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A synthetic air information system (SADS) is an alternative air information system that tin produce synthetic air information quantities without directly measuring the air data. Information technology uses other information such as GPS, wind information, the aircraft's attitude, and aerodynamic properties to gauge or infer the air data quantities. Though air data includes altitude, airspeed, pressures, air temperature, Mach number, and flow angles (e.chiliad., Angle of Attack and Angle of sideslip), existing known SADS primarily focuses on estimating airspeed, Angle of Attack, and Angle of sideslip. SADS is used to monitor the principal air data system if there is an anomaly due to sensor faults or organisation faults.^{[ane] [two]} Information technology can as well be potentially used as a backup to provide air data estimates for whatsoever aerial vehicle.

Functionality [edit]

Constructed air data systems can potentially reduce adventure past creating an extra layer of redundancy (analytical redundancy) to the mechanical air data arrangement such every bit the Pitot-static systems and angle vanes. It can also exist used to detect failures of other subsystems through data compatibility checks.

History [edit]

The thought of SADS has been around since the 1980s. The bones idea is to use not air information sensors such as Inertial Measurement Unit of measurement (IMU) and GPS fused with vehicle dynamics models to estimate air data triplet airspeed, angle of Assault, and angle of sideslip (either separately or combined). Virtually of the earlier work used vehicle dynamics models to judge air data in both aircraft^{[3] [4] [5] [6]} and spacecraft^{[7] [viii]} applications. This arroyo is sometimes referred to as the aerodynamic model-based SADS.^{[nine] [10] [eleven]} Nevertheless, the aerodynamic model-based SADS is challenging to implement because it is difficult to obtain accurate vehicle dynamics models possessing the fidelity needed to yield the required accuracy in the air data estimates. To accost this issue, model-free SADS has been proposed recently.^{[12] [13] [14]} The model-costless SADS does non require the vehicle dynamics models. Instead, information technology relies on the accuracy of the Inertial navigation system (INS) and Three-Dimensional (3D) wind estimates.

SADS has gained a lot of renewed interest later the Air France Flight 447 accident in 2009. Several universities and regime agencies such as the Academy of Minnesota, Delft Academy of Applied science, NASA Langley Research Centre, and the Institute of Flight Mechanics and Flying Command at Technische Universität München, have been researching the SADS related topics. Recent patents related to SADS have been filed by the leading air data organization producers such as Collins Aerospace^{[15] [sixteen]} and Honeywell.^[17] Moreover, the recent two Boeing 737 MAX accidents (Lion Air Flight 610 (2018) and Ethiopian Airlines Flight 302 (2019)) have brought SADS into the spotlight again,^[18] which is detailed by the study. In particular, synthetic airspeed^[19] has become a focal signal to better Boeing aircraft's safety.

Commercial Shipping [edit]

SADS has been implemented in some of the most advanced modern commercial aircraft such as the Boeing 787. The ADS on Boeing 787 calculates a synthetic airspeed from the angle of assault measurement, inertial data, accurate Elevator coefficient, and aircraft mass (validated after takeoff).^[twenty] The constructed airspeed has helped the Boeing 787 recover from the erroneous airspeed measurement.^[21]

Unmanned Aerial Vehicles [edit]

SADS has also been implemented for Unmanned aerial vehicle UAVs (drones).^[22] The motivation of SADS for UAVs is that about of the depression-cost air information systems on the minor Unmanned Aircraft System (UAS) are not reliable. Also, having multiple air data sensors (e.g., Pitot tubes) on small UAVs is non viable due to stringent size, weight, and ability constraints. SADS can significantly increase drones' overall reliability in both Line-Of-Sight and Beyond Visual Line-Of-Sight (BVLOS) drone operations. Contempo academic enquiry has focused on improving SADS'south accuracy, fault detectability, and reliability of the ADS used on small UAS by leveraging SADS.^{[23] [24]}

Air Data Triplet [edit]

The airspeed ${\textstyle V_{a}}$ , bending of attack ${\textstyle \alpha }$ , and bending of sideslip ${\textstyle \beta }$ represents the air information triplet, and the current land of art of SADS is to estimate these iii quantities either together or separately. 1 fashion to estimate the air data triplet is use the air current triangle relationship. Mathematically, the wind triangle equation is shown beneath:

$${\displaystyle {\big [}{\brainstorm{array}{ccc}u&v&westward\finish{array}}{\big ]}^{T}={\bf {C}}_{northward}^{b}({\boldsymbol {\psi }}_{nb}^{n})\left[{\bf {v}}^{n}-{\bf {West}}^{n}\right]}$$

where ${\textstyle u}$ , ${\textstyle v}$ , and ${\textstyle w}$ are the translation velocity components expressed in the body frame, ${\textstyle {\bf {C}}_{n}^{b}({\boldsymbol {\psi }}_{nb}^{n})}$ is the coordinate transformation from N-Eastward-Down (NED) frame to the torso frame. The vector ${\textstyle {\boldsymbol {\psi }}_{nb}^{due north}={\big [}\phi ~\theta ~\psi {\big ]}^{T}}$ represents the attitude vector in roll, pitch, and yaw. The ${\textstyle {\bf {5}}^{north}}$ and ${\textstyle {\bf {W}}^{north}}$ represent the footing velocity and wind vector in the NED frame respectively.

If the ${\textstyle u}$ , ${\textstyle v}$ , and ${\textstyle due west}$ are known, the airspeed ${\textstyle V_{a}}$ , angle of set on ${\textstyle \alpha }$ and angle of skid ${\textstyle \beta }$ tin can be calculated every bit the post-obit:^[25]

$${\displaystyle V_{a}={\sqrt {u^{2}+five^{two}+w^{2}}}}$$

$${\displaystyle \alpha =\tan ^{-1}\left({\dfrac {u}{five}}\right)}$$

$${\displaystyle \beta =\sin ^{-1}\left({\dfrac {v}{\sqrt {u^{2}+v^{2}+w^{2}}}}\right)}$$

Method [edit]

There are various methods to estimate or "synthesize" airspeed, angle of attack, and angle of sideslip without direct using the measured air information. For instance, synthetic airspeed can be computed past using the footing velocity, angle of set on, wind velocity, aeroplane'south pitch mental attitude and heading. The ground velocity is usually provided by GPS. The angle of attack measurements can come from the angle vanes. The air current velocity tin be obtained by the airborne weather radar. The mental attitude of the airplane can be computed from the inertial navigation system. The exact computation of the synthetic airspeed can vary (e.g., small-angle approximation tin be made to simplify the computation), merely it is primarily based on the kinematic wind triangle equation. This method is sometimes referred to as the model-free SADS method; at that place is no vehicle model dynamics involved.

The model-based SADS leverages the vehicle dynamics model to help estimate the air data quantities. In item, the aerodynamic coefficients are used to compute synthetic air data. For example, bending of assail ${\textstyle \blastoff }$ tin can be synthesized if the Elevator coefficient ${\textstyle C_{L}}$ , Mach number ${\textstyle One thousand}$ , and altitude ${\textstyle h}$ are known. Mathematically,^[3]

$${\displaystyle \alpha =f(C_{L},~1000,~h)}$$

The function ${\textstyle f(\cdot )}$ that relates ${\textstyle C_{Fifty}}$ to ${\textstyle \blastoff }$ can be empirically determined by curve fitting the aerodynamic data. The accuracy of the model-based SADS depends on the accuracy of the aerodynamic coefficient. This accuracy constraint might non an issue for high operation aircraft such as F-fifteen, but it can be quite difficult for low-toll UAVs.

Many model-based and model-gratuitous SADS utilize classical estimation methods such as Kalman filtering and least squares extensively to estimate air information when sensor fusion and real-time computing are required. Other non-conventional methods such as data-driven learning or car learning based air data interpretation algorithms take emerged in the final decade,^{[26] [27]} but they are difficult to be certified due to the complication of the algorithms.

Come across also [edit]

• Air data computer
• Inertial navigation system
• Redundancy (engineering)
• Kalman filter
• Acronyms and abbreviations in avionics
• Fundamental Air Data Estimator, used on the F-xiv, the first microprocessor-based ADC

References [edit]

1. ^ Freeman, Paul and Seiler, Peter and Balas, Gary J (2013). "Air data system fault modeling and detection". Command Engineering Practice. ELSEVIER. 21 (10): 1290–1301. doi:10.1016/j.conengprac.2013.05.007. {{cite journal}}: CS1 maint: multiple names: authors list (link)
2. ^ M. L. Fravolini, G. Del Core, U. Papa, P. Valigi and M. R. Napolitano (2019). "Data-Driven Schemes for Robust Error Detection of Air Information Organisation Sensors". IEEE Transactions on Control Systems Technology. IEEE. 27 (1): 234–248. doi:x.1109/TCST.2017.2758345. S2CID 54464524. {{cite journal}}: CS1 maint: multiple names: authors list (link)
3. ^ ^{a b} Zeis, J. E. J. (1988). "Angle of Attack and Skid Estimation Using an Inertial Reference Platform" (PDF). One thousand.S. Thesis, U.South. Air Forcefulness Inst. of Technology, Wright-Patterson AFB.
4. ^ Colgren, R., Frye, Chiliad., and Olson, W. (1999). "A Proposed Organization Architecture for Estimation of Angle-of-Assault and Skid Angle". Guidance, Navigation, and Command Conference and Showroom. AIAA. doi:10.2514/half dozen.1999-4078. {{cite journal}}: CS1 maint: multiple names: authors list (link)
5. ^ Wise, Kevin (2004). "Computational air information system for estimating bending-of-attack and angle-of-sideslip". European Patent Office.
6. ^ Wise, K. (2006). "Flight Testing of the X-45A J-UCAS Computational Alpha-Beta Organisation". AIAA Guidance, Navigation, and Command Conference and Showroom. Guidance, Navigation, and Control Conference and Exhibit. AIAA. doi:ten.2514/6.2006-6215. ISBN978-ane-62410-046-viii.
7. ^ Nebula, F., Palumbo, R., Morani, G., and Corraro, F. (2009). "Virtual Air Information System Compages for Infinite Reentry Applications". Journal of Spacecraft and Rockets. Journal of Spacecraft and Rockets, AIAA. 46 (4): 818–828. Bibcode:2009JSpRo..46..818N. doi:x.2514/1.42485. {{cite journal}}: CS1 maint: multiple names: authors list (link)
8. ^ Karlgaard, C. D., and Schoenenberger, Chiliad. (2018). "Planetary Probe Entry Atmosphere Estimation Using Synthetic Air Information Arrangement". Journal of Spacecraft and Rockets. AIAA. 55 (3): 599–610. Bibcode:2009JSpRo..46..818N. doi:10.2514/one.42485. {{cite journal}}: CS1 maint: multiple names: authors list (link)
9. ^ Prevarication, F. A. P. and Gebre-Egziabher, D. (2013). "Synthetic Air Data Organization". Journal of Aircraft. Journal of Aircraft, AIAA. 50 (4): 1234–1249. doi:ten.2514/i.C032177. {{cite periodical}}: CS1 maint: multiple names: authors list (link)
10. ^ Lie, F. A. P. (2014). "Constructed air data estimation: a case study of model-aided estimation". PHD Thesis. University of Minnesota Twin Cities. Bibcode:2014PhDT.......205L.
11. ^ Tian, P., and Chao, H. (2018). "Model Aided Estimation of Angle of Set on, Sideslip Angle, and 3D Wind without Menstruum Angle Measurements". Guidance, Navigation, and Control Conference. AIAA. doi:10.2514/6.2018-1844. ISBN978-ane-62410-526-5. {{cite periodical}}: CS1 maint: multiple names: authors listing (link)
12. ^ Rhudy, M., Larrabee, T., Chao, H., Gu, Y. and Napolitano, M. (2013). "UAV Attitude, Heading, and Wind Interpretation Using GPS/INS and an Air Information System". Journal of Aircraft. AIAA Guidance, Navigation, and Control (GNC) Conference. doi:ten.2514/half dozen.2013-5201. ISBN978-1-62410-224-0. {{cite journal}}: CS1 maint: multiple names: authors listing (link)
13. ^ Johansen, T. A., Cristofaro, A., SØrensen, G., Hansen, J. Yard., and Fossen, T. I. (2015). "On Estimation of Wind Velocity, Angle-of-Attack and Sideslip Angle of Pocket-size UAVs Using Standard Sensors". 2015 International Conference on Unmanned Shipping Systems (ICUAS). IEEE: 510–519. doi:10.1109/ICUAS.2015.7152330. ISBN978-1-4799-6010-1. S2CID 12478001. {{cite periodical}}: CS1 maint: multiple names: authors list (link)
14. ^ Sun, K., Regan, C. D. and Gebre-Egziabher, D. (2019). "Observability and Performance Analysis of a Model-Costless Synthetic Air Data Calculator". Journal of Aircraft. Periodical of Aircraft, AIAA. 56 (4): 1471–1486. doi:10.2514/one.C035290. {{cite journal}}: CS1 maint: multiple names: authors list (link)
15. ^ Derrick David Hongerholt and Dennis James Cronin (2004). "Fault isolation method and apparatus in artificial intelligence based air information systems". Usa.
16. ^ Kaare Josef Anderson, Brian Daniel Matheis, Derrick D. Hongerholt, William Kunik (2018). "Synthetic air data output generation". United States. {{cite spider web}}: CS1 maint: multiple names: authors listing (link)
17. ^ Frank Rajkumar Elias, Visvanathan Thanigai, Nathan Peter Wesley (2011). "Alternate airspeed computation when air data computer (adc) fails". European Patent Office. {{cite web}}: CS1 maint: multiple names: authors listing (link)
18. ^ Alan Levin (August 23, 2020). "Boeing Boosts 737 Max Rubber With Spacecraft, Drone Technology".
19. ^ The House Committee on Transportation & Infrastructure (2020). "Terminal COMMITTEE REPORT: BOEING 737 MAX" (PDF).
20. ^ Dodt, T. (2011). "Introducing the 787 - Upshot on Major Investigation and Interesting Tidbits" (PDF).
21. ^ Australian Transport Safety Bureau (2015). "Erratic airspeed indications Boeing 787-eight, VH-VKE" (PDF).
22. ^ Dominicus, Kerry (2020). "Reliable Air Data Solutions For Small-scale Unmanned Aircraft Systems". PHD Thesis. University of Minnesota Twin Cities.
23. ^ S. Hansen and K. Blanke (2014). "Diagnosis of Airspeed Measurement Faults for Unmanned Aerial Vehicles". IEEE Transactions on Aerospace and Electronic Systems. IEEE. 50 (1): 224–239. doi:x.1109/TAES.2013.120420. hdl:11250/2392884. S2CID 206595814.
24. ^ Sunday, G. and Gebre-Egziabher, D. (2020). "A Fault Detection and Isolation Design for a Dual Pitot Tube Air Data Arrangement". IEEE/ION Position, Location and Navigation Symposium (PLANS). IEEE: 62–72. doi:10.1109/PLANS46316.2020.9110179. ISBN978-1-7281-0244-3. S2CID 219592787. {{cite journal}}: CS1 maint: multiple names: authors list (link)
25. ^ Klein, V. and Morelli, Due east. A. (2006). Shipping Arrangement Identification Theory and Practice. ISBN9781563478321. {{cite book}}: CS1 maint: multiple names: authors list (link)
26. ^ H. Long and S. Vocal (2009). "Method of Estimating Bending-of-Attack and Sideslip Angel Based on Data Fusion". Second International Conference on Intelligent Computation Technology and Automation. IEEE: 641–644. doi:x.1109/ICICTA.2009.160. ISBN978-0-7695-3804-iv. S2CID 14321728.
27. ^ K. T. Borup, T. I. Fossen and T. A. Johansen (2020). "A Machine Learning Approach for Estimating Air Data Parameters of Small Stock-still-Fly UAVs Using Distributed Pressure Sensors". IEEE Transactions on Aerospace and Electronic Systems. IEEE. 56 (three): 2157–2173. doi:10.1109/TAES.2019.2945383. S2CID 210003963.

External links [edit]

• "Aircraft Handbooks & Manuals". U.S. Department of Transportation, Federal Aviation Administration.
• "14 CFR Role 135 Air Carrier and Operator Certification". U.S. Department of Transportation, Federal Aviation Assistants.

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Source: https://en.wikipedia.org/wiki/Synthetic_air_data_system

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