Aviation Hazard Research Articles

How Pilot Fatigue Affects Operational Flight Risk in Scheduled Commercial Aviation

Abstract: Pilot fatigue remains a hazard in scheduled commercial aviation, contributing to 31 aircraft accidents between 2005 and 2022, according to International Air Transport Association (IATA) (2023) . Therefore, this study further investigated fatigue and its impact on operational flight risk in commercial aviation. More than 16,000 safety reports were analyzed using mixed methods. The results showed that fatigue-related occurrences were more likely to be reported confidentially. Factors contributing to an increased risk of fatigue included pilots’ personal factors, inadequate rest periods, or poor hotel environments. Fatigue was observed in 6% of all safety-related occurrences analyzed, but these events consumed 9% of the airlines’ risk budget. Further empirical research is required focusing on the influence of pilots’ personal factors on fatigue risk.

Overview of the Space Weather Impact on the Navigation and Communication Systems

This article summarizes and analyzes the space weather impact on the Earth´s surface, aviation – especially navigation and communication aids, satellite functions ensuring communication, and GPS function. Forecasting the geomagnetic storms, solar radiation storms, radio blackouts, X-ray flux, and others is difficult due to the high variability of the space weather conditions and sun activity. It can change every hour and the exact space weather conditions and the impact on aviation for the future are hard to predict. This article shows some predictions and historical data regarding space weather activity, where the stated values were collected from the NOAA page. The article states the daily maximums or the maximums of predicted phenomena activity. Because of the complexity and the facts mentioned above, the historical data and predictions show the summary and most important phenomena of the day. The article consists of 3 main chapters: 1. Space Weather 2. Space Weather Impact on Technological Vulnerability 3. Space Weather Condition Overview in Data. The last chapter in this article is a conclusion that consists of and mentions some information about the used alternative methods when a GPS lapse occurs. Even though there are some alternate systems, but still using satellite-based navigation and communication aids is still the first option, and space weather events highly affect the systems and are a big hazard for aviation.

Ice accretion compositions in ice crystal icing

Ice crystal icing is a major hazard in aviation from the perspectives of aerodynamic deterioration, flight safety and operability. To counter this challenge, existing ice prediction tools require more accurate modeling of sticking efficiency of glaciated ice crystals on heated substrates. As part of the present study, icing wind tunnel experiments have been performed to quantify the volumetric liquid water fraction in the ice layers, the sticking efficiency of impacting ice crystals and the maximum thickness of ice layers accreted on a dedicated test article for a wide range of experimental conditions. To achieve this objective, two measurement techniques, calorimetry and capacitive measurements, have been employed to quantify the average liquid water content and the distribution of liquid in the ice layers. The experiments show that when switching from negative to positive wet bulb temperatures, the sticking efficiency and maximum thickness of ice layers increase significantly. A similar trend is observed for increasing substrate heat flux, which results in an increasing liquid fraction in the ice layer measured by calorimetry, as well as increasing sticking efficiency and maximum thickness of the ice layers. Increasing the ice water content does not have a significant influence on the liquid content, but leads to more pronounced erosion effects. The conducted experiments provide an unprecedented insight into the liquid distribution in ice accretions and its effect on the sticking efficiency. These results provide a sound basis for more accurate modeling of sticking efficiency and enhances the understanding of ice accretion processes.

Identifying and characterising trapped lee waves using deep learning techniques

Trapped lee waves, and resultant turbulent rotors downstream, present a hazard for aviation and land‐based transport. Though high‐resolution numerical weather prediction models can represent such phenomena, there is currently no simple and reliable automated method for detecting the extent and characteristics of these waves in model output. Spectral transform methods have traditionally been used to detect and characterise regions of wave activity in model and observational data; however, these methods can be slow and have their limitations. Machine‐learning (ML) techniques offer a new and potentially fruitful method of tackling this problem. We demonstrate that a deep‐learning model can be trained to accurately recognise and label coherent regions of lee waves from vertical velocity data on a single level from a high‐resolution numerical weather prediction (NWP) model. Using transfer learning, wave characteristics (wavelength, orientation, and amplitude) can be extracted from the trained segmentation model. The use of synthetic wave fields with prescribed wave characteristics makes this transfer learning possible without the need to characterise real complex wave fields. Addition of noise to the synthetic data makes the models more robust when applied to more complex and noisy NWP data. The collection of trained models produced provides a valuable tool to investigate the prevalence and nature of lee wave activity, as well as a new way for forecasters to detect resolved waves. The deep‐learning model was more capable and quicker at detecting and characterising lee waves than a spectral technique was. This work is just one example of how already established ML techniques can be used to detect and characterise complex weather phenomena from NWP model output and observational data, and how the careful use of synthetic data can reduce the requirements for large volumes of hand‐labelled training data for ML models.

A machine learning study to predict wind-driven water runback characteristics

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.

Integrating Ice Protection and Noise Abatement Systems for Aircraft Application: A Review

Aircraft icing remains a key aviation hazard as the global fleet of aircraft in various sectors continues to expand, posing a serious threat to flight safety. As previously stated, the growth of this type of aircraft has been accompanied by an increase in noise levels, and aircraft is reportedly the second most bothersome noise source after traffic. However, integrating an acoustic liner with anti-icing techniques on the leading edge of a nacelle would not efficiently eliminate forward radiated noise and improve the thermal performance of the anti-icing system. Hence, it is of the utmost importance to research the integration of ice protection and noise abatement systems for aircraft applications. This review discusses the integration of ice accretion and noise abatement systems in aircraft applications. The prominence of this review is to explain significant features such as ice protection systems, Computational Fluid Dynamics in ice protection, noise abatement systems, and the integration of ice protection systems and noise abatement systems wherever they are described.

Airspace Contamination by Volcanic Ash from Sequences of Etna Paroxysms: Coupling the WRF-Chem Dispersion Model with Near-Source L-Band Radar Observations

Volcanic emissions (ash, gas, aerosols) dispersed in the atmosphere during explosive eruptions generate hazards affecting aviation, human health, air quality, and the environment. We document for the first time the contamination of airspace by very fine volcanic ash due to sequences of transient ash plumes from Mount Etna. The atmospheric dispersal of sub-10 μm (PM10) ash is modelled using the WRF-Chem model, coupled online with meteorology and aerosols and offline with mass eruption rates (MERs) derived from near-vent Doppler radar measurements and inferred plume altitudes. We analyze two sequences of paroxysms with widely varied volcanological conditions and contrasted meteorological synoptic patterns in October–December 2013 and on 3–5 December 2015. We analyze the PM10 ash dispersal simulation maps in terms of time-averaged columnar ash density, concentration at specified flight levels averaged over the entire sequence interval, and daily average concentration during selected paroxysm days at these flight levels. The very fine ash from such eruption sequences is shown to easily contaminate the airspace around the volcano within a radius of about 1000 km in a matter of a few days. Synoptic patterns with relatively weak tropospheric currents lead to the accumulation of PM10 ash at a regional scale all around Etna. In this context, closely interspersed paroxysms tend to accumulate very fine ash more diffusively at a lower troposphere and in stretched ash clouds higher up in the troposphere. Low-pressure, high-winds weather systems tend to stretch ash clouds into ~100 km wide clouds, forming large-scale vortices 800–1600 km in diameter. Daily average PM10 ash concentrations commonly exceed the aviation hazard threshold, up to 1000 km downwind from the volcano and up to the upper troposphere for intense paroxysms. Vertical distributions show ash cloud thicknesses in the range 0.7–3 km, and PM10 sometimes stagnates at ground level, which represent a potential health hazard.

A Methodology for Classifying Attractive Sources Related to Airport Birdstrike by Using Geospatial Tools

The urban and rural environment around airport grounds, being an attractive site for wildlife, is subjected to special attention in relation to the risk of wildlife collision with aircrafts according to specific aviation norms. Therefore, investigation into significant attractive sources in airport surroundings is needed to contribute to ‘wildlife strike’ monitoring in order to activate efficient countermeasures for limitation and control in view of aviation safety while at the same time increasing wildlife protection. On this basis, the study was focused on attractive sites related to the birdstrike hazard in airport surroundings. The methodology described in this paper investigated the spatial distribution characteristics of bird strike influencing factors with an open-access approach to data handling. Remote sensing imagery and open-source GIS tools were utilised to apply suitability analysis to the surfaces involved, such as airport obstacle limitation surfaces, protected areas, vegetation, and water bodies. The methodology was applied to a case study of the airport of Catania, Italy. The results showed that application of geospatial tools to suitability analysis allowed for the identification of areas that have the greatest influence on the birdstrike hazard for aviation. This approach has made it possible to analyse these areas from the point of view of airport safety and the monitoring of ecological areas and corridors of high naturalistic value in order to protect them, providing a contribution toward sustainable management of the birstrike issue.

Assessment of the Impacts of Thunderstorm on Flight Operations at Murtala Mohammed International Airport, Ikeja, Lagos State, Nigeria

The inefficiency of the aviation industry and the persistent rise in aviation hazards have been linked to weather phenomena. As a result, researchers are looking for better solutions to the problem. The study examined the impact of thunderstorms on flight operations at Murtala Mohammed International Airport, Lagos. The data on thunderstorms and flight operations were sourced from Nigerian Meteorological Agency (NiMet) and Nigerian Airspace Management Agency (NAMA) respectively. In order to meet the research target, descriptive statistics (mean, standard deviation, and charts) and inferential statistics (Pearson’s Product Moment Correlation (PPMC) and Regression) were used. The significance level for all inferential analyses was set at 5% (0.05). The study revealed that 77.4% of thunderstorms occurred during the rainy season (April-October) while 22.6% occurred during the dry season (November-March). It also revealed some fluctuating movements of a thunderstorm in the study area. According to the findings, thunderstorms occur most frequently at the airport in June and less frequently in January and December. The study also discovered that thunderstorms at the airport are positively and significantly related to flight delays and cancellations, while the association between flight diversions and thunderstorm occurrence is positive but statistically insignificant. Furthermore, flight delays, flight diversions, and flight cancellations interact positively among themselves. The regression result of the study revealed that a 1% increase in thunderstorm occurrence leads to a 19.4% increase in flight delay, a 7.1% increase in flight cancellation, and a 4.3% increase in flight diversion. As a result, the study presented various regression models that may be utilized to make predictions. The study proposes consistent thunderstorm observation at the airport and steady forecasts using the regression models, based on the findings. However, it further recommends that pilots, air traffic controllers, and meteorologists be trained and retrained so that they can provide better and more efficient services.

Determining the height of deep volcanic eruptions over the tropical western Pacific with Himawari-8

Volcanic eruptions are significant aviation hazards due to the formation of airborne volcanic ash clouds. Further, deep eruptions that reach the upper troposphere and lower stratosphere may have significant weather and climate impacts. A key variable for both dispersion model forecasting for aviation hazards and understanding climate impacts is the volcanic plume height. This work presents a method to quickly and reliably estimate the maximum plume heights of volcanic eruptions that interact with the tropical tropopause layer in the tropical western Pacific region. The method uses infrared (11.2 μm) data from Himawari-8 to identify ‘stratospheric warm spots’ in optically thick portions of the eruption cloud top by searching for reversals in the local-brightness temperature gradient. The brightness temperature of these warm spots is converted to height using seasonal stratospheric reference temperature profiles derived from 20 years of radiosonde data from 17 stations spread throughout the western Pacific. An approach for estimating the height of cold ‘overshooting tops’ is also adopted. Based on the radiosonde data, estimates of the uncertainty in the plume height depend on the height and range within 0.5–5.0 km. A case study of the 19 December 2021 eruption of Hunga Tonga-Hunga Ha’apai demonstrates the technique. The heights are robustly determined with this simple technique and compare well with height estimates of eruptions in the literature that use more complex satellite techniques.

Development of Simulation Models Supporting Next-Generation Airborne Weather Radar for High Ice Water Content Monitoring

In this article, a method of extending airborne weather radar modeling to incorporate high-ice-water-content (HIWC) conditions has been developed. A novel aspect is incorporating flight test measurement data, including forward-looking radar measurements and in situ microphysics probes data, into the model and part of the evaluations of modeling. The simulation models assume a dual-polarized, airborne forward-looking radar, while for single-polarized operations, the index-of-dispersion is included as a helpful indicator for HIWC detection. The radar system simulation models are useful for design evaluations for the next generation of airborne aviation hazard monitoring and incorporate HIWC hazard detection algorithms. Example applications of the simulator, such as hazard detection based on the simulated HIWC flight encounter scenarios based on specific numeric weather prediction (NWP) model outputs, are discussed.

Icing wind tunnel measurements of supercooled large droplets using the 12 mm total water content cone of the Nevzorov probe

Abstract. Supercooled large droplet (SLD) icing can occur behind the protected surfaces of an aircraft and create severe aerodynamic disturbances, which represent a safety hazard for aviation. Liquid water content (LWC) measurements in icing conditions that contain SLDs require instruments that are able to sample unimodal and bimodal droplet size distributions with droplet diameters from 2 to 2000 µm. No standardized detection method exists for this task. A candidate instrument, which is currently used in icing wind tunnel (IWT) research, is the Nevzorov probe. In addition to the standard 8 mm total water content (TWC) collector cone, a novel instrument version also features a 12 mm diameter cone, which might be advantageous for collecting the large droplets characteristic of SLD conditions. In the scope of the two EU projects, SENSors and certifiable hybrid architectures for safer aviation in ICing Environment (SENS4ICE) and ICE GENESIS, we performed measurement campaigns in SLD icing conditions in IWTs in Germany, Austria and the USA. We obtained a comprehensive data set of measurements from the LWC sensor, the 8 mm cone sensor and the 12 mm cone sensor of the Nevzorov probe, and from the tunnel reference instrumentation. In combination with measurements of the particle size distribution, we experimentally derive a collision efficiency curve that is based on a suitable functional form for the new 12 mm cone for median volume diameters (MVDs) between 12 and 58 µm and wind tunnel speeds from 40 to 85 m s−1. Knowledge of this curve allows us to correct the LWC measurements of the 12 mm cone (LWC12) in particular for the inevitably high decrease in collision efficiency for small droplet diameters. In unimodal SLD conditions, with MVDs between 128 and 720 µm, LWC12 generally agrees within ±20 % with the tunnel LWC reference values from a WCM-2000 and an isokinetic probe. An increase in the difference between LWC12 and the WCM-2000 measurements at larger MVDs indicates better droplet collection properties of the 12 mm cone. Similarly, the favorable detector dimensions of the 12 mm cone explain a 7 % enhanced detection efficiency compared to the 8 mm cone; however this difference falls within the instrumental uncertainties. Data collected in various bimodal SLD conditions with MVDs between 16 and 534 µm and LWCs between 0.22 and 0.72 g m−3 also show an agreement within ±20 % between LWC12 and the tunnel LWC, which demonstrates the suitability of the Nevzorov sensor head with the 12 mm cone for measurements of LWC in Appendix O icing conditions.

Technique for comparison of backscatter coefficients derived from in situ cloud probe measurements with concurrent airborne lidar

Abstract. Jet engine power loss due to ice particle accumulation is a recognized aviation hazard occurring in cloud conditions difficult to forecast or visually recognize. High-altitude cirrus clouds can have ice particle concentrations high enough to be dangerous; therefore, pilots must be informed when aircraft enter such environments. One approach to determining ice particle concentration is an onboard lidar system. Concurrent lidar measurements are compared to backscatter coefficients derived from particle size distributions obtained from wing-mounted, in situ probes during four case studies consisting of sixty-second flight segments at different temperatures: +7 and +4 ∘C for water droplet analysis, and −33 and −46 ∘C for ice particle analysis. Backscatter coefficients derived from external cloud probes (ECP) are correlated (0.91) with measurements by an airborne lidar system known as the Optical Ice Detector (OID). Differences between OID and ECP backscatter coefficients range from less than 1 to over 3 standard deviations in terms of uncertainties. The backscatter coefficients are mostly in agreement for liquid clouds and are in disagreement for the −33 and −46 ∘C cases, with ECP-derived backscatter coefficients lower than the OID for three out of the four cases. Measurements over four 60 s research flight segments show that measured total water content is correlated (0.74) with the OID backscatter coefficient, which indicates that the OID is a useful instrument for determining ice particle concentrations over a broad range of environments, including at ice water contents as low as 0.02 g m−3. Additionally, concurrent measurements from cloud imaging probes and the OID provide improved knowledge of cloud conditions, which may help in understanding cloud processes.

Semantic segmentation of explosive volcanic plumes through deep learning

Tracking explosive volcanic phenomena can provide important information for hazard monitoring and volcano research. Perhaps the simplest forms of monitoring instruments are visible-wavelength cameras, which are routinely deployed on volcanoes around the globe. Here, we present the development of deep learning models, based on convolutional neural networks (CNNs), to perform semantic segmentation of explosive volcanic plumes on visible imagery, therefore classifying each pixel of an image as either explosive plume or not explosive plume. We have developed 3 models, each with average validation accuracies of >97% under 10-fold cross-validation; although we do highlight that, due to the limited training and validation dataset, this value is likely an overestimate of real-world performance. We then present model deployment for automated retrieval of plume height, rise speed and propagation direction, all parameters which can have great utility particularly in ash dispersion modelling and associated aviation hazard identification. The 3 trained models are freely available for download at https://doi.org/10.15131/shef.data.17061509.

Comparative Study of Uniform and Graded Meshes for Solving Convection-Diffusion Equation with Quadratic Source

Due to its fundamental nature, the problems of convection-diffusion are discussed in various aviation, science and engineering applications. Among major applications are in the study of the dynamics of aircraft wake vortex and its interaction with turbulent jet which is a very serious hazard in aviation. Other applications include those in the investigation of intrusive sampling of jet engine exhaust gases, and the effectiveness of hot fluid injection in the removal of ice on aircraft wings. The numerical solutions of convection-diffusion require propermeshing schemes. Among major meshes in computational fluid dynamics are those of uniform, piecewise-uniform, graded, and hybrid over which the solutions of discretized governing equations are found. Bad solutions as spurious fluctuations, over-or under-predictions, and excessive computation time might be the results of unwitting application of the meshes. Accentuating comparative effectiveness of two meshes, namely uniform mesh and graded mesh with mesh expansion factor, this paper takes the solution of aconvection-diffusion equation with quadratic source term into account. The problem is solved by assigning several values of mesh expansion factor to graded mesh, while mesh number is kept constant. The factors are calculated based on the generalization oftheir logarithmically linear relationship with low Peclet numbers derived in previous work. Based on the values of Peclet number, five test cases are considered. Graded mesh is proven relatively more robust, particularly due the solution on the mesh beingfree from spurious fluctuation. Furthermore, the accuracy level of the solution of up to two order of magnitude higher is obtained. The mesh expansion factor therefore contributes to a stable and highly accurate solution corresponding to all interested Peclet numbers.

Dynamic melting behavior of volcanic ash subjected to thermal shock relevant to aviation hazards

The ingestion of volcanic ash by aircrafts is widely recognized as a potential hazard for aviation. By melting, adhering, and spreading, ash can permanently damage the thermal barrier coatings in jet engines; consequently, it is important to investigate the melting behavior of volcanic ash. Previous work has concentrated on the relatively isothermal process of melting at a slow heating rate using “characteristic temperatures” identified according to different geometrical characteristics. Characteristic temperatures serve well as the parameters to quantify the successive and continuous melting processes of volcanic ash under the approximately homogeneous isothermal condition. However, when the ash is ingested by a jet engine, there are large and transient thermal gradients, which differs from the near absence of temperature gradients induced in a sample throughout slow and constant heating. Therefore, experiments using slow heating rates and characteristic temperatures are limited in their utility, and new techniques are required. Here, we designed a thermal shock experiment to better reproduce the conditions of volcanic ash encountering a jet engine and proposed three characteristic times to discretize and quantify the continuous melting process of volcanic ash under thermal shock conditions. Three characteristic times are characterized by shape transitions of the specimens to spherical (St1), to hemispherical (Ht2), and to flow state (Ft3). Ash sampled from Eyjafjallajökull volcano, Iceland, was compacted into cylinders with diameters of 1–3 mm, and then thermally shocked to a target temperature of 1251, 1296, 1341, 1384 or 1427 °C. The three characteristic times follow exponential functions of the target temperature. Additionally, in the sintering regime, a retardation of densification was exhibited in the smaller cylinder employed here, whereas the subsequent spreading process was enhanced, resulting in a positive correlation between the size of the volcanic ash cylinder and the characteristic times in the spreading phase. We present a universal empirical function for the hemispherical state involving dimensionless target temperature and the ash's thermal properties. The function provides a fundamental basis for a dynamic melting timescale predicting model for yet smaller volcanic ash particles under even more intense transient thermal loading conditions than those studied here.

High Ice Water Content Conditions Associated with Wintertime Elevated Convection in the Midwest

AbstractAircraft flying through areas of high ice water content (HIWC) can experience engine damage and/or failure. HIWC is typically associated with convection and the microphysical properties of tropical oceanic and coastal convection are well documented as a result of several field campaigns in the past decade. HIWC appears to be less common in extratropical convection, but instances of HIWC-related aircraft issues have been recorded in extratropical weather, even during winter. The present study documents the microphysical properties of HIWC between −25° and 0°C and the meteorological and thermodynamic conditions around that HIWC from five flights from the In-Cloud Icing and Large-Drop Experiment (ICICLE) in the midwestern United States in February 2019. All five cases contained elevated convection above a strong low-level temperature inversion. Values for top-of-inversion mixing ratios and total column precipitable water were about 5 g kg−1and 20 mm, respectively, according to soundings near each case. A maximum ice water content of 2.1 g m−3was observed over a length scale of about 500 m, and ice particle size distributions had mean volume equivalent diameters around 1000μm. Supercooled drizzle droplets were also observed in the vicinity of the HIWC, raising questions about the possible role of secondary ice production via the freezing and shattering of supercooled large droplets in HIWC formation. The generalizability of these results is limited by the small number of cases, but they provide some of the first in situ observations of extratropical winter HIWC and highlight the need for continued research on these conditions.Significance StatementHigh ice water content (HIWC) conditions can cause engine damage, stall, and failure in aircraft and can cause air data probes to report erroneous values. Most research on HIWC has focused on tropical convection. This paper serves to draw more attention to the aviation hazard posed by extratropical winter HIWC and the need for additional research into these environments. The cases examined also contained supercooled precipitation-sized liquid droplets. Combined with other recent research, these observations may help to motivate laboratory experiments investigating the role of secondary ice production via droplet freezing and shattering in the formation of HIWC.

Microburst, Windshear, Gust Front, and Vortex Detection in Mega Airport Using a Single Coherent Doppler Wind Lidar

Accurate wind shear detection is crucial for aviation safety, especially in landing and departure. A new approach for windshear alerting is proposed and demonstrated. This approach monitors orthogonal wind components in multiple runways using single coherent Doppler wind lidar (CDWL). First, the two orthogonal components of the wind field are retrieved from radial speed by an updated variational method. Then, the heading wind and cross wind on different runways are calculated simultaneously, without the location restriction of the single lidar. Finally, a windshear alerting message is generated through quantitatively evaluating the distribution of shear ramps over the monitoring area. The new CDWL-based approach for windshear alerting is implemented at the Beijing Daxing International Airport. The retrieved horizontal wind from the lidar is consistent with that from anemometers. Thanks to its high spatial/temporal resolution, some meteorological phenomena of aviation hazards, including microburst, windshear, gust front, and vortex are well captured. Particularly, all 10 windshear cases reported by crews are successfully identified during the windshear verification experiment, demonstrating the effectiveness and robustness of the new approach.

The Ability of the ICE-T Microphysics Scheme in HARMONIE-AROME to Predict Aircraft Icing

AbstractIn-cloud icing is a major hazard for aviation traffic and forecasting of these events is an important task for weather agencies worldwide. A common tool utilized by aviation forecasters is an icing intensity index based on supercooled liquid water from numerical weather prediction models. We seek to validate the modified microphysics scheme, ICE-T, in the HARMONIE-AROME numerical weather prediction model with respect to aircraft icing. Icing intensities and supercooled liquid water derived from two 3-month winter season simulations with the original microphysics code, CTRL, and ICE-T are compared with pilot reports of icing and satellite retrieved values of liquid and ice water content fromCloudSat–CALIPSOand liquid water path from AMSR-2. The results show increased supercooled liquid water and higher icing indices in ICE-T. Several different thresholds and sizes of neighborhood areas for icing forecasts were tested out, and ICE-T captures more of the reported icing events for all thresholds and nearly all neighborhood areas. With a higher frequency of forecasted icing, a higher false alarm ratio cannot be ruled out, but is not possible to quantify due to the lack of no-icing observations. The increased liquid water content in ICE-T shows a better match with the retrieved satellite observations, yet the values are still greatly underestimated at lower levels. Future studies should investigate this issue further, as liquid water content also has implications for downstream processes such as the cloud radiative effect, latent heat release, and precipitation.

Silicate ash-resistant novel thermal barrier coatings in gas turbines

Gas turbines, used to propel aircraft or generate electricity, are vulnerable to attack by environmental silicate debris. When silicate ash is ingested into gas turbines, it melts and may adhere to the hot-section components of gas turbines (typically operating at > 1250 °C), infiltrating into their thermal barrier coatings (TBCs). These ash-related TBC issues pose a critical hazard for aircraft aviation. Here, we have developed here a novel NdYbZr2O7 coating substrate which addresses these challenges. We have quantitatively analyzed the in-situ wetting process of three representative silicate ash compositions—CMAS, volcanic ash and fly ash, and compare their infiltration behavior in the newly-developed NdYbZr2O7 coating substrate. The widely differing chemical compositions of the three ash samples yield differences in their high temperature viscosities which in turn impact their respective propensities for wetting and infiltration. CMAS exhibits the greatest dynamic wettability due to its lower viscosity over the entire range of temperature. Volcanic ash exhibits the largest infiltration depth, probably related to the relatively high solubility of rare-earth oxides (RE2O3) in volcanic ash melt. Nevertheless, compared with a traditional YSZ coating, the NdYbZr2O7 coating exhibits a better corrosion resistance in the presence of all three silicate ash samples due to the rapid precipitation of (Nd, Yb)-apatite and c-ZrO2 phases, which build a dense reaction layer, inhibiting further melt infiltration.