Optimal Ascent Trajectories and Feasibility of Next-Generation Orbital Spacecraft View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1998-06

AUTHORS

A. Miele, S. Mancuso

ABSTRACT

This paper deals with the optimization of the ascent trajectories for single-stage-to-orbit (SSTO) and two-stage-to-orbit (TSTO) rocket-powered spacecraft. The maximum payload weight problem is studied for various combinations of initial thrust-to-weight ratio, engine specific impulse, and spacecraft structural factor. For TSTO rocket-powered spacecraft, two cases are studied: uniform structural factor and nonuniform structural factor between stages.The main conclusions are that: the design of SSTO configurations might be comfortably feasible, marginally feasible, or unfeasible, depending on the parameter values assumed; the design of TSTO configurations is not only feasible, but the payload appears to be considerably larger than that of SSTO configurations; for the case of a nonuniform structural factor, the most attactive TSTO design appears to be a first-stage structure made of only tanks and a second-stage structure made of engines, tanks, electronics, and so on.Improvements in engine specific impulse and spacecraft structural factor are desirable and crucial for SSTO feasibility; indeed, aerodynamic improvements do not yield significant improvements in payload weight.For SSTO configurations, the maximum payload weight behaves almost linearly with respect to the engine specific impulse and the spacecraft structural factor. The same property holds for TSTO configurations as long as the ratio of the structural factors of Stage 2 and Stage 1 is held constant. With reference to the specific impulse/structural factor domain, this property leads to the construction of a zero-payload line separating the feasibility region (positive payload) from the unfeasibility region (negative payload). More... »

PAGES

519-550

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/a:1022633924359

DOI

http://dx.doi.org/10.1023/a:1022633924359

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036016843


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