Additional Results Of Ice Accretion Scaling At Sld Conditions

Author : Edward Rocco
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (992 download)

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Download or read book Super-cooled Large Droplet Experimental Reproduction, Ice Shape Modeling, and Scaling Law Assessment written by Edward Rocco and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The simulation of icing conditions is sought for potential aircraft certification,and therefore test facilities that can generate conditions able to reproduce theice accretion phenomena are necessary. The icing conditions that aircraft endureare outlined in The Federal Aviation Administration regulations for airframe icingas described in Federal Aviation Regulation (FAR) 14 Part 25 Appendix C andPart 33 Appendix O. Multiple icing facilities exist for FAR 14 Part 25 AppendixC conditions, however developing facilities that can replicate super-cooled largedroplet (SLD) clouds and bi-modal SLD clouds (cloud with concentrations ofAppendix C and SLD conditions often observed in flight test) related to AppendixO is difficult due to the shortcomings of horizontal wind tunnels when generatingSLD particles (gravity effects on the large droplets). In the presented research effort,The Adverse Environment Rotor Test State (AERTS) at Penn State is assessedas a low-cost alternative to horizontal wind tunnels for the reproduction of SLDconditions. Current ice modeling techniques are also investigated for SLD regimes,existing Appendix C ice scaling techniques are evaluated in the SLD regime, andbi-modal SLD cloud impingement limits and ice shapes are investigated. Mentionedevaluation of ice accretion modeling tools is conducted via ice shape correlationsbetween experimental result and predictions.Firstly, the AERTS facility was calibrated in the SLD regime. Median VolumeDiameter (MVD) and Liquid Water Content (LWC) are the test parametersnecessary to calibrate for the reproduction of flight conditions. Phase DopplerInterferometer (PDI) data of cloud MVD was used to demonstrate that the existingnozzle spray system can provide relative MVD control of an SLD cloud. LWCcalibration is generally achieved in an icing facility utilizing a rime ice shape toensure freezing fractions close to unity (all encountered droplets freeze on impactwithout splashing or flowing aft). A rime shape in the SLD regime is unachievabledue to large particle splashing, and thus the effect splashing has on effective collectionefficiency must be considered in the LWC calculation. LEWICE, the nationsstandard ice prediction software, contains a droplet splashing model based on lowspeed test data (20 m/s). The LEWICE splashing model, coupled with a literaturebased empirical LWC adjustment, necessary due to test speeds beyond the 20 m/slimit, was utilized to effectively calibrate the LWC in the AERTS facility within16%.Secondly, ice shape modeling software known to be valid in Appendix C conditionswere assessed in the SLD regime. LEWICE, with and without an improvedheat transfer model (known as the AERTS prediction) was compared to six (6)AERTS test cases, three (3) of which had literature reference shapes. Overall, theAERTS test cases and literature reference case shapes were similar, but differencesin horn formation were observed. Overall, the ice prediction modeling tools werein agreement with the AERTS test cases, and the AERTS prediction providedimprovements in shape prediction when compared to LEWICE. When comparingthe deviation of the generated ice shapes to the prediction models, the AERTSprediction, on average, provided a 28.4% ice stagnation thickness prediction improvementsand 24.1% horn angle prediction improvements to LEWICE predictions.This is consistent with the prediction performance of LEWICE when including theheat transfer model improvements that were observed in previous, Appendix Ccondition, research efforts.Thirdly, ice condition scaling laws known to be valid in the Appendix C regimewere evaluated in SLD conditions. The modified Ruff scaling method was previouslytested at the NASA Glenn Icing Research Tunnel for SLD, but investigation of thescaling laws in other test facilities was requested to further understand SLD scaling.The results of this research, comparing six (6) scaling tests with the six (6) SLDtests previously mentioned, suggests that the ice scaling laws apply in the SLDregime as previously discussed in the literature. The mean deviation of stagnationthickness, horn angle, and horn protrusion of scale to reference test cases wereobserved to be 1.60%, 4.45%, and 1.46%, respectively. Furthermore, scalabilitydid not appear to degrade despite a large range of MVD, LWC, temperature, andspeed tested.Finally, a bi-modal cloud was studied in the SLD regime. The AERTS facilitywas modified with two independent cloud spray systems to generate a bi-modalcloud. In an SLD cloud, ice impingement limits are farther aft than in Appendix Cconditions, which is of concern for de-icing system design. Therefore, impingementlimit behavior of bi-modal clouds often observed in nature, must be understood.Impingement limits are defined by collection efficiency; a function of particletrajectory and thus MVD. Therefore, the impingement limit of a bi-modal SLD cloud should be that of a unimodal SLD cloud of the same MVD. To assess theimpingement limit trend, four (4) conditions resulting in sixteen (16) tests and fortyeight(48) data points were executed. The SLD impingement limit being that of thebi-modal cloud was observed experimentally, with a -1.58% 8.44% mean deviationof the upper impingement limit to the LEWICE prediction of the SLD impingementlimit, and a -11.0% 8.41% mean deviation of the lower impingement limit to theLEWICE prediction. When observing shape trends in the bi-modal scenario, the iceshape qualities transitioned from the 0% SLD to the 100% SLD shape consistentlyas SLD cloud content was increased. When comparing the deviation of four(4)generated ice shapes to the prediction models, the AERTS prediction forecast, onaverage, 21.4% ice stagnation thickness prediction improvements, and 18.5% hornangle prediction improvements when compared to LEWICE prediction deviations.vi.