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Boundary Layer Theory
No full textBoundary Layer Theory is a comprehensive introduction to the physics of the boundary layer, and the latest numerical methods for describing it. The increases in computing power of recent decades have expanded our capability to investigate complex fluid phenomena such as boundary layer turbulence, and research in this field has lead to important advances for aerospace, chemical, thermal, and hydraulic engineering among other areas. With worked examples and problem exercises in every chapter, this book helps readers to understand the physics of the boundary layer before going through the related numerical solutions, and moving on to the latest numerical methods as they are used in research globally. Topical research areas such as non-Newtonian fluid-boundary layers, effects of magnetohydrodynamics on boundary layers, and three dimensional boundary layer effects are all addressed, making this the ideal starting point for any fluids engineer approaching a research topic in boundary layer
Spectral sampling of boron diffusion in Ni alloys: Cr and Mo effects on bulk and grain boundary transport
No full textExcerpt: Understanding how light interstitials migrate in chemically complex alloys is essential for predicting defect dynamics and long-term stability. Here, we introduce a spectral sampling framework to quantify boron diffusion activation energies in Ni and demonstrate how substitutional solutes (Cr, Mo) reshape interstitial point defect transport in both the bulk and along crystallographic defects. In the bulk, boron migration energy distributions exhibit distinct modality tied to solute identity and spatial arrangement: both Cr and Mo raise barriers in symmetric cages but induce directional asymmetry in partially decorated environments
Large-Scale CubeSat Chassis
No full textThe present invention relates to large-scale CubeSat chassis and methods of making and using same. The disclosed CubeSat comprises specific structural features that can yield up to at least a 27 U CubeSat that has comparable or even better performance and benefits than that of smaller CubeSats. Such volume increase coupled with the structural integrity of Applicants\u27 CubeSats opens up a wide range of CubeSat performance increases including, but not limited to, larger payloads, more diverse payloads and larger CubeSat fuel capacities. Such increased payload capabilities includes, for example, increased focal length capabilities and such improved maneuverability results in, for example, decreased ground sample distance, increased optical resolution, increased ability to change orbits, increased structural stability. In addition, the judicious selection of such specific structural features can result increased payload capabilities and improved maneuverability
Scalable, Occlusion-Aware Geolocation of Ground-Based GNSS Jammers with Networked Smartphones
No full textExcerpt: This study addresses these limitations and demonstrates how smartphone networks can successfully locate a jamming source by integrating cloud-based environmental occlusion modeling with scalable, efficient localization algorithms
Influence of Personnel Factors on Air Force Fighter Mishaps via Bayesian Regression
No full textAviation safety in the United States (U.S.) military has received growing attention in recent years due to numerous high-profile mishaps. Despite the increased attention, there have been few quantitative analyses of the relationship between pilot attributes and mishap rates. In this study, we use nearly 15 years of U.S. Air Force (USAF) safety and administrative records to investigate the relationship between pilot attributes and fighter aviation mishap rates. First, we present an analysis of flight mishap rates for different mishap classes and fighter aircraft types, referred to as a mission design series (MDS). Second, we quantify pilot attributes and present an analysis of fighter pilot populations across time and MDS. We then model the association between pilot attributes and annual rate of class A, B, and C flight mishaps, which we refer to as high-class mishaps (HCMs), using a Bayesian regression framework. Our results show prior flight experience and key characteristics of an MDS pilot community are associated with the rate of HCMs. Specifically, we find that MDS pilot communities with 10 more flight hours in the past year are, on average, associated with a 5% decrease in HCM rate. Additionally, we find that a 0.1 standard deviation increase in the proportion of pilots who are instructor pilots, distinguished graduates from commissioning source, and graduate degree recipients is associated with a reduction in major aviation mishaps by 2.1%, 2.0%, and 1.3%, respectively. These findings have significant financial implications, given that the cost of a single HCM starts at 200M. In addition to our model results, our efforts to quantify pilot attributes and model the relationship between personnel factors and mishap rates using Bayesian regression and predictive projection for feature selection represent a valuable methodological contribution to aviation accident analysis
Development of a Neuromorphic-Friendly Spiking Neural Network for RF Event-based Classification
No full textThis paper provides details for the most recent step taken in RndF-to-CNN-to-SNN classifier transition activity supporting an envisioned RF “event radio” concept. Successful results here include the transition from CNNs to neuromorphic-friendly CNN-derived SNNs and pique sufficient interest for pursuing next-step hardware demonstrations. Consistent with earlier RndF and CNN works that used the same experimentally collected WirelessHART signals, SNN results here show that two-dimensional event-based fingerprinting is best overall using events detected in burst Gabor transform responses. The approximate %CΔ≈−2% decrease in average percent correct classification performance resulting from RF eventization encoding is effectively offset by a complementary %CΔ≈+2% to +3% increase that occurs with the CNN-to-SNN transition. This level of neuromorphic-friendly SNN performance is promising when considering the potential 10X-100X energy efficiencies that remain to be demonstrated
Predicting the graphitization and mechanical properties of pyrolyzed carbyne polymers
No full textExcerpt: Carbyne is a one-dimensional chain of carbon atoms that has high elastic modulus and thermal conductivity. However, its mechanical properties vary with temperature. We use molecular dynamics to investigate the bond structure of polymers formed from cumulenic and polyynic carbyne pyrolyzed at high temperatures after quenching and predict the polymers’ mechanical properties. We observe that nanostructures begin to form during pyrolysis at 1,000K, and there is a major transformation from sp-hybridized carbyne to sp2-, and sp3-hybridized polymers after heating the carbyne up to 3,000K. Abstract © Elsevier
Multi-class Classification of Satellite Orbits for Database Quality Control
No full textThe Joint Spectrum Center (JSC) Equipment, Tactical, and Space (JETS) database contains 9,539 satellite records. When new data is ingested the satellite orbit type needs to be identified, which is currently a manual process. To save time, this work explores automating the process using machine learning. Several statistical machine learning and neural network models were developed and compared using the weighted averages of precision, recall, and F1 score metrics. The number of records used in training and testing was 1,024 with a 60/20/20 train, validation, and test split. Six orbital parameters were initially used to fit the models, but three parameters (the mean motion, eccentricity, and inclination) were most important in determining orbit type. A decision tree model with the three most important orbital parameters as inputs best identified the seven target orbit types. The weighted averages of the precision, recall, and F1 score on the test data were 0.991, 0.990, and 0.990 respectively. This compared favorably to the F1 metrics for a random classifier (0.106) and a model that always predicted the majority class (0.103)