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The state-of-practice for aircraft manufacturers to diagnose guidance & control faults and obtain full flight envelope protection at all times is to provide high levels of dissimilar hardware redundancy. This ensures sufficient available control action and allows performing coherency tests, cross and consistency checks, voting mechanisms and built-in test techniques of varying sophistication. This hardware-redundancy based fault detection and diagnosis (FDD) approach is nowadays the standard industrial practice and fits also into current aircraft certification processes while ensuring the highest level of safety standards. In the context of future “sustainable” aircraft (More Affordable, Smarter, Cleaner and Quieter), the Electrical Flight Control System (EFCS) design objectives, originating from structural loads design constraints, are becoming more and more stringent.

In the framework of the aircraft global optimization, for future and upcoming programs, current research interests include more Electrical Flight Control System (EFCS) autonomy for a more easy-to-handle aircraft. A possible solution is to increase the number of redundant flight parameter sensors but to the detriment of the aircraft weight and so to the cost and performances. This paper proposes an algorithm using PLS (Partial Least Squares) to estimate a flight parameter from independent sensor measurements. The estimates are then used as so-called “software” or “virtual” sensors, allowing aircraft weight saving. This algorithm is based on an iterative processing and thus can be used in real time in the embedded flight control computer. Furthermore, the resulting flight parameter estimates can be used to detect failures. Different detection strategies are proposed and results show that this method can lead to robust detections.

Traditionally, software in avionics has been totally separated from open-world software, in order to avoid any interaction that could corrupt critical on-board systems. However, new aircraft generations need more interaction with off-board systems to offer extended services, which makes these information flows potentially dangerous. In a previous work, we have proposed the use of virtualization to ensure dependability of critical applications despite bidirectional communication between critical on-board systems and untrusted off-board systems. We have developed a test bed to assess the performance impact induced by the use of virtualization. In this work, various configurations have been experimented that range from a basic machine without an OS up to the complete architecture featuring a hypervisor and an OS running in a virtual machine. Several tests (computation, memory, network) are carried out, and timing measures are collected on different hypervisors.

This study presents the results of the ICE GENESIS 2021 Swiss Jura Flight Campaign in a way that is readily usable for ice accretion modelling and aims at improving the description of snow particles for model inputs. 2D images from two OAP probes, namely 2D-S and PIP, have been used to extract 3D shape parameters in the oblate spheroid assumption, as there are the diameter of the sphere of equivalent volume as ellipsoid, sphericity, orthogonal sphericity, and an estimation of bulk density of individual ice crystals through a mass-geometry parametrization. Innovative shape recognition algorithm, based on Convolutional Neural Network, has been used to identify ice crystal shapes based on these images and produce shape-specific mass particle size distributions to describe cloud ice content quantitatively in details. 3D shape descriptors and bulk density have been extracted for all the data collected in cloud environments described in the regulation as icing conditions.

Measurements in snow conditions performed in the past were rarely initiated and best suited for pure and extremely detailed quantification of microphysical properties of a series of microphysical parameters, needed for accretion modelling. Within the European ICE GENESIS project, a considerable effort of natural snow measurements has been made during winter 2020/21. Instrumental means, both in-situ and remote sensing were deployed on the ATR-42 aircraft, as well as on the ground (ground station at ‘Les Eplatures’ airport in the Swiss Jura Mountains with ATR-42 overflights). Snow clouds and precipitation in the atmospheric column were sampled with the aircraft, whereas ground based and airborne radar systems allowed extending the observations of snow properties beyond the flight level chosen for the in situ measurements.

The RTCA SC-230 committee began working on minimum operational performance standards (MOPS) for ice crystal detection using weather radar in 2018. The resulting MOPS document will be released in 2023. This paper presents the rationale, summarizes key requirements, and discusses means of validation for an ice crystal detection function incorporated in an airborne weather radar system.

The PLANET System was used for real-time satellite data transmission during the HAIC-HIWC Darwin field campaign (January to March 2014). The basic system was initially providing aircraft tracking, chat, weather text messages (METAR, TAF, etc.), and aeronautical information (NOTAMs) in a standalone application. In the framework of the HAIC project, many improvements were made in order to fulfill requirements of the onboard and ground science teams for the field campaign. The aim of this paper is to present the main improvements of the system that were implemented for the Darwin field campaign. New features of the system are related to the hardware component, the communication protocol, weather and tracking display, geomarkers on the map, and image processing and compression before onboard transfer.

In order to comply with applicable certification regulations, airframers have to demonstrate safe operation of their aircraft in icing conditions. Part of this demonstration is often a numerical prediction of the potential ice accretion on unprotected surfaces. The software ONICE2D, originally developed at the Office National d'Études et de Recherche Aérospatial (ONERA), is used at Airbus for predicting ice accretions on wing-like geometries. The original version of the software uses a flow solution of the 2D full-potential equation on a structured C-grid as basis for an ice accretion prediction. Because of known limitations of this approach, an interface was added between ONICE2D and TAU [6], a hybrid flow solver for the Navier-Stokes equations. The paper first details the approach selected to implement the interface to the hybrid flow solver TAU. It continues to explain how an automatic impingement and ice accretion calculation on multi-element configurations has been achieved.

The High Altitude Ice Crystals (HAIC) Sub-Project 3 (SP3) focuses on the detection of cloud regions with high ice water content (IWC) from current available remote sensing observations of space-based geostationary and low-orbit missions. The SP3 activities are aimed at supporting operationally the two up-coming HAIC flight campaigns (the first one in May 2015 in Cayenne, French Guyana; the second one in January 2016 in Darwin, Australia) and ultimately provide near real-time cloud monitoring to Air Traffic Management. More in detail the SP3 activities focus on the detection of high IWC from space-borne geostationary Meteosat daytime imagery, explore the synergy of concurrent multi-spectral multiple-technique observations from the low-orbit A-Train mission to identify specific signatures in high IWC cloud regions, and finally develop a satellite-based nowcasting tool to track and monitor convective systems over the Tropical Atlantic.

Accurate calculation of the catch efficiency β is of paramount importance for any ice accretion calculation since β is the most important factor in determining the mass of ice accretion. A new scheme has been proposed recently in [1] for accurately calculating β on a discretized two-dimensional geometry based on the results of a Lagrangian droplet trajectory integrator (start and impact conditions). This paper proposes an extension to the algorithm in Ref. [1], which is applicable to three-dimensional surfaces with arbitrary surface discretization. The 3D algorithm maintains the positive attributes of the original 2D algorithm, namely mass conservation of the impinging water, capability to deal with overlapping impingement regions and with crossing trajectories, computational efficiency of the algorithm, and low number of trajectories required to reach good accuracy in catch efficiency.

ASRP (Assembly Simulation of Riveting Process) software is a special tool for assembly process modelling for large scale airframe parts. On the base of variation simulation, ASRP provides a convenient way to analyze, verify and optimize the arrangement of temporary fasteners. During the assembly of airframe certain criteria on residual gap between parts must be fulfilled. The numerical approach implemented in ASRP allows to evaluate the quality of contact on every stage of assembly process and solve verification and optimization problems for temporary fastener patterns. The paper is devoted to description of several specialized approaches that combine statistical analysis of measured data and numerical simulation using high-performance computing for optimization of fastener patterns, calculation of forces in fasteners needed to close initial gaps, and identification of hazardous areas in junction regions via ASRP software.

“Smart” refers to tools that are “specific, measurable, achievable, reasonable/realistic, and time bound.” Smart assembly tools are used in many industries, including automotive, aerospace, and space for measuring, inspecting, gauging, drilling, and installing all existing fastening systems. Inside the Industry 4.0 environment, these tools have a huge influence on Information and Communication Technology (ICT), assembly cost reduction, process control, and even the product and process quality. These four domains—and their undefined nature—are the focus of this SAE EDGE™ Research Report. The technical issues identified here need to be discussed, the goals clarifying the scope of the industry-wide need to be aligned, and the issues requiring standardization need prioritized. NOTE: SAE EDGE Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry.

Modern aircraft, such as A380 or A350 for Airbus, are very well connected in flight to ground stations through wireless communications. For maintenance and operations purpose, the aircraft is programmed to send regularly information such as flight reports based on the BITE messages (Built-In Test Equipment) or standard reports based on the value of physical parameters. Moreover, Airbus is capable of sending requests (called uplinks) to the aircraft to retrieve the value of different parameters in almost real-time. This ability, associated with adequate process, improves significantly the reaction time of the diagnostic and prognostic solutions that Airbus can provide to its customers. Traditionally Health Monitoring is considered useful when the Potential to Functional failure (P-F) interval is greater than one flight cycle.