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 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.
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 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
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
A fundamental understanding of the icing process for aircraft requires a more thorough analysis of the thermodynamics of supercooled droplet impingement. To better study such thermodynamic processes, a novel temperature sensor that functions within supercooled water and ice crystals was developed. The temperature sensor is non-intrusive and provides temperature and phase change information for both liquid water and solid ice. The temperature sensor is an optical sensor based on the luminophore pyranine. The use of pyranine allows for the measurement of spatially and temporally resolved temperature fields for icing applications. The sensitivity of the sensor is -9.2±0.1%/K for temperature measurement in the solid phase and 0.8±0.1%/K for the liquid phase. The performance of the sensor was demonstrated through a calibration process using spectral analysis, the observation of the melting process of a rectangular prism created from the luminescent ice, and the study of the temperature
Considerable amounts of water accumulate in aircraft fuel tanks due to condensation of vapor during flight or directly during fueling with contaminated kerosene. This can result in a misreading of the fuel meters. In certain aircraft types, ice blocks resulting from the low temperatures at high altitude flights or in winter time can even interfere with the nozzles of the fuel supply pipes from the tanks to the engines. Therefore, as part of the maintenance operations, water has to be drained in certain intervals ensuring that no remaining ice is present. In the absence of an established method for determining residual ice blocks inside, the aircraft operator has to wait long enough, in some cases too long, to start the draining procedure, leading potentially to an unnecessary long ground time. A promising technology to determine melting ice uses acoustic signals generated and emitted during ice melting. With acoustic emissions, mainly situated in the ultrasonic frequency range, a very
The paper describes the upgrade and validation of a Cartesian solver able of estimating the mass deposition of super-cooled large droplets (SLD) on aerodynamic surfaces. A decoupled approach is applied in which the air-flow field is first computed by using a RANS method and then passed to an Eulerian solver for obtaining the water-field. Both tools are based on a finite-volume (FV) approach based on locally refined Cartesian meshes and immersed boundaries. The use of semi-empirical models allow to take into account the primary effects due to splashing and bouncing of large droplets on aerodynamic surfaces. Here, we discuss the results of a numerical campaign with the aim of estimating the accuracy of two mass-deposition models on benchmarks from different experimental databases. Besides, for some cases we compare the present results with the ones obtained by using a body-conforming method.
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