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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.

In the framework of the European ICE GENESIS project (https://www.ice-genesis.eu/), a field experiment was conducted in the Swiss Jura in January 2021 in order to characterize snow microphysical properties and document snow conditions for aviation industry purposes. Complementary to companion papers reporting on snow properties, this study presents an investigation on mixed-phase conditions sampled during the ICE GENESIS field campaign. Using in situ measurement of the liquid and total water content, the ice mass fraction is calculated and serves as a criteria to identify mixed-phase conditions. In the end, mixed phase conditions were identified in almost 30 % of the 3800 km long cloud samples included in the ICE GENESIS dataset. The data suggests that the occurrence of mixed-phase does not clearly depend on temperature in the 0 to -10 °C range, but varies significantly from one cloud system to another.

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.

In its entirety, automation is part of an integrated, multi-disciplinary product development process including the design, process, production, logistics, and systems approach—it depends on all these areas, but it also influences them as well. Automation in aerospace manufacturing is present throughout the entire supply chain, from elementary part manufacturing at suppliers up to final assembly, and a clear understanding of all the benefits (and drawbacks) of automation would help designers and engineers select the right designs for and levels of automation. The Right Level of Automation Within Industry 4.0 examines all impacts of automation that should be known by designers, manufacturers, and companies before investments in automation-related decisions are made—regardless of the which industry they work in. The process and the set of criteria discussed in this report will help decision makers select the right level of automation.

Dents on the aircraft skin are frequent and may easily go undetected during airworthiness checks, as their inspection process is tedious and extremely subject to human factors and environmental conditions. Nowadays, 3D scanning technologies are being proposed for more reliable, human-independent measurements, yet the process of inspection and reporting remains laborious and time consuming because data acquisition and validation are still carried out by the engineer. For full automation of dent inspection, the acquired point cloud data must be analysed via a reliable segmentation algorithm, releasing humans from the search and evaluation of damage. This paper reports on two developments towards automated dent inspection. The first is a method to generate a synthetic dataset of dented surfaces to train a fully convolutional neural network. The training of machine learning algorithms needs a substantial volume of dent data, which is not readily available.

Increased production rates and cost reduction are affecting manufacturing in all mobility industry sectors. One enabling methodology that could achieve these goals in the burgeoning “Industry 4.0” environment is the optimized deterministic assembly (DA) approach. It always forms the same final structure and has a strong link to design-for-assembly and design-for-automation. The entire supply chain is considered, with drastic savings at the final assembly line level through recurring costs and lead-time reduction. Unsettled Technology Areas in Deterministic Assembly Approaches for Industry 4.0 examines the evolution of previous assembly principles that lead up to and enable the DA approach, related simulation methodologies, and undefined and unsolved links between these domains. Click here to access the full SAE EDGETM Research Report portfolio.

Every state-of-art aircraft has a complex distributed systems of avionics Line Replaceable Units/Modules (LRUs/LRMs), networked by several Data buses. These LRUs are becoming more complex because of an increasing number of new functions need to be integrated into avionics architecture. Moreover, the complexity of the overall avionics architecture and its impact on cable length, weight, power consumption, reliability and maintainability of avionics systems encouraged manufacturers to incorporate efficient avionics architectures in their aircraft design process. The evolution of avionics data buses and architectures have moved from distributed analog and federated architecture to digital integrated modular avionics (IMA). IMA architecture allows suppliers to develop their own LRUs/LRMs capable of specific features that can then be offered to Original Equipment Manufacturers (OEMs) as Commercial-Off-The-Shelf (COTS) products.

Bus drivers are a group at risk of often suffering from musculoskeletal problems, such as low-back pain, while bus passengers on the last-row seats experience accelerations of high values. In this paper, the contribution of K-seat in decreasing the above concern is investigated with a detailed simulation study. The K-seat model, a seat with a suspension that functions according to the KDamper concept, which combines a negative stiffness element with a passive one, is benchmarked against the conventional passive seat (PS) in terms of comfort when applied to different bus users’ seats. More specifically, it is tested in the driver’s and two different passengers’ seats, one from the rear overhang and one from the middle part. For the benchmark shake, both are optimized by applying excitations that correspond to real intercity bus floor responses when it drives over a real road profile.

This SAE EDGE™ Research Report builds a comprehensive picture of the current state-of-the-art of human-robot applications, identifying key issues to unlock the technology’s potential. It brings together views of recognized thought leaders to understand and deconstruct the myths and realities of human- robot collaboration, and how it could eventually have the impact envisaged by many. Current thinking suggests that the emerging technology of human-robot collaboration provides an ideal solution, combining the flexibility and skill of human operators with the precision, repeatability, and reliability of robots. Yet, the topic tends to generate intense reactions ranging from a “brave new future” for aircraft manufacturing and assembly, to workers living in fear of a robot invasion and lost jobs. It is widely acknowledged that the application of robotics and automation in aerospace manufacturing is significantly lower than might be expected.

“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.

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.

In response to the ice crystal icing hazard identified twenty years ago, aviation industry, regulation authorities, and research centers joined forces into the HAIC-HIWC international collaboration launched in 2012. Two flight campaigns were conducted in the high ice water content areas of tropical mesoscale convective systems in order to characterize this environment conducive to ice crystal icing. Statistics on cloud microphysical properties, such as Ice Water Content (IWC) or Mass Median Diameter (MMD), derived from the dataset of in situ measurements are now being used to support icing certification rulemaking and anti-icing systems design (engine and air data probe) activities. This technical paper focuses on methodological aspects of the derivation of MMD. MMD are estimated from PSD and IWC using a multistep process in which the mass retrieval method is a critical step.

Digital Twin (DT) is a dynamic digital representation of a real-world asset, process or system. Industry 4.0 has recognised DT as the game changer for manufacturing industries in their digital transformation journey. DT will play a significant role in improving consistency, seamless process development and the possibility of reuse in subsequent stages across the complete lifecycle of the product. As the concept of DT is novel, there are several challenges that exist related to its phase of development and implementation, especially in high value manufacturing sector. The paper presents a thematic analysis of current academic literature and industrial knowledge. Based on this, eleven key challenges of DT were identified and further discussed. This work is intended to provide an understanding of the current state of knowledge around DT and formulate the future research directions.

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared.

The flow field and body aerodynamic loads on the DrivAer reference model have been extensively investigated since its introduction in 2012. However, there is a relative lack of information relating to the models wake development resulting from the different rear-body configurations, particularly in the far-field. Given current interest in the aerodynamic interaction between two or more vehicles, the results from a preliminary CFD study are presented to address the development of the wake from the Fastback, Notchback, and Estateback DrivAer configurations. The primary focus is on the differences in the far-field wake and simulations are assessed in the range up to three vehicle lengths downstream, at Reynolds and Mach numbers of 5.2×106 and 0.13, respectively. Wake development is modelled using the results from a Reynolds-Averaged Navier-Stokes (RANS) simulation within a computational mesh having nominally 1.0×107 cells.

The intent of this paper is to provide a general overview of the main engineering and test activities conducted in order to support A350XWB Ice and Rain Protection Systems certification. Several means of compliance have been used to demonstrate compliance with applicable Certification Basis (CS 25 at Amendment 8 + CS 25.795 at Amendment 9, FAR 25 up to Amendment 129) and Environmental protection requirements. The EASA Type Certificate for the A350XWB was received the 30th September 2014 after 7 years of development and verification that the design performs as required, with five A350XWB test aircraft accumulating more than 2600 flight test hours and over 600 flights. The flight tests were performed in dry air and measured natural icing conditions to demonstrate the performance of all ice and rain protection systems and to support the compliance demonstration with CS 25.1419 and CS25.21g.

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.

The installation of essential systems into aircraft wings involves numerous labour-intensive processes. Many human operators are required to perform complex manual tasks over long periods of time in very challenging physical positions due to the limited access and confined space. This level of human activity in poor ergonomic conditions directly impacts on speed and quality of production but also, in the longer term, can cause costly human resource problems from operators' cumulative development of musculoskeletal injuries. These problems are exacerbated in areas of the wing which house multiple systems components because the volume of manual work and number of operators is higher but the available space is reduced. To improve the efficiency of manual work processes which cannot yet be automated we therefore need to consider how we might redesign systems installations in the enclosed wing environment to better enable operator access and reduce production time.

Global aviation is growing exponentially and there is a great emphasis on trajectory optimization to reduce the overall environmental impact caused by aircraft. Many optimization techniques exist and are being studied for this purpose. The CLEAN SKY Joint Technology Initiative for aeronautics and Air transport, a European research activity run under the Seventh Framework program, is a collaborative initiative involving industry, research organizations and academia to introduce novel technologies to improve the environmental impact of aviation. As part of the overall research activities, “green” aircraft trajectories are addressed in the Systems for Green Operations (SGO) Integrated Technology Demonstrator. This paper studies the impact of large commercial aircraft trajectories optimized for different objectives applied to the on board systems.

Airbus business and Extended Enterprise require more and more involvement of design and built suppliers, tier 1 but also across the complete supply chain i.e. tier 2-n. These suppliers are not working only for Aerospace industry and may have different cultures. The pressure on cost and overall efficiency is high and everybody has to cope with obsolescence and new regulation (e.g. REACH (Registration, Evaluation and Authorization and Restriction of Chemicals)). So it became very important for Airbus to clarify the criteria under which a change can be done without Airbus review, and criteria under which a change can be done without Airworthiness authority review.