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Journal articles on the topic 'Flush air data sensing'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

Shyam Mohan, N., M. Jayakumar, T. Sivamurugan, K. C. Finitha, S. B. Vidya, Jayanta Dhoaya, N. Remesh, M. Prasath, Shashi Krishna, and Aisha Sidhique. "Flush Air Data Sensing System." Current Science 114, no. 01 (January 10, 2018): 68. http://dx.doi.org/10.18520/cs/v114/i01/68-73.

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2

Srivastava, Ankur, Andrew J. Meade, and Kurtis R. Long. "Learning Air-Data Parameters for Flush Air Data Sensing Systems." Journal of Aerospace Computing, Information, and Communication 9, no. 3 (November 2012): 110–24. http://dx.doi.org/10.2514/1.54947.

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3

Zhan, Ye, Li Ming Chang, Jun Li, and Ming Shu Jiao. "Study on Flush Air Data Sensing Technology." Advanced Materials Research 962-965 (June 2014): 2766–69. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2766.

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

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4

ZHOU, WeiJiang, GuoHui DOU, XiuXin DOU, WuYue LIU, and GuangQiang CHEN. "Flush air data sensing system design for air breathing air-to-air missile." SCIENTIA SINICA Technologica 46, no. 11 (October 28, 2016): 1193–206. http://dx.doi.org/10.1360/n092016-00258.

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5

Rohloff, Thomas J., Stephen A. Whitmore, and Ivan Catton. "Fault-Tolerant Neural Network Algorithm for Flush Air Data Sensing." Journal of Aircraft 36, no. 3 (May 1999): 541–49. http://dx.doi.org/10.2514/2.2489.

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6

KUNISHIGE, Tatsuki, Koichi YONAMOTO, Takahiro FUJIKAWA, Guna Surendra GOSSAMSETTI, and Daisuke MORIYAMA. "Analysis on Singularity in Flush-Type Air Data Sensing Algorithm." Proceedings of Conference of Kyushu Branch 2018.71 (2018): J35. http://dx.doi.org/10.1299/jsmekyushu.2018.71.j35.

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7

Chen, Guangqiang, Bingyan Chen, Pengfei Li, Peng Bai, and Chunqun Ji. "Study on Algorithms of Flush Air Data Sensing System for HypersonicVehicle." Procedia Engineering 99 (2015): 860–65. http://dx.doi.org/10.1016/j.proeng.2014.12.613.

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8

Gao, Qinghua, Zhengguang Shen, Jingyu Dong, and Jingchun Yuan. "Faults Self-detection of Self-validating Flush Air Data Sensing System." International Journal of Control and Automation 10, no. 3 (March 31, 2017): 227–40. http://dx.doi.org/10.14257/ijca.2017.10.3.19.

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9

Samy, Ihab, Ian Postlethwaite, Da-Wei Gu, and John Green. "Neural-Network-Based Flush Air Data Sensing System Demonstrated on a Mini Air Vehicle." Journal of Aircraft 47, no. 1 (January 2010): 18–31. http://dx.doi.org/10.2514/1.44157.

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10

Ding, Zhijian, Huan Zhou, Feng Wang, Dongsheng Wu, Yingchuan Wu, and Yuanyuan He. "An implementation of the cubature Kalman filter for estimating trajectory parameters and air data of a hypersonic vehicle." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 12 (April 3, 2019): 4554–70. http://dx.doi.org/10.1177/0954410019835977.

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Trajectory parameters (including the position, velocity, and attitude angles of a vehicle) and air data (consisting of the flow angles, the Mach number, and the freestream static pressure) are vital data for the analysis and evaluation process in the hypersonic flight tests. This paper describes a data fusion estimation algorithm for a flush air data sensing system/inertial navigation system/global positioning system integrated system, which is used to estimate the trajectory parameters and air data for an unpowered hypersonic vehicle. In the approach, the raw outputs of flush air data sensing system (i.e. the surface pressure measurements) are integrated with global positioning system results (the vehicle’s position and velocity) and inertial navigation system measurements (including the acceleration and the angular velocity measurements) by using a nonlinear Kalman filter algorithm. Firstly, the system state vector is defined with the trajectory parameters, the biases of the inertial sensors and the winds. Then, the system dynamic models are built based on the motion equations of an unpowered hypersonic vehicle, the inertial sensor error models and the wind model. Besides, the system measurement vector is designed with the global positioning system results and the flush air data sensing system raw outputs. Based on these works, the system state is directly estimated by using the cubature Kalman filter algorithm. After that, the air data is calculated based on the estimated values and a high-fidelity model of atmosphere. Simulation cases are implemented to assess the performance of the proposed algorithm. The results show that the proposed method could estimate the trajectory parameters and air data for hypersonic vehicle with a high precision.

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11

Karlgaard, Christopher D., Prasad Kutty, and Mark Schoenenberger. "Coupled Inertial Navigation and Flush Air Data Sensing Algorithm for Atmosphere Estimation." Journal of Spacecraft and Rockets 54, no. 1 (January 2017): 128–40. http://dx.doi.org/10.2514/1.a33331.

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12

Wang, Dayi, Maodeng Li, and Xiangyu Huang. "Analytical Solutions of Generalized Triples Algorithm for Flush Air-Data Sensing Systems." Journal of Guidance, Control, and Dynamics 40, no. 5 (May 2017): 1314–20. http://dx.doi.org/10.2514/1.g000689.

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13

KUTSUNA, Yuki, Gossamsetti GUNA SURENDRA, Koichi YONEMOTO, Takaaki MATSUMOTO, and Satoshi NONAKA. "504 Fault-tolerant Flush Air Data Sensing System for Subsonic to Supersonic Regions." Proceedings of Conference of Kyushu Branch 2015.68 (2015): 183–84. http://dx.doi.org/10.1299/jsmekyushu.2015.68.183.

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14

MORIYAMA, Daisuke, Koichi YONAMOTO, Takahiro FUJIKAWA, and Guna Surendra GOSSAMSETTI. "Design and Analysis of Flush-type Air Data Sensing System for Winged Rocket." Proceedings of Conference of Kyushu Branch 2017.70 (2017): 505. http://dx.doi.org/10.1299/jsmekyushu.2017.70.505.

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15

Lee, Chang-Ho. "Study of the Flush Air Data Sensing System for Subsonic and Supersonic Flows." Journal of the Korean Society for Aeronautical & Space Sciences 47, no. 12 (December 31, 2019): 831–40. http://dx.doi.org/10.5139/jksas.2019.47.12.831.

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16

Gossamsetti, GunaSurendra, Koichi Yonemoto, and Takahiro Fujikawa. "DESIGN AND DEVELOPMENT OF FLUSH AIR DATA SENSING (FADS) SYSTEM FOR WINGED ROCKET." International Journal of Advanced Research 6, no. 10 (September 30, 2018): 551–58. http://dx.doi.org/10.21474/ijar01/7837.

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17

Samy, Ihab, Ian Postlethwaite, and Dawei Gu. "Subsonic Tests of a Flush Air Data Sensing System Applied to a Fixed-Wing Micro Air Vehicle." Journal of Intelligent and Robotic Systems 54, no. 1-3 (July 25, 2008): 275–95. http://dx.doi.org/10.1007/s10846-008-9266-x.

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18

Lee, Chang-Ho, Young-Min Park, Byeong-Hee Chang, and Yung-Gyo Lee. "Calibration and Flight Test Results of Air Data Sensing System using Flush Pressure Ports." Journal of the Korean Society for Aeronautical & Space Sciences 45, no. 7 (July 31, 2017): 531–38. http://dx.doi.org/10.5139/jksas.2017.45.7.531.

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19

Takahashi, Hidemi, Masatoshi Kodera, and Kouichiro Tani. "Flush Air Data Sensing System for a Sharp-Nosed Hypersonic Vehicle with Curved-Wedge Forebody." AIAA Journal 58, no. 11 (November 2020): 4819–31. http://dx.doi.org/10.2514/1.j059429.

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TODAKA, Akira, Yuki Kutsuna, Koichi YONEMOTO, Takaaki Matsumoto, and Satoshi NONAKA. "809 Preliminary Research on Fault Tolerant Flush Air Data Sensing (FADS) System for Supersonic Flight." Proceedings of Conference of Kyushu Branch 2014.67 (2014): _809–1_—_809–2_. http://dx.doi.org/10.1299/jsmekyushu.2014.67._809-1_.

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21

MORIYAMA, Daisuke, Koichi YONAMOTO, Takahiro FUJIKAWA, and Guna Surendra GOSSAMSETTI. "Calibration and Accuracy Verification of Flush-Type Air Data Sensing System for Experimental Winged Rocket." Proceedings of Conference of Kyushu Branch 2019.72 (2019): C15. http://dx.doi.org/10.1299/jsmekyushu.2019.72.c15.

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22

Liu, Yanbin, Dibo Xiao, and Yuping Lu. "Comparative Study on a Solving Model and Algorithm for a Flush Air Data Sensing System." Sensors 14, no. 5 (May 23, 2014): 9210–26. http://dx.doi.org/10.3390/s140509210.

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23

Srivastava, Ankur, and Andrew J. Meade. "A Comprehensive Probabilistic Framework to Learn Air Data from Surface Pressure Measurements." International Journal of Aerospace Engineering 2015 (2015): 1–19. http://dx.doi.org/10.1155/2015/183712.

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Use of probabilistic techniques has been demonstrated to learn air data parameters from surface pressure measurements. Integration of numerical models with wind tunnel data and sequential experiment design of wind tunnel runs has been demonstrated in the calibration of a flush air data sensing anemometer system. Development and implementation of a metamodeling method, Sequential Function Approximation (SFA), are presented which lies at the core of the discussed probabilistic framework. SFA is presented as a tool capable of nonlinear statistical inference, uncertainty reduction by fusion of data with physical models of variable fidelity, and sequential experiment design. This work presents the development and application of these tools in the calibration of FADS for a Runway Assisted Landing Site (RALS) control tower. However, the multidisciplinary nature of this work is general in nature and is potentially applicable to a variety of mechanical and aerospace engineering problems.

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24

Liu, YanBin, and DiBo Xiao. "Trade-off design of measurement tap configuration and solving model for a flush air data sensing system." Measurement 90 (August 2016): 278–85. http://dx.doi.org/10.1016/j.measurement.2016.04.068.

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25

KUTSUNA, Yuki, Akira TODAKA, Gaku Sasaki, Koichi YONEMOTO, and Takaaki MATSUMOTO. "509 Development of Flash type Air-data Sensing System and Supersonic Airflow Generator." Proceedings of Conference of Kyushu Branch 2013.66 (2013): 157–58. http://dx.doi.org/10.1299/jsmekyushu.2013.66.157.

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26

Jia, Qianlei, Weiguo Zhang, Jingping Shi, Guangwen Li, and Xiaoxiong Liu. "Research on Fault Detection Method of FADS System." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 6 (December 2020): 1210–17. http://dx.doi.org/10.1051/jnwpu/20203861210.

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In order to solve the fault detection problem of flush air data sensing (FADS), an advanced airborne sensor, a new method is proposed in this paper. First, the high-precision FADS model is established on the basis of the database obtained from the CFD software and aerodynamics knowledge. Then, the distribution characteristics of each group of signals under fault condition are derived through strict formulas. Meanwhile, the threshold of alarm times is designed with statistical knowledge. For verifying the effectiveness of the newly proposed method, a comparison with other two widely adopted methods, including the methods based on parity equation and Chi-square χ2 distribution, is conducted under different measurement noise. Simulation results show that the proposed fault detection method for FADS possess higher accuracy and stronger anti-interference.

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27

Illingworth, A. J., D. Cimini, C. Gaffard, M. Haeffelin, V. Lehmann, U. Löhnert, E. J. O’Connor, and D. Ruffieux. "Exploiting Existing Ground-Based Remote Sensing Networks to Improve High-Resolution Weather Forecasts." Bulletin of the American Meteorological Society 96, no. 12 (December 1, 2015): 2107–25. http://dx.doi.org/10.1175/bams-d-13-00283.1.

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Abstract A new generation of high-resolution (1 km) forecast models promises to revolutionize the prediction of hazardous weather such as windstorms, flash floods, and poor air quality. To realize this promise, a dense observing network, focusing on the lower few kilometers of the atmosphere, is required to verify these new forecast models with the ultimate goal of assimilating the data. At present there are insufficient systematic observations of the vertical profiles of water vapor, temperature, wind, and aerosols; a major constraint is the absence of funding to install new networks. A recent research program financed by the European Union, tasked with addressing this lack of observations, demonstrated that the assimilation of observations from an existing wind profiler network reduces forecast errors, provided that the individual instruments are strategically located and properly maintained. Additionally, it identified three further existing European networks of instruments that are currently underexploited, but with minimal expense they could deliver quality-controlled data to national weather services in near–real time, so the data could be assimilated into forecast models. Specifically, 1) several hundred automatic lidars and ceilometers can provide backscatter profiles associated with aerosol and cloud properties and structures with 30-m vertical resolution every minute; 2) more than 20 Doppler lidars, a fairly new technology, can measure vertical and horizontal winds in the lower atmosphere with a vertical resolution of 30 m every 5 min; and 3) about 30 microwave profilers can estimate profiles of temperature and humidity in the lower few kilometers every 10 min. Examples of potential benefits from these instruments are presented.

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Miglietta, M. M., and A. Regano. "An observational and numerical study of a flash-flood event over south-eastern Italy." Natural Hazards and Earth System Sciences 8, no. 6 (December 15, 2008): 1417–30. http://dx.doi.org/10.5194/nhess-8-1417-2008.

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Abstract. A flash-flood episode affecting a small area in Apulia (south-eastern Italy) on 22 October 2005 is documented. A rainfall amount of 160 mm was recorded in a 6 h interval in the central part of the region, producing severe damage and causing six fatalities. Synoptic maps, observations from surface stations and remote-sensing data are used here to describe the evolution of the rainfall system. The vertical profiles show features similar to those observed in other orographic heavy-rain events, such as a low-level jet, a conditionally unstable environment, and a nearly saturated warm low-level air mass. The low hills in the centre of the region play an important role in the release of the instability and the localisation of the rainfall, providing the uplift necessary to the air parcels to reach the level of free convection. Numerical simulations are performed in order to understand the mechanisms responsible for the heavy rain event. The Weather Research and Forecasting model (WRF) is setup in a 2-way nesting configuration including two domains. The model is able to realistically simulate the evolution of the precipitation system and to capture fairly well the localisation, the amount and the timing of the rainfall. The simulations suggest the important synergy of low and upper-tropospheric features which act as the triggering mechanism for the development of convection. A sensitivity experiment confirms the importance of the orography for the development of convective cells.

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Leon, David C., Jeffrey R. French, Sonia Lasher-Trapp, Alan M. Blyth, Steven J. Abel, Susan Ballard, Andrew Barrett, et al. "The Convective Precipitation Experiment (COPE): Investigating the Origins of Heavy Precipitation in the Southwestern United Kingdom." Bulletin of the American Meteorological Society 97, no. 6 (June 1, 2016): 1003–20. http://dx.doi.org/10.1175/bams-d-14-00157.1.

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Abstract The Convective Precipitation Experiment (COPE) was a joint U.K.–U.S. field campaign held during the summer of 2013 in the southwest peninsula of England, designed to study convective clouds that produce heavy rain leading to flash floods. The clouds form along convergence lines that develop regularly as a result of the topography. Major flash floods have occurred in the past, most famously at Boscastle in 2004. It has been suggested that much of the rain was produced by warm rain processes, similar to some flash floods that have occurred in the United States. The overarching goal of COPE is to improve quantitative convective precipitation forecasting by understanding the interactions of the cloud microphysics and dynamics and thereby to improve numerical weather prediction (NWP) model skill for forecasts of flash floods. Two research aircraft, the University of Wyoming King Air and the U.K. BAe 146, obtained detailed in situ and remote sensing measurements in, around, and below storms on several days. A new fast-scanning X-band dual-polarization Doppler radar made 360° volume scans over 10 elevation angles approximately every 5 min and was augmented by two Met Office C-band radars and the Chilbolton S-band radar. Detailed aerosol measurements were made on the aircraft and on the ground. This paper i) provides an overview of the COPE field campaign and the resulting dataset, ii) presents examples of heavy convective rainfall in clouds containing ice and also in relatively shallow clouds through the warm rain process alone, and iii) explains how COPE data will be used to improve high-resolution NWP models for operational use.

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30

Zhang, Qing Feng. "Optimization Design of the Flush Air Data System." Applied Mechanics and Materials 533 (February 2014): 312–15. http://dx.doi.org/10.4028/www.scientific.net/amm.533.312.

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A new configuration of multi sensor flush air data system was proposed and the detecting principles of the flush air data system was discussed. A large number of redundant information pick up from multi sensors embedded in aircraft data system put forward the requirements of information optimization. Characteristic value of air data defined in this chapter meet the active control system of aircraft redundancy requirements of total pressure, static pressure, angle of attack and sideslip angle of atmospheric data, was the foundation of the multi sensor information fusion system.

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31

Johnston, Ian A., Peter A. Jacobs, and Takayuki Shimoda. "Flush Air Data System Calibration Using Numerical Simulation." Journal of Spacecraft and Rockets 35, no. 6 (November 1998): 812–20. http://dx.doi.org/10.2514/2.3404.

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32

Schleutker, Thorn, Ali Gülhan, Bart Van Hove, and Özgür Karatekin. "ExoMars Flush Air Data System: Experimental and Numerical Investigation." Journal of Spacecraft and Rockets 56, no. 4 (July 2019): 971–82. http://dx.doi.org/10.2514/1.a34185.

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33

Soldatkin, V. M., V. V. Soldatkin, A. V. Nikitin, and G. P. Sokolova. "Ensuring Dynamic Accuracy of Aircraft’s Air Data System with Motionless Flush-Mounted Receiver of Flow." Mekhatronika, Avtomatizatsiya, Upravlenie 21, no. 9 (September 7, 2020): 535–43. http://dx.doi.org/10.17587/mau.21.535-543.

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The article views, that draw-backs of aircraft’s traditional air data systems (ADS), built based installed in incoming air flow and installed outside the fuselage the pitot tube booms, temperature braking receivers, vane sensors of incidence angle and gliding angle are eliminated in original ADS with motionless flush-mounted receiver of flow. The functional scheme of aircraft’s air data system with motionless flush-mounted receiver of flow, built based on the original ion-mark sensor of aerodynamic angle and true airspeed, on receiving board of which the hole-receiver is installed to perceive the static pressure of incoming air flow. Models of operator sensitivity and dynamic errors of instrumentation channels due to random stationary atmospheric turbulence and random flow pulsations at location of the ion-mark sensor on fuselage of the aircraft are presented. Recommended to use the optimal linear Wiener filter, the synthesis method of which is revealed on example of the true airspeed instrumentation channel to reduce the stationary dynamic errors of instrumentation channels of air data system with motionless flush-mounted receiver due to atmospheric turbulence. Recommended to use the principle of integration to reduce the stationary random dynamic errors of instrumentation channels of air data system with motionless flush-mounted receiver due to flow pulsations near fuselage at location of ion-mark sensor. Proposed to use aeromechanical measuring and computing system built based VIMI method with Luenberger observer as an additional component of integrated air data system. Integrated system simulates the movement of aircraft in this flight mode and by flight parameters measured with high accuracy using flush-mounted receivers "restores" air signals included in equations of movement of aircraft. The structure, method and algorithms for determining air signals in channels of aeromechanical measuring and computing system with a Luenberger observer are presented. Using the example of true airspeed measurement, the analysis and quantitative assessment of residual dynamic error of integrating channel of integrated aircraft’s air data system with motionless flush-mounted receiver of flow is carried out.

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34

Laurence III, Roger J., and Brian M. Argrow. "Numerical Calibration of a Low-Speed sUAS Flush Air Data System." Journal of Atmospheric and Oceanic Technology 36, no. 8 (August 2019): 1577–90. http://dx.doi.org/10.1175/jtech-d-18-0208.1.

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AbstractA method using computational fluid dynamics to numerically calibrate a flush air data system is presented. A small unmanned aircraft system (sUAS) has been equipped with a flush air data system and experimentally tested. The flush air data system uses computational fluid dynamics to train neural networks and is validated using the in-flight data that were previously collected. Results of the flight validation are presented, along with ways to improve the accuracy of the system. Several different calibration approaches are presented and compared with each other. The best-case results with the in-flight calibration are 0.59° and 0.66° for angle of attack and sideslip, respectively, whereas the best-case results when calibrated with computational fluid dynamics data are 0.78° and 0.90°. It is also possible to estimate other air data parameters, such as dynamic pressure, static pressure, and density, with neural networks, but the direct calculation is more accurate. Calibrating the system numerically, such as with the use of computational fluid dynamics, removes the need for any calibration flights. Although not as accurate as the in-flight calibration, numerical calibration is possible and can save the user time and expense.

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35

Ellsworth, Joel C., and Stephen A. Whitmore. "Simulation of a Flush Air-Data System for Transatmospheric Vehicles." Journal of Spacecraft and Rockets 45, no. 4 (July 2008): 716–32. http://dx.doi.org/10.2514/1.33541.

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36

Madhavanpillai, Jayakumar, Jayanta Dhoaya, Vidya Saraswathi Balakrishnan, Remesh Narayanan, Finitha Kallely Chacko, and Shyam Mohan Narayanan. "Inverse Flush Air Data System (FADS) for Real Time Simulations." Journal of The Institution of Engineers (India): Series C 98, no. 6 (October 13, 2017): 705–13. http://dx.doi.org/10.1007/s40032-017-0398-2.

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Srinatha Sastry, C. V., K. S. Raman, and B. Lakshman Babu. "Failure management scheme for use in a flush air data system." Aircraft Design 4, no. 4 (December 2001): 151–62. http://dx.doi.org/10.1016/s1369-8869(01)00012-x.

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Van Hove, Bart, Özgür Karatekin, Thorn Schleutker, and Ali Gülhan. "ExoMars Flush Air Data System: Entry Simulation and Atmospheric Reconstruction Method." Journal of Spacecraft and Rockets 56, no. 4 (July 2019): 1205–20. http://dx.doi.org/10.2514/1.a34187.

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39

Satheesan, Arun, N. Remesh, M. Jayakumar, N. ShyamMohan, and S. Swaminathan. "Recalibration of Flush Air Data System (FADS) on Pressure Port/Sensor Failures." IFAC Proceedings Volumes 45, no. 1 (2012): 179–84. http://dx.doi.org/10.3182/20120213-3-in-4034.00034.

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40

Milz, M., T. v. Clarmann, P. Bernath, C. Boone, S. A. Buehler, S. Chauhan, B. Deuber, et al. "Validation of water vapour profiles (version 13) retrieved by the IMK/IAA scientific retrieval processor based on full resolution spectra measured by MIPAS on board Envisat." Atmospheric Measurement Techniques 2, no. 2 (July 27, 2009): 379–99. http://dx.doi.org/10.5194/amt-2-379-2009.

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Abstract. Vertical profiles of stratospheric water vapour measured by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) with the full resolution mode between September 2002 and March 2004 and retrieved with the IMK/IAA scientific retrieval processor were compared to a number of independent measurements in order to estimate the bias and to validate the existing precision estimates of the MIPAS data. The estimated precision for MIPAS is 5 to 10% in the stratosphere, depending on altitude, latitude, and season. The independent instruments were: the Halogen Occultation Experiment (HALOE), the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), the Improved Limb Atmospheric Spectrometer-II (ILAS-II), the Polar Ozone and Aerosol Measurement (POAM III) instrument, the Middle Atmospheric Water Vapour Radiometer (MIAWARA), the Michelson Interferometer for Passive Atmospheric Sounding, balloon-borne version (MIPAS-B), the Airborne Microwave Stratospheric Observing System (AMSOS), the Fluorescent Stratospheric Hygrometer for Balloon (FLASH-B), the NOAA frostpoint hygrometer, and the Fast In Situ Hygrometer (FISH). For the in-situ measurements and the ground based, air- and balloon borne remote sensing instruments, the measurements are restricted to central and northern Europe. The comparisons to satellite-borne instruments are predominantly at mid- to high latitudes on both hemispheres. In the stratosphere there is no clear indication of a bias in MIPAS data, because the independent measurements in some cases are drier and in some cases are moister than the MIPAS measurements. Compared to the infrared measurements of MIPAS, measurements in the ultraviolet and visible have a tendency to be high, whereas microwave measurements have a tendency to be low. The results of χ2-based precision validation are somewhat controversial among the comparison estimates. However, for comparison instruments whose error budget also includes errors due to uncertainties in spectrally interfering species and where good coincidences were found, the χ2 values found are in the expected range or even below. This suggests that there is no evidence of systematically underestimated MIPAS random errors.

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41

Crowther, W. J., and P. J. Lamont. "A neural network approach to the calibration of a flush air data system." Aeronautical Journal 105, no. 1044 (February 2001): 85–95. http://dx.doi.org/10.1017/s0001924000011532.

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Abstract A flush air data system uses surface pressure measurements to obtain speed and aerodynamic orientation of a flight vehicle. This paper investigates the use of a neural network to calibrate the air data system on a low-observable aircraft forebody with 22 pressure tappings. Wind tunnel data were obtained for between 0 and 25° angle of attack, 0 to 10° sideslip for speeds of 32, 37 and 45ms-1. Experimental data were used to train multilayer perceptron neural networks. A calibration accuracy of 0·322ms-1, 0·811° and 0·552° for speed, alpha and beta respectively was achieved just using the first five tappings on the nose cone. Increasing the number of tappings used as inputs to the neural network reduces the calibration error. A neural network with 22 inputs gives a best accuracy of 0·095ms-1, 0·15° and 0·085° for speed, α and β respectively. Trained networks show poor robustness to single sensor failures. Robustness is improved by preprocessing input data with an autoassociative network or by introducing sensor redundancy.

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Jablonski, Piotr G., and Sang-im Lee. "Painted Redstarts (Myioborus Pictus) Attack Larger Prey when Using Flush-Pursue Strategy." Open Ornithology Journal 11, no. 1 (March 30, 2018): 34–38. http://dx.doi.org/10.2174/1874453201811010034.

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Introduction:Prey escape reaction in insects is an antipredatory adaptation that is mediated by prey neural escape circuits with specific sensory properties.Methods:Certain insectivorous birds, flush-pursuers, exploit this visual sensitivity by employing conspicuous pivoting movements of spread tail and wings to flush the prey into the air where it is available for chase in aerial pursuits. Although it is known that this strategy increases the number of insects attacked, no information has been published on the size distribution of arthropods attacked using flush-pursue strategyvs. traditional gleaning and pecking off substrate strategy.Results:Based on one season of observational data of foraging redstarts (Myioborus pictus) we show that prey items that were flushed and chased were on average larger than prey pecked off of substrates.Conclusion:This may be one of the benefits from flush-pursue foraging – a strategy that is probably costly in terms of energy demands.

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Menzies, M. A. "Integrated air data sensors." Aeronautical Journal 105, no. 1046 (April 2001): 223–29. http://dx.doi.org/10.1017/s000192400002546x.

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Abstract Avionics Specialties in partnership with Honeywell has developed an integrated air data pressure sensor known as the integrated multi-function probe (IMFP). This continuously flow-aligned pressure-sensing device was developed using a multidisciplinary design approach encompassing computational fluid dynamic, (CFD), flow analysis, and experimentation. The computational investigation was performed using the finite element code, CFDesign, by Blue Ridge Numerics and consisted of pressure distribution studies for varying flows around conical sections. The application and integration of this technology to previously developed flow alignment and pressure transducing products lead to design similarity analysis saving considerable development time. Initial experimentation used to develop and refine the design and analysis consisted of laboratory bench testing, low and high-speed wind tunnel investigations, and subsonic flight-testing. Final performance verification was accomplished through data reduction and analysis of an extensive flight test program using a Lockheed Martin F-16 Fighting Falcon. The analysis, design and performance verification of this innovative pressure-sensing device was investigated for direct comparison to conventional Pitot-static performance with particular emphasis toward military fighter aircraft applications.

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Li, Fu, and Zhen Luo. "Effects of Pressure Ports Layout on Angle of Attack and Sideslip Estimation in the Flush Air Data System." Advanced Materials Research 383-390 (November 2011): 2996–3000. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.2996.

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Flush air data system (FADS) have been successfully used on the nose tip of large manned/unmanned air vehicles instead of a traditional noseboom air data system. In order to integrate FADS with strapdown inertial navigation, high accuracy of angle of attack and sideslip is required. The estimated accuracy of angle of attack and sideslip in three types of FADS, which have different pressure ports layout, is compared using the nonlinear least squares theory. Evaluation function is provided to evaluate the accuracy of angle of attack and sideslip in different pressure ports layout. The results show that more sensors and radiation-type can provide high accuracy.

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Wang, L., and Y. Huang. "MOBILE ATMOSPHERIC SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W2 (November 16, 2017): 217–21. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w2-217-2017.

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Atmospheric quality dramatically deteriorates over the past decades around themetropolitan areas of China. Due to the coal combustion, industrial air pollution, vehicle waste emission, etc., the public health suffers from exposure to such air pollution as fine particles of particulates, sulfur and carbon dioxide, etc. Many meteorological stations have been built to monitor the condition of air quality over the city. However, they are installed at fixed sites and cover quite a small region. The monitoring results of these stations usually do NOT coincide with the public perception of the air quality. This paper is motivated to mimic the human breathing along the citys transportation network by the mobile sensing vehicle of atmospheric quality. To obtain the quantitative perception of air quality, the Environmental Monitoring Vehicle of Wuhan University (EMV-WHU) has been developed to automatically collect the data of air pollutants. The EMV-WHU is equipped with GPS/IMU, sensors of PM2.5, carbon dioxide, anemometer, temperature, humidity, noise, and illumination, as well as the visual and infrared camera. All the devices and sensors are well collaborated with the customized synchronization mechanism. Each sort of atmospheric data is accompanied with the uniform spatial and temporal label of high precision. Different spatial and data-mining techniques, such as spatial correlation analysis, logistic regression, spatial clustering, are employed to provide the periodic report of the roadside air quality. With the EMV-WHU, constant collection of the atmospheric data along the Luoyu Road of Wuhan city has been conducted at the daily peak and non-peak time for half a year. Experimental results demonstrated that the EMV is very efficient and accurate for the perception of air quality. Comparative findings with the meteorological stations also show the intelligence of big data analysis and mining of all sorts of EMV measurement of air quality. It is promising for the aerial and emergent air quality monitoring over the sky of big cities, if EMV-WHU be miniaturized for the unmanned aerial vehicles(UAV) in the future.

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Cole, K. D., and J. V. Beck. "Conjugated Heat Transfer From a Hot-Film Probe for Transient Air Flow." Journal of Heat Transfer 110, no. 2 (May 1, 1988): 290–96. http://dx.doi.org/10.1115/1.3250482.

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A numerical heat transfer model of a flush-mounted hot-film probe is presented for transient air flow. The model geometry involves simultaneous heat flow in both the air and the adjacent slab wall. Two cases are considered, steady laminar flow and laminar flow with a low-frequency oscillating component. In the steady-flow case, the numerical results compare favorably with steady calibration data from the literature. In the unsteady flow case, transfer function results are consistent with literature experimental data. In both cases, agreement with the literature data is obtained by calibrating the numerical model at zero flow.

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Golkar, Foroogh, Ali Akbar Sabziparvar, Reza Khanbilvardi, Mohammad Jafar Nazemosadat, Shahrokh Zand- Parsa, and Yousef Rezaei. "Estimation of instantaneous air temperature using remote sensing data." International Journal of Remote Sensing 39, no. 1 (October 1, 2017): 258–75. http://dx.doi.org/10.1080/01431161.2017.1382743.

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Ha, Quang Phuc, Santanu Metia, and Manh Duong Phung. "Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring." IEEE Sensors Journal 20, no. 8 (April 15, 2020): 4430–41. http://dx.doi.org/10.1109/jsen.2020.2964396.

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Gabrys, Jennifer, Helen Pritchard, and Benjamin Barratt. "Just good enough data: Figuring data citizenships through air pollution sensing and data stories." Big Data & Society 3, no. 2 (December 2016): 205395171667967. http://dx.doi.org/10.1177/2053951716679677.

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Sun, Y. J., J. F. Wang, R. H. Zhang, R. R. Gillies, Y. Xue, and Y. C. Bo. "Air temperature retrieval from remote sensing data based on thermodynamics." Theoretical and Applied Climatology 80, no. 1 (October 14, 2004): 37–48. http://dx.doi.org/10.1007/s00704-004-0079-y.

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