Kitabı oku: «Introduction to Flight Testing», sayfa 6

3.1.3 Outside Air Temperature

Knowledge of ambient (freestream) temperature of the flight environment is important for calculating the freestream density from the ideal gas law and for determination of true airspeed. The challenge of measuring outside air temperature (OAT) from a moving aircraft is related to the frame of reference of the air (the desired temperature) versus the aircraft reference frame (where temperature is measured). Since the air is moving relative to the aircraft, the temperature measured in the aircraft frame of reference will be higher. If a temperature probe completely stagnates the flow (i.e., with no loss of energy), the isentropic Mach relation,

(3.1)

may be used to find the freestream temperature (T_∞) from measurements of the stagnation temperature (T₀) and the freestream Mach number (M_∞), where γ = 1.4 is the ratio of specific heats for air. However, most temperature probes do not fully recover the full stagnation temperature, so a recovery factor (k) must be introduced. Furthermore, there may be differences between the local Mach number (M_ℓ) and the freestream Mach number, due to local acceleration or deceleration of the flow around the aircraft body. Thus, we can modify Eq. (3.1) to accommodate these two factors,

(3.2)

where M_ℓ and k can be found by calibration (Gracey 1980).

3.1.4 Other Instrumentation

Other relevant instruments on a traditional cockpit panel include a clock (for timing various events), the engine tachometer (for measuring engine speed in RPM), manifold pressure for the engine air intake, fuel flow rate, and total fuel burned. In traditional flight testing at the university level, readings from the “steam gauge” instruments must be manually recorded via pen and paper. These manual data recording techniques can be effective, as long as the time scale of any transient characteristic of the flight maneuver is long relative to the speed at which data can be manually recorded. For example, the rate of climb in a sustained, steady climb can be easily recorded by hand with a stopwatch and the altimeter, while the level acceleration of an aircraft would be much more difficult to manually record since velocity is changing quickly over time.

3.2 Glass Cockpit Instruments

Glass panel avionics displays (Figures 3.6–3.8) are becoming increasingly common on small GA aircraft. Glass panels measure and display the same data as traditional instruments, but the organization and display of data is greatly improved for better scanning and interpretation by the pilot. The glass panel display is usually physically segmented into a primary flight display (PFD), where critical data such as airspeed, altitude, heading, and attitude are displayed (left screen in Figures 3.6 and 3.7), and a multifunction display (MFD) where secondary data such as engine performance, navigation, terrain, traffic, etc. are displayed (right screen in Figures 3.6 and 3.8). We will discuss the glass panel display of the same data streams that are found on a traditional six pack of instruments, followed by a few notes on recording data from glass panel avionics systems. The following discussion refers to Figures 3.7 and 3.8.

Figure 3.6 Overview of aircraft cockpit instrumentation for a glass panel avionics display in a Cirrus SR20.

Figure 3.7 Detailed view of the primary flight display (PFD) on a Cirrus SR20, with key indications identified.

Measurements such as airspeed and altitude continue to be made in the same manner, where total pressure and static pressure are used to calculate the indicated airspeed. Since the calibration of airspeed is known, and real‐time measurements of pressure and OAT are available, the flight computer can calculate the true airspeed in real time. Since global positioning system (GPS) data are also available for measuring ground speed, the flight computer can also calculate the winds aloft, which represent the difference between true airspeed and ground speed. (See Chapter 8 for detailed definitions of these speeds, and details on how they are computed.) Indicated airspeed is prominently displayed on the left side of the glass panel on a linear, sliding scale along with a digital readout in the center of the scale. Altitude is also displayed on a linear sliding scale, but on the right side of the screen. The Kollsman setting (reference pressure) is displayed immediately beneath the altitude scale. On the right side of the altitude ticker is a display of the rate of climb in numerical and graphical form.

The attitude indicator is displayed in much the same way as it is on a traditional attitude indicator; one critical difference is that the artificial horizon completely spans the width of the display for better situational awareness for the pilot. The heading indicator is in the bottom center of the PFD, with heading information displayed in much the same way as on the analog instrument. Both the attitude indicator and the heading indicator are based on microelectromechanical systems (MEMS)‐based gyroscopes, magnetometers, and accelerometers. The details of these sensor schemes will be discussed in the following subsections. OAT is typically displayed on the lower left side of the PFD. Engine parameters such as engine speed, percent power, manifold pressure, fuel flow rate, fuel burned, exhaust gas temperature, oil pressure, oil temperature, etc. are displayed on the MFD (see Figure 3.8).

Figure 3.8 Detailed view of the multifunction display (MFD) on a Cirrus SR20, with key engine parameters identified.

The airspeed and altitude tickers, along with the heading indicator, have accompanying magenta trend bars (on the inside edge of each ticker or along the circumference of the heading indicator) that indicate the projected value that will be true 6 seconds in the future, based on current rates of change. Rate of turn can be inferred from the length of the magenta bar on the heading indicator (e.g., a standard rate turn would have a magenta bar extending 18° from the center).

Data from glass cockpit displays may also be recorded manually (pen and paper), or in some cases, limited data streams are available in digital form from the avionics suite itself. For example, the Avidyne FlightMax Entegra avionics suite records aircraft data including latitude, longitude, pressure altitude, density altitude, exhaust gas temperatures and cylinder head temperatures for all cylinders, oil temperature, oil pressure, engine RPM, OAT, and manifold pressure. While this data stream is predominantly focused on engine parameters (for engine health monitoring), it can be a useful supplement to other data streams used in flight testing. The avionics suite records the data at a rate of one sample per 6 seconds (0.167 Hz) and stores it in internal data storage. The data can be retrieved after the flight via the USB port on the front of the avionics panel.

The fundamental principles for the sensors at the heart of an aircraft's glass panel avionics are the same as those underlying traditional cockpit instruments. Glass cockpit sensors are based on gyroscopic principles and measurement of physical quantities such as pressure and temperature, but there are critical differences. Sensors such as magnetometers, accelerometers, and rate gyroscopes are grouped together into an inertial measurement unit (IMU), known as the attitude and heading reference system (AHRS), that fuses the sensor data streams to provide real‐time computations of an aircraft's heading and orientation in space. A second major subsystem is the air data computer (ADC), which computes aircraft speed, altitude, and rate of change of altitude based on measurements of total pressure, static pressure, and OAT. The third major subsystem used in glass panel avionics is the navigation instruments. In modern avionics, this primarily relies upon a global navigation satellite system (GNSS) receiver (i.e., a GPS receiver) but also includes radio receivers for radio‐based navigation aids. A significant advantage of glass panel avionics is that derived quantities such as winds aloft, density altitude, true airspeed, etc. can be determined real time in flight via the onboard computer at the heart of the avionics system.

3.3 Flight Test Instrumentation

Recent developments in MEMS have revolutionized sensor design and fabrication, making these sensors available at low cost and in small packages. Complex sensors such as gyroscopes and magnetometers can be fabricated with an extremely small form factor, allowing for them to be installed in very compact devices (see Figure 3.9). Most notably, the rapid development of MEMS has been driven by the proliferation of smartphones, which have many of the same sensors as those found on aircraft. MEMS developments have also enabled integration of these instruments into the cockpit – the modern avionics glass cockpit systems discussed in the previous section rely upon MEMS sensors for determining aircraft state. Alternatively, small external sensor packages such as smartphones, or the custom‐built unit, illustrated in Figure 3.9, can be mounted in any convenient location within the aircraft cockpit for simple installation and reasonably accurate DAQ. These comprehensive sensor suites are roughly equivalent to the AHRS that supports glass panel avionics.

Figure 3.9 Modern flight testing board with built‐in GPS, accelerometers, gyroscopes, magnetometers, and pressure transducer.

Source: Photo courtesy of Matthew H. McCrink.

The most significant deficiency of these instrumentation packages is the lack of air data measurements such as total pressure, static pressure, and OAT. Measurement of these properties requires direct access to dedicated instrumentation on the aircraft (the pitot‐static system), which is not normally possible on GA aircraft commonly accessible to students in an aircraft flight testing course. However, there are techniques (“work‐arounds”) for inferring these missing flight data. OAT changes very slowly at a given altitude, allowing it to be manually read and recorded at sparse intervals at a particular flight condition. Freestream static pressure can be measured to fairly good approximation by measuring cockpit pressure using a MEMS barometer on the DAQ device (see Gregory and McCrink 2016 for details). Total pressure (and, thus true airspeed) cannot be measured in flight without access to a pitot probe. However, using the techniques described in Chapter 8, true airspeed may be found by measuring ground speed during flight at a single test condition along three separate headings.

Key sensors in a typical external DAQ unit are a satellite navigation receiver and antenna (e.g., the GPS), 3‐axis gyroscopes, 3‐axis accelerometers, 3‐axis magnetometers, and a pressure transducer (barometer). A brief overview of each of these sensors is discussed as follows, but a much more detailed discussion is available in Titterton and Weston (2004).

3.3.1 Global Navigation Satellite System

One example of a global navigation satellite system used for determining position in 3D space (latitude, longitude, and altitude) is the GPS (for the remainder of this book, GPS will be considered synonymous with GNSS, although there are other satellite‐based navigation systems in use such as the Russian GLONASS, the European Galileo, or the Chinese BeiDou systems.). GPS signals offer very high positioning accuracy, typically resolving location to within a few meters or less. The basis for the measurement is transmission of a modulated carrier signal with a known pseudorandom code, a time stamp based on a highly stable atomic clock on board the satellite, and the precise location of the satellite in space. The GPS receiver infers the distance to each satellite being received based on the time required for the signal to traverse the distance from the satellite to the receiver (based on the ultrastable clock on each satellite). Since the position of each satellite in space is known with high accuracy and precision, and the speed of light is well known, the receiver can infer the distance to each satellite by phase aligning the pseudorandom code. The receiver then uses multilateration techniques to infer its own location in space based on these distances – the calculated distance from a particular satellite restricts the receiver location to being somewhere on a spherical shell centered on the satellite. The intersection of at least three spherical shells results in position being defined as a single point in space. Note that the clock on the receiver itself generally does not have a high degree of accuracy, so this represents an unknown in the calculation of position. Therefore, there are four unknowns in the computation: latitude, longitude, altitude, and instantaneous time, so the GPS receiver must have at least four satellites in view simultaneously in order to determine an accurate position estimate. GPS receivers can reliably report latitude and longitude with high accuracy, but altitude indications typically have much more error. In particular, smartphone‐based GPS receivers lack an external antenna and rely upon algorithms that are optimized for terrestrial applications. Thus, altitude reported by smartphone‐based GPS sensors is generally unreliable and should not be used for measurement of altitude in aircraft flight testing (see Gregory and McCrink 2016 for details). General overviews of satellite‐based navigation schemes are provided by Misra and Enge (2010) and Kaplan and Hegarty (2017).

Several key error sources limit the accuracy of GPS position estimates. These errors include propagation delays due to dispersion of the signal by the ionosphere (termed ionospheric delay), uncertainty in the time due to drift of the atomic clocks on board the satellites, and uncertainty in the location of the satellites (ephemeris error). Advanced signal processing algorithms have been introduced to correct for these errors, but the most basic correction scheme is to introduce a real‐time error correction term from a receiver at a fixed, accurately known position. These correction schemes include differential GPS (DGPS), real‐time kinematic (RTK) GPS, and the FAA's Wide Area Augmentation System (WAAS). We will provide a brief overview of each of these as follows.

Differential GPS can provide refined position and velocity histories for flight test applications (Sabatini and Palmerini 2008). A typical DGPS architecture for flight testing is shown in Figure 3.10. The system consists of a reference receiver located at a known location that has been previously surveyed, and one or more DGPS user (mobile) receivers mounted on a test aircraft. The reference receiver antenna, differential correction processing system, and datalink equipment are collectively called the reference station. Both the user receiver and the reference receiver data can be collected and stored for later processing, or sent to the desired location in real time via the datalink. DGPS is based on the principle that receivers in the same vicinity will simultaneously experience common errors on a particular satellite ranging signal. In general, mobile receivers use measurements from the reference receiver to remove the common errors. The limiting factor for DGPS is that the mobile and fixed receivers need to be in proximity to one another such that they experience the error sources in the same way and to facilitate radio communication from the fixed reference receiver to the mobile receiver on board the aircraft. Thus, DGPS is most applicable to local‐area flight operations such as takeoff and landing flight tests.

Figure 3.10 Typical differential GPS architecture.

RTK GPS is a technique similar to DGPS, but offers higher accuracy. Similar to DGPS, RTK provides a correction to the position estimate, which can be transmitted in real time to the mobile GPS receiver or stored for subsequent analysis. The correction factor is determined by measuring the distance to the satellite using a different technique from traditional GPS. Instead of relying solely on the pseudorandom code transmitted by the satellites, RTK GPS uses statistical methods to estimate the number of cycles present in the waveform between the receiver and the satellite and then multiplies the number of cycles by the wavelength (19 cm for the L1 signal) to infer range. There is some resulting error in the estimated distance, due to ambiguity in determining the correct integer number of cycles due to phase differences. The RTK technique can provide remarkable positioning accuracy, improving the position estimate to 1 cm accuracy. However, RTK GPS also requires that the mobile receiver be in the same vicinity as a fixed reference station, which limits its applicability to downrange flight tests.

In the aviation realm, an augmentation system has been recently developed in order to improve upon the baseline accuracy of the satellite‐based position measurement system and enable precision instrument approaches without requiring nearby ground‐based reference stations. The Wide Area Augmentation System is a satellite‐based augmentation system developed by the US Federal Aviation Administration that covers the majority of North America. It is based on a system of ground‐based reference stations that calculate the local difference between the GPS‐indicated position and the station's actual position (surveyed to very high accuracy). This error data, expressed as a deviation correction, is uplinked in real time to geostationary WAAS satellites at least once every five seconds, which then broadcast the correction to aircraft throughout the national airspace. WAAS GPS corrects for positioning errors predominantly resulting from ionospheric disturbances, which add phase distortion to received GPS signals. The WAAS correction is broadcast over the same frequency bands used for the baseline GPS signal, which reduces system cost and complexity. The resulting accuracy of a WAAS GPS receiver is improved to approximately 2‐m in the horizontal and vertical directions, which is an order of magnitude improvement relative to the baseline accuracy of standard GPS.

An important point to recognize when using GPS data for flight test is that the position from a GPS receiver is reported as decimal degrees for latitude and longitude, with a sign convention for positive being North of the equator and East of the prime meridian (e.g., the latitude/longitude coordinates 40.074199 ° , – 83.07968° are in North America). If relative distance is desired (say, in units of ft, m, or nautical miles), then some type of transformation is needed to convert the difference between latitude/longitude coordinate pairs into distance. This transformation is commonly done in the fields of geodesy and navigation, where a model of the Earth's shape must be assumed. Generally speaking, Earth is in the shape of an ellipsoid that bulges near the equator (relative to a perfect sphere) due to the rotation of the planet. One of the most common Earth models is the World Geodetic System 1984 ellipsoid (WGS84), which is periodically refined and revised (National Imagery and Mapping Agency 2000; Pavlis et al. 2012). WGS84 forms the basis of GPS position reporting, with latitude and longitude forming a measure of distance within the WGS84 coordinate system. However, these coordinates must be transformed to local Cartesian coordinates before distance calculations can be made for flight testing. This transformation can be done via the Universal Transverse Mercator conformal projection. This system involves the segmentation of Earth's surface into 60 zones, each measuring 6° of longitude wide. Bands of latitude measuring 8° high are sometimes used to further subdivide the zones. The transformation for each zone from WGS84 to Cartesian coordinates can be performed by methods described by Snyder (1987), and a wide array of MATLAB toolboxes are available for this purpose (e.g., see Wasmeier 2015).