Kyushu Institute of Technology Academic Repository

Kyutacarは九州工業大学で生産された研究成果を オープンアクセスで提供する機関リポジトリシステムです。 Kyutacar is open-access repository of research by members of the Kyushu Institute of Technology.

The primary goal of single-stage-to-orbit technology is to reduce the cost of transportation to and from space radically. The winged rocket developed by Kyushu Institute of Technology is a test bed and technology demonstrator for this effort. Winged rocket is one of the ideal types of space transportation system that has a high potential of reusability, operational flexibility including abort making it possible to fly back to its launch point. This vehicle can be the pioneer for the future space transportation such as cargo and human space flight. To successfully complete these missions, estimation of air data parameters such as angle of attack, Sideslip angle, Mach number and dynamic pressure during the flight is essential. Conventional pitot tubes are not suitable for operation in re-entry flight environments due to extreme heating of the nose with small radius. Therefore the concept of FADS (Flush Air Data Sensing) system, by which the aerodynamic pressure is measured on the airframe surfaces, has been proposed. This air data sensing system allows the continuous operation of high temperature supersonic and hypersonic re-entry flight. However, in the extreme thermal environment, pressure ports and sensors still have a high risk of failure that makes the acquisition of the air data unreliable, to result in loss of flight controllability. This research proposes innovative fault tolerant FADS by utilizing a large number of pressures holes on the airframe nose. Chapter 1 discusses the outline and importance of this research based on the background of previous researches. Chapter 2 discusses the FADS wind tunnel test model with 17 pressure holes and test cases carried out for calibration of air data estimation. Chapter 3 discusses the air data estimation algorithm using the geometric model and aerodynamic models to calculate the angle of attack, Sideslip angle, Mach number and dynamic pressure. To perform this estimation, the air data must be related to the surface pressures by the aerodynamic model that covers over a wide range of Mach number, which is derived from the closed form potential flow solution for a blunt body applicable to subsonic speeds and the modified Newtonian flow model applicable to hypersonic speeds. Both the potential flow and the Newtonian flow describe the surface pressure at each port in terms of the geometrical incident flow angle. On the other hand, the geometric model represents the location of the pressure ports of the FADS wind tunnel test model. Based on the geometric model and aerodynamic model of the FADS, the aerodynamic parameters such as angle of attack, Sideslip angle, Mach number and dynamic pressure are estimated by the selection of some sets of various pressure port combinations. Since there are four aerodynamic parameters and a calibration parameter of the aerodynamic model to be estimated, at least five surface pressure port measurements must be available to derive the entire air data state. Using five pressure measurements to estimate the air data is equivalent to a high order spline fit and results in an air data estimating algorithm, which is sensitive to noise in the measured pressures. Providing an additional sixth sensing location mitigates the noise sensitivity, increases redundancy options, and results in a system that gives overall superior performance. On the other hand, wind tunnel test data, which have been performed for various speeds in both subsonic and supersonic regions with different angle of attack and sideslip angle, are used for the validation and calibration of the wind test model. Once the air data parameters are estimated, it is necessary to correct the mainstream angle of attack and the Sideslip angle using the flow correction angle parameters derived from the wind tunnel data. Additionally, a shape and compressibility parameter also called as calibration coefficient is estimated with respect to various Mach number and angle of attack. Chapter 4 discusses the results and analysis of the estimation algorithm carried out using the wind tunnel test data, and the improvements made for the estimation algorithm based on the analysis is proposed. It is found that the accuracy of the Sideslip angle deteriorates as high the angle of attack increases. In order to understand the cause of the deterioration, a theoretical approach using the pressure distribution method was employed. In this method, the pressure at each port is estimated using the geometrical and aerodynamic model for various speeds and attitudes which is the inverse of the FADS estimation algorithm. From this study, it was understood that at some particular attitude of the FADS module the pressure at some of the ports was equal. During the estimation of angle of attack or Sideslip angle, we select some sets of pressure port combinations and in these combinations if two surface pressure ports have same value, the expression for estimation will become indefinite. This causes the indeterminate values called singularity points, when estimating angle of attack and Sideslip angle. It was understood that, these singularity points are the reason for deterioration of the sideslip angle accuracy at higher angle of attack. Chapter 5 discusses the fault tolerance capability of the FADS system for the fault detection and isolation scheme when singularity points arise during flight. To overcome this issue, the selection of appropriate ports and increasing the number of ports from 6 to 9 for the estimation is one of the suitable solutions for improving the accuracy and fault tolerance. Therefore, for angle of attack estimation 10 combinations of pressure ports and for sideslip angle estimation 55 combinations can be employed in the algorithm. When the port combinations are increased, there is a wide selection of combinations for various attitudes of the flight to eliminate the combinations that cause the singularity and still achieve the accuracy. Additionally, the FADS hardware for WIRES vehicles is summarized in this section. Chapter 6 summarizes the research results of the algorithm with the fault tolerance capability and future works to be carried out in this field are included.

内容記述

九州工業大学博士学位論文 学位記番号：工博甲第465号 学位授与年月日：平成31年3月25日

その他のタイトル

耐故障性を有するフラッシュエアデータシステムの研究開発

目次

1. Introduction|2. Wind Tunnel Experiments|3. Air Data Sensing Algorithm|4. Results and Analysis|5. Fault tolerance Capability and Results|6. Conclusions