Super-cooled Large Droplet Experimental Reproduction, Ice Shape Modeling, and Scaling Law Assessment
Open Access
- Author:
- Rocco, Edward Thomas
- Graduate Program:
- Aerospace Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- June 13, 2016
- Committee Members:
- Jose Palacios, Thesis Advisor/Co-Advisor
Philip J. Morris, Committee Member - Keywords:
- SLD Accretion
Super-cooled Large Droplets
Aircraft Icing
Ice Scaling Methods
Ice Accretion Testing - Abstract:
- The simulation of icing conditions is sought for potential aircraft certification, and therefore test facilities that can generate conditions able to reproduce the ice accretion phenomena are necessary. The icing conditions that aircraft endure are outlined in The Federal Aviation Administration regulations for airframe icing as described in Federal Aviation Regulation (FAR) 14 Part 25 Appendix C and Part 33 Appendix O. Multiple icing facilities exist for FAR 14 Part 25 Appendix C conditions, however developing facilities that can replicate super-cooled large droplet (SLD) clouds and bi-modal SLD clouds (cloud with concentrations of Appendix C and SLD conditions often observed in flight test) related to Appendix O is difficult due to the shortcomings of horizontal wind tunnels when generating SLD particles (gravity effects on the large droplets). In the presented research effort, The Adverse Environment Rotor Test State (AERTS) at Penn State is assessed as a low-cost alternative to horizontal wind tunnels for the reproduction of SLD conditions. Current ice modeling techniques are also investigated for SLD regimes, existing Appendix C ice scaling techniques are evaluated in the SLD regime, and bi-modal SLD cloud impingement limits and ice shapes are investigated. Mentioned evaluation of ice accretion modeling tools is conducted via ice shape correlations between experimental result and predictions. Firstly, the AERTS facility was calibrated in the SLD regime. Median Volume Diameter (MVD) and Liquid Water Content (LWC) are the test parameters necessary to calibrate for the reproduction of flight conditions. Phase Doppler Interferometer (PDI) data of cloud MVD was used to demonstrate that the existing nozzle spray system can provide relative MVD control of an SLD cloud. LWC calibration is generally achieved in an icing facility utilizing a rime ice shape to ensure freezing fractions close to unity (all encountered droplets freeze on impact without splashing or flowing aft). A rime shape in the SLD regime is unachievable due to large particle splashing, and thus the effect splashing has on effective collection efficiency must be considered in the LWC calculation. LEWICE, the nation’s standard ice prediction software, contains a droplet splashing model based on low speed test data (20 m/s). The LEWICE splashing model, coupled with a literature based empirical LWC adjustment, necessary due to test speeds beyond the 20 m/s limit, was utilized to effectively calibrate the LWC in the AERTS facility within 16%. Secondly, ice shape modeling software known to be valid in Appendix C conditions were assessed in the SLD regime. LEWICE, with and without an improved heat transfer model (known as the AERTS prediction) was compared to six (6) AERTS test cases, three (3) of which had literature reference shapes. Overall, the AERTS test cases and literature reference case shapes were similar, but differences in horn formation were observed. Overall, the ice prediction modeling tools were in agreement with the AERTS test cases, and the AERTS prediction provided improvements in shape prediction when compared to LEWICE. When comparing the deviation of the generated ice shapes to the prediction models, the AERTS prediction, on average, provided a 28.4% ice stagnation thickness prediction improvements and 24.1% horn angle prediction improvements to LEWICE predictions. This is consistent with the prediction performance of LEWICE when including the heat transfer model improvements that were observed in previous, Appendix C condition, research efforts. Thirdly, ice condition scaling laws known to be valid in the Appendix C regime were evaluated in SLD conditions. The modified Ruff scaling method was previously tested at the NASA Glenn Icing Research Tunnel for SLD, but investigation of the scaling 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) SLD tests previously mentioned, suggests that the ice scaling laws apply in the SLD regime as previously discussed in the literature. The mean deviation of stagnation thickness, horn angle, and horn protrusion of scale to reference test cases were observed to be 1.60%, 4.45%, and 1.46%, respectively. Furthermore, scalability did not appear to degrade despite a large range of MVD, LWC, temperature, and speed tested. Finally, a bi-modal cloud was studied in the SLD regime. The AERTS facility was modified with two independent cloud spray systems to generate a bi-modal cloud. In an SLD cloud, ice impingement limits are farther aft than in Appendix C conditions, which is of concern for de-icing system design. Therefore, impingement limit behavior of bi-modal clouds often observed in nature, must be understood. Impingement limits are defined by collection efficiency; a function of particle trajectory 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 the impingement limit trend, four (4) conditions resulting in sixteen (16) tests and fortyeight (48) data points were executed. The SLD impingement limit being that of the bi-modal cloud was observed experimentally, with a -1.58% ±8.44% mean deviation of the upper impingement limit to the LEWICE prediction of the SLD impingement limit, and a -11.0% ±8.41% mean deviation of the lower impingement limit to the LEWICE prediction. When observing shape trends in the bi-modal scenario, the ice shape qualities transitioned from the 0% SLD to the 100% SLD shape consistently as SLD cloud content was increased. When comparing the deviation of four(4) generated ice shapes to the prediction models, the AERTS prediction forecast, on average, 21.4% ice stagnation thickness prediction improvements, and 18.5% horn angle prediction improvements when compared to LEWICE prediction deviations. vi