Flush Air Data Sensing System Research Articles

Redundancy Design of FADS System for Complex Leading- Edge Aircraft Based on Neural Network

The flush airdata sensing system (FADS) calculates flight parameters such as angle of attack, sideslip angle, Mach number, incoming flow pressure and static pressure based on the pressure measurement of the aircraft surface. It can effectively solve the problem that the leading edge of the pitot tube protruding from the fuselage cannot adapt to the severe aerodynamic heating faced by hypersonic aircraft during the cruise phase, and at the same time meet the aircraft's demand for stealth performance. At present, there is little analysis and research on the use of neural network methods and FADS systems for complex leading edge aircraft. In view of the subsonic/transonic conditions of hypersonic aircraft returning autonomously during the landing phase, the redundancy design and verification of the head FADS system of complex leading edge aircraft are carried out considering the influence of thin leading edge and inlet components. 15 pressure measuring holes are opened in the head of the complex leading edge aircraft. Through a large number of refined numerical simulations, the pressure database of the aircraft under different incoming flow conditions is established, and the typical working conditions are verified by wind tunnel tests. For the complex leading edge aircraft, 4 sets of neural network algorithms are established based on pressure data and redundant design research is carried out, including 1 set of 9-hole algorithm and 3 sets of redundant algorithms. Among them, the 9-hole algorithm has a higher accuracy, with the solution error of the angle of attack within 0.07∘, the solution error of the sideslip angle within 0.3∘, the solution error of the Mach number within 0.0012, and the relative error of the solution of the incoming flow pressure and static pressure within 1.5%. In addition, a system solution process with a certain fault tolerance was established, which can continue to maintain the effective output of the incoming flow parameters in the event of failure of any single pressure measuring hole.

An optical-scan method for measuring the as installed surface port incidence angles for flush air data sensing (FADS) systems

The Flush Air Data Sensing (FADS) System, where air data are inferred from non-intrusive surface pressure measurements, uses natural contours of the vehicle forebody, wing leading edge, or probe. Although multiple methods have been developed to derive airdata from the sensed pressure matrix, all methods rely on accurate knowledge of local surface contours at the port locations. One of the most well-developed solution methods curve-fits the surface pressure distribution against the associated surface incidence angles using a quasi-Newtonian model. The well-known "Triples" algorithm extracts airdata from the curve-fit model. This solution method requires precise knowledge of as-installed incidence angles, i.e. the angles between the surface normal and the longitudinal axis of the vehicle. This study investigates the feasibility and accuracy of using an inexpensive optical-scanning system to measure the in-situ FADS pressure ports surface incidence angles. Here, two legacy 3-D printed probe shapes, as previously tested during a series of very low-speed wind tunnel tests, were used to develop and evaluate this method. The shapes 1) a hemispherical head cylindrical forebody, and 2) a Rankine-Body, were scanned along the longitudinal axis and the resulting point-cloud was edited using open-source software to generate three concentric "loops" surrounding each surface port. Each annular loop was assumed as co-planar with the surface port, and the singular-value decomposition (SVD) was used calculate the local surface gradient vector from the null-space solution. From the resulting gradient vector, geometric relationships calculate the port's polar coordinates including the surface incidence angle. For both body contours the resulting calculations are compared to the "known" design surface angles as prescribed for the 3-D prints. Error plots are presented for each individual ring-set, and for the collected set using all three ring together. For the collected data sets, the incidence angle calculations are accurate to within a quarter-degree.

Fault-tolerant FADS system development for a hypersonic vehicle via neural network algorithms

Hypersonic vehicles suffer from extreme aerodynamic heating during flights, especially around the area of leading edge due to its small curvature. Therefore, flush air data sensing (FADS) system has been developed to perform accurate measurement of the air data parameters. In the present study, the method to develop the FADS algorithms with fail-operational capability for a sharp-nosed hypersonic vehicle is provided. To be specific, the FADS system implemented with 16 airframe-integrated pressure ports is used as a case study. Numerical simulations of different freestream conditions have been conducted to generate the database for the FADS targeting in 2≤Ma≤5 and 0km≤H≤30km. Four groups of neural network algorithms have been developed based on four different pressure port configurations, and the accuracy has been validated by 280 groups of simulations. Particularly, the algorithms based on the 16-port configuration show an excellent ability to serve as the main solver of the FADS, where 99.5% of the angle-of-attack estimations are within the error band ±0.2∘. The accuracy of the algorithms is discussed in terms of port configuration. Furthermore, diagnosis of the system health is present in the paper. A fault-tolerant FADS system architecture has been designed, which is capable of continuously sensing the air data in the case that multi-port failure occurs, with a reduction in the system accuracy.

Comparing a 3-d printed hemispherical-head and Rankine-body probe shapes for very low speed flush air data system (FADS) measurements

This study investigates the feasibility of using Flush Air Data Sensing (FADS) System technology for air data measurements at the very low-airspeeds, where many Unmanned Aerial Vehicles (UAVs) operate. FADS is a non-intrusive alternative to pitot probes, where the vehicle nosecone, wing leading edge, or other aerodynamic surfaces are configured with multiple pressure-ports distributed along the windward face. Although FADS technology has been used for a variety of high-speed aircraft, FADS has never been applied to very low-airspeed flight regimes. This study reports on wind tunnel tests of two 3-D printed shapes: 1) a cylindrical body with a hemispherical head, and 2) a Rankine-Body. These body shapes can act as a vehicle analog to a wide range of three-dimensional shapes and account for both blunt leading edge and trailing after body flow characteristics. For this study the "probes" were printed with 5 pressure ports and the associated flow channels aligned at 0o, +22.5o and +45o direction-angles along the vertical centerlines of the models. Sensed pressure data were curve-fit, developing quasi-potential flow calibration models for each probe, with coefficients compiled as a function of geometric angle-of-attack and tunnel airspeed. The calibration models account for end-to-end systematic effects, including the mounting sting flow compression, up wash, and tunnel blockage. Using the derived calibration models and the sensed pressure data, the effective angles-of-attack were re-calculated using the well-known "Triples" algorithm. The associated airspeed and dynamic pressure are estimated from the sensed pressure data using non-linear regression. The resulting estimates are compared to the tunnel reference conditions. Generally, both probe shapes performed well, with the redundant 5-port arrangement allowing for significant noise rejection. Both probes achieved RMS airspeed errors of less than 5%, angle-of-attack errors less than 1 deg., and dynamic pressure errors of less than 12 pascals, across airspeeds ranging from 5 to 25 m/sec. The sensed Airdata measurements at the lowest airspeeds (5 m/sec), exhibited similar accuracy to those sensed at the highest airspeeds (25 m/sec), verifying the applicability of FADS technology to very low airspeed flight regimes.

Simplified Real-Time Flush Air-Data Sensing System for Sharp-Nosed Hypersonic Vehicles

This paper discusses a flush air-data sensing (FADS) system for a sharp-nosed hypersonic vehicle designed to estimate flight air data in real time at hypersonic speeds. The design’s target condition is Mach 5.0 to 7.0 with an angle of attack within 5 deg. The FADS system estimates air data by relating them to surface pressures measured from surface-mounted ports on the vehicle forebody. Unique combinations of pressure ratios were used to estimate the freestream dynamic pressure, which was a primary parameter for the flight experiment, the angle of attack, and the Mach number. The proposed FADS estimation algorithm was validated through numerical simulation, which was also used to generate datasets of surface pressures for given flight conditions. To handle possible sensor errors related to estimation accuracy in real-time estimation for a flight experiment, redundant systems were implemented. The results indicated that the designed FADS system can estimate air data within the uncertainty of 4.8% for a single estimator by considering sensor errors for freestream dynamic pressures in the range of 0–100 kPa, including targeted and off-design flight conditions. The proposed algorithm can estimate the air data with an acceptable level of uncertainty while retaining the robustness of estimation to sensor failures with low computational cost.

Flush Airdata System on a Flying Wing Based on Machine Learning Algorithms

By using an array of pressure sensors distributed on the surface of an aircraft to measure the pressure of each port, the flush airdata sensing (FADS) system is widely applied in many modern aircraft and unmanned aerial vehicles (UAVs). Normally, the pressure transducers of the FADS system should be mounted on the leading edge of the aircraft, where they are sensitive to changes in pressure. For UAVs, however, the leading edge of the nose and wing may not be available for pressure transducers. In addition, the number of transducers is limited to 8–10, making it difficult to maintain accuracy in the normal method for FADS systems. An FADS system model for an unmanned flying wing was developed, and the pressure transducers were all located outside the regions of the leading edge areas. The locations of the transducers were selected by using the mean impact value (MIV), and ensemble neural networks were developed to predict the airdata with a very limited number of transducers. Furthermore, an error detection method was also developed based on artificial neural networks and random forests. The FADS system model can accurately detect the malfunctioning port and use the correct pressure combination to predict the Mach number, angle of attack, and angle of sideslip with high accuracy.

A fault detection method for FADS system based on interval-valued neutrosophic sets, belief rule base, and D-S evidence reasoning

Fault detection, with the characteristics of strong uncertainty and randomness, has always been one of the research hotspots in the field of aerospace. Considering that devices will inevitably encounter various unknown interference in the process of use, which greatly limits the performance of many traditional fault detection methods. Therefore, the main aim of this paper is to address this problem from the perspective of uncertainty and randomness of measurement signal. In information engineering, interval-valued neutrosophic sets (IVNSs), belief rule base (BRB), and Dempster-Shafer (D-S) evidence reasoning are always characterized by the strong ability in revealing uncertainty, but each has its drawbacks. As a result, the three theories are firstly combined in this paper to form a powerful fault detection algorithm. Besides, a series of innovations are proposed to improve the method, including a new score function based on p-norm for IVNSs and a new approach of calculating the similarity between IVNSs, which are both proved by authoritative prerequisites. To illustrate the effectiveness of the proposed method, flush air data sensing (FADS), a technologically advanced airborne sensor, is adopted in this paper. The aerodynamic model of FADS is analyzed in detail using knowledge of aerodynamics under subsonic and supersonic conditions, meanwhile, the high-precision model is established based on the aerodynamic database obtained from CFD software. For further confirming the validity and feasibility, a comparison with the methods based on parity equation, χ2 distribution, and information fusion method ordered weighted averaging (OWA) with three sets of weight vectors are conducted.

Flush Air Data Sensing System for a Sharp-Nosed Hypersonic Vehicle with Curved-Wedge Forebody

A new algorithm for a flush air data sensing (FADS) system was developed to estimate flight data and attitude angles for a sharp-nosed hypersonic vehicle in a framework of a hypersonic flight demonstration project directed by the Japan Aerospace Exploration Agency at the Kakuda Space Center. The design’s target condition is Mach 5.0–7.0 with angles of attack and sideslip within 6°. The FADS system relates air data and pressures from surface-mounted pressure ports on the forebody of the vehicle to generate lookup tables that are used to estimate the angle of attack, freestream Mach number, static pressure, and dynamic pressure. These data are also used to generate an angle-of-sideslip estimate from triples algorithm, which has been well-established for a blunt-nosed body. The estimation algorithm used by the designed FADS system is validated through numerical simulation, which was also used to generate lookup tables and wind tunnel experiments. Results indicated that the designed FADS system can estimate air data within the uncertainty of 6% for freestream conditions and within 0.7° for the angles of attack and sideslip in the range of targeted flight conditions.

아음속 및 초음속 유동의 플러시 대기자료 측정장치 연구

플러시 대기자료 측정장치는 비행체 표면에서 측정되는 압력 데이터를 이용하여 대기자료를 예측한다. FADS는 돌출된 프로브가 없으므로 고성능 항공기, 스텔스 비행체 및 극초음속 비행체에 적합하다. 본 논문에서는 구-원추 형상을 갖는 비행체에 대해서 아음속부터 초음속 비행까지 대기자료를 예측할 수 있는 FADS의 교정 절차와 계산 알고리즘을 제시한다. 표면 압력 데이터 측정을 위해 노즈부 표면에 5개 플러시 압력공들을 마련하였다. 유동각 예측과 압력 관련 변수의 예측을 분리하는 개념이며, 아음속 유동의 포텐셜 유동해와 극초음속 유동의 수정 뉴톤식을 결합한 압력모델을 사용한다. 교정 압력 데이터는 Euler 방정식을 푸는 전산유체역학 코드를 만들어서 마흐수 0.5 ~ 3.0의 범위에서 구축하였다. 비행 마흐 수 0.6~3.0, 받음각과 옆미끄럼각은 각각 –10° ~ +10°의 범위에서 여러 비행조건에 대해서 테스트를 수행하였다. 예측된 대기자료는 받음각, 옆미끄럼각, 마흐수, 자유류 정압이며 참고 데이터와 비교하여 정확도를 분석하였다.

Vibration Testing of Absolute Pressure Sensor for a Flush Air Data System (FADS)

ABSTRACT Flush Air Data Sensing System (FADS) is an important subsystem required for a winged re-entry vehicle. It essentially makes use of surface pressure measurements located at a suitable position in the vehicle for deriving the air data parameters of the vehicle such as angle of attack, angle of sideslip, Mach number, etc. These air data parameters are used by the control and guidance system for carrying out gain scheduling, trim scheduling of control surfaces and overall flight management. The accuracy of the pressure sensors used in FADS is critical in achieving the targeted accuracy of the air data parameters. In the FADS system considered in this paper, piezoresistive MEMS-based pressure sensors are used. The FADS system is located in the nosecone of the vehicle which is a region prone to vibration during transportation as well as during flight. It is essential that the pressure sensors perform with the desired accuracies in spite of the vibration environment. Sine and random vibration testing are carried out as part of qualification testing of the vibration sensors of FADS. Analysis of the test results shows that the accuracies demanded by the system are provided by the pressure sensors under the vibration environment.

Flush Air Data Sensing System

Flush air data sensing system (FADS) forms a mission-critical subsystem in re-entry vehicles. It makes use of surface pressure measurements from the nose cap of the vehicle for deriving air data parameters such as angle of attack, angle of sideslip, Mach number, etc. of the vehicle. These parameters are used by the flight control and guidance systems, and also assist in the overall mission management. The overall system engineering of FADS, including selection of pressure transducers, tubing size, port geometry, FADS algorithm and associated processing electronics along with the integration scheme is addressed in this article. Details of the qualification tests carried out in wind tunnel for end-to-end verification of the entire FADS system are covered in brief. Majority of the tests were carried out in a low-speed wind tunnel at a wind speed of 65 m/s (Mach number 0.2). The flight performance of FADS is also discussed in this article.

Radio/FADS/IMU integrated navigation for Mars entry

Supposing future orbiting and landing collaborative exploration mission as the potential project background, this paper addresses the issue of Mars entry integrated navigation using radio beacon, flush air data sensing system (FADS), and inertial measurement unit (IMU). The range and Doppler information sensed from an orbiting radio beacon, the dynamic pressure and heating data sensed from flush air data sensing system, and acceleration and attitude angular rate outputs from an inertial measurement unit are integrated in an unscented Kalman filter to perform state estimation and suppress the system and measurement noise. Computer simulations show that the proposed integrated navigation scheme can enhance the navigation accuracy, which enables precise entry guidance for the given Mars orbiting and landing collaborative exploration mission.

Inverse Flush Air Data System (FADS) for Real Time Simulations

Flush Air Data Sensing System (FADS) forms a mission critical sub system in future reentry vehicles. FADS makes use of surface pressure measurements from the nose cap of the vehicle for deriving the air data parameters of the vehicle such as angle of attack, angle of sideslip, Mach number, etc. These parameters find use in the flight control and guidance systems, and also assist in the overall mission management. The FADS under consideration in this paper makes use of nine pressure ports located in the nose cap of a technology demonstrator vehicle. In flight, the air data parameters are obtained from the FADS estimation algorithm using the pressure data at the nine pressure ports. But, these pressure data will not be available, for testing the FADS package during ground simulation. So, an inverse software to FADS which estimates the pressure data at the pressure ports for a given flight condition is developed. These pressure data at the nine ports will go as input to the FADS package during ground simulation. The software is run to generate the pressure data for the descent phase trajectory of the technology demonstrator. This data is used again to generate the air data parameters from FADS algorithm. The computed results from FADS algorithm match well with the trajectory data.

Flush air data sensing system design for air breathing air-to-air missile

The key technical difficulties and design flow of flush air data sensing (FADS) system design for air breathing air-to-air missile are analyzed. The aspirated air-to-air missile large slenderness than layout, high maneuverability, high angle of attack and low cost control characteristics proposed a high precision, multi-points of fault diagnosis and fault-tolerant and anti overload of fads system design method. Mainly includes the algorithm design based on strategy of pressure model, hierarchical diagnosis and multi-models of re-configurable fault-tolerant, the error source of the system control model design, high precision and anti overload pressure measurement module design and solution machine module design. According to the shape of a typical air breathing air-to-air missile, the FADS system principle prototype is designed, which is based on 9 pressure measuring points layout and flight Mach number 1.5–4.0. Prototype real-time solution test is done in 1.2 m×1.2 m supersonic wind tunnel. Test results show that: relative error of static pressure measurement is no more than 6.9%, measurement error of Mach number is no more than 0.10, the measurement errors of the angle of attack and side slip angle are no more than 1°. The measurement error of the reconstructed model is close to the baseline model error; algorithm fault diagnosis points is less than or equal to 3, the scope of fault diagnosis of P1 deviation is less than or equal to −20% or greater than or equal to 40%, P2–P9 deviation is less than or equal to −30% or greater than or equal to 30%. The algorithm has less than or equal to 2 fault tolerant ability. The measurement error level, fault diagnosis and tolerance ability of the system can meet the engineering requirements.

Trade-off design of measurement tap configuration and solving model for a flush air data sensing system

This paper presents a trade-off design scheme with consideration of the pressure-tap configuration and solving model for a Flush Air Data Sensing System (FADS). First, a mechanism model of FADS is built for the basic structure in order to transform the measured pressures to the required air data. Then, several iteration algorithms are introduced for FADS to obtain the converging results of the solving model. Furthermore, four kinds of the pressure-tap configurations are designed in the relation to the basic structure, and the issues on the iteration convergence and modeling errors are discussed to analyze the compromise relations between the pressure-tap configuration and solving model. Lastly, a simulation example is applied to verify the feasibility of this proposed scheme, and at the same time some suggestions in the real application are provided for FADS.

Study on Flush Air Data Sensing Technology

Accurate measurement of atmospheric data is important for the control and navigation of aircraft. With the development of Aerospace Technology, the traditional probe air data sensing system has been could not meet the requirements of advanced flight. For this problem, the flush air data sensing system (FADS) has been proposed. The paper listed and compared some FADS algorithms and summarizes the characteristics and using conditions of several algorithm based on introducing the FADS measuring principle and compositions.

Recalibration of Flush Air Data System (FADS) on Pressure Port/Sensor Failures

Flush Air Data sensing System (FADS) forms a mission critical sub system in future reusable space transportation systems. FADS makes use of surface pressure measurements from the nose cap of the vehicle for deriving the air data parameters of the vehicle like angle of attack, angle of sideslip, Mach number, etc. These parameters are used by the control and guidance system of the vehicle for reentry flight management as well as for subsequent mission phases upto landing. The on board algorithm makes use of calibration coefficients derived from wind tunnel data to achieve better accuracy in the estimated air data parameters. In the event of faults in FADS due to port blockages or pressure sensor failures, recalibration is essential for achieving the desired accuracy of air data estimates. This paper describes recalibration of FADS using surface fit method in the event of pressure port blockage or sensor failures. The method is based on the storage of coefficients of a 4th order surface fit corresponding to the wind tunnel calibration data and recalibration using these polynomial coefficients.

Unmanned air vehicle air data estimation using a matrix of pressure sensors: a comparison of neural networks and look-up tables

Flush airdata sensing (FADS) systems are cost- and weight- effective alternatives to current air data booms for measuring important air data parameters such as airspeed, angle of attack, sideslip, etc. Most applications consider large manned/unmanned air vehicles where the Pitot-static tube is located at the nose tip. However, traditional air data booms can be physically impractical for micro- (unmanned) air vehicles (MAVs) and, in this article, a FADS system mounted on the wing leading edge of a MAV flown at low speeds of Mach 0.07 (wind tunnel experiments under corresponding conditions) is designed. Moreover, two approaches for converting the FADS system pressure to meaningful air data are compared: a neural network (NN) approach and a look-up table (LUT). Results have shown that instrumentation weight and cost were reduced by 80 per cent and 97 per cent, respectively, in comparison to a traditional air data boom. Overall, the NN estimation accuracies were 0.51°, 0.44 lb/ft2, and 0.62 m/s and the LUT estimation accuracies 1.32°, 0.11 lb/ft2, and 0.88 m/s for the angle of attack, static pressure, and airspeed, respectively. It was also found that the LUT has faster execution times while the NN was in most cases more robust to sensor faults. However, while the LUT requires high memory usage, especially for higher dimensions, the NN can be executed in a few lines of code.

Subsonic Tests of a Flush Air Data Sensing System Applied to a Fixed-Wing Micro Air Vehicle

Flush air data sensing (FADS) systems have been successfully tested on the nose tip of large manned/unmanned air vehicles. In this paper we investigate the application of a FADS system on the wing leading edge of a micro (unmanned) air vehicle (MAV) flown at speed as low as Mach 0.07. The motivation behind this project is driven by the need to find alternative solutions to air data booms which are physically impractical for MAVs. Overall an 80% and 97% decrease in instrumentation weight and cost respectively were achieved. Air data modelling is implemented via a radial basis function (RBF) neural network (NN) trained with the extended minimum resource allocating network (EMRAN) algorithm. Wind tunnel data were used to train and test the NN, where estimation accuracies of 0.51°, 0.44 lb/ft2 and 0.62 m/s were achieved for angle of attack, static pressure and wind speed respectively. Sensor faults were investigated and it was found that the use of an autoassociative NN to reproduce input data improved the NN robustness to single and multiple sensor faults. Additionally a simple NN domain of validity test demonstrated how the careful selection of the NN training data set is crucial for accurate estimations.

Improved Algorithms for Flush Airdata Sensing System

The Flush Airdata Sensing (FADS) system and its pressure model are presented briefly. The improved algorithm for calculating the impact pressure, static pressure and modifying coefficient are studied. First, the non-linear equations are simplified using Moore-Penrose inverse. Then the impact pressure and static pressure are computed with the improved iteration and BP neural network. Both the two improved algorithms meet the requirements of the flush airdata sensing system on precision, reliability and speed. BP neural network has great advantages on real-time requirements, for it needs only 5% time to reach the required precision comparing to the original algorithm.