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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.

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.

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.

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.

Porous wall permeability is one of the most critical factors for the estimation of backpressure, a key performance indicator in automotive particulate filters. Current experimental and analytical filter models could be calibrated to predict the permeability of a specific filter. However, they fail to provide a reliable estimation for the dependence of the permeability on key parameters such as wall porosity and pore size. This study presents a novel methodology for experimentally determining the permeability of filter walls. The results from four substrates with different porosities and pore sizes are compared with several popular permeability estimation methods (experimental and analytical), and their validity for this application is assessed. It is shown that none of the assessed methods predict all permeability trends for all substrates, for cold or hot flow, indicating that other wall properties besides porosity and pore size are important.

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.

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 purpose of this paper to is to review the methodology applied by Collins Aerospace to develop, test and qualify a more robust surface ply rubber compound that has demonstrable improvements in durability and performance at sub-freezing temperatures. Using in-service products as a reference, pneumatic deicers in use on regional turboprop applications were selected as a basis for operational characteristics and observed failure modes. Custom test campaigns were developed by Collins to comparatively evaluate key characteristics of the surface ply material including low temperature elasticity, erosion durability, and fluid susceptibility. Collins’ proprietary engineered rubber formulations were individually evaluated and built into fully functional test deicers for component level testing to DO-160G environmental exposure, comparative ice shed performance in Collins’ Icing Wind Tunnel and erosion in Collins’ Rain Erosion Silo.

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.

Structural optimization and automation have gained a significant mileage in recent past due to advent of advancements in Computer Aided Design (CAD) and Engineering tools. The conventional approach of design cycle often results in bulkier products requiring optimization activity as a dedicated task resulting in increased overall design time. This paper discusses knowledge based integrated parametric product design philosophy which is a synergistic approach inclusive of all the functions such as design, performance, stress, manufacturing, and automation to produce optimized designs meeting all the functional requirements at preliminary design stage of the product cycle itself. This involves establishing a basic standard architecture for a product family under consideration based on legacy knowledge reflecting the Design Failure Mode Effects Analysis (DFMEA), Design for Manufacturing & Assembly (DFMA) and data driven standard works.

In the development of electric actuators, the electric motor to drive the actuator is quite often selected from a set of available motors that have been previously used on other similar programs, or based on legacy experience, or from those that are commercially available seeming to fit for the purpose. Scheduling and budgetary constraints pose a restriction on design and development of a new electric motor specifically for the required application. Generally, these electric motors have minimal weight but deliver maximum output power because of which they tend to heat up rapidly. Such rapid heating can lead to issues such as insulation damage or weakening of the strengths of permanent magnets used in the rotors of permanent magnet induction motors. In such cases, very early in the design phase, it becomes necessary to estimate the temperature rise of the electric motor in a cost effective and rapid way so that the best suitable motor that gives minimal temperature rise is selected.

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.

The accurate prediction of system behavior and the stress response for vibration loads depends on how well the boundary conditions, joints, interfaces, mass, and stiffness are defined. Often, in vibration analysis several assumptions would be made due to the lack of the analytical tool capabilities, the state of art Finite Element Analysis (FEA) software still cannot handle nonlinear modal analysis. This paper attempts to develop methodology to deal with the nonlinear vibration analysis using the existing Finite Element (FE) tools. Two different aspects of nonlinearities are considered, one is impact of nonlinearities at global level in terms of predicting natural frequency & mode shapes and the other aspect is consideration of nonlinearities local to the contacts, enabling prediction of realistic stresses in the vicinity of the contacts.

The FE analysis of complex systems with lot of intricate features and fillets modeled in detail would result in a huge FE model size making it difficult to handle. Therefore, to reduce the computation time, defeaturing of such regions are carried out as a common practice and these critical stress concentration regions can be studied using submodeling approach later based on response superimposed from the global models. This method is widely practiced and is quite easy to implement for static and harmonic analysis problems. However, there is no well documented methodology exists for submodeling in the random vibration environment. In case of Random vibration analysis, the cut boundary displacements from Power Spectral Density (PSD) analysis would result in unrealistic stresses.

The present work represents the continuation of the introductory study presented in part I [11] where the experimental plan, the measurement system and the tools developed for the testing of a modern Wankel engine were illustrated. In this paper the motored data coming from the subsequent stage of the testing are presented. The AIE 225CS Wankel rotary engine produced by Advanced Innovative Engineering UK, installed in the test cell of the University of Bath and equipped with pressure transducers selected for the particular application, has been preliminarily tested under motored conditions in order to validate the data acquisition software on the real application and the correct determination of the Top Dead Centre (TDC) location which is of foremost importance in the computation of parameters such as the indicated work and the combustion heat release when the engine is tested later under fired conditions.

Engine oil systems drive and de-aerate air-oil solutions in a two-phase flow to provide an appropriate amount of oil lubrication and cooling. especially in aero-engine and starter-generator component and system. The oil lubrication systems combine three important functions of the Main Oil Pump (MOP) for lubrication and scavenging: the de-aeration and de-oiling of the air-oil mixture generated in the bearing and gearbox sumps and pumping the oil towards the tank. These are critical functions for the aero-engine and starter-generator. An aero-engine lubrication system along with an integrated pump and separation of gas-liquid mixture has been developed and characterized experimentally to increase Collins Aerospace Engine and Control Systems research and development productivity. This system has also improved engine and starter-generator reliability and system performance.

Permanent magnet (PM) electrical machine has far-reaching impacts in aviation electrification due to the continuous development in high power density and high efficiency electrical drives. The primary barrier to acceptance of permanent magnet machines for safety-critical starter-generator systems is its low fault-tolerance capability and low reliability (for the conventional designs). This article investigates a modular triple three-phase PM starter-generator comprehensively, including the tradeoff of fault-tolerant topology, optimization design process, analysis of electromagnetic (highlight the post-fault analysis) and thermal behavior, respectively. The triple three-phase segmented topology proposed meet the fault-tolerant requirement along with complete electrical, magnetic, and thermal isolation. There would be cost penalty on the proposed topology, but it gets offset by the ease of manufacturing of coils and their insertion.

Evolving of aircraft design towards further electrification requires safe and fault-free operation of all the components. More electric aircraft are increasingly utilizing electro-mechanical actuators (EMA). EMAs are prone to jamming and subsequent failure due to large forces on the shaft. Large forces are generated due to the high reflected inertia of the electric machine rotor. To limit the force acting on the shaft, a torque limiting device is connected to the power train which can separate the rotating mass of the electric machine from the power train. In this paper, a concept of integration of torque limiter and the electric machine rotor is presented to reduce overall volume and mass. It is connected closely with the rotor, within the motor envelope. A commercially available torque limiter and an electric machine designed for actuator application are used to demonstrate the concept. While essential for safety, the torque limiter adds to the mass and size of the overall EMA.

Particulate emissions from gasoline direct injection (GDI) engines continue to be a topic of substantial research interest. Forthcoming regulation both in the USA and the EU will further reduce their emission and drive innovation. Substantial research effort is spent undertaking experiments to understand, characterize, and research particle number (PN) emissions from engines and vehicles. Recent advances in computing power, data storage, and understanding of artificial intelligence algorithms now mean that these are becoming an important tool in engine research. In this work a random forest (RF) algorithm is used for the prediction of PN emissions from a highly boosted (up to 32 bar BMEP) GDI engine. Particle size, concentration, and the accumulation mode geometric standard deviation (GSD) are all predicted by the model. The results are analysed and an in depth study on parameter importance is carried out.