Super-cooled large droplets (SLDs) present a unique experimental challenge for conventional horizontal spray icing wind tunnels due to gravity effects on the droplets and coalescence between the particles. Limited ice accretion shapes in this regime exist, making the verification of modeling tools and the validation of the applicability of scaling methods difficult. Furthermore, SLD accretions often occur in nature in a bimodal icing cloud, providing an additional facility challenge related to the reproduction of such clouds. The objective of this research effort is to expand the available SLD dataset to assess the capability of an existent ice accretion tool (LEWICE 2D) to predict such ice shapes. The effort also attempts to further verify the applicability of ice shape scaling methods in the SLD regime. Finally, bimodal SLD cloud reproduction and ice shape prediction is investigated. The Adverse Environment Rotor Test Stand (AERTS) at Penn State was assessed as an alternative facility to icing wind tunnels for ice accretion testing in the SLD regime. Laser diffraction measurement data analysis demonstrated that the icing nozzles used in the facility can produce an SLD within $\pm 11.9\%$ of requested values. Techniques to measure liquid water content (LWC) in the facility are also presented. LWC was controllable within $\pm 16\%$. LEWICE ice shapes predicted were obtained using legacy empirical heat transfer models as well as an updated empirical heat transfer function developed at Penn State. When comparing modeling results to experimental shapes from the literature and the AERTS, LEWICE with the updated heat transfer function provided stagnation thicknesses within 5.6% of experimental values and horn protrusion within 16%. The modified Ruff scaling ice shape method was effective in the SLD regime, providing a mean deviations of 2.5% between reference and scaled ice shape characteristics. Finally, characteristics of a bimodal ice shapes were predicted by LEWICE. When comparing modeling results to experimental shapes, LEWICE with the updated heat transfer function provided predictions within 10.3% of the experimental stagnation thickness measured. It also provided horn angles with discrepancies of less than 12%. Icing limits of a bimodal icing cloud were observed to be that of a single mode SLD cloud of the same MVD, with an overall deviation of $\pm 12.1\%$ when comparing the experimental database to LEWICE.

由于重力对液滴的影响以及颗粒之间的聚结，过冷的大液滴（SLD）对常规的水平喷雾结冰风洞提出了独特的实验挑战。在这种情况下存在有限的积冰形状，因此难以验证建模工具和验证缩放方法的适用性。此外，自然界中常会在双峰结冰云中发生SLD增生，从而带来了与此类云的繁殖有关的附加设施挑战。这项研究工作的目的是扩展可用的SLD数据集，以评估现有的积冰工具（LEWICE 2D）预测这种冰形状的能力。这项工作还试图进一步验证冰形状缩放方法在SLD方案中的适用性。最后，研究了双峰SLD云的复制和冰形预测。宾夕法尼亚州立大学的不良环境转子试验台（AERTS）被评估为在SLD制度中为风洞结冰进行冰积土测试的替代设施。激光衍射测量数据分析表明，该设施中使用的结冰喷嘴可在$\pm 11.9％$要求的值。还介绍了用于测量设施中液态水含量（LWC）的技术。LWC在$\pm 16％$。使用传统的经验传热模型以及在宾夕法尼亚州开发的更新的经验传热函数，获得了LEWICE预测的冰形状。当将建模结果与文献和AERTS的实验形状进行比较时，具有更新后的传热功能的LEWICE提供的滞流厚度在实验值的5.6％之内，而喇叭角在16％之内。改进的Ruff结垢冰形方法在SLD模式下有效，在参考和结垢冰形特征之间的平均偏差为2.5％。最后，LEWICE预测了双峰冰形的特征。当将模型结果与实验形状进行比较时，具有更新传热功能的LEWICE提供的预测值在测得的实验停滞厚度的10.3％以内。它还提供了小于12％的差异的喇叭角。观察到双峰结冰的结冰极限是相同MVD的单模SLD云的结冰极限，总偏差为$\pm 12.1％$ 比较实验数据库和LEWICE时。