Plate Lines - Mitigating Wake Turbulence Risks and Increasing Runway Throughput

So-called plate lines have been developed at the DLR Institute of Atmospheric Physics in order to shorten the lifetime of wake vortices generated by landing aircraft. Installing plate lines underneath the glide path mitigates wake vortex encounter risks and prevents go-arounds. In combination with a modern separation scheme, delays can be reduced and runway capacity can be increased

Estimating Aircraft Landing Weights from Mode S Data

Aircraft weights prior to touchdown are assessed employing equations suggested for estimates of descent speeds depending on aircraft gross mass following BADA (Base of Aircraft Data). The required aircraft type, calibrated airspeed and air density data are derived from Mode S data protocols. Landing weights of 3328 aircraft approaching Vienna airport, provided by Austrian Airlines, serve as reference gross masses. Average aircraft masses during final approach vary between 85% and 93% of the maximum landing weight depending on the aircraft type. A simple correction for the observed inclination of pilots to fly somewhat faster than prescribed in reference handbooks eliminates the bias of the mass estimates in the current data base, while the respective standard deviation amounts to approximately 5%

Assessment of Aircraft Separation Reduction Potential for Arrivals Facilitated by Plate Lines

Wake vortex related minimum aircraft separations for arrivals were evaluated utilizing plate lines. A plate line consists of a series of vertical plates placed in front of the runway that accelerate the decay of wake vortices. This study evaluates the potential to reduce minimum aircraft separations and the potential to increase safety due to the accelerated vortex decay. The presented method follows the methodology of the proposal for revised wake turbulence categorization RECAT-EU as much as possible in order to be compliant with the respective certified safety assessment. Wake vortex data measured by lidar with and without plates during a measurement campaign at Vienna International Airport, Austria, were used to generate generic non-dimensional decay curves for all captured aircraft types under so-called reasonable worst-case weather conditions with and without plates. The observed wake vortex behavior without plates was fitted to the generic decay curve from RECAT-EU. The application of the same method to the measurements with plates yields a generic decay curve representing the accelerated decay triggered by the plates. Applied to the RECAT-EU minimum separation scheme potential separation reductions ranging from 12% to 15% were evaluated. The same method was applied to the RECAT-EU-PWS pairwise separations, yielding a potential separation reduction due to the plates of 12% to 24% (average 14.8%) or a potential circulation reduction of about 20% to 30%. An assessment of the associated encounter risk showed significant benefits from plate lines indicating that the collaborative introduction of RECAT-EU-PWS together with plate lines may bring about increased airport capacity and safety at the same time when comparing it to the RECAT-EU scheme which is already operational at four European airports

Plate lines to enhance wake vortex decay for reduced separations between landing aircraft

To mitigate the risk of wake vortex encounters during final approach, so-called plate lines have been developed. Data collected during a six-month measurement campaign at Vienna International Airport is used to assess the potential for reducing minimum aircraft separations facilitated by plate lines during approach and landing following the RECAT-EU methodology for revised wake turbulence categorization. To ensure that no other parameters controlling wake vortex decay bias the analysis, it is verified that wind speed, atmospheric turbulence, thermal stratification, and flight altitude reside in similar ranges with and without the plates. The analysis follows the steps of the RECAT-EU method to generate non-dimensional so-called reasonable worst-case circulation decay curves; one as a reference for nominal operations without plates and one representing the accelerated wake vortex decay brought about by the plate lines. The difference between the two circulation decay curves corresponds to the non-dimensional time-based aircraft separation reduction potential that can be translated into distance-based separation gains. Depending on the particular RECAT-EU category combination, the attained aircraft separation reduction potential ranges between 12% and 15%. Constricting the analysis to wake vortices generated by one aircraft type representing the Upper Heavy RECAT-EU category, the separation reduction potential amounts up to 20%

Investigating Artificial Neural Networks for Detecting Aircraft Wake Vortices in Lidar Measurements

Convolutional Neural Networks (CNNs) are employed to identify wake vortices via their two-dimensional position and circulation strength in Light Detection and Ranging (lidar) measurement scans. A campaign at Vienna International Airport delivered data that so far has only been processed with a traditional lidar processing algorithm, namely the Radial Velocity (RV) method. Its not fully automated nature led to only a fraction of scans from the overall data set to be evaluated. Here we present ways to use CNNs for this task. A scoring algorithm engineered for verifying CNN detections has been implemented. In particular green detections (those marked as correct CNN detections by the scoring algorithm) can confidently be used for further analysis about the wake vortex encounter hazard. With this approach we end up with a significantly more processed and characterized lidar data compared to that so far delivered by the RV method

Investigating Errors of Wake Vortex Retrievals Using High Fidelity Lidar Simulations

Wake vortex characterization algorithms for Light Detection and Ranging (lidar) measurements are vital for airport operation studies considering both efficiency of aircraft throughput and the related safety issue. To date the operational accuracy of algorithms such as the Radial Velocity (RV) method, particularly in turbulent atmosphere, has not been quantified thoroughly. In real lidar scans, the true flow field and the characteristics of the contained coherent structures, such as wake vortices, are unknown. Thus, the error of algorithms such as the RV method has not yet been considered beyond theoretical estimations. In this work we tackle the unavailability of a ground-truth by simulating virtual lidar instruments employing high fidelity Large Eddy Simulations (LES) of a landing aircraft. Within the numerical simulations the characteristics of the wake vortices are fully known, so that the accuracy of algorithms such as the RV method can be investigated and quantified. Virtual lidar scans generated by our proposed LES Lidar Simulator (LLS) focus on accurately representing the filtering effect of real lidar via a range gate weighing function. Comparisons to real lidar measurements and the simulated wake of the LES suggest that first accuracy estimations of the RV method can already be performed with the present LLS version. We observe that theoretical RV method characterization errors are significant underestimations, particularly the strength of vortices appears to be overestimated. These results manifest the necessity to investigate errors inherent to wake vortex characterization algorithms from lidar measurements also in further atmospheric conditions and aircraft landing scenarios

Characterizing Wake Vortices of Landing Aircraft Using Artificial Neural Networks and LiDAR Measurements

The characterization of wake vortices with Light Detection and Ranging (LiDAR) instruments is commonly facilitated using analytical algorithms such as the Radial Velocities (RV) method. However, these can either not be employed for all LiDAR types, require time-consuming semi-automatic processing, or lack accuracy requirements for fast-time hazard prediction at airports. The approach taken in this paper employs Artificial Neural Networks (ANNs) for the estimation of the location and strength of the primary wake vortices trailing behind landing aircraft, going beyond the qualitative wake vortex identification of previous literature. Custom Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) architectures are generated, and compared to state of the art LiDAR processing algorithms. For this, LiDAR measurements taken at Vienna International Airport that were processed with the RV method are utilized for supervised training of the networks. In addition, feature engineering is performed, allowing to increase the performance of the ANNs by mitigating crosswind effects, optimizing measurement grid positions, and minimizing interfering boundary layer effects. Results indicate the superior performance of the custom CNNs over the custom MLPs in nearly all characterization parameters, while the evaluation speed of a single LiDAR scan turns out to be substantially faster compared to the current state of the art RV method. The custom CNN architecture results in circulation errors as low as 26 m^2/s and localization errors as low as 13 m. A hazard prediction reliability of up to 91% is obtained, given the accuracy of the RV method which constitutes a natural limit of the performance capabilities of ANNs

Characterizing aircraft wake vortex position and strength using LiDAR measurements processed with artificial neural networks

The position and strength of wake vortices captured by LiDAR (Light Detection and Ranging) instruments are usually determined by conventional approaches such as the Radial Velocity (RV) method. Promising wake vortex detection results of LiDAR measurements using machine learning and operational drawbacks of the comparatively slow traditional processing methods motivate exploring the suitability of Artificial Neural Networks (ANNs) for quantitatively estimating the position and strength of aircraft wake vortices. The ANNs are trained by a unique data set of wake vortices generated by aircraft during final approach, which are labeled using the RV method. First comparisons reveal the potential of custom Convolutional Neural Networks in comparison to readily available resources as well as traditional LiDAR processing algorithms

Kinetic modeling of stem cell transcriptome dynamics to identify regulatory modules of normal and disturbed neuroectodermal differentiation

Thousands of transcriptome data sets are available, but approaches for their use in dynamic cell response modelling are few, especially for processes affected simultaneously by two orthogonal influencing variables. We approached this problem for neuroepithelial development of human pluripotent stem cells (differentiation variable), in the presence or absence of valproic acid (signaling variable). Using few basic assumptions (sequential differentiation states of cells; discrete on/off states for individual genes in these states), and time-resolved transcriptome data, a comprehensive model of spontaneous and perturbed gene expression dynamics was developed. The model made reliable predictions (average correlation of 0.85 between predicted and subsequently tested expression values). Even regulations predicted to be non-monotonic were successfully validated by PCR in new sets of experiments. Transient patterns of gene regulation were identified from model predictions. They pointed towards activation of Wnt signaling as a candidate pathway leading to a redirection of differentiation away from neuroepithelial cells towards neural crest. Intervention experiments, using a Wnt/beta-catenin antagonist, led to a phenotypic rescue of this disturbed differentiation. Thus, our broadly applicable model allows the analysis of transcriptome changes in complex time/perturbation matrices

Radiation Response of Murine Embryonic Stem Cells

To understand the mechanisms of disturbed differentiation and development by radiation, murine CGR8 embryonic stem cells (mESCs) were exposed to ionizing radiation and differentiated by forming embryoid bodies (EBs). The colony forming ability test was applied for survival and the MTT test for viability determination after X-irradiation. Cell cycle progression was determined by flow cytometry of propidium iodide-stained cells, and DNA double strand break (DSB) induction and repair by γH2AX immunofluorescence. The radiosensitivity of mESCs was slightly higher compared to the murine osteoblast cell line OCT-1. The viability 72 h after X-irradiation decreased dose-dependently and was higher in the presence of leukemia inhibitory factor (LIF). Cells exposed to 2 or 7 Gy underwent a transient G2 arrest. X-irradiation induced γH2AX foci and they disappeared within 72 h. After 72 h of X-ray exposure, RNA was isolated and analyzed using genome-wide microarrays. The gene expression analysis revealed amongst others a regulation of developmental genes (Ada, Baz1a, Calcoco2, Htra1, Nefh, S100a6 and Rassf6), downregulation of genes involved in glycolysis and pyruvate metabolism whereas upregulation of genes related to the p53 signaling pathway. X-irradiated mESCs formed EBs and differentiated toward cardiomyocytes but their beating frequencies were lower compared to EBs from unirradiated cells. These results suggest that X-irradiation of mESCs deregulate genes related to the developmental process. The most significant biological processes found to be altered by X-irradiation in mESCs were the development of cardiovascular, nervous, circulatory and renal system. These results may explain the X-irradiation induced-embryonic lethality and malformations observed in animal studies