Post processing of multiple GPS receivers to enhance baseline accuracy

Inexpensive, commercial available off-the-shelf (COTS) Global Positioning Receivers (GPS) have typical accuracy of ±3 meters when augmented by the Wide Areas Augmentation System (WAAS). There exist applications that require position measurements between two moving targets. The focus of this work is to explore the viability of using clusters of COTS GPS receivers for relative position measurements to improve their accuracy. An experimental study was performed using two clusters, each with five GPS receivers, with a fixed distance of 4.5 m between the clusters. Although the relative position was fixed, the entire system of ten GPS receivers was on a mobile platform. Data was recorded while moving the system over a rectangular track with a perimeter distance of 7564 m. The data was post processed and yielded approximately 1 meter accuracy for the relative position vector between the two clusters.

Aerothermodynamic cycle analysis of a dual-spool, separate-exhaust turbofan engine with an interstage turbine burner

This study focuses on a specific engine, i.e., a dual-spool, separate-flow turbofan engine with an Interstage Turbine Burner (ITB). This conventional turbofan engine has been modified to include a secondary isobaric burner, i.e., ITB, in a transition duct between the high-pressure turbine and the low-pressure turbine. The preliminary design phase for this modified engine starts with the aerothermodynamics cycle analysis is consisting of parametric (i.e., on-design) and performance (i.e., off-design) cycle analyses. In parametric analysis, the modified engine performance parameters are evaluated and compared with baseline engine in terms of design limitation (maximum turbine inlet temperature), flight conditions (such as flight Mach condition, ambient temperature and pressure), and design choices (such as compressor pressure ratio, fan pressure ratio, fan bypass ratio etc.). A turbine cooling model is also included to account for the effect of cooling air on engine performance. The results from the on-design analysis confirmed the advantage of using ITB, i.e., higher specific thrust with small increases in thrust specific fuel consumption, less cooling air, and less NOx production, provided that the main burner exit temperature and ITB exit temperature are properly specified. It is also important to identify the critical ITB temperature, beyond which the ITB is turned off and has no advantage at all. With the encouraging results from parametric cycle analysis, a detailed performance cycle analysis of the identical engine is also conducted for steady-stateengine performance prediction. The results from off-design cycle analysis show that the ITB engine at full throttle setting has enhanced performance over baseline engine. Furthermore, ITB engine operating at partial throttle settings will exhibit higher thrust at lower specific fuel consumption and improved thermal efficiency over the baseline engine. A mission analysis is also presented to predict the fuel consumptions in certain mission phases. Excel macrocode, Visual Basic for Application, and Excel neuron cells are combined to facilitate Excel software to perform these cycle analyses. These user-friendly programs compute and plot the data sequentially without forcing users to open other types of post-processing programs.

Computational fluids domain reduction to a simplified fluid network

The primary goal of this project is to demonstrate the practical use of data mining algorithms to cluster a solved steady-state computational fluids simulation (CFD) flow domain into a simplified lumped-parameter network. A commercial-quality code, “cfdMine” was created using a volume-weighted k-means clustering that that can accomplish the clustering of a 20 million cell CFD domain on a single CPU in several hours or less. Additionally agglomeration and k-means Mahalanobis were added as optional post-processing steps to further enhance the separation of the clusters. The resultant nodal network is considered a reduced-order model and can be solved transiently at a very minimal computational cost. The reduced order network is then instantiated in the commercial thermal solver MuSES to perform transient conjugate heat transfer using convection predicted using a lumped network (based on steady-state CFD). When inserting the lumped nodal network into a MuSES model, the potential for developing a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track temperatures near specific objects (such as equipment in vehicles).