Thesis(M.S.)--Auburn University, 2007. / Abstract. Vita. Includes bibliographic references (p.133-135).

Source: Masters Abstracts International, Volume: 45-06, page: 3140. Adviser: Thomas Stock. Includes bibliographical references.

ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada / In an experimental flight test campaign, the usage of a real time Ground Telemetry System (GTS) provides mandatory support for three basic essential services: a) Safety storage of Flight Tests Instrumentation (FTI) data, in the occurrence of a critical aircraft failure; b) Monitoring of critical flight safety parameters to avoid the occurrence of accidents; and c) Monitoring of selected parameters that validates all tests points. At the operational side the test ranges typically works in two phases: a) In real time where the GTS crew performs test validation and test point selection with Telemetry data; and b) In post mission where the engineering crew performs data analysis and reduction with airborne recorded data. This process is time consuming because recorded data has to be downloaded, converted to Engineering Units (EU), sliced, filtered and processed. The main reason for the usage of this less efficient process relies in the fact that the real time Telemetry data is less reliable as compared to recorded data (i.e. it contains more noise and some dropouts). With the introduction of new technologies (i.e. i-NET) the telemetry link could be very reliable, so the GTS could perform data reduction analysis immediately after the receipt of all valid tests points, while the aircraft is still flying in a quasi-real time environment. To achieve this goal the Brazilian Flight Test Group (GEEV) along with EMBRAER and with the support of Financiadora de Estudos e Projetos (FINEP) started the development of a series of Decision Aid Tools that performs data reduction analysis into the GTS in quasi-real time. This paper presents the development and the evaluation of a tool used in Air Data System Calibration Flight Tests Campaign. The application receives the Telemetry data over either a TCP/IP or a SCRAMnet Network, performs data analysis and test point validation in real time and when all points are gathered it performs the data reduction analysis and automatically creates HTML formatted tests reports. The tool evaluation was carried out with the instruction flights for the 2009 Brazilian Flight Test School (CEV). The results present a great efficiency gain for the overall Flight Test Campaign.

In this work the problem of calibrating Flush Air Data Sensing (FADS) has been addressed. The inverse problem of extracting freestream wind speed and angle of attack from pressure measurements has been solved. The aim of this work was to develop machine learning and statistical tools to optimize design and calibration of FADS systems. Experimental and Computational Fluid Dynamics (EFD and CFD) solve the forward problem of determining the pressure distribution given the wind velocity profile and bluff body geometry. In this work three ways are presented in which machine learning techniques can improve calibration of FADS systems. First, a scattered data approximation scheme, called Sequential Function Approximation (SFA) that successfully solved the current inverse problem was developed. The proposed scheme is a greedy and self-adaptive technique that constructs reliable and robust estimates without any user-interaction. Wind speed and direction prediction algorithms were developed for two FADS problems. One where pressure sensors are installed on a surface vessel and the other where sensors are installed on the Runway Assisted Landing Site (RALS) control tower. Second, a Tikhonov regularization based data-model fusion technique with SFA was developed to fuse low fidelity CFD solutions with noisy and sparse wind tunnel data. The purpose of this data model fusion approach was to obtain high fidelity, smooth and noiseless flow field solutions by using only a few discrete experimental measurements and a low fidelity numerical solution. This physics based regularization technique gave better flow field solutions compared to smoothness based solutions when wind tunnel data is sparse and incomplete. Third, a sequential design strategy was developed with SFA using Active Learning techniques from the machine learning theory and Optimal Design of Experiments from statistics for regression and classification problems. Uncertainty Sampling was used with SFA to demonstrate the effectiveness of active learning versus passive learning on a cavity flow classification problem. A sequential G-optimal design procedure was also developed with SFA for regression problems. The effectiveness of this approach was demonstrated on a simulated problem and the above mentioned FADS problem.

Data visualisation in a virtual environment (VE) opens up new ways of presenting data and makes it possible for the observer to explore data in an immersive way. However, it also comes with a number of challenges. One of these challenges is data distinguishability. The data needs to be distinguishable against the background, but in a VE where the user can move around and observe the data from different perspectives, the backdrop will be constantly changing. This thesis studies this challenge and contributes knowledge to current research about data visualisation in VEs. The research question When in a detailed virtual environment, what impact does the varying background have on distinguishability of visualised data? is answered using a digital self -completion questionnaire and four hypotheses.  The data were not able to clearly determine if one of the colourmap used (YellowRed, Rainbow) was overall more effective than the other one. However, the rainbow colourmap did have marginally better results and was chosen by more participants as their preferred colourmap. The results did show that a larger number of participants disagreed that the light background made the data easier to distinguish in comparison to a dark backdrop. The results showed that more participants found it easier to see the data when seen from above than when from below. The two colourmaps were not equally effective regarding how well they could show both the VE and the data: The results indicating that the YellowRed colourmap was better at showing the details of the VE but not as good at distinguishing the data, whilst the Rainbow colourmap had the reverse results being better at distinguishing the data but less effective at showing the background.  The thesis concludes that it has fulfilled its goal of establishing a starting point for further studies, further studies that, according to the author, is woefully needed.

ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California / Telemetry data are usually collected for analysis at some later time and can be monitored to follow the progress of a test. In the case of an Air Data System the signals from the sensors are sent to a computer that calculates the air data parameters for use on multiple LabView-generated displays, as well as to the Data Acquisition System. The readouts on the multiple displays need to be real-time so they are useful to the flight crew. Equations that control the different air data values are determined by what telemetry data are available and the preference of those doing the test planning. These systems need to display the information in a format useful to the flight crew and be reliable.

This thesis presents the calibration processes of the pitot-static system, which is a part of the air data system of a military transport aircraft through flight tests. Tower fly-by method is used for air data system calibration. Altitude error caused by the position of the static port on the aircraft is determined by analyzing the data collected during four sorties with different weight, flap and landing gear configurations. The same data has been used to determine the airspeed measurement error. It has been shown that both the altitude and airspeed errors are within the allowable limits specified by FAR 25. Same method is also used for trailing cone calibration that is used for high altitude test flights for RVSM certification.

This thesis presents an estimator to assist or replace a fighter aircraft’s air datasystem (ADS). The estimator is based on machine learning and LSTM neuralnetworks and uses the statistical correlation between states to estimate the angleof attack, angle of sideslip and Mach number using only the internal sensorsof the aircraft. The model is trained and extensively tested on a fighter jetsimulation model and shows promising results. The methodology and accuracyof the estimator are discussed, together with how a real-world implementationwould work. The estimators presented should act as a proof of concept of thepower of neural networks in state estimation, whilst the report discusses theirstrengths and weaknesses. The estimators can estimate the three targets wellin a vast envelope of altitudes, speeds, winds and manoeuvres. However, thetechnology is quite far from real-world implementation as it lacks transparencybut shows promising potential for future development. / Det här examensarbetet presenterar en estimator för att hjälpa eller ersätta ettstridsflygplans luftdatasystem (ADS). Estimatorn är baserad på maskininlärningoch LSTM neurala nätverk och använder statistisk korrelation mellan tillstånd föratt uppskatta anfallsvinkeln, sidglidningsvinkel och Mach-tal endast med hjälpav flygplanets interna sensorer. Modellen är tränad och utförligt testad på ensimuleringsmodell för stridsflygplan och visar lovande resultat. Estimatornsmetodik och noggrannhet diskuteras, tillsammans med hur en implementeringi verkligheten skulle fungera. De presenterade estimatorerna bör fungera somett “proof of concept” för kraften hos neurala nätverk för tillståndsuppskattning,medan rapporten diskuterar deras styrkor och svagheter. Estimatorerna kanuppskatta de tre tillstånden väl i ett stort spektra av altituder, hastigheter, vindaroch manövrar. Tekniken är dock ganska långt ifrån en verklig implementeringeftersom den saknar transparens, men visar lovande potential för framtidautveckling.