Browse Topic: Icing and ice detection
This SAE Aerospace Recommended Practice (ARP) provides recommended practices for the calibration and acceptance of icing wind tunnels to be used in testing of aircraft components and systems and for the development of simulated ice shapes. This document is not directly applicable to air-breathing propulsion test facilities configured for the purposes of engine icing tests, which are covered in AIR6189. This document also does not provide recommended practices for creating Supercooled Large Drop (SLD) or ice crystal conditions, since information on these conditions is not sufficiently mature for a recommended practice document at the time of publication of ARP5905A. Use of facilities as part of an aircraft’s ice protection Certification Plan should be reviewed and accepted by the applicable regulatory agency prior to testing. Following acceptance of a test plan, data generated in these facilities may be submitted to regulatory agencies for use in the certification of aircraft ice
This SAE Aerospace Standard (AS)/Minimum Operational Performance Specification (MOPS) specifies the minimum performance requirements of remote on-ground ice detection systems (ROGIDS). These systems are ground based. They provide information that indicates whether frozen contamination is present on aircraft surfaces. Section 1 provides information required to understand the need for the ROGIDS, ROGIDS characteristics, and tests that are defined in subsequent sections. It describes typical ROGIDS applications and operational objectives and is the basis for the performance criteria stated in Sections 3 through 5. Section 2 provides reference information, including related documents, definitions, and abbreviations. Section 3 contains general design requirements for the ROGIDS. Section 4 contains the Minimum Operational Performance Requirements for the ROGIDS, which define performance in icing conditions likely to be encountered during ground operations. Section 5 describes environmental
Ice build-up on aircraft and wind turbines can impact the safety and efficiency of their systems
Ice prediction capabilities for Unmanned Aerial Systems (UAS) is of growing interest as UAS designs and applications become more diverse. This report summarizes the current state-of-the-art in modeling aircraft icing within a computational framework as well as a recent U.S. Army DEVCOM AvMC effort to evaluate ice prediction models for current use and future integration into the Computational Research and Engineering Acquisition Tools and Environments (CREATE) Air Vehicle (AV) framework. U.S. Army Combat Capabilities Development Command, Redstone Arsenal, Alabama Historically, smaller Unmanned Aerial Systems (UAS), such as Class 2 RQ-1B Raven and Class 3 RQ-7Bv2 Shadow, have been restricted to not be approved to fly in icing conditions under the assumption that any ice accretion would cause an unacceptable risk of loss of the aircraft. However, interest exists in better understanding potential icing accretion on UAS to determine if less extreme icing conditions could result in only
Historically, smaller Unmanned Aerial Systems (UAS), such as Class 2 RQ-1B Raven and Class 3 RQ-7Bv2 Shadow, have been restricted to not be approved to fly in icing conditions under the assumption that any ice accretion would cause an unacceptable risk of loss of the aircraft. However, interest exists in better understanding potential icing accretion on UAS to determine if less extreme icing conditions could result in only partial degradation and not total loss of the vehicle for the purpose of expanding approved flight envelopes. Icing accretion can be tested during a flight test, which is considered unacceptable due to lack of controlled conditions and risk to the UAS or in a controlled experiment, by using wind tunnel testing to evaluate a single icing condition. Cryogenic wind tunnel tests, such as those conducted at the National Aeronautical and Space Administration (NASA) Glenn Icing Research Tunnel (IRT), Cleveland, OH, as shown in figures 1 and 2, are prohibitively expensive
Brake squeal is a common phenomenon across all types of vehicles. It becomes prominent in the absence of other noise sources, as in the case of electric vehicles. Earlier simulation attempts date back to late nineties and early 2000s. Identification of unstable modes of the coupled system of brake rotor and pads, and occasionally some caliper components, was the primary goal. Simulating the rotation of the rotor along with squeezing of the pads was attempted in a multi-body dynamics tools with flexible representation of rotor and pads. Though this gave some insights into the dynamics of stopping mechanism, squeal required capturing the nonlinearities of the contact in a more rigorous sense. Also, efforts were made to capture noise from vibrations using boundary- and finite- element methods [1]. In this attempt at digitalizing a brake dynamometer, the author used a nonlinear implicit solver to mimic the dynamics and transient vibro-acoustic solver to convert transient vibrations to
The European Union’s Horizon 2020 programme has funded the SENS4ICE (Sensors for Certifiable Hybrid Architectures for Safer Aviation in Icing Environment) international collaboration flagship programme. Under this programme a number of different organizations have developed ice detection technologies, specifically aimed at providing information to differentiate between ‘classical’ Appendix C icing conditions and the larger droplets found in Appendix O icing. As a partner within the SENS4ICE project, AeroTex UK has developed an ice detection concept called the Atmospheric Icing Patch (AIP). The sensor utilizes a network of iso-thermal sensors to detect icing and differentiate between small and large droplet icing conditions. This paper discusses the development of the sensor technology with a focus on the outcomes of the flight testing performed on the Embraer Phenom 300 platform during early 2023. The work in the programme is built on previous studies performed by AeroTex UK into a
The term “3 inch ice shapes” has assumed numerous definitions throughout the years. At times it has been used to generally characterize large glaze ice accretions on the major aerodynamic surfaces (wing, horizontal stabilizer, vertical stabilizer) for evaluating aerodynamic performance and handling qualities after a prolonged icing encounter. It has also been used as a more direct criterion while determining or enforcing sectional ice shape characteristics such as the maximum pinnacle height. It is the authors’ observation that over the years, the interpretation and application of this term has evolved and is now broadly misunderstood. Compounding the situation is, at present, a seemingly contradictory set of guidance among (and even within) the various international regulatory agencies resulting in an ambiguous set of expectations for design and certification specialists. The focus of this paper is to provide a more complete and accurate historical accounting of “3 inch ice shapes
In this work, ice accretion is investigated on a fundamental level using a novel Eulerian phase field approach that captures the phase interface. This method, unlike the Allen-Cahn method, does not lead to spurious phase change (artificial mass loss). This method is also straightforward to implement and avoids normal vector reconstructions along the interface or ghost cells. Additionally, it has well-defined and novel stiffness constraints for accuracy and stability that define parameters in the model such as the kinetic coefficient μ and the interface regularization coefficient γ. An incompressible solver is constructed and used to verify the new method using an analytical Stefan problem solution in both 1D and 2D domains
Super-cooled large drops present serious threats to aviation safety and as a result, the problem has been addressed by the FAA with the additional icing certification requirement. SLD clouds often consist of bi-modal drop size spectra leading to great challenges when it comes to simulating and characterizing these conditions in situ and in icing wind tunnels. Legacy instrumentation for measuring drop size distributions and liquid water content has been challenged under these conditions. In this report, a high-resolution particle imaging instrument is described; this instrument addresses the need for measuring drop size distributions and liquid water content over a wide range of drop sizes (10 to 2500 μm or larger). A high-throughput megapixel digital camera is used to record shadow images of the particles. High-quality illumination of the particle field is provided with high-power LED illumination with driving electronics designed to provide pulse durations as short as 25ns with
The Current Icing Product (CIP; Bernstein et al. 2005) and Forecast Icing Product (FIP; Wolff et al. 2009) were originally developed by the United States’ National Center for Atmospheric Research (NCAR) under sponsorship of the Federal Aviation Administration (FAA) in the mid 2000’s and provide operational icing guidance to users through the NOAA Aviation Weather Center (AWC). The current operational version of FIP uses the Rapid Refresh (RAP; Benjamin et al. 2016) numerical weather prediction (NWP) model to provide hourly forecasts of Icing Probability, Icing Severity, and Supercooled Large Drop (SLD) Potential. Forecasts are provided out to 18 hours over the Contiguous United States (CONUS) at 15 flight levels between 1,000 ft and FL290, inclusive, and at a 13-km horizontal resolution. CIP provides similar hourly output on the same grid, but utilizes geostationary satellite data, ground-based radar data, Meteorological Terminal Air Reports (METARS), lightning data, and voice pilot
To support an industry wide response to an EASA proposed Special Condition regarding the threat of in-flight supercooled liquid water icing conditions at altitudes above FL300, Boeing 777 fleet data were used to estimate the frequency and severity of such icing occurrences. The data were from the calendar year 2019 and included ~ 950,000 airline revenue flights from around the world by multiple operators. The unique architecture of the Primary Ice Detection System (PIDS) on that model, in addition to robust meteorological data that was able to be correlated, afforded an opportunity to conservatively estimate the Total Water Exposure (TWE) and thus the Liquid Water Content (LWC) of the icing encounters captured at FL295 and above. This paper will outline the key methods used and present the findings
This study examines the impact of slip in aero-thermal conditions of supercooled large droplets (SLD) produced in an Icing Wind Tunnel (IWT) on the ice accretion characteristics. The study identifies potential biases in the SLD model development based on IWT data and numerical predictions that assume the SLD to be in aerothermal equilibrium with the IWT airflow. To obtain realistic temperature and velocity data for each droplet size class in the test section of the Braunschweig Icing Wind Tunnel (BIWT), a Lagrangian droplet tracking solver was used within a Monte Carlo framework. Results showed that SLDs experience considerable slips in velocity and temperature due to their higher inertia and short residence time in the Braunschweig IWT. Large droplets were found to be warmer and slower than the flow in the test section, with larger droplets experiencing larger aerothermal slips. To examine the impact of these slips, numerical ice accretion simulations were performed on a NACA 0012
Wind tunnel tests were performed on an 8.9-percent scale semispan wing in the Wichita State University 7x10-foot wind tunnel with simulated ice accretion shapes. Simulated ice shapes from large-droplet clouds, simple-geometry ice horn shapes, and simple-geometry spanwise ridge shapes typical of runback icing were tested. Three Reynolds number and Mach number combinations were tested over a range of angles of attack. Aerodynamic forces and moments were acquired from the tunnel balance and surface pressures and oil flow visualizations were acquired. This research supplements the Swept Wing Icing Program recently concluded by NASA, FAA, ONERA, and their partners by testing new ice shapes on the same wind tunnel model. Additional surface roughness was added to simulate large-droplet ice accretion aft of the highly three-dimensional primary ice shape, and it had little effect on the wing aerodynamic performance. Spanwise ridge simulations produced large increases in drag and small increases
Icing wind tunnel testing was performed as part of the Republic of Korea certification of the Light Civil Helicopter (LCH) for inadvertent flight in icing conditions. The test was aimed at the compliance demonstration of the engine and air intake with dry-media Inlet Barrier Filter (IBF) and was performed with an Arriel 2C2 engine in turbojet configuration. Testing took place at the sea level ambient pressure Large Climatic Wind Tunnel (CWT) at Rail Tec Arsenal (RTA) in Vienna, Austria, by an integrated test team comprising engineers from the Royal Netherlands Aerospace Centre (NLR), Korea Aerospace Industries (KAI), and Safran Helicopter Engines. The test matrix covered the AC29-2C Appendix C 10,000 ft icing envelope, as well as simulated ground icing conditions, considering both a clean and artificially contaminated IBF. Beyond the aforementioned certification conditions, exploratory testing was performed in conditions with Supercooled Large Droplets (SLD) and rain. The test set-up
The paper describes a tools’ suite able of analyzing numerically 3D ice-accretion problems of aeronautical interest. The methodology consists of linking different modules each of them performing a specific function inside the ice-simulation chain. It has been specifically designed from the beginning with multi-step capability in mind. Such a feature plays a key role when studying the dynamic evolution of the icing process. Indeed, the latter has the character of a multi-physic and time-dependent phenomenon which foresees a strong interaction of the air- and water fields with the wall thermodynamics. Our multi-layer approach assumes that the physical problem can be discretized by a series of pseudo-steady conditions. The simulation process starts with the automatic generation of a Cartesian three-dimensional mesh which represents the input for the immersed boundary (IB) RANS solver. Once obtained, the air-phase is used by the Eulerian tool to solve the transport of the water-phase on
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