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Abstract

A miniature air data system based on a seven-hole probe was developed for real-time, in-flight measurement of wind flow deviation angle and inertial parameters. The system is very small in size and weighs only 150 g, especially suitable for flight tests with miniature unmanned aerial vehicles (UAVs). It is capable of measuring angle of attack (AOA) and angle of sideslip (AOS), ranging up to ±78°. The exposed part is a slender probe fixed on the nose of the aircraft, which is only 4 mm in diameter. A spin flight test was conducted with a delta-wing, slender body unmanned aircraft; experimental results demonstrate the complete spinning process. The synchronized measurement of AOA, AOS and angular rate clearly revealed the different phases of spin entry. Furthermore, a spin prediction criterion based on the sideslip changes at high AOA is given. Flight test results show that the prediction approach based on air data can forecast spin entry earlier than the conventional approach based on angular rate, which makes incipient spin detection possible.

Keywords

Air data system seven-hole probe flight test angle of attack spin prediction oscillatory spin

Introduction

Spin is one of the most dangerous flight conditions. Its features include high angle of attack (AOA)/angle of sideslip (AOS), high sink rate and rapid yaw movement, which is considered to be the most serious threat to flight safety. Therefore, during the aircraft development process, it is a mandatory requirement that its aerodynamic configuration is unlikely to spin or easy to recover from spin. However, for fighters, there are increased requirements for maneuverability. To meet such requirements, current fighters sometimes operate at high AOA where many configurations experience a severe degradation in stability and control characteristics, resulting in a significant number of stall/spin accidents.¹

Although limited by the control computer, unmanned aerial vehicles (UAVs) fly within the normal envelope and are unlikely to stall/spin. But once it occurs, due to the time delay of remote control and the lack of feedback, it is impossible for UAV ground pilots to perform appropriate spin recovery maneuvers. In 2001, a Global Hawk RQ-4A UAV crashed after a spin caused by structural failure of the V-tail.² It is just one of the large number of UAV accidents.

Piloting strategies for spin recovery vary with the mass distribution and tail configuration of the aircraft. For most of the aircraft, recovery controls are ailerons neutral, opposite rudder and stick forward, and there are also abnormal controls with ailerons with or against the spin, elevator first and rudder neutral.³ In any case, recognizing the right direction of spin should be the first step. However, it is extremely difficult for pilots to distinguish the attitude of the spinning aircraft; instruments in the cockpit may be inaccurate or lose efficacy and give pilots a false sense of the attitude. Moreover, oscillations during spin can be so extreme as to cause erect spins to go inverted or to confuse the pilot as to the actual direction of spin rotation.⁴ From this point, there is a need for an automatic spin detection system, which will allow enough time for pilots or UAV flight control computers to recover if the detection apparatus signals the threat before spin entry, and provides the type of spin at the moment of spin entry.

In the past decades, study of the mechanism of stall and spin attracted increasing attention. To solve the most challenging aerodynamic problem, an accurate experimental technique is required. Experimental methods include flight test, spin wind tunnel test, free-drop and model flight test. A spin wind tunnel provides upward airstream, guaranteeing the aircraft model will float within the wind tunnel. The parameters of spin motion over time are measured, and spin models are tested with various configurations (different payload, center of gravity, external stores, etc.). This supplies experimental data for aircraft designers and a recovery method for pilots. For example, the Langley spin tunnel had conducted a list of spin test. Analysis of the data showed that model tests satisfactorily predicted full-scale recovery characteristics approximately 90% of the time.⁵

However, not all the processes of spin are possible to test in the wind tunnel, for example, the spin entry process from stall. Besides, spin models will be more likely to enter into flat spin in a wind tunnel than in real flight, and thus the sensitivity of spin cannot be immediately determined with a spin wind tunnel. Subscale model flight tests set up a bridge between a wind tunnel test and a full-size flight test. The flight characteristics obtained from a powered model flight test would augment the aerodynamic data obtained from the wind-tunnel investigation.⁶ For example, the use of the Airborne Subscale Transport Aircraft Research (AirSTAR) facility at NASA Langley Research Center allows for the rapid prototyping and testing of control algorithms in high fidelity simulation and flight testing environments.⁷

The rapid development of miniature unmanned aircraft enabled the low-cost, highly flexible aerodynamic flight experiments. Researchers from Stanford University have studied automatic stall/spin detection approaches using commercial off-the-shelf model aircraft and flight data systems.⁸ Moreover, researchers at University of Illinois at Urbana-Champaign have conducted a list of experiments using a model aircraft to investigate the effects of various control surface deflections and combinations, motor power settings and orientations on the spin characteristics of a single-engine subscale aircraft.⁹

The air data system is an essential part of avionics, providing airspeed, AOA, AOS, temperature and barometric altitude. These parameters are vital for normal flight as well as flight tests. Generally the AOA and AOS are measured by wind vanes. Their drawbacks include high startup wind speed, mechanical friction, slow response and bulk (for miniature UAV). Because of these problems, most of the miniature UAV flight tests do not use AOA/AOS sensors, but calculate AOA and AOS from inertial parameters. However, the calculation method is not accurate at unsteady aerodynamic conditions, especially at spin.

In this paper, a miniature seven-hole probe was developed to measure the air data during spin processes, which is small enough for subscale model aircraft. The seven-hole probe system was tested on a delta-wing unmanned aircraft, and a list of spin flight tests were conducted. Additionally, a spin detection approach based on the real-time air data is proposed.

Development of miniature seven-hole probe system

Multihole probes

Multihole pressure probes have long been used to obtain velocity and pressure information in fluid flows.¹⁰ One of them is the seven-hole probe (Figure 1), frequently used for flow field scanning in the wind tunnel. Wind velocity and wind direction are acquired through the calculation of the pressure value from different pressure taps. The seven-hole probe is able to measure a flow deflection angle up to 78°, with an accuracy below 1% span. The total pressure and static pressure from a certain point in the flow field can be gauged at the same time. There are also examples where multihole probes are applied to flight tests.^11–13 Moreover, pressure taps drilled directly on the nose of the aircraft, the FADS (Flush Airdata Sensing) system, is another solution for the military aircraft and spacecraft where a protrusive probe is undesirable.¹⁴

Figure 1.

The 4 mm seven-hole probe.

In order to minimize the size, a miniature seven-hole probe (4 mm in diameter) was used in this experiment. It has been calibrated following the method of Gerner et al.¹⁵

Hardware architecture of embedded systems

Conventional multihole probes are connected to pressure transducers, and a multi-channel data acquisition card measures the voltage of each transducer. Then the voltages are converted into pressure by softwares on the computer. However, the miniature UAV that is used in this research has a limited payload, so the challenge of this new task is to design an integrated pressure module that achieves the same function as the conventional system. It is accomplished by using an embedded system, which has been widely applied in commercial drones. Embedded systems are very small in size and integrate various communication interfaces, so as to provide a real-time and multi-tasking system. The ARM Cortex-M3 micro controller is used in the system, which is able to read pressure data from transducers, process matrix operations and communicate with ground station through a wireless data link. Figure 2 demonstrates the architecture of the hardware system, which is divided into three parts, the seven-hole probe, the digital pressure module and the data acquisition module.

Figure 2.

Embedded systems architecture.

The probe is fixed on the nose of the experimental aircraft model and connected to the digital pressure sensors through silicone tubes. The reference pressure tubes of each sensor are connected together into a single static tube that is connected to a cavity inside the fuselage. The cavity is connected to the fuselage through a small vent, open to atmosphere indirectly since the fuselage is not airtight. Thus the fuselage buffers the turbulence from the outside airstream. The reference pressure is not precisely equivalent to the atmosphere during maneuvering flight such as post-stall, but the reference pressure has been canceled in the calculation formula of the seven-hole probe. Therefore, the accuracy of flow angle and airspeed is not sensitive to the reference pressure. The digital pressure module is designed as the associated equipment of a certain probe, and its calibration matrix is stored in an EEPROM chip within the module, accessible by the micro controller. The calibration data in EEPROM and the processing software will not be changed frequently. Therefore, one certain probe is attached to one relevant module, which are non-interchangeable. Moreover, a high resolution barometric pressure sensor provides data to be used in calculating local atmospheric density, a necessary parameter for obtaining airspeed.

The data acquisition module (Figure 2(right)) is designed to be highly flexible. The ultimate goal of this module is to allow the non-experts to do various flight experiments without being concerned with how to program. When it works, air data, attitude data, GPS data and altitude will be recorded into a Micro SD Card (Secure digital card) automatically and printed on the Organic Light-Emitting Diode (OLED) screen simultaneously. In addition, there is a Xbee data link that sends attitude data to the ground station or receives control commands. As is shown in Figure 2, the data acquisition module contains a commercial off-the-shelf inertial measurement unit that measures attitude data. Based on the quaternion inertial navigation method and Micro-Electro-Mechanical System (MEMS) sensors, the unit outputs the value of acceleration, angular rate and attitude angle at a maximum speed of 200 Hz. Such data is recorded synchronously with the air data from the seven-hole probe. All these parameters greatly facilitate researchers to do flight experiments and the module can also be mounted to other vehicles conveniently. Furthermore, the future proposed research is expected to introduce an automatic spin recovery maneuver, so several additional Input/Output (IO) ports are reserved which can be used to generate a Pulse Width Modulation (PWM) signal for servo motors. Detailed parameters of the system are listed in Table 1.

Table 1.

Parameters of seven-hole probe and embedded system.

Item	Parameter
Probe diameter	4 mm
Probe length	200 mm
Airspeed range	5–30 m/s
AOA range	±78°
AOS range	±78°
Pressure sensor range	±2 in H₂O
Weight	150 g
Inertial parameters	Roll, pitch and yaw angles (ϕ, θ, ψ)
	Roll, pitch and yaw rates (p, q, r)
Data acquisition rate	100 Hz
Storage medium	TF card
Power supply	7.2 V
External ports	GPS, Data-link, ADC, IO ports

AOA: angle of attack; AOS: angle of sideslip; Micro SD Card: (Secure digital card); GPS: Global Positioning System; ADC: Analog-to-Digital Converter.

Pressure sensors and controllers are expected to be light-weight and small-sized. In this work, digital pressure sensors with built-in analog–digital converters are adopted and integrated on a single printed circuit board. The circuit and associated measurement electronics are carefully designed to reduce the size. As is shown in Figure 3, the dimension of the sensor board is 67 mm × 52 mm (Figure 3, left) and the data acquisition module is 80 mm × 50 mm (Figure 3, right). Together with the probe and battery (Figure 3, center), the weight of the whole system is only 150 g, bringing great convenience to future research.

Figure 3.

Printed circuit board. Left: digital pressure sensor module. Center: battery. Right: data acquisition module.

Dynamic response of the tubing system

The sampling rate of the pressure sensors is 600 Hz. Limited by the pressure tubes, the dynamic performance of the system deteriorates. Dynamic response analysis of the tubing system was therefore conducted using acoustic synthetic jet actuator and Kulite pressure sensors. The probe described in this article consists of seven stainless steel tubes. Each tube has an internal diameter of 0.65 mm and a length of 200 mm. The tubes are connected to the pressure sensors respectively through seven rubber tubes with larger diameter. The discontinuity in tube radius offers the possibility of non-linearities.¹⁶ The experimental apparatus is shown in Figure 4. A sinusoidal pulsing pressure was generated by the acoustic synthetic jet actuator applied to the stainless steel tube and rubber tube which have the same diameter and length as the tubes in the seven-hole probe. In order to measure the dynamic response quantitatively, two Kulite pressure sensors are mounted on the end of the rubber tube and the other vent of the jet actuator respectively. The two pressure sensors are sampled synchronously by a data acquisition device, and then the amplitude response and phase response are calculated, as shown in Figure 5. The original sinusoidal signal in the measurement process changed from 20 Hz to 1 kHz, with a 10 Hz step.

Figure 4.

Experimental apparatus of dynamic response measurement.

Figure 5.

Dynamic response of the seven-hole probe tubing system. (a) Amplitude response; (b) Phase response.

Figure 5 shows the attenuation under different excitation frequencies. Apparently, a heavily damped response with a low frequency range was obtained since the narrower tube section is placed at the beginning.¹⁷ With a given excitation frequency of 100 Hz, the amplitude ratio is 0.706 and the phase lag is −96°. The amplitude ratio decreased to 0.343 at 600 Hz, with a −387.3° phase lag. Therefore, the probe is not suitable for high-frequency systems such as turbulence measurement. However, the spin flight test does not require high sampling frequency, since the oscillating frequency of the oscillatory spin is less than 20 Hz. At this frequency, the amplitude ratio is 0.994, which is negligible for flight test application. In this paper, the original sampling rate was 600 Hz, and it was further reduced to 100 Hz after median filtering. Thus the final output rate of the seven-hole probe is 100 Hz.

Air data processing

Air data consists of dynamic pressure (q), static pressure (P_∞), temperature (t) and AOA (α)/AOS (β). These parameters are essential to analyze spin process and indicate the aerodynamic force directly. All these parameters except temperature are derived from the pressure values of the sensor module. The micro controller on the module reads data from eight sensors (one of them is a spare) through a Serial Peripheral Interface (SPI) interface at a frequency of 600 Hz. Noise is reduced by median filtering, and then the data is processed by matrix operation and resolved to obtain the flow angle and velocity. The software will first compare the pressure values from seven holes of the probe. If the center hole has a maximum pressure, it is considered to be a low flow angle; otherwise, it is classified as a high flow angle. The detailed formulas follow Gerner's algorithm.¹⁵

For flight tests, both accuracy and speed of the acquisition are required. Normally, to perform one mathematical operation of the seven-hole probe, the program has to look up a coefficient table of 400 figures, before doing several matrix manipulations and trigonometric functions. In the solution of this paper, with the help of a high-performance ARM processor, a whole manipulation only requires 230 μs, and the system output rate is 100 Hz. The rate can be accelerated up to 600 Hz under specific circumstances for dynamic measurement with shorter pressure tubes.

Synchronization precision

The synchronous acquisition of multi-channel signals is one of the critical technologies for subscale model flight test. Synchronous wind and attitude data provides an important reference for the aerodynamic analysis of the spin. During the spin entry, the flow direction usually changes before the inertial forces, and thus precise, synchronized data contributes to the measurement of the time interval between them. The seven-hole probe system consists of two separated electronic parts that are controlled by two processors respectively. Each part consists of several sensors with different sampling rates (higher than 100 Hz). Although all the parameters are finally recorded at 100 Hz, the pressure transducers, accelerators, gyroscopes and compass are not precisely synchronized.

As is shown in Figure 6, the program consists of three loops: the data fetch and computation loops are functions in the digital pressure sensor module and the IMU (inertial measurement unit) loop runs in the data acquisition module. The P₁₋₇(n) refers to one reading of the pressure value, and the value will be calculated every six readings. The computation and IMU loops both run at 100 Hz. In the data fetch loop, the pressure values are measured 600 times per second so the micro processor reads data sequentially from seven sensors every 1.67 ms. The interval is negligible between reading the first and the seventh sensor. Subsequently pressure values are sent to the computation loop just after the sixth reading (shown in Figure 6, at the beginning of P₁₋₇(6)), where a median filter is applied, and then the filtered data are calculated using a list of matrices. Filtering and calculating require 3.5 ms before transmitting the air data of 19 bytes through RS232 (A standard for serial communication transmission of data) which requires 1.65 ms, at a baud rate of 115200. The air data is transmitted to the IMU loop where inertial data is measured and saved to the storage card. The IMU loop captures inertial data after a complete frame of air data is received. Therefore, there is a time lag between air data and inertial data. Take the third sample of data fetch loop as the sampling instant of air data, and then the interval is 9.3 ms. At the time when inertial data is saved, the air data is actually the state of 9.3 ms ago. Theoretically the air data curves should be moved forward in the final results; however, considering that the air data will finally be used as spin entry prediction, the curves remain in their original condition to reflect the true time lag.

Figure 6.

Timing diagram of data-processing, 100 Hz output rate.

In order to validate the dynamic performance when the airflow direction changes rapidly, experiments were conducted in the low turbulence close circuit low-speed wind tunnel (Figure 7). A swing rig is hinged to the wind tunnel by a rotating shaft, which is driven by a crank mechanism. The model of the slender body is fixed in the middle of the swing rig and a seven-hole probe is mounted on the tip of the slender body, simulating the forebody of the experimental unmanned aircraft. During the test, the rig swings sinusoidally in horizontal direction with an amplitude of 60° at 0.5 Hz. The maximum angular velocity is 100°/s, and the wind speed is 15 m/s. Meanwhile, an angle encoder connected with the shaft records the angle of rotation, as a true-value of the AOA. Experimental results are shown in Figure 8. The time delay of seven-hole probe is demonstrated by comparing the two measured curves. Approximately, the time delay of the seven-hole probe is 10 ms, which is close to the theoretical prediction of 9.3 ms, acceptable for the purpose of a subscale model flight test. Meanwhile, the subsequent flight test results (see Figure 11 later) show that the maximum angular rate occurs at spin entry, which is 75°/s, and thus a 10 ms delay caused angular deviation of 0.75°. This deviation needs to be considered in further quantitative studies.

Figure 7.

Swing mechanism for slender-body model.

Figure 8.

Time delay of the flow angle.

The circumferential pressure distribution of the forebody is measured by a circle of pressure taps on the slender model, at a location 3.5 times the diameter away from the tip. Further, the lateral force coefficient of the cross section can be derived by integrating the circumferential pressure distribution, and thus the relation curves between AOA and side force coefficient (C_z) are obtained. Due to the aerodynamic characteristics of the slender body at a high AOA, side force occurs when the AOA surpasses a certain degree. Figure 9 shows that C_z (the reference area is the cross section of the forebody) increased dramatically when the AOA surpassed 25°, and there are peaks at 43° and 58°. Up to a certain extent, the result provides a reference for the subsequent flight test. However, the dynamic performance of C_z may be different. For example, side force is produced at a higher degree when the AOA increases rapidly. Moreover, the characteristics of the slender body lack repeatability due to its complicated vortex structure at high AOAs, and a slight difference of the experimental conditions will cause completely different side forces.

Figure 9.

Side force coefficient at different angle of attack.

Vehicle design

The aircraft model for spin test is a delta-wing, twin vertical tail configuration (see Figure 10). For the sake of concentrating on spin research, the fuselage is simplified to a slender body, with a cone-shaped nose. The front part of the fuselage is manufactured by high-accuracy stereolithography appearance, followed by a transition section that transformed from cylinder to cuboid. The front part is connected to an avionics cabin on the upside of the wing, which contains electronic modules and provides a reference pressure for pressure sensors as well. The battery and remote control receiver are mounted in a pod beneath the wing and connected to a brushless motor with a push propeller. On the trailing edge of the wing, there are three control surfaces, which are enlarged in order to improve the maneuverability and easily enter/recover from spin. Table 2 shows the detailed parameters of the aircraft.

Figure 10.

The miniature UAV (unmanned aerial vehicle) for spin flight test.

Table 2.

Aircraft design parameters.

Item	Parameter
Length	950 mm
Wing span	860 mm
Wing area (S_W)	0.28 m²
Root chord ( $c_{W_{r}}$ )	610 mm
Tip chord ( $c_{W_{t}}$ )	102 mm
Taper ratio (λ)	8.4
Aspect ratio (AR)	2.64
Sweep of inboard wing	57°
Sweep of outboard wing	46°
Area of vertical tail (S_V)	0.025 m²
Height of vertical tail	200 mm
Take-off weight	750 g
CG location	25% $\bar{c}$
Motor	2212 1400 KV
Propeller	8040/8060
Battery	Li-Po 11.1 v 1300 mAh
Maximum thrust	approx.700 g

CG: Center of Gravity.

Figure 11.

Air data and attitude data of a typical spin.

As a general configuration of the modern military aircraft, a tailless delta wing is suitable for supersonic flight as well as low-speed maneuvers with a high AOA. The leading edge vortex shedding from the wing leading edge provides additional lift during high-AOA maneuvers, and enhanced the post-stall performance of fighters. However, delta wings have some particular aerodynamic issues. With limited lateral stability and an unstable forebody vortex, the wing rock phenomenon frequently occurs.¹⁸ Additionally, a slender fuselage with a cone radome may cause phantom yaw that is induced by sideforce.¹⁹ With the purpose of spin research on delta wings, their specific aerodynamic characteristics are factors to consider.

Spin flight test

Spin may be triggered by multiple causes; nevertheless the asymmetric lift and drag over the wings is a primary reason.²⁰ Moreover, vortex breakdown (slender delta wing), particular shape of cross section and improper operation contribute to the spin development as well. At the time when an aircraft is reaching a stalling AOA and both wings reach maximum lift coefficient(C_Lmax), a lateral disturbance (gust, control surface maneuver or side force of the forebody) will trigger sideslip that leads to a different AOA of the two sides. Consequently, the retreating wing with a higher AOA drops and further increases the AOA; thereby the unbalanced lift and drag between the advancing wing and retreating wing are reinforced, and the aircraft will enter into spin in a short time. Accordingly, the initial sideslip is a sign of spin entry to some extent. In fact, the development of spin entry is so rapid that pilots can hardly take actions to stop the asymmetric stall. To make matters worse, when the sideslip occurs, their first response is to control ailerons against the rolling direction, which increases the AOA of the downward wing and causes a greater rolling moment. Therefore, most of the previous studies are focused on the spin recovery method within the first turn or fully developed spin beyond several turns.

For the model used in this study, its aerodynamic performance deteriorates significantly in high-AOA flight, because the vertical tail may fall into the wake zone of the delta wing. Therefore the lateral static stability drops due to a lower efficiency of the vertical tail. At the same time, the slender body may generate a side force. As a matter of fact, it is more likely to enter into left spin in the flight tests of this model, in spite of different wind direction and aileron position. Also, the left spin presents a higher spin rate. Since the power was reduced to zero before stall, propeller torque was not considered. Meanwhile the aileron lost efficacy at a high AOA, so aileron trim will not generate an additional rolling moment. This phenomenon strongly implies the existence of an asymmetric vortex, presumably caused by a micro asymmetry in tip shape after the installation of the seven-hole probe. It is evident that the forebody greatly affects the side force as well as spin characteristics, indicating a potential direction of further study. During the flight test, the aircraft first climbed to a safe altitude around 150 m and kept level flight. Then the pilot gradually reduced the throttle and pulled back the stick on the remote controller. The coordinated maneuvers are expected to keep the aircraft maintaining an altitude before it spins, and increasing its AOA smoothly until the aircraft reached its stalling angle, following by a stall/spin process. The pilot applied a full-elevator deflection and idle thrust during spin entry and developed spin, and recovered after six spins by pushing forward the stick and increasing throttle.

Figure 11 presents the air data and attitude data of a spin process. The AOA increases from 44 s and stalls at around 45 s, along with the airspeed reducing. The spin occurs at 46 s, followed by a periodical yaw motion and a high AOA of 60°. Accordingly, the altitude of the aircraft drops at a sink rate of 7.1 m/s. The spin parameters are shown in Table 3.

Table 3.

Parameters of a typical spin.

Item	Parameter
α	≈60°
Sink rate (V)	7.1 m/s
Spin rate (ω)	33 r/min
Dimensionless spin rate $(ω b 2 V)$	0.067
Re	0.225 × 10⁶
Oscillation rate	1.06 Hz
Oscillation amplitude (AOA)	±10°
Oscillation amplitude (AOS)	±20°

It can be seen that a sinusoidal motion occurs during the developed spin. The aircraft oscillates in the directions of rotation and pitch, and the rolling motion further causes a significant sideslip oscillation (as well as AOA, but much weaker), which is presumably caused by reduced roll damping. Due to the low aspect ratio and mass being concentrated within the slender fuselage, it has a larger moment of inertia in the Y-axis than in X-axis(I_Y > I_X), which is classified as a fuselage-loaded airplane. Most of the modern fighter airplanes are fuselage-loaded, which helps to give them very distinctive spin characteristics.²¹ Moreover, the phenomenon of sideslip oscillation is also observed in previously published articles.^1,6,22

Spin prediction approach

Analysis of the spin entry

For modern fighter pilots, their first step towards spin recovery training is to distinguish the direction of spin (left, right or inverted), before engaging the appropriate operation. If the pilot distinguished the spin mode from the early stage, when the control surfaces were still effective, the pilot might be able to stop the spin development. However, predicting the spin mode is not easy at the incipient stage, which is usually accompanied by wing rock and vibration. A yawing motion of the aircraft may indicate spin entry or just a wing rock, which is difficult for pilots to identify. But when the yaw rate is recognizable, the spin has already started.

In the stage of post-stall, increasing the AOS will trigger spin entry. Therefore, the changing of sideslip at high incidence angle can be chosen as an indicator of spin entry. The different phases of spin entry can be clearly revealed when plotting the data points of AOA, AOS and yaw rate into a scatter diagram (see Figure 12). In the diagram, the parameters of α, β, r are on the X-, Y-, Z-axes respectively. The points represent a time span from level flight to developed spin, roughly matching the first half of Figure 11. Apparently, data points from level flight are concentrated within a small area around zero (grey points). When the pilot reduced the throttle and pulled back the stick, the AOA began rising constantly until reaching the stalling AOA. Afterwards, there is a rapid increase in the AOS, indicating a nose slice of the aircraft, namely, the asymmetric forebody vortex was formed. The AOA is 24.2° at this point, which approximately agrees with previous wind tunnel experimental results. The stage after the nose slice is shown in blue points, in which the AOA and AOS keep rising. Subsequently the spin entry occurs at 46 s, refer to Figure 11, where α = 49.7°, β = 29.2°, and the yaw rate then increased dramatically with a decrease of AOS (yellow points). Due to a significant increasing yawing motion, spin mode in this stage is recognizable by pilots, but there is only a short period of time to perform a recovery maneuver, before entering into developed spin, when recovery becomes more difficult. Finally, after one turn of yaw angle, when r = 180.1°, it developed into oscillatory spin with coupling movement of yaw rate, AOA and AOS (red points). Obviously, the data points in this stage are represented in a circular fashion, because of the deviation between angular rate vector and velocity vector.^23,24 Note that spin recovery data is not included in Figure 12.

Figure 12.

Spin entry stages from a typical spin maneuver. (a) Dividing points of spin phases; (b) Yaw rate vs. AOS; (c) Yaw rate vs AOA; (d) AOA vs. AOS.

Conventional spin prediction techniques are based on inertial parameters (e.g. angular rate) and alarm signals or engaging automatic spin recovery procedure when the angular rate surpasses a given threshold.²⁵ Similarly, pilots will also focus on the angular rate since inertial force is a most intuitive sense for human beings. As is shown in Figure 12(b), the yellow segment presents a rapid change in yaw rate, and the fast deteriorating maneuverability leaves little time for pilots to stop the plane from entering into oscillation. However, it is obvious that α and β have great changes before the yellow segment, which can be considered as a prediction. The blue segment covers the changing of α and β. If early corrective actions are taken in this segment, ailerons with spin direction, for example, spin can be avoided. As a delta-wing aircraft with slender fuselage, side force of the forebody is a primary cause of initial sideslip and further leads to spin entry. Thus, a sideslip at a high AOA can be regarded as a prediction of spin entry.

Spin departure criteria and prediction results

The initial sideslip deviation before spin entry is observed in every spin process of the flight tests, since the yaw movement at the beginning of spin will inevitably cause sideslip, when there is still a forward vector of velocity. However the sideslip will decrease and oscillate around zero after the spin is developed, because the vector of velocity points downward causing a high AOA. Thus the upward flow brings a lateral component when there is a roll angle, and then the lateral component is measured as a sideslip by the probe. Consequently, a changing roll angle induces an oscillating sideslip. Therefore, only an initial sideslip between stalling AOA and spinning AOA is valid as a prediction.

Finally, the departure criterion for spin prediction is set as the first time that the AOS surpasses ±10° when the AOA is higher than 20°, before developed spin. Flight test results indicate that the model spins shortly after it surpasses the given criteria. The flight data of α, β, r from six spins are shown in Figure 13, and spin prediction is flagged with vertical lines in black, where air data parameters surpass the criterion. Meanwhile, the spin entry points are temporarily defined as ${\bar{r}}_{spin} / 2 (100^{\circ} / s)$ (average yaw rate at developed spin), flagged with vertical lines in red, in order to provide a time reference. It can be inferred from the diagrams that spins 1, 3, 5 present rapid spin entry with a fast rising AOA, and further the AOS reaches a prediction criterion when α > 40°, β = 10°, followed by spin entry in a half second. In contrast, the deviation of flow angles change smoothly in spins 2, 4, 6, and the prediction flags are ahead of spin entry by 0.9 s ∼ 1.7 s. In addition, the wing rock is observed during this time. Compared with inertial parameters, the air data reflects the incipient spin stage earlier. It can be seen that black vertical lines occur at the beginning of yaw rate rising or shortly before its rising. Although it is only hundreds of milliseconds in advance, it is possible for automatic recovery systems to engage a maneuver. Moreover, the control surfaces are effective at the prediction time when the AOA is not too high, which is beneficial to the recovery.

Figure 13.

Spin prediction results using the air data threshold.

Conclusions

The seven-hole probe module proved to be reliable and practical. The air data and inertial parameters collected by the module clearly reproduce the spin process, providing a convenient research tool for spin study. In addition, a delta-wing, slender body model was designed. Flight tests carrying the module were conducted to study the spin entry process as well as oscillatory spin. The attitude data from different spin entries revealed that the AOA/AOS indicated departure in advance, compared with inertial parameters. Furthermore, a spin prediction criterion was given based on the sideslip deviation at a high AOA. According to the flight test results, the spin entries were predicted before a noticeable increase in angular rate, allowing the pilots or automatic controller to engage an incipient recovery maneuver before developed spin.

The limitation of the prediction criterion is the lack of different configurations of the aircraft model, as well as the lack of various center of gravity (CG) position or mass distribution, and thus the criterion is exclusively for a certain model. As for future work, various configurations will be studied to widen the applicable range, and the seven-hole probe will be substituted by the FADS system to improve the dynamic response. Moreover, the air data and inertial data will be combined to make the prediction more reliable and robust. Further, an automatic spin recovery controller will be designed, which is able to respond at the beginning of spin entry, applicable for both manned and unmanned aircraft, reducing the risk of spinning.

Footnotes

Acknowledgements

The authors would like to thank Yaju Ge and Ziqiang Zhang for their help with portions of the development of the printed circuit board and embedded system programming. The authors would also like to thank Zhiyuan Hu for the initial development of the pressure sensor module.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: A project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

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