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Articles 1 - 5 of 5
Full-Text Articles in Other Aerospace Engineering
Physics-Informed Deep Learning For Pilot Parameter Estimation And Pilot-Induced Oscillation Characterization, Stephen A. Brutch
Physics-Informed Deep Learning For Pilot Parameter Estimation And Pilot-Induced Oscillation Characterization, Stephen A. Brutch
Doctoral Dissertations and Master's Theses
This thesis investigates the issue of loss-of-control in flight which is a driving contributor to fatal aviation accidents. The two main contributors tackled in this research are human pilot error and categories of pilot-induced-oscillations. The primary objective is to develop models that can capture the mathematical parameters that relate to human pilot control under the widely used McRuer Mathematical pilot model. The goal is that if the mathematical parameters that relate to human pilot control can be monitored during flight then the pilot’s input to the control system and pilot-induced-oscillations (PIO) can be monitored during flight to avoid loss-of-control. This …
Air Passenger Demand Forecast Through The Use Of Artificial Neural Network Algorithms, Juan Gerardo Muros Anguita, Oscar Díaz Olariaga
Air Passenger Demand Forecast Through The Use Of Artificial Neural Network Algorithms, Juan Gerardo Muros Anguita, Oscar Díaz Olariaga
International Journal of Aviation, Aeronautics, and Aerospace
Airport planning depends to a large extent on the levels of activity that are anticipated. To plan the facilities and infrastructures of an airport system and to be able to satisfy future needs, it is essential to predict the level and distribution of demand. This document presents a short- and medium-term forecast of the demand for air passengers carried out through a specific case study (Colombia), in which the impact of the pandemic period due to COVID-19 on air traffic was taken into account. To make the forecast, an algorithm that implements techniques based on Artificial Neural Networks (ANN) (Machine …
Air Passenger Demand Forecast Through The Use Of Artificial Neural Network Algorithms, Juan Gerardo Muros Anguita, Oscar Díaz Olariaga
Air Passenger Demand Forecast Through The Use Of Artificial Neural Network Algorithms, Juan Gerardo Muros Anguita, Oscar Díaz Olariaga
International Journal of Aviation, Aeronautics, and Aerospace
Airport planning depends to a large extent on the levels of activity that are anticipated. In order to plan facilities and infrastructures of an airport system and to be able to satisfy future needs, it is essential to predict the level and distribution of demand. This document presents a short- and medium-term forecast of the demand for air passengers carried out through a specific case study (Colombia), in which the impact of the pandemic period due to COVID-19 on air traffic was taken into account. To make the forecast, an algorithm that implements techniques based on Artificial Neural Networks (ANN) …
Soarnet, Deep Learning Thermal Detection For Free Flight, Jake T. Tallman
Soarnet, Deep Learning Thermal Detection For Free Flight, Jake T. Tallman
Master's Theses
Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, and conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of feet of elevation gain, however estimating thermal locations is not …
Next-Generation Re-Entry Aerothermodynamic Modeling Of Space Debris Using Machine Learning, Nicholas Sia
Next-Generation Re-Entry Aerothermodynamic Modeling Of Space Debris Using Machine Learning, Nicholas Sia
Graduate Theses, Dissertations, and Problem Reports (ETD)
The number of resident space objects re-entering the atmosphere is expected to rise with increased space activity over recent years and future projections. Predicting the survival and impact location of the medium to large sized re-entering objects becomes important as they can cause on ground casualties and damage to property. Uncertainties associated with the re-entry process makes necessary a probabilistic approach, which can be computationally expensive when using high-fidelity numerical methods for estimating aerothermodynamic properties. To date, object-oriented analysis is the dominant tool used for atmospheric re-entry modeling and simulation, where aerothermodynamic coefficients are used to determine the risk a …