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Assessment of Noncommercial Icing Prediction Capabilities for Army Applications

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.

Figure 1. The common research model in IRT was involved in this research project.

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 and time consuming to evaluate a wide array of icing conditions on multiple UAS. The ability to simulate aircraft icing using computational methods permits evaluation across a number of vehicles and icing scenarios for a fraction of the cost and time.

Figure 2. Ice accretion on a powered force model rotor in IRT.

The aerospace scientific community has recently developed interest in ice prediction capabilities within a computational framework. In 2021, the first American Institute for Aeronautics and Astronautics (AIAA) Ice Prediction Workshop was held in conjunction with the AIAA Aviation Forum [2]. Twenty participants from academia, industry, and government evaluated ice accretion on Two-Dimensional (2-D) and Three-Dimensional (3-D) geometries where experimental ice shapes were publicly available by using a wide range of solvers to assess the state-of-the-art in icing prediction tools. Kestrel and Helios, the Computational Research and Engineering Acquisition Tools and Environments (CREATE) Air Vehicle (AV) simulation tools for fixed-wing and rotorcraft evaluation, do not have ice prediction capabilities.

DEVCOM AvMC conducted an assessment of NASA-developed icing prediction codes for potential application to Army UAS aerodynamic modeling predictions. Current capabilities are considered to be lacking for DEVCOM AvMC use due to 2-D formulation and a lack of CFD-based streamlined iterative solution methods. NASA is planning to address these issues with the public release of GlennICE. At this time, Commercial Off-The-Shelf (COTS) codes are considered to be the best path forward.

This work was performed by Amanda G. Kolpitcke, Kevin C. Losser, and Zachary M. Hall for the U.S. Army Combat Capabilities Development Command. For more information, download the Technical Support Package (free white paper) below. FCDDAMS-2301

Topics:
Aeronautics Aerospace Aircraft Aircraft Aviation Icing and ice detection Safety testing and procedures Unmanned Air Vehicles/Systems (UAV/UAS) Unmanned aerial vehicles Vehicles Wind tunnel tests
This Brief includes a Technical Support Package (TSP).
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Assessment of Noncommercial Icing Prediction Capabilities for Army Applications

(reference FCDDAMS-2301) is currently available for download from the TSP library.

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    Aerospace & Defense Technology Magazine

    This article first appeared in the December, 2023 issue of Aerospace & Defense Technology Magazine (Vol. 8 No. 7).

    Read more articles from this issue here.

    Read more articles from the archives here.

    Overview

    The technical report titled "Assessment of Noncommercial Icing Prediction Capabilities for Army Applications," authored by Amanda G. Kolpitcke, Kevin C. Losser, and Zachary M. Hall, evaluates various noncommercial tools and methodologies for predicting icing, which is critical for Army aviation and missile operations. Released in June 2023, the report is part of the efforts by the Software, Simulation, Systems Engineering and Integration Directorate of the Combat Capabilities Development Command Aviation & Missile Center.

    The document begins with an introduction to the importance of accurate icing prediction in military applications, where adverse weather can significantly impact mission success. It outlines the current state-of-the-art in icing prediction technologies, including specific tools such as LEWICE, GlennICE, and FENSAP-ICE, which are discussed in detail regarding their capabilities and limitations.

    The report is structured into several key sections. The first part reviews the existing computational tools for icing prediction, highlighting their operational effectiveness and areas for improvement. The authors then present the goals of the DEVCOM AVMC (Development Command Aviation and Missile Center) in enhancing icing prediction capabilities, emphasizing the need for more reliable and accurate models.

    Subsequent sections delve into two-dimensional and three-dimensional icing prediction methodologies, including potential-based and computational fluid dynamics (CFD)-based approaches. Each method is assessed for its capabilities, limitations, and applicability to Army needs. The report also discusses future work and advancements in tools like GlennICE and FENSAP-ICE, suggesting directions for further research and development.

    The conclusions drawn from the assessment highlight the necessity for continued investment in noncommercial icing prediction technologies to ensure that Army operations can effectively mitigate the risks posed by icing conditions. The report underscores the importance of integrating advanced predictive models into operational planning and decision-making processes.

    Overall, this technical report serves as a comprehensive resource for understanding the current landscape of icing prediction technologies and their relevance to military applications, providing insights that could lead to improved operational readiness and safety in adverse weather conditions.

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