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1	Hypersonic Flight Vehicle Roughness Characterization and Effects of Roughness Arrays on Crossflow under Mach 6 Quiet Flow Cassandra Jennifer Butler (18431619) 26 April 2024 (has links) <p dir="ltr">Experiments were performed in the Boeing/AFOSR Mach-6 Quiet Tunnel to study the effect of flight-derived discrete roughness elements repeated in an axisymmetric pattern near the nose of a sharp 7° cone. The aim of the roughness array was to simulate natural vehicle roughness and attempt to introduce a deterministic roughness pattern with the ability to cancel out the instabilities caused by the natural roughness. The cone was pitched at a 6° of attack to determine the three-dimensional flow field effects of the roughness elements. Tests were also ran at 0° of attack for comparison. Quiet flow testing included the designed-for freestream unit Reynolds number of 10.8x10<sup>6</sup>, and Reynolds numbers above and below. In noisy flow, comparable Reynolds numbers were also tested at to isolate the effects of noise in a conventional flow wind tunnel.</p><p dir="ltr">Infrared thermography and surface pressure sensors were used to document the behavior of the boundary layer. It was found that the roughness pattern was in general unsuccessful in controlling the added boundary layer instabilities as intended at 6° of attack, but it did create different instability amplitudes and heating patterns. Additionally, it was determined to reduce Mack's second-mode instability amplitudes at 0° of attack.</p><p dir="ltr">Additionally, work was done to document and characterize the roughness patterns found on samples of hypersonic glide vehicles PRIME (SV-5D or X-23) and ASSET (ASV-3). These samples were taken in the form of molded impressions of the surface which were able to be analyzed with an optical profilometer and considered for future experimental distributed roughness studies.</p> Boundary-layer transition Quiet Tunnels roughness elements hypersonic glide vehicles
2	Machine Learning for Speech Forensics and Hypersonic Vehicle Applications Emily R Bartusiak (6630773) 06 December 2022 (has links) <p>Synthesized speech may be used for nefarious purposes, such as fraud, spoofing, and misinformation campaigns. We present several speech forensics methods based on deep learning to protect against such attacks. First, we use a convolutional neural network (CNN) and transformers to detect synthesized speech. Then, we investigate closed set and open set speech synthesizer attribution. We use a transformer to attribute a speech signal to its source (i.e., to identify the speech synthesizer that created it). Additionally, we show that our approach separates different known and unknown speech synthesizers in its latent space, even though it has not seen any of the unknown speech synthesizers during training. Next, we explore machine learning for an objective in the aerospace domain.</p> <p><br></p> <p>Compared to conventional ballistic vehicles and cruise vehicles, hypersonic glide vehicles (HGVs) exhibit unprecedented abilities. They travel faster than Mach 5 and maneuver to evade defense systems and hinder prediction of their final destinations. We investigate machine learning for identifying different HGVs and a conic reentry vehicle (CRV) based on their aerodynamic state estimates. We also propose a HGV flight phase prediction method. Inspired by natural language processing (NLP), we model flight phases as “words” and HGV trajectories as “sentences.” Next, we learn a “grammar” from the HGV trajectories that describes their flight phase transition patterns. Given “words” from the initial part of a HGV trajectory and the “grammar”, we predict future “words” in the “sentence” (i.e., future HGV flight phases in the trajectory). We demonstrate that this approach successfully predicts future flight phases for HGV trajectories, especially in scenarios with limited training data. We also show that it can be used in a transfer learning scenario to predict flight phases of HGV trajectories that exhibit new maneuvers and behaviors never seen before during training.</p> Audio processing Computer vision Digital forensics Deep learning machine learning deep learning speech forensics media forensics convolutional neural network transformer convolutional transformer ensemble spectrogram analysis mel spectrogram analysis synthesized speech detection synthesized speech attribution closed set open set t-stochastic neighbor embedding latent space analysis hypersonics hypersonic glide vehicles vehicle classification flight phase prediction stochastic grammar k-nearest neighbors support vector machine probabilistic context-free grammar automatic distillation of structure generalized earley parser transfer learning

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Hypersonic Flight Vehicle Roughness Characterization and Effects of Roughness Arrays on Crossflow under Mach 6 Quiet Flow

Machine Learning for Speech Forensics and Hypersonic Vehicle Applications