The successful demonstration of long-lived nitric oxide (NO) fluorescence for molecular tagging velocimetry (MTV) measurements is described in this Letter. Using 1 + 1 resonance-enhanced multiphoton ionization (REMPI) of NO at a wavelength near 226 nm, targeting the overlapping Q₁(7) and Q₂₁(7) lines of the A–X (0, 0) electronic system, the lifetime of the NO MTV signal was observed to be approximately 8.6 µs within a 100-Torr cell containing 2% NO in nitrogen. This is in stark contrast to the commonly reported single photon NO fluorescence, which has a much shorter calculated lifetime of approximately 43 ns at this pressure and NO volume fraction. While the shorter lifetime fluorescence can be useful for molecular tagging velocimetry with single laser excitation within very high-speed flows at some thermodynamic conditions, the longer lived fluorescence shows the potential for an order of magnitude more accurate and precise velocimetry, particularly within lower speed regions of hypersonic flow fields such as wakes and boundary layers. The physical mechanism responsible for the generation of this long-lived signal is detailed. Furthermore, the effectiveness of this technique is showcased in a high-speed jet flow, where it is employed for precise flow velocity measurements.

The unsteady runback behavior of wind-driven runback water film (WDRWF) flows over aircraft surfaces has a significant impact on the aircraft icing process, one of the most significant aviation hazards in cold weather. The limited understanding of the complex multiphase interactions between freestream airflow, water film motion, and solid airframe surface makes conventional theoretical/numerical methods unable to precisely simulate WDRWF flow. Machine learning-based techniques can accurately capture complex physics using data, making it an attractive alternative to conventional methods. In this study, machine learning methods are used to predict the evolution of the front contact point (FCP) of WDRWF flow and film thickness distribution (FTD) of WDRWF flow. For FCP prediction, the performance of the Light Gradient-Boosting Machine (LightGBM) and Multi-Layer Perceptron is compared quantitatively. They perform well in capturing intermittent and smooth features, respectively. For the prediction of the spatial-temporal evolution of FTD, a computationally efficient deep neural network architecture named ConvLSTM-AutoEncoder was developed, which predicts a future FTD based on a sequence of FTDs in the past. The robustness of the ConvLSTM-AutoEncoder model to noisy input FTD is demonstrated. The generalizability of the three models is evaluated by applying the trained models to unexplored datasets. Based on the proposed techniques' generalizability, robustness, and computational efficiency, machine learning-based methods are demonstrated to be powerful tools in predicting the complex unsteady characteristics of the multiphase WDRWF flows.

In-flight icing presents a critical safety hazard for unmanned aerial vehicles (UAVs), resulting in ice accumulation on propeller surfaces that compromise UAV aerodynamic performance and operational integrity. While hybrid anti-/de-icing systems (i.e., combining active heating with passive superhydrophobic coatings) have been developed recently to efficiently address this challenge, conventional active heating sub-systems utilized in the hybrid anti-/de-icing systems face significant limitations when applied to curved geometries of UAV propeller blades. This necessitates the development of innovative self-heating superhydrophobic coatings that can conform perfectly to complex surface topographies. Carbon-based electrothermal coatings, particularly those incorporating graphite and carbon nanotubes, represent a promising approach for ice mitigation applications. This study presents a comprehensive experimental investigation into the development and optimization of a novel self-heating carbon nanotube (CNT)-based superhydrophobic coating specifically designed for UAV icing mitigation. The coating’s anti-/de-icing efficacy was evaluated through a comprehensive experimental campaign conducted on a rotating UAV propeller under typical glaze icing conditions within an advanced icing research tunnel facility. The durability of the coating was also examined in a rain erosion test rig under the continuous high-speed impingement of water droplets. Experimental results demonstrate the successful application of the proposed sprayable self-heating superhydrophobic coating in UAV icing mitigation, providing valuable insights into the viability of CNT-based electrothermal coatings for practical UAV icing protection. This work contributes to the advancement of icing protection technologies for un-manned aerial systems operating in adverse weather conditions.

While aircraft icing poses a critical safety risk and can degrade the aerodynamic performance of aircraft significantly, accurate prediction of aircraft icing events requires precise characterization of atmospheric icing conditions, particularly the properties of airborne supercooled water droplets (SWDs) and ice crystals in icing clouds. This study introduces the development of, to our knowledge, a novel digital inline holography (DIH) system for simultaneous characterization and classification of airborne SWDs and ice crystals for aircraft icing studies. The measurement accuracy of the DIH system was analyzed based on a quantitative comparison of the DIH measurements against the nominal values of the standard particles certificated by the National Institute of Standards and Technology (NIST). By leveraging the unique Icing Research Tunnel of Iowa State University (ISU-IRT), the unique capacity of the DIH system is demonstrated to achieve in situ measurements to characterize the airborne SWDs and ice crystals in terms of median volume diameter (MVD), liquid water content (LWC), ice water content (IWC), and their size and shape distributions under typical atmospheric icing conditions within aircraft inflight icing envelopes. A shape-based nondimensional parameter (i.e., circularity) is demonstrated for the first time to successfully differentiate the airborne SWDs from ice crystals in mixed-phase icing clouds for better monitoring and forecasting icing events, thus ensuring safer and more efficient operations of aircraft in cold climates.