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Journal Article

Uncertainty of the Ice Particles Median Mass Diameters Retrieved from the HAIC-HIWC Dataset: A Study of the Influence of the Mass Retrieval Method

2019-06-10
2019-01-1983
In response to the ice crystal icing hazard identified twenty years ago, aviation industry, regulation authorities, and research centers joined forces into the HAIC-HIWC international collaboration launched in 2012. Two flight campaigns were conducted in the high ice water content areas of tropical mesoscale convective systems in order to characterize this environment conducive to ice crystal icing. Statistics on cloud microphysical properties, such as Ice Water Content (IWC) or Mass Median Diameter (MMD), derived from the dataset of in situ measurements are now being used to support icing certification rulemaking and anti-icing systems design (engine and air data probe) activities. This technical paper focuses on methodological aspects of the derivation of MMD. MMD are estimated from PSD and IWC using a multistep process in which the mass retrieval method is a critical step.
Technical Paper

Snow Particle Characterization. Part B: Morphology Dependent Study of Snow Crystal 3D Properties Using a Convolutional Neural Network (CNN)

2023-06-15
2023-01-1486
This study presents the results of the ICE GENESIS 2021 Swiss Jura Flight Campaign in a way that is readily usable for ice accretion modelling and aims at improving the description of snow particles for model inputs. 2D images from two OAP probes, namely 2D-S and PIP, have been used to extract 3D shape parameters in the oblate spheroid assumption, as there are the diameter of the sphere of equivalent volume as ellipsoid, sphericity, orthogonal sphericity, and an estimation of bulk density of individual ice crystals through a mass-geometry parametrization. Innovative shape recognition algorithm, based on Convolutional Neural Network, has been used to identify ice crystal shapes based on these images and produce shape-specific mass particle size distributions to describe cloud ice content quantitatively in details. 3D shape descriptors and bulk density have been extracted for all the data collected in cloud environments described in the regulation as icing conditions.
Technical Paper

ONICE2D and DROP3D SLD Capability Assessment

2011-06-13
2011-38-0088
In 1994, an ATR-72 crashed at Roselawn, Indiana, USA. It has been speculated that accident was due to Supercooled Large Droplet (SLD) icing. This accident led to a modification of the regulation rules with the definition of the Appendix O which includes freezing drizzle and freezing rain icing conditions. The associated NPRM (Notice of Proposed Rule Making) has been distributed to industry for comments on 29th June 2010 and could be applicable by beginning 2012. In order to comply with this new rule, the simulation tools, as Acceptable Means of Compliance, have to be improved and validated for these conditions. The paper presents the work performed within Airbus to review, improve and assess simulation tools capability to accurately predict physical phenomena related to SLD. It focuses in particular on splashing and bouncing phenomena which have been highlighted as the first order effects.
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