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Distributed Conflict Detection And Optimal 4d Trajectory Resolution Leveraging Polynomial Based Methods, Michael Klinefelter, Austin Stone, Joshua Miller, Cameron K. Peterson, John Salmon
Distributed Conflict Detection And Optimal 4d Trajectory Resolution Leveraging Polynomial Based Methods, Michael Klinefelter, Austin Stone, Joshua Miller, Cameron K. Peterson, John Salmon
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This paper presents a methodology for distributed conflict detection and resolution of aircraft following time-dependent flight paths. We use parametric fifth-order polynomial splines to define the full, time-based paths of vehicles. This representation can be exploited to rapidly detect conflicts and calculate optimal resolution solutions that minimize deviations from the original path. Conflicts are identified using a Sturm sequencing procedure and resolutions are found using gradient-based optimization techniques. Simulations show the locally optimal resolution of complex multi-vehicle conflicts and large-scale scenarios. Also, a method of fitting the flight path model to data sets is presented and flight path trajectories are …
Comparison Of Empirical And Analytical Eigenfunctions As Bases For Reduced Order Methods In Heat Transfer, Jakob G. Bates, Matthew R. Jones, Christopher R. Dillon, John Tencer
Comparison Of Empirical And Analytical Eigenfunctions As Bases For Reduced Order Methods In Heat Transfer, Jakob G. Bates, Matthew R. Jones, Christopher R. Dillon, John Tencer
Student Works
Reduced order methods using spectral representations show promise in facilitating and accelerating heat transfer analyses. This paper proposes a taxonomy for reduced order methods, classifying a method as reduced order compression, modelling, or analysis. The performance of bases formed with analytical and empirical eigenfunctions are compared for representative reduced order tasks. The Akaike Information Criterion is applied in a novel way to compare the performance of these bases. The present study finds that both bases are parsimonious for reduced order compression tasks. Empirical eigenfunctions are more robust to for reduced order modelling with variations in modelling parameters such as thermal …