satellites and onboard avionics of the ADS-B Out capable aircraft. Based on the standard technical provisions of the ADS-B Out, the use of ADS-B data is expected to be a possible approach to facilitate the flight data analysis for general aviation. This research explored the use of ADS-B data to facilitate flight data analysis for general aviation. Researchers started the current study phase from analyzing the structure and content of the ADSB message by referring to the ICAO technical provisions (2008) and the operational performance standard of ADS-B from the Radio Technical Commission for Aeronautics (RTCA) (2009). Based upon the findings of the ADS-B data structure and content, a set of retrievable aircraft parameters was identified, and additional aircraft parameters were derived from the basic ADS-B information. Furthermore, sets of flight metrics were developed using the aircraft parameters broadcasted by ADS-B Out. The development of flight metrics was expected to be essential for measuring flight operational performance to support flight data analysis. In addition, exceedance detection was adopted to analyze the flight metrics in flight data analysis. ADS-B data were collected using an ADS-B receiver, and 40 sets of ADS-B data were selected to detect five operational exceedances of the Cirrus SR-20 aircraft of the Purdue Fleet. Exceedances were detected from the 40 sets of data. However, researchers noticed that the sparse ADS-B data caused by the low reception rate might affect the exceedance detection. Therefore, a preliminary analysis was conducted to investigate the difference of exceedance detection using ADS-B data with different reception rates. The results of analysis indicated that sparse ADS-B data could affect the detection of exceedances, but some exceedances might be less sensitive to the sparse data. Based on the findings of this research, recommendations were proposed for future studies

Microscopic Vehicle Emission Modelling

Vehicle emission models are widely used to estimate air pollution from road transport. This estimation can then be considered for transport management and traffic control policies, to quantify their impacts on urban air quality. The focus of this study is to investigate the relationship between vehicle dynamics and tailpipe emission by statistical methods. These methods are: log- polynomial and classified log-polynomial model based on acceleration and deceleration, lagged regression and transfer function model based on time series analysis, gear-based emission model based on estimated transmission gear components, and the general additive model for location, scale and shape (GAMLSS) based on spline functions. The dataset for this study is second-by-second emission laboratory measurements of four different vehicle types while following a driving cycle recorded in urban, suburban and motorway areas of London. The vehicles can be categorized by size (compact and saloon), fuel type (petrol and diesel) and transmission type (manual and automatic). For each vehicle type, CO2, CO and NOx emissions are estimated in each second of driving by the speed profile as the main explanatory variable. The six emission models developed in this study are: Log-polynomial (LP), classified log-polynomial (CLP), lagged regression (LR), transfer function (TF), gear-based and GAMLSS. These are evaluated using the BIC, total emission recovery and statistical time series analysis of the residuals. The GAMLSS model consistently has the best BIC values for all vehicle and emission types, while the recovery ratio of this model is within 1% for all vehicle types. In addition, statistical analysis of the ACF/PACF time series plots shows that the GAMLSS emission model is clearer from the significant lags compared to the parametric models (LP, TF, Gear-based, gear-based and CLP). Among the parametric models, the classified models represent the emission relationship better than others. The best BIC values (after GAMLSS) were achieved by the gear- based and the CLP emission models. These results indicate that the GAMLSS approach which uses spline functions and flexible error structure performs better than the other models investigated here. This model is validated by 10- fold cross-validation approach which shows that the prediction power of the GAMLSS emission model exceeds that of the parametric models. The models are evaluated by the BIC values, total emission recovery and analysis of the residuals. Based on these criteria, the GAMLSS emission model is the most effective, especially for CO and NOx emission modelling. This model is then validated by the K-fold cross-validation process. The suggestion for future research is to evaluate the performance of the developed models with track and real driving emission (RDE) tests. The calibrated model then will be implemented to a traffic microsimulation, where different transportation management and traffic policies can be simulated and evaluated by their impacts on air quality

Numerical modeling of gas turbine cooled blades

In contrast to methods that do not take into account multiconnectivity in a broad sense of this term, we develop mathematical models and highly effective combination (BIEM and FDM) numerical methods of calculation of stationary and quasi‐stationary temperature field of a profile part of a blade with convective cooling (from the point of view of realization on PC). The theoretical substantiation of these methods is proved by appropriate theorems. For it, converging quadrature processes have been developed and the estimations of errors in the terms of A. Ziqmound continuity modules have been received. First Published Online: 14 Oct 201

Efficient multidimensional wideband parameter estimation for OFDM based joint radar and communication systems

In this paper, we propose a new pre-processing technique for efficient multidimensional wideband parameter estimation. One application is provided by an orthogonal frequency division multiplexing-(OFDM) based joint radar and communication system, which uses SIMO architecture. In this paper, the estimated parameters are given by the range (time delay), the relative velocity, and the direction of arrival (DoA) pairs of the dominant radar targets. Due to the wideband assumption, the received signals on different subcarriers are incoherent and, therefore, cannot fully exploit the frequency diversity of the OFDM waveform. To estimate the parameters jointly and coherently on different subcarriers, we propose an interpolation-based coherent multidimensional parameter estimation framework, where the wideband measurements are transformed into an equivalent narrowband system. Then, narrowband multidimensional parameter estimation algorithms can be applied. In particular, a wideband $R$ -D periodogram is introduced as a benchmark algorithm, and we develop the $R$ -D Wideband Unitary Tensor-ESPRIT algorithm. The simulations show that the proposed coherent parameter estimation method significantly outperforms the direct application of narrowband parameter estimation algorithms to the wideband measurements. If the fractional bandwidth is significant and the SNR is not too low, the estimates provided by the narrowband estimation algorithms can become inconsistent. Moreover, the interpolation order should be chosen according to the SNR regime. In the low SNR regime, interpolation with a lower-order (i.e., linear interpolation) is recommended. For higher SNRs, we propose an interpolation with higher-order polynomials, e.g., fourth-order (cubic splines) or even higher

Terminal Area Simulation System User's Guide - Version 10.0

The Terminal Area Simulation System (TASS) is a three-dimensional, time-dependent, large eddy simulation model that has been developed for studies of wake vortex and weather hazards to aviation, along with other atmospheric turbulence, and cloud-scale weather phenomenology. This document describes the source code for TASS version 10.0 and provides users with needed documentation to run the model. The source code is programed in Fortran language and is formulated to take advantage of vector and efficient multi-processor scaling for execution on massively-parallel supercomputer clusters. The code contains different initialization modules allowing the study of aircraft wake vortex interaction with the atmosphere and ground, atmospheric turbulence, atmospheric boundary layers, precipitating convective clouds, hail storms, gust fronts, microburst windshear, supercell and mesoscale convective systems, tornadic storms, and ring vortices. The model is able to operate in either two- or three-dimensions with equations numerically formulated on a Cartesian grid. The primary output from the TASS is time-dependent domain fields generated by the prognostic equations and diagnosed variables. This document will enable a user to understand the general logic of TASS, and will show how to configure and initialize the model domain. Also described are the formats of the input and output files, as well as the parameters that control the input and output

Mathematic Models for Aircraft Trajectory Design: A Survey

Presented at the 2013 ENRI International Workshop on ATM/CNS (EIWAC2013), Tokyo, Japan, February 2013.Air traffic management ensure the safety of flight by optimizing flows and maintaining separation between aircraft. After giving some definitions, some typical feature of aircraft trajectories are presented. Trajectories are objects belonging to spaces with infinite dimensions. The naive way to address such problem is to sample trajectories at some regular points and to create a big vector of positions (and or speeds). In order to manipulate such objects with algorithms, one must reduce the dimension of the search space by using more efficient representations. Some dimension reduction tricks are then presented for which advantages and drawbacks are presented. Then, front propagation approaches are introduced with a focus on Fast Marching Algorithms and Ordered upwind algorithms. An example of application of such algorithm to a real instance of air traffic control problem is also given. When aircraft dynamics have to be included in the model, optimal control approaches are really efficient. We present also some application to aircraft trajectory design. Finally, we introduce some path planning techniques via natural language processing and mathematical programming