Two passive add-on porous fairings, comprised of diamond grids varying in size, are numerically and experimentally investigated for their effectiveness in mitigating landing gear noise. The baseline landing gear, a modified version of the LAGOON landing gear with its inner rim cavities closed, along with two configurations equipped with diamond-lattice fairings, are numerically simulated using the Improved Delayed Detached-Eddy Simulation (IDDES) in combination with the Ffowcs Williams and Hawkings (FW-H) analogy. Instead of resolving the detailed flow features through the diamond-lattice fairings, a numerical model is employed to represent the effect of fairing. Prior to integrating the numerical model into the simulations of landing gears, rigorous validation of the model against experimental data in a channel flow is performed. Subsequently, the predicted flow fields and far-field noise spectra of the baseline and controlled landing gears are validated against the experiments conducted in the anechoic A-Tunnel at Delft University of Technology. The results indicate that implementing a diamond-lattice fairing upstream of the landing gear can effectively diminish far-field noise in the frequency range exceeding 200 Hz. For the baseline landing gear, the torque link and brakes are potent noise sources. For the controlled landing gears, both diamond-lattice fairings mitigate the pressure fluctuations on the torque link and brakes, leading to a reduction of surface noise sources. The noise directivity shows that the DL 4.5mm fairing produces a noise reduction of 2-6 dB whereas the DL 2.5mm fairing generates a noise reduction of 3-7 dB across all radial directions. These findings pave the way for the low-noise design of aircraft landing gears.@en

A solid fairing and a wire-mesh fairing consisting of very fine wires and pores are numerically and experimentally investigated for the mitigation of landing gear noise. A slightly modified LAGOON landing gear and two configurations, one equipped with a solid fairing and the other with a wire-mesh fairing, are numerically simulated using the Improved Delayed Detached-Eddy Simulation (IDDES) in combination with the Ffowcs Williams and Hawkings (FW-H) analogy. Instead of resolving the detailed flow features through the wire mesh, a recently proposed numerical model is used to represent the effect of the wire-mesh fairing. The simulated flow fields and the far-field noise spectra are validated against the experiments conducted in an anechoic wind tunnel. The superiority of the recently proposed wire-mesh model over a classical wire-mesh model in modelling both the aerodynamic and aeroacoustic effects of the wire mesh is demonstrated. Results also show that the dense wire-mesh fairing functions very similarly to the solid fairing and that significant noise can be reduced through the installation of a solid fairing or a wire-mesh fairing upstream of the landing gears. For the baseline landing gear, the torque link and the brakes are identified noise sources. With the aerodynamic penalty of a 50% increase in drag, both fairings mitigate the pressure fluctuation on the torque link and brakes, resulting in the reduction of surface noise sources. The noise directivity shows that a solid fairing or a dense wire-mesh fairing contributes to a noise reduction of 4-6 dB in all radial directions. The findings in this study pave the way for the low-noise design of aircraft landing gears.

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Extracting the cerebral anterior vessel tree of patients with an intracranial large vessel occlusion (LVO) is relevant to investigate potential biomarkers that can contribute to treatment decision making. The purpose of our work is to develop a method that can achieve this from routinely acquired computed tomography angiography (CTA) and computed tomography perfusion (CTP) images. To this end, we regard the anterior vessel tree as a set of bifurcations and connected centerlines. The method consists of a proximal policy optimization (PPO) based deep reinforcement learning (DRL) approach for tracking centerlines, a convolutional neural network based bifurcation detector, and a breadth-first vessel tree construction approach taking the tracking and bifurcation detection results as input. We experimentally determine the added values of various components of the tracker. Both DRL vessel tracking and CNN bifurcation detection were assessed in a cross validation experiment using 115 subjects. The anterior vessel tree formation was evaluated on an independent test set of 25 subjects, and compared to interobserver variation on a small subset of images. The DRL tracking result achieves a median overlapping rate until the first error (1.8 mm off the reference standard) of 100, [46, 100] % on 8032 vessels over 115 subjects. The bifurcation detector reaches an average recall and precision of 76% and 87% respectively during the vessel tree formation process. The final vessel tree formation achieves a median recall of 68% and precision of 70%, which is in line with the interobserver agreement.

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The service performances of asphalt pavement, especially rutting, will be inevitably affected by climate change. However, existing studies have generally focused on the rutting depth and rutting life, and thus became insufficient for comprehensively evaluating the influence of climate change on rutting over the service life. A resilience assessment method for asphalt pavement rutting is developed to solve the above problem. First, the original resilience method is extended to fit the system whose performance level continues to decline. Then, the calculation formulas of rutting resilience are derived by combining the rutting prediction model and the level assessment model. Subsequently, the influence degrees of climate change in representative cities on rutting resilience are studied. The results suggest that neglecting climate change in rutting design of asphalt pavement will lead to insufficient resilience, especially in northern China. Furthermore, the predicted temperature under RCP8.5 should be employed for asphalt pavement design.

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