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Effective contrail management while ensuring operational and economic efficiencies for flight services is essential for providing services with minimal adverse environmental impact. The paper explores various aspects of contrail management applicable to different platforms such as Unmanned vehicles, Commercial airliners and Business & regional jets. The aspects unique to each platform such as flight levels of operation, fuel types, flight endurance and radius of operation have been analyzed. Expanse of 5G network is resulting in increased flight activity at flight levels not envisaged hitherto. The paper also dwells on the ramifications of the increased proliferation of different platforms at newer flight levels from the perspective of contrail management.

Aviation industry has been continuously striving for reducing the number of flight crew in the aircraft cockpit for balancing operational efficiency with the flight economics. Concepts like Reduced Crew Operations (RCO) and Single Pilot Operations (SPO) are being experimented in this direction. In RCO and SPO, additional aid/system is needed for reducing the pilot’s workload and to help him/her in taking right decisions. Weather situational awareness and management of weather-related threats are significant part of the workload the pilot is subjected during the flight. Weather information presented to the pilot in the cockpit is obtained either from an onboard weather radar on larger commercial aircrafts or from other sources like Air Traffic Control, ADS-B Flight Information Services, Connected weather services, etc.

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.

Aviation industry is striving to leverage the technological advancements in connectivity, computation and data analytics. Scalable and robust connectivity enables futuristic applications like smart cabins, prognostic health management (PHM) and AI/ML based analytics for effective decision making leading to flight operational efficiency, optimized maintenance planning and aircraft downtime reduction. Wireless Sensor Networks (WSN) are gaining prominence on the aircraft for providing large scale connectivity solution that are essential for implementing various health monitoring applications like Structural Health Monitoring (SHM), Prognostic Health Management (PHM), etc. and control applications like smart lighting, smart seats, smart lavatory, etc. These applications help in improving passenger experience, flight operational efficiency, optimized maintenance planning and aircraft downtime reduction.

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 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.

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 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.

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.

Protecting against atmospheric icing conditions is critical for the safety of aircraft during flight. Sensors and probes are often used to indicate the presence of icing conditions, enabling the aircraft to engage their ice protection systems and exit the icing cloud. Supercooled large drop icing conditions, which are defined in Appendix O of 14 CFR Part 25, pose additional aircraft certification challenges and requirements as compared to conventional icing conditions, which are defined in Appendix C of 14 CFR Part 25. For this reason, developing sensors that can not only indicate the presence of ice, but can also differentiate between Appendix O and Appendix C icing conditions, is of particular interest to the aviation industry and to federal agencies. Developing detectors capable of meeting this challenge is the focus of SENS4ICE, a European Union sponsored project.

Since the early days of aviation, when an AC-type generator became a primary source of electrical power for all aircraft systems, the demand for electrical power has steadily grown. Following rapid technology and scientific advancements in the aerospace industry, the complexity and criticality of all aircraft systems have increased to the point where multiple independent and isolated electrical power sources are required. In such an environment, with two or more variable-frequency AC-type generators that can be simultaneously activated to provide electrical power to the aircraft power distribution system, a safe power transfer process becomes a major priority. This means that any two independent aircraft AC power sources with different frequencies or phase angles cannot be connected simultaneously to a common power bus.

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.

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Pitot probes and Total Air Temperature (TAT) probes are critical to aircraft performance. They are also susceptible to becoming overwhelmed and produce erroneous outputs when flying in icing conditions, especially in high altitude ice crystal situations. When the probes are overwhelmed with ice crystals, it can have significant impacts to aircraft operations. Through design and process iterations, Collins Aerospace (also known as Rosemount Aerospace™), has developed new Appendix D compliant pitot and TAT probes that are much more capable in high ice crystal content icing environments which greatly reduce the adverse risks to the aircraft and engine systems that depend on these probes.

Multiphase CFD simulations of air and water play a critical role in aircraft icing analysis. Specifically for air data sensors mounted near the front of an aircraft, simulations that predict the concentration of water surrounding an aircraft fuselage are necessary for understanding their performance in icing conditions. Those simulations can aid in sensor design and placement, and are central for defining critical conditions to test during icing qualification campaigns. There are several methods available in CFD that solve a multiphase flow field. Two of the most common methods used are Lagrangian and Eulerian. While these methods are similar, important differences can be viewed in the results, specifically in how the water shadow zones are predicted. This paper compares a Lagrangian and Eulerian CFD method for solving a multiphase flow field, and assesses their performance for use for analyzing installation locations and critical icing conditions of air data probes.

This paper provides an overview of the state-of-art multiscale “Icing Simulation Framework” capability developed at Raytheon Technologies Research Center. Specifically, the application of this framework to simulate droplet runback and runback icing will be presented. In summary, this high-fidelity framework tracks the physical mechanisms associated with droplet dynamics, ice nucleation, growth and interaction with the environment (e.g. adhesion, crystal growth, evaporation, sublimation, etc.) across all relevant scales (including nucleation at <10-7m to ~10-6m of coating/environment interaction to 10-2m of the component) which allows a rigorous investigation of how different environmental (e.g. LWC, MVD, pressure, velocity and temperature) and substrate (e.g. coating molecular and macroscopic specifications) characteristics affect the icing behavior.

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.

For nearly a century, ice build-up on aircraft surfaces has presented a safety concern for the aviation industry. Pilot observations of visible moisture and temperature has been used a primary means to detect conditions conducive to ice accretion on aircraft critical surfaces. To help relieve flight crew workload and improve aircraft safety, various ice detection systems have been developed. Some ice detection systems have been successfully certified as the primary means of detecting ice, negating the need for the flight crew to actively monitor for icing conditions. To achieve certification as a Primary ice detection system requires detailed substantiation of ice detector performance over the full range of icing conditions and aircraft flight conditions. Some notable events in the aviation industry have highlighted certain areas of the icing envelope that require special attention.

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.