Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Other Aerospace Engineering (3)
- Aerodynamics and Fluid Mechanics (1)
- Artificial Intelligence and Robotics (1)
- Automotive Engineering (1)
- Aviation (1)
-
- Civil and Environmental Engineering (1)
- Computer Sciences (1)
- Dynamical Systems (1)
- Mathematics (1)
- Navigation, Guidance, Control, and Dynamics (1)
- Numerical Analysis and Scientific Computing (1)
- Physical Sciences and Mathematics (1)
- Probability (1)
- Statistics and Probability (1)
- Transportation Engineering (1)
- Institution
- Publication
- Publication Type
Articles 1 - 4 of 4
Full-Text Articles in Aerospace Engineering
Machine Learning To Predict Warhead Fragmentation In-Flight Behavior From Static Data, Katharine Larsen
Machine Learning To Predict Warhead Fragmentation In-Flight Behavior From Static Data, Katharine Larsen
Doctoral Dissertations and Master's Theses
Accurate characterization of fragment fly-out properties from high-speed warhead detonations is essential for estimation of collateral damage and lethality for a given weapon. Real warhead dynamic detonation tests are rare, costly, and often unrealizable with current technology, leaving fragmentation experiments limited to static arena tests and numerical simulations. Stereoscopic imaging techniques can now provide static arena tests with time-dependent tracks of individual fragments, each with characteristics such as fragment IDs and their respective position vector. Simulation methods can account for the dynamic case but can exclude relevant dynamics experienced in real-life warhead detonations. This research leverages machine learning methodologies to …
Machine Learning In Aerodynamic Shape Optimization, Jichao Li, Xiaosong Du, Joaquim R.R.A. Martins
Machine Learning In Aerodynamic Shape Optimization, Jichao Li, Xiaosong Du, Joaquim R.R.A. Martins
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and provide a perspective on the state-of-the-art and future directions. We first introduce conventional ASO and current challenges. Next, we introduce ML fundamentals and detail ML algorithms that have been successful in ASO. Then, we review ML applications to ASO addressing three aspects: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, …
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) …
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 …