5Conclusion

Author
Leonardo Pinero-Perez
Written
2018 · Thesis, University of Texas at Arlington
Original
Full thesis · 105 pages
Contents
Chapter 1 · 2 · 3 · 4 · 5 · Appendix A: Nomenclature · References · Acknowledgments and Biographical Information

Converted from the original PDF on with Claude Opus 5.5. The wording is unchanged, except: my name is spelled without accents, and Tables 1.1 and 1.2, pictures in the PDF, are typed out. Equations, figures and numbering follow the original. Conversion not yet checked against the PDF.

5.1Concluding Remarks

This thesis has laid the groundwork for assessing the topology of re-entry vehicle configurations based on six geometric parameters. These parameters can approximate several historical designs, which can be assessed on this topology.

The performance analysis included assessing payload, inclination change capability (using both synergetic and propulsive mission profiles), and a descent analysis. The descent analysis had three main components: cross-range, g-loading, and heating. These performance parameters were then weighted by importance to converge on historical designs, where it was found that payload capability needed more of a weighting value to compensate for high structural weight. In this way, the weighting value here was related to the assumed structural index.

The optimization routine brought about several interesting observations, such as proof of the importance of the physical model. Whenever a solution was converged upon, this led to insight on the important variables to the model. The algorithm converged on an aircraft that eventually ignored lifting capability, favoring a minimum-volume design to reduce structural mass. This was the result of a high logic-weight on the payload fraction.

By the nature of the topic, there is much room for additional analysis to improve on the algorithm developed by the author. Some of the major points for improvement are discussed below.

5.2Future Work

The work done in this thesis is only a foundation of a design methodology that will require more analysis methods to include more design considerations. For example, the most glaring hole in these analyses is that of determination of aircraft stability throughout the entire flight regime. A stable static margin was assumed since the delta wing starts at the aft chord endpoint, and the payload was assumed at the front of the spacecraft. More geometric parameters are required to ensure stability to factor into the “mission success” variable in the vehicle assessment function.

While this re-entry vehicle is analyzed for hypersonic aerodynamics, design for subsonic regimes are still required for advantageous operation. The author has assumed from literature that 8% of the structural weight was allocated to the decoupled mode, as applied to capsule conceptual design. However, assuming a decoupled mode does not utilize the high-lift capability of higher-performing designs.

The descent profile is somewhat disconnected from the geometric parameters, as assumptions were made to determine the lift and drag coefficients apart from merely the ratio between the two. However, the geometric analysis provides a starting point for computing these forces to generate vehicle parameters to accurately model the descent profile for a particular configuration. Future work should pay closer attention to the heating of the spacecraft, as this was not deeply covered in this thesis (thermal-protection system parameters assumed to be covered by the structural index).

The addition of planetary bodies other than earth are speculative, as no manned missions have been conducted in this environment at the time of this writing. In light of this, future work could possibly be written in a time where there is actually historical validation for the analysis conducted here. At this point, there could be a continuous link between the parameters of planetary bodies and their respective vehicle performances. In this case, some manned vehicles may have mission profiles requiring a configuration which is a compromise between two environments. This will require an extension of the optimization algorithm and a supplementing solution space generator.

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