Effects of the Wind Field on the Synthetic Measurement of the Aerodynamic Angles of an Aerial Vehicle

The estimation of the angle of attack and sideslip angle is of fundamental importance for the situational awareness of an aerial vehicle. Historically, several accidents occurred due to failures of the traditional protruding probes applied to measure these two angles. The MIDAS project aims to design and develop a certifiable Air Data System capable of providing the entire set of Air Data, integrating a synthetic estimation of the aerodynamic angles. All operating conditions shall be taken into account, even those related to atmospheric phenomena. The wind effects (both steady and unsteady) represent a very challenging topic when design a synthetic sensor because of its intrinsic nature. In fact, the airflow surrounding the AC can be affected by several phenomena with a very wide range of characteristics (e.g. speed and direction range) that can be hardly simulated during the design stage. It is clear that the atmosphere condition (both steady and unsteady) can affect this particular kind of sensor, and it must be analysed the error in the presence of the wind. The paper shows the estimation error due to the steady wind field and correction to be applied to previous synthetic sensors design in order to be reliable both in still air and in presence of the wind

2D Closed-Form Solution for the Measurement of the Angle of Attack and Sideslip Angle

At the beginning of 2021, the measurement of the Angle of Attack and of the Angle of Sideslip is still mainly conducted with physical protruding probes. Although several alternative methods have been proposed in literature, the attention is generally focused on data-driven methods and little discussion is conducted on the mathematical problem. If the formulation that allows to associate the aerodynamic angles to other flight parameters has a closed-form solution is still an open question in the field. This paper provides a closed-form solution for a restricted problem where one of the two angle is known. Moreover, a linearized solution is provided. The result section gives evidence of the approach in simulated environment, showing the advantages of the nonlinear solution with respect to the linear one

Sensitivity Analysis of a Certifiable Synthetic Sensor for Aerodynamic Angle Estimation

Nowadays, some alternative methods exist for the replacement of physical vanes (or probes) for aerodynamic angles (angle of attack and sideslip) with synthetic solutions. The results are promising and there is a growing interest for the industry in this particular solution. However, a lack of methods has been observed to estimate their performance and to compare them. The MIDAS project, funded in the Clean Sky 2 frame, will provide the aerospace community with an innovative modular digital air data system (ADS) based on synthetic sensors for aerodynamic angles. To meet the system requirement specifications given by the project leader, a method of uncertainty estimation must be implemented. This paper proposes a method of estimation of the overall uncertainty based on a consolidated metrological procedure. This method holds a certain degree of generality because it can be applied to different kinds of architecture of the synthetic sensor. In this paper, it has been applied to the preliminary design of the synthetic sensor of the MIDAS air data system and the results have been reported as example

A Data-Driven Approach to Identify Flight Test Data Suitable to Design Angle of Attack Synthetic Sensor for Flight Control Systems

Digital avionic solutions enable advanced flight control systems to be available also on smaller aircraft. One of the safety-critical segments is the air data system. Innovative architectures allow the use of synthetic sensors that can introduce significant technological and safety advances. The application to aerodynamic angles seems the most promising towards certified applications. In this area, the best procedures concerning the design of synthetic sensors are still an open question within the field. An example is given by the MIDAS project funded in the frame of Clean Sky 2. This paper proposes two data-driven methods that allow to improve performance over the entire flight envelope with particular attention to steady state flight conditions. The training set obtained is considerably undersized with consequent reduction of computational costs. These methods are validated with a real case and they will be used as part of the MIDAS life cycle. The first method, called Data-Driven Identification and Generation of Quasi-Steady States (DIGS), is based on the (i) identification of the lift curve of the aircraft; (ii) augmentation of the training set with artificial flight data points. DIGS’s main aim is to reduce the issue of unbalanced training set. The second method, called Similar Flight Test Data Pruning (SFDP), deals with data reduction based on the isolation of quasi-unique points. Results give an evidence of the validity of the methods for the MIDAS project that can be easily adopted for generic synthetic sensor design for flight control system applications

Sensitivity Analysis of a Neural Network based Avionic System by Simulated Fault and Noise Injection

The application of virtual sensor is widely discussed in literature as a cost effective solution compared to classical physical architectures. RAMS (Reliability, Availability, Maintainability and Safety) performance of the entire avionic system seem to be greatly improved using analytical redundancy. However, commercial applications are still uncommon. A complete analysis of the behavior of these models must be conducted before implementing them as an effective alternative for aircraft sensors. In this paper, a virtual sensor based on neural network called Smart-ADAHRS (Smart Air Data, Attitude and Heading Reference System) is analyzed through simulation. The model simulates realistic input signals of typical inertial and air data MEMS (Micro Electro-Mechanical Systems) sensors. A procedure to define the background noise model is applied and two different cases are shown. The first considers only the sensor noise whereas the latter uses the same procedure with the operative flight noise. Flight tests have been conducted to measure the disturbances on the inertial and air data sensors. Comparison of the Power Spectral Density function is carried out between operative and background noise. A model for GNSS (Global Navigation Satellite System) receiver, complete with constellation simulator and atmospheric delay evaluation, is also implemented. Eventually, a simple multi-sensor data fusion technique is modeled. Results show good robustness of the Smart-ADAHRS to the sensor faults and a marginal sensitivity to the temperature-related faults. Solution for this kind of degradation is indicated at the end of the paper. Influences of noise on input signals is also discussed

Advantages of Neural Network Based Air Data Estimation for Unmanned Aerial Vehicles

Redundancy requirements for UAV (Unmanned Aerial Vehicle) are hardly faced due to the generally restricted amount of available space and allowable weight for the aircraft systems, limiting their exploitation. Essential equipment as the Air Data, Attitude and Heading Reference Systems (ADAHRS) require several external probes to measure significant data as the Angle of Attack or the Sideslip Angle. Previous research focused on the analysis of a patented technology named Smart-ADAHRS (Smart Air Data, Attitude and Heading Reference System) as an alternative method to obtain reliable and accurate estimates of the aerodynamic angles. This solution is based on an innovative sensor fusion algorithm implementing soft computing techniques and it allows to obtain a simplified inertial and air data system reducing external devices. In fact, only one external source of dynamic and static pressures is needed. This paper focuses on the benefits which would be gained by the implementation of this system in UAV applications. A simplification of the entire ADAHRS architecture will bring to reduce the overall cost together with improved safety performance. Smart-ADAHRS has currently reached Technology Readiness Level (TRL) 6. Real flight tests took place on ultralight aircraft equipped with a suitable Flight Test Instrumentation (FTI). The output of the algorithm using the flight test measurements demonstrates the capability for this fusion algorithm to embed in a single device multiple physical and virtual sensors. Any source of dynamic and static pressure can be integrated with this system gaining a significant improvement in terms of versatility

Ill-conditioned problems improvement adapting Joseph covariance formula to non-linear Bayesian filters

Integration of Unmanned Aerial Vehicles (UAVs) into civil airspace is becoming a fundamental requirement to satisfy the even more consumer growing demand. The limiting issues for this integration are related to the development of a reliable Sense and Avoid (SAA) system able to equate the human eye performances. Multisensor data fusion techniques are generally used in order to overcome single sensor shortcomings. Although much research addresses toward the realisation of better performing sensors, system degradation could arise from bad numerical behaviours injected by the specific fusion algorithm. Bayesian estimators are the most widely used techniques to perform this task but they could be affected by round-off errors. To improve filter instabilities, induced by ill-conditioned matrices, an alternative numerical approach, based on the Joseph form of the state covariance matrix update applied to non-linear systems is presented. The novelty of this technique lies on taking advantage from the higher order accuracy ensured by Sigma-Point Kalman Filters for solving non-linear inference problems, and using the more numerically robust Joseph update equation

The Origins of [CII] Emission in Local Star-forming Galaxies

The [CII] 158um fine-structure line is the brightest emission line observed in local star-forming galaxies. As a major coolant of the gas-phase interstellar medium, [CII] balances the heating, including that due to far-ultraviolet photons, which heat the gas via the photoelectric effect. However, the origin of [CII] emission remains unclear, because C+ can be found in multiple phases of the interstellar medium. Here we measure the fractions of [CII] emission originating in the ionized and neutral gas phases of a sample of nearby galaxies. We use the [NII] 205um fine-structure line to trace the ionized medium, thereby eliminating the strong density dependence that exists in the ratio of [CII]/[NII] 122um. Using the FIR [CII] and [NII] emission detected by the KINGFISH and Beyond the Peak Herschel programs, we show that 60-80% of [CII] emission originates from neutral gas. We find that the fraction of [CII] originating in the neutral medium has a weak dependence on dust temperature and the surface density of star formation, and a stronger dependence on the gas-phase metallicity. In metal-rich environments, the relatively cooler ionized gas makes substantially larger contributions to total [CII] emission than at low abundance, contrary to prior expectations. Approximate calibrations of this metallicity trend are provided.Comment: 8 pages, accepted for publication in Ap

The emission by dust and stars of nearby galaxies in the Herschel KINGFISH survey

Using new far-infrared imaging from the Herschel Space Observatory with ancillary data from ultraviolet (UV) to submillimeter wavelengths, we estimate the total emission from dust and stars of 62 nearby galaxies in the KINGFISH survey in a way that is as empirical and model independent as possible. We collect and exploit these data in order to measure from the spectral energy distributions (SEDs) precisely how much stellar radiation is intercepted and re-radiated by dust, and how this quantity varies with galaxy properties. By including SPIRE data, we are more sensitive to emission from cold dust grains than previous analyses at shorter wavelengths, allowing for more accurate estimates of dust temperatures and masses. The dust/stellar flux ratio, which we measure by integrating the SEDs, has a range of nearly three decades (from 10(-2.2) to 10(0.5)). The inclusion of SPIRE data shows that estimates based on data not reaching these far-IR wavelengths are biased low by 17% on average. We find that the dust/stellar flux ratio varies with morphology and total infrared (IR) luminosity, with dwarf galaxies having faint luminosities, spirals having relatively high dust/stellar ratios and IR luminosities, and some early types having low dust/stellar ratios. We also find that dust/stellar flux ratios are related to gas-phase metallicity ((log(f(dust)/f(*)) over bar) = -0.66 +/- 0.08 and -0.22 +/- 0.12 for metal-poor and intermediate-metallicity galaxies, respectively), while the dust/stellar mass ratios are less so (differing by approximate to 0.2 dex); the more metal-rich galaxies span a much wider range of the flux ratios. In addition, the substantial scatter between dust/stellar flux and dust/stellar mass indicates that the former is a poor proxy of the latter. Comparing the dust/stellar flux ratios and dust temperatures, we also show that early types tend to have slightly warmer temperatures (by up to 5 K) than spiral galaxies, which may be due to more intense interstellar radiation fields, or possibly to different dust grain compositions. Finally, we show that early types and early-type spirals have a strong correlation between the dust/stellar flux ratio and specific star formation rate, which suggests that the relatively bright far-IR emission of some of these galaxies is due to ongoing (if limited) star formation as well as to the radiation field from older stars, which is heating the dust grains