FLIGHT PARAMETERS OF AN AIRCRAFT

TECHNICAL FIELD

The present invention relates to a method and system of calculation and consolidation of flight parameters of an aircraft.

BACKGROUND ART

Almost all operating aircraft, both civil and military, use sensors that protrude from the surfaces of the aircraft, in particular floating fins for measuring incidence and sideslip angles. Thee sensors are able to directly measure quantities such as flow angles and local velocities and, in general, flight parameters useful for determining the aircraft ' s attitude, altitude and flight speed. The algorithms used for processing the data measured by these sensors, as well as their peculiarities tied to the location of the sensors on the aircraft, their characteristics and the system reliability/redundancy requirements, are developed for virtually the sole purpose of measurement calibration.

Static pressure sensors of the type available on the market, for example pressure sensors made using MEMS technology, have the advantage of small size and weight and do not protrude from the surface on which they are mounted. The use of such pressure sensors is therefore desirable. However, to obtain one or more of the above-mentioned flight parameters starting from static pressure measurements, complex algorithms are needed, which require considerable calculating capacity. Known algorithms for the calculation of flight parameters starting from static pressure measurements are typically based on using artificial intelligence and neural networks and guarantee functionality for calculation of the flight parameters and detection of any failures in the pressure sensors.

However, solutions of known type have the drawback of requiring a high calculating capacity (with consequent energy consumption) and, if a failure of one or more pressure sensors is detected, do not allow the exclusion of this/these sensor (s). In practice, these solutions of known type "live" with the failure. The information coming from faulty sensors is still used in the calculation of the flight parameters.

DISCLOSURE OF INVENTION

The object of the present invention is to provide a method and system of calculation and consolidation of flight parameters of an aircraft that overcome the drawbacks of the known art.

According to the present invention, a method and system of calculation and consolidation of flight parameters of an aircraft are provided as defined in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the present invention, some preferred embodiments will now be described, purely by way of a non- limitative example, with reference to the attached drawings, where:

- Figures la and lb show views from above and below of an aircraft equipped with static pressure sensors at a plurality of locations; and

- Figure 2 shows, by means of a flowchart, a method of calculation and validation of flight parameters according to one embodiment of the present invention.

BEST MODE FOR CARRYING OUT THE INVENTION

The method according to the present invention combines improved flight parameter calculation functionality with functionality for identifying and isolating malfunctioning sensors .

In particular, the method according to the present invention is based on the use of mathematical and physical models that bind the flight parameters to be calculated (or, more in general, state variables) to parameters measured in flight (local static pressures) . The use of these models enables easier validation of the entire process with respect to the known .art, with positive repercussions in terms of traceability and verification in a certification procedure.

The local static pressure measurements are obtained by using a plurality N of pressure sensors Si-S _N , of known type and available on the market, positioned in a plurality of respective locations on an aircraft. The pressure sensors can be of any type and made using any manufacturing technology (for example, MEMS technology) , provided that they can be "buried" in the body of the aircraft in Figures la and lb, in the sense that they do not significantly protrude from the surface of the aircraft's body. For example, such sensors do not significantly protrude when they do not constitute a radar signature element.

Figures la and lb show an aircraft 1 by way of example, seen respectively from above and below, and comprising a plurality N of pressure sensors Si-S _N with each one arranged at a respective location ii-i _N on the aircraft 1 and possible housed in a respective casing able to protect each respective sensor from the external environment. In particular, each sensor Si-S _N is integrated into the body of the aircraft 1 such that it is substantially coplanar with the body of the aircraft 1 at the location ii-i _N in which it is positioned, or arranged within the body of the aircraft 1. In general, the sensors Si-S _N , or in any case the casings that house a respective sensor Si-S _N , do not protrude from the surface of the body of the aircraft 1 at the locations ii-i _N where they are positioned. In order to allow the sensors Si-S _N to correctly acquire static pressure information, the body of the aircraft 1 has one or more holes (not shown) at each location ii-i _N that form the only interface of the pressure sensors Si-S _N with the outside of the aircraft 1. The use of sensors integrated in the body of the aircraft 1 ensures a low contribution of the sensors Si-S _N to the radar signature of the aircraft 1.

As mentioned, in use, each sensor S _I -S _N detects a static pressure value at location ii-i _N , and generates a transduced signal Xi-X _N indicative of the static pressure value measured at a given moment in time (or, alternatively, an average value over a predetermined period of time) . The transduced signal Xi-X _N is, in particular, an electrical signal, suitable for being received and processed by a computer 5, placed on board the aircraft 1. The computer 5 is further configured to implement a calculation algorithm of the type shown in the flowchart in Figure 2.

The number of sensors S _X -S _N is chosen so as to comply with the system' s redundancy and reliability requirements and therefore so as to support a number of failures of single sensors Si-S without compromising the calculating capacity of the flight parameters, keeping their accuracy within a required limit (depending, for example, on the required aircraft-level specifications, as provided by the aircraft's designer to ensure controllability of the aircraft, and/or on the basis of any national legislations . The static pressure information of a single sensor Si-S _N actually weighs on the calculated value of the flight parameters differently according to the total number N of sensors Si-S _N : the larger the total number N of sensors S _X -S _N/ the smaller this weight. The applicant has established that an ideal number N of sensors S; _L -S _n (in terms of accuracy of calculated flight parameter values and resilience to possible failures) is 9. In general, it is preferable to have N≥7. Nevertheless, it is clear that it is possible to calculate flight parameters according to the present invention also using a number N of sensors Si-S _N less than 7, accepting a drop in the accuracy of the calculated flight parameters values and a reduced ability to compensate for the possible failure of one or more sensors Si-S _N . The choice of the locations ii-i _N for the sensors S _X -S _N on aircraft 1 is guided by the need to maximize the sensitivity of the acquired pressure values to variations in the flight parameters during the flight of the aircraft 1. Assessments for. defining the "best" locations can be carried out, for example, by using computer simulators.

The applicant has established that the areas where changes in pressure corresponding to variations in the flight parameters (i.e. the gradients) are greatest (absolute values intended) are locations ii-i _N shown in Figures la and lb.

Figure 2 shows, by means of a flowchart, the steps a method of calculation of flight parameters starting from the pressure measurements taken by the sensors S _X -S _N . The mathematical model for calculation of the flight parameters is developed from the aerodynamic mathematical model, in particular the aerodynamic model in the following equation (1) : P _l0 AOA,AOS,Mach) = Ps _∞ + Cp _i (AOA,AOS,Mach) *^yPs _∞ Mach ² (1) where Pi _OC i is the local static pressure measured by a sensor Si-S _N at the respective location ii-i _N , AOA is the angle of attack (sometimes called the angle of incidence) and indicated by letter a, AOS is the angle of sideslip and indicated by letter β, Mach represents the flight Mach number, Ps _∞ is the flight static pressure, Cpi is the pressure coefficient related to the location ii-i _N considered, and γ is the ratio between specific heat at constant pressure and the specific heat at constant volume and is a constant having a value, for air, of between 1.3 and 1.5, in particular approximately 1.4 in the operating temperature range (between -40 and +40 °C) .

The pressure coefficient Cpi represents the link between the flight parameters to be calculated (state variables, Ps∞, AOA, AOS and Mach) and the local static pressures Pi _OC i measured in flight (dependent on the flight parameters AOA, AOS and ach) . The values of the pressure coefficient Cpi at the various locations Ϊ _Ι -Ί is obtained by using computer simulators, and/or experimental testing in wind tunnels. The thus-obtained values for the ¹ pressure coefficient Cpi are then tabulated in matrix form.

In greater detail, with regards to an experimental approach, construction of the arrays of Cpi values is achieved as specified in steps a-f below:

a. A Mach value is set;

b. An angle of sideslip AOS value of zero is set;

c. The value of the angle of attack AOA is varied in predetermined steps, generally between 0.5 degrees and 1.0 degree; a Cpi value is obtained for each pair of values (AOS; AOA) ;

d. Step c is repeated, setting a non-zero value for the angle of sideslip AOS; in particular, the repetition of step c must be carried out the same number of times as the values of angle of sideslip AOS for which it is intended to collect Cpi values. In general, for AOS, values between 0 degrees and 10 degrees with a 2-degree step, both positive and negative, are selected;

e. For each pair of values (AOS; AOA), a Cpi value is acquired on the basis of the previous step;

f. Steps b-e are repeated, setting a Mach value different from the value set in step a; in particular the repetition of steps b-f must be carried out the same number of times as the values of Mach for which it is intended to collect Cpi values. In general, Mach values between 0.2 and the maximum envisaged by requirements are selected, with a variable step and preferably closer together near to the maximum (for example, a step of 0.1) . At low Mach speeds, a step of 0.2 is adequate. In this way, for each Mach value considered, it is possible to build a table (or array) M containing a plurality of pressure coefficient Cpi values for each pair of values (AOS; AOA) considered. Considering P flight ach values, P arrays Mi-M _P are thus created. Characterizing equation (1) for each pressure sensor Si - S _N present on the aircraft 1, gives a system of non- linear equations, in which the subscript "i" takes i values from 1 to N: P^^ F^Ps^Mach.AO^ AOS) (2) where the Pi _OC i variables constitute the known terms (measured by the sensors Si-S _N ) and Ps _∞ , AOA, AOS and Mach are the flight parameters to be calculated, which constitute the unknown terms of a system of equations as better expressed hereinafter. The function F± in equation (2) is the aerodynamic model as per equation (1) .

The main characteristic of this system of equations is non- linearity, already present in the aerodynamic model, made explicit by the square of the Mach number (Mach ² ) and contained in the relation, defined in points and described by the arrays Mi-M _P , which bind the pressure coefficients Cpi and the flight parameters (Ps», AOA, AOS and Mach) .

Therefore, the resolution of the system of equations according to step 20 in Figure 2 is interactively driven, starting from the consolidated solution at the previous time of calculation (initial condition) and performing linearization of the system. The iteration is implemented by making the system linear around the solution of the previous iteration and then calculating the variation of the state variables (in this case, the flight parameters Ps∞, AOA, AOS and Mach) . The Newton-Raphson method, known in the literature, is used for this purpose. For a single sensor Si-S _N , placed at the respective location ix-iu, the pressure variation with respect to the previous time can be written in the manner defined by the following equation (3): ;F nF BF. cF ι \

SR . = SPs _∞ + 5AOA + 5AOS + '— dMach + o 5VAR) (3 ) l ^0Cl dP _∞ ^∞ dAOA 3AOS dMach ^{j J} where subscript "i" identifies each location ii-i _N , and where VAR is the following vector:

VAR = [Ps _∞ ,Mach, AOA, AOS] (4) where j is the index of the locations of vector VAR (i.e., for j=l, one has for j=3, one has VAR ₃ =A0A; for j =4 , one has VAR ₄ =AOS) .

By defining array J as the gradient of array F ( [J] = [VF] ) , the notation of equation (3) can be simplified as follows:

j

It should be noted that quantities preceded by the symbol "δ" represent the perturbations with respect to values/quantities defined at the previous time of calculation. As already mentioned, the initial condition of each calculation cycle is set equal to the solution calculated at the time of the previous calculation. Backtracking, it is necessary to initialize the calculation the very first time, as there is obviously no previously calculated solution. The initialization is carried out using predetermined values, for example the values regarding the aircraft 1 when stationary (on the ground) .

Ignoring terms above the first order (i.e. performing a linearization operation) and considering equation (3) or, equivalently, equation (4), written for all the sensors and in matrix notation, it is possible to represent the system of equations (4) in the following manner:

where the number of rows represents the number of information items (signals Xi-X _N ) or, equivalently, the sensors Si-S _N (used for calculation of the flight parameters) and the number of columns represents the number of unknown parameters (the actual flight parameters) .

Therefore, defining the flight parameters that must be calculated in the current iteration as Ps _∞NE w, AOA _N E _W , AOS _NEW and Machu _EW/ and those regarding the previous iteration (or the initialization parameters at the first iteration) as PS _∞0 LD AOAOLD, AOSOLD and Mach _0L D, gives:

(7)

Formula (7) is solved iteratively until a consolidated solution is reached, as described hereinafter.

Starting from formula (7) , the state variables vector updated during the iterations (the one with the "NEW" suffix) is obtained by inverting array J as shown in the following formula (8) :

Mach _NEW

AOA _NEW

_ AOS _NEW _ For an overdetermined system (number of known parameters greater than those unknown) , the inversion of array J is carried out by applying the linear or ordinary least squares method:

where [(V ^r )(V )] ^~ ' V ^T F analytically represents the operation of inverting array J. This gives:

For each iteration, array J is calculated again using the CPi values stored in the P arrays Μχ-Μρ, starting from the values of the flight parameters calculated in the previous iteration (those with the "OLD" suffix) . In other words, on the basis of the Mach, AOS and AOA values calculated in the previous iteration, for each location ii-i _N , a corresponding value of Cpi is obtained from arrays Mi-M _P ; then, using the thus- obtained value of Cpi together with the pressure value PS» ₀ LD in the " F" formula (equation (1) ) , it is possible to perform the operation described by formula (9) . As already mentioned, these step are performed for each location ii-i _Nf or, in other words, for each sensor Si-S _N .

As mentioned above, on the first iteration, when current values of the flight parameters Ps» _0LD , AOAQLD , A0S ₀ LD and Macho _LD , do not exist, the latter are considered having their ground value, with the aircraft stationary, prior to take-off. Otherwise, the values of PS _∞0 LD , AOAQLD , AOS ₀ LD and Mach ₀ LD used are the last values calculated (and stored) during the iterations . The convergence of the iterative calculation is evaluated by comparing the variation of the calculated flight parameters between two successive iterations and a predetermined tolerance Th: when the variation of these parameters is below the established tolerance Th, the iterative calculation has satisfied the convergence criterion. Instead, if the criterion is not satisfied, a new iteration is carried out using the updated state variables vector as the initial condition of the new iteration.

The use of iterative algorithms makes the introduction of convergence control criteria important, these having the purpose of ensuring that it is always possible to obtain a solution for each instance of calculation.

Therefore, in step 30 in Figure 2, an estimate is made of the plurality of static pressure measurements at the locations ± _x - i _N of the sensors Sx-S _N .

Once convergence of the iterative resolution process is achieved, the flight parameters calculated in step 20 are then used to provide an estimate of the static pressure values at the locations ii~i _N by means of the aerodynamic model defined by equation (1) . In practice, by inserting the values of the flight parameters Ps _∞ , AOA, AOS and Mach calculated in step 20 into equation (1) , an estimated pressure value Yi-Y _N for each sensor Si-S _N is obtained. The comparison between these estimates Y _I -Y _N and the value Xi- X _N effectively measured at the locations ii-i _N provides an indication of the quality of the solution found: the difference (or residual) between the estimated pressure values and the measured ones (Xi-Y _lf ... , X _N -Y _N ) represents the calculation error or level of approximation of the flight parameters with respect to their "real" values. The comparison in step 30 enables, step 40 , identifying possible failures of one or more sensors Si - S _N . In fact, ^' on the basis of the definition of "residual" (i.e., Xi -Yi , . . . , X _N - Y _N ) provided in the previous paragraph, the functionality of identifying the failure of a single sensor Si - S _N is obtained through the statistical analysis of these residuals, in particular by calculating the standard deviation of the residuals. To render- the process of identifying individual failures effective and resilient, the residuals are calculated, as well as starting from the consolidated solution by using all "N" sensors Si - S _N of the system, also from those obtained by the use of sensor subgroups. Each subgroup is constituted by all of the pressure sensors Si - S _N less one. With this logical approach, it is possible to identify a number "N" of mutually different subgroups, each composed of N-l sensors. By calculating a value of standard deviation of the residuals for each subgroup, it is possible to compare the values of standard deviation associated with each subgroup with one other. Failure identification is achieved by comparing these values of standard deviation with each another.

The standard value associated with the subgroup that excludes the faulty sensor (i.e. that provides local static pressure values not consistent with those of the other locations) is significantly smaller than those of the other subgroups. By defining one or more thresholds, ^" the pressure sensor missing from the subgroup having a standard value significantly smaller than those of the other subgroups is the pressure sensor that has failed.

This monitoring of residuals enables identifying possible failure of a sensor, represented by possible "freezing" of the reported data or drifting in its reading. Once the failure of a single sensor is identified, step 50, the flight parameters that are consolidated and transmitted, for example to the flight control system, are those related to the subgroup that excludes the failed sensor, thus rendering the failure isolation functionality effective.

From the foregoing, it is evident that the method according to the present invention represents a significant development with respect to that described in the literature and known in the state of the art.

Application of the described methodology allows the calculation of flight parameters through the use of sensors buried in the surface of the aircraft, giving the system characteristics of low contribution to the radar signature during flight, less exposure to the risk of bird impact during flight, and easier handling for operators during ground operations (less risks of damaging the sensors) . With regard to low radar signature aircraft, the main advantage of this methodology of calculation of the flight parameters is represented by the use of mathematical models that enable easier validation of the entire process, with positive repercussions in terms of traceability and verification in a possible certification procedure.

Finally, it is clear that modifications and variants can be made to the invention set forth herein without departing from the scope of the present invention, as defined in the appended claims.