Search Results

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.

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 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.

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.

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.

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.

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.

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.