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The aerospace industry is noticing significant shift towards More Electric Aircraft (MEA). The advancement of electrical technology the systems are being transformed towards electric compared to the conventional pneumatic or hydraulic systems. This has led to an increased demand in electrical power from 150 Kilo Watts in the conventional airplane to 1 Mega Watts in More Electric Aircraft. More electric systems, call for increased electrical wiring harness to connect various systems in the aircraft. These harnesses consist of power and data cables. Wireless communication technology is being matured for data communication, leading to reduction of wire harness for data. As of now, the length of wires in large commercial aircraft is over 100miles and it may not be surprising if the electrification of aircraft drive this too much longer.

The growth in global economies has led to a world that has become much more mobile in the last few decades. The number of enplanements has increased and is expected to continue to do so at an annual average rate of 1.8% through 2039 [1]. Prior to the COVID-19 pandemic, the number of aircraft in service was expected to increase annually to meet the travel demand. Next-generation, more-complex aircraft were scheduled to replace the older aircraft at a pace that still allowed sufficient capacity to meet the increasing demand. The events of 2020 have driven the industry to accelerate retirement of older aircraft while deferring the introduction of new aircraft. While the length of the industry recovery period cannot be predicted, most analysts believe that demand for travel will return once a vaccine is widely available.

In the aerospace industry, competition is high and the need to ensure safety and security while managing costs is paramount. Furthermore, stakeholders—who gain the most by working together—do not necessarily trust each other. Now, mix that with changing enterprise technologies, management of historical records, and customized legacy systems. This issue touches all aspects of the aerospace industry, from frequent flyer miles to aircraft maintenance and drives tremendous inefficiency and cost. Technology that augments, rather than replaces, is needed to transform these complex systems into efficient, digital processes. Blockchain technology offers collaborative opportunities for solving some of the data problems that have long challenged the industry. This SAE EDGE™ Research Report by Rhonda D. Walthall examines how blockchain technology could impact the aerospace industry and addresses some of the unsettled concerns surrounding its implementation.

The aircraft jet engine is one of the most complex multivariable systems with multiple inputs and multiple outputs. To attempt to optimize control functions or to address diagnostic problems, a detailed knowledge of all jet engine design parameters and performances is required. Although jet engines have been around for almost a century, there are only a few companies in the world presently designing and manufacturing them; as such these companies possess detailed knowledge of all relevant design characteristics and performance parameters. In the event where jet engine technical details are unknown, or only a few of them are known from manufacturer’s catalogues, the challenge becomes how to calculate and extrapolate critical performance parameters based on only fuel flow, jet exhaust temperature and total thrust.

In recent years, the increasing complexity of modern aerospace systems has driven the rapid adoption of robust Model-Based Systems Engineering (MBSE). MBSE is a development methodology centered around computational models, which are instrumental in supporting the design and analysis of intricate systems. In this context, the Architecture Analysis and Design Language (AADL) and Systems Modeling Language (SysML) are two prominent modeling languages for specifying and analyzing the structure and behavior of a cyber-physical system. Both languages have their own specific use cases and tool environments and are typically employed to model different aspects of system design. Although multiple software tools are available for transforming models from one language to another, their effectiveness is limited by fundamental differences in the semantics of each language.

Aircraft Manufacturing procedures are very critical which always require skillful engineers who must adhere to various process and procedure during daily work. The challenge is not only to identify right tools and manuals but also to keep track of operator usage data and behavior. With Augmented reality (AR), intelligent tools and analytics, we can provide a new lease of life to first-line assembly engineers. The objective is how AR can help us to reduce rework or scrap with the concept of Industry 4.0 [1] were integrating with cutting edge technologies like machine learning and the internet of things (IoT) to meet the fundamental requirements during manufacturing. Smart Manufacturing consists of four major components Cyber-physical systems, IoT, cloud computing and cognitive computing.

In response to new safety regulations regarding aircraft icing, Collins Aerospace has developed and tested an Optical Ice Detector (OID) capable of discriminating among icing conditions appropriate to Appendix C and Appendix O of 14 CFR Part 25 and Appendix D of Part 33. The OID is a short-range, polarimetric lidar that samples the airstream up to ten meters beyond the skin of the aircraft. The intensity and extinction of the backscatter light correlate with bulk properties of the cloud, such as water content and phase. Backscatter scintillation (combined with the outside air temperature from another probe) signals the presence of supercooled large droplets (SLD) within the cloud-a capability incorporated into the OID to meet the requirements of Appendix O. Recent laboratory and flight tests of the Optical Ice Detector have confirmed the efficacy of the OID to discriminate among the various icing conditions.

A GE Aviation Systems report documents that the National Oceanic and Atmospheric Administration (NOAA) provided weather forecast data has a bias of 15 knots and a standard deviation of 13.3 knots for the 40 flights considered for the research. It also had a 0.47 bias in the temperature with a standard deviation of 0.27. The temperature errors are not as significant as the wind. There is a potential opportunity to reduce the operational cost by improving the weather forecast. The flight management system (FMS) currently uses the weather forecast, available before takeoff, to identify an optimized flight path with minimum operational costs depending on the selected speed mode. Such a flight plan could be optimum for a shorter flight because these flight path planning algorithms are very less susceptible to the accuracy of the weather forecast.

The Collins Aerospace Optical Ice Detector is a short-range polarimetric cloud lidar designed to detect and discriminate among all types of icing conditions with the use of a single sensor. Recent flight tests of the Optical Ice Detector (OID) aboard a fully instrumented atmospheric research aircraft have allowed comparisons of measurements made by the OID with those of standard cloud research probes. The tests included some icing conditions appropriate to the most recent updates to the icing regulations. Cloud detection, discrimination of mixed phase, and quantification of cloud liquid water content for a cloud within the realm of Appendix C were all demonstrated. The duration of the tests (eight hours total) has allowed the compilation of data from the OID and cloud probes for a more comprehensive comparison. The OID measurements and those of the research probes agree favorably given the uncertainties inherent in these instruments.

In response to safety regulations regarding aircraft icing, Collins Aerospace has developed and tested a new generation of optical ice detectors (OID Lite) intended to discriminate among icing conditions described by Appendix C and Appendix O of 14 CFR Part 25 and Appendix D of Part 33. The OID Lite is a flush-mounted, short-range, polarimetric optical sensor that samples the airstream up to two meters beyond the skin of the aircraft. The intensity and polarization of the backscatter light correlate with bulk properties of the cloud, such as cloud density and phase. Drizzle-sized droplets, mixed within a small droplet cloud, appear as scintillation spikes in the lidar signal when it is processed pulse-by-pulse. Scintillation in the backscatter (in combination with the outside air temperature monitored by another probe) signals the presence of supercooled large droplets (SLD) within the cloud—a capability incorporated into the OID Lite to meet the requirements of Appendix O.

The number of Unmanned Aircraft Systems (UAS) has been growing over the past few years and will continue to grow at a faster pace in the near future. UAS faces many challenges in certification, airspace management, operations, supply chain, and maintenance. Blockchain, defined as a distributed ledger technology for the enterprise that features immutability, traceability, automation, data privacy, and security, can help address some of these challenges. However, blockchain also has certain drawbacks and, additionally, it is still not fully mature. Hence it is essential to study how blockchain can help UAS. This Aerospace Information Report (AIR) presents the current opportunities, challenges of UAS operating at or below 400 ft Above Ground Level (AGL) altitude for commercial use and how blockchain can help meet these challenges. It also provides requirements for developing a blockchain solution for UAS along with the need for the standardization of blockchain enabled processes.

Unmanned Aircraft Systems (UAS) have been growing over the past few years and will continue to grow at a faster pace in future. UAS faces many challenges in certification, airspace management, operations, supply chain, and maintenance. Blockchain, defined as a distributed ledger technology for the enterprise that features immutability, traceability, automation, data privacy, and security, can help address some of these challenges. However, blockchain also has certain challenges and is still evolving. Hence it is essential to study on how blockchain can help UAS. G-31 technical committee of SAE International responsible for electronic transactions for aerospace has published AIR 7356 [1] entitled Opportunities, Challenges and Requirements for use of Blockchain in Unmanned Aircraft Systems Operating below 400ft above ground level for Commercial Use. This paper is a teaser for AIR 7356 [1] document.

The main aim of operating the navigation database server from ground station (Web/cloud) is to operate a single navigation database server across all aircrafts and navigation database updates can be performed at one place. which will be effective and quick, thus no need to update the navigation database in each flight for every 28 days. UAM refers to a safe and efficient air transportation system that uses transformative new airborne technology, manned and unmanned, to move people and goods in a metropolitan area, operating the navigation data base server from ground station might be the first step towards including the FMS system in urban air mobility (UAM). the proposed system can run as standalone application and provides serveries to all aircrafts from single resource; thus, the system will provide services with low cost.

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.

As the complexity of systems expands with increasing emphasis for digital transformation, the aerospace industry is generating big data to meet customer requirements. The ability to that data to solve challenging problems is limited by many factors, including the capabilities of current classical computing systems. Impact of Quantum Computing in Aerospace discusses how quantum computing systems offer (possibly quadratic to exponentially) greater computational power over classical computers. The power of quantum computing is tremendous and has many potential impacts on the aerospace industry; however, there are also many unsettled topics surrounding the future of the technology. Click here to access the full SAE EDGETM Research Report portfolio.

Avionics systems are currently undergoing a transition from single core processor architectures to multi-core processor architectures. This transition enables significant advantages in reduction in size, weight, power (SWaP) and cost. However, avionics hardware and software certification policies and guidance are evolving as research and experience is gained with multi-core processor architectures. The unique challenges of using multi-core processors in certified avionics will be discussed. The requirements for a virtualization platform supporting multiple real-time operating system (RTOS) partitions on a multi-core processor used in safety-critical avionics systems are defined, including the ability to support multiple design assurance levels (DAL) on multiple cores, fault isolation and containment, static configuration as per ARINC 653, role-based development as per DO-297, and robust partitioning to reduce cost of incremental certification.

Electric aircraft have emerged as a promising solution for sustainable aviation, aiming to reduce greenhouse gas emissions and noise pollution. Efficiently estimating and optimizing energy consumption in these aircraft is crucial for enhancing their design, operation, and overall performance. This paper presents a novel framework for analyzing and modeling energy consumption patterns in lightweight electric aircraft. A mathematical model is developed, encompassing key factors such as aircraft weight, velocity, wing area, air density, coefficient of drag, and battery efficiency. This model estimates the total energy consumption during steady-level flight, considering the power requirements for propulsion, electrical systems, and auxiliary loads. The model serves as the foundation for analyzing energy consumption patterns and optimizing the performance of lightweight electric aircraft.

The aerospace ecosystem is a complex system of systems comprising of many stakeholders in exchanging technical, design, development, certification, operational, and maintenance data across the different lifecycle stages of an aircraft from concept, engineering, manufacturing, operations, and maintenance to its disposal. Many standards have been developed to standardize and improve the effectiveness, efficiency, and security of the data transfer processes in the aerospace ecosystem. There are still challenges in data transfer due to the lack of standards in certain areas and lack of awareness and implementation of some standards. G-31 standards committee of SAE International has conducted a study on the available digital data standards in aircraft asset life cycle to understand the current and future landscapes of the needed digital data standards and identify gaps. This technical paper presents the study conducted by the G-31 technical committee.

The aircraft lifecycle involves thousands of transactions and an enormous amount of data being exchanged across the stakeholders in the aircraft ecosystem. This data pertains to various aircraft life cycle stages such as design, manufacturing, certification, operations, maintenance, and disposal of the aircraft. All participants in the aerospace ecosystem want to leverage the data to deliver insight and add value to their customers through existing and new services while protecting their own intellectual property. The exchange of data between stakeholders in the ecosystem is involved and growing exponentially. This necessitates the need for standards on data interoperability to support efficient maintenance, logistics, operations, and design improvements for both commercial and military aircraft ecosystems. A digital thread defines an approach and a system which connects the data flows and represents a holistic view of an asset data across its lifecycle.

Certification standards for high-assurance systems include objectives for demonstrating compliance of process artifacts such as requirements and code with style guidelines and other standards. With the emergence of model-based development, similar objectives have been specified that apply to models. Demonstration of compliance is often achieved by employing a static analysis linter tool. This paper describes Resolint, an open-source, lightweight linter tool for checking compliance of Architecture Analysis and Design Language (AADL) models with modeling guidelines. AADL enables engineers to describe the key elements of distributed, real-time, embedded system architectures with a sufficiently rigorous semantics. In addition, AADL provides an annex mechanism for extending the base language, enabling new kinds of analyses and tool support. Resolint uses the AADL annex capability to provide a language for specifying style guide rule sets.