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Drag Modulated Aerocapture (DMA) for Venus orbiter missions relies on the deployable aeroshell ADEPT to perform orbit insertion Delta-V
NASA Ames Entry Systems and Technology Division has been leading development and testing of the Adaptable, Deployable, Entry and Placement Technology (ADEPT). A teaming partnership with JPL has been established to study and develop a compelling application of the ADEPT mechanically deployable hypersonic decelerator as part of the Venus Drag Modulated Aerocapture (VDMA) JPL R&TD effort. The primary work of this activity is to develop a design tool for the ADEPT drag skirt (e.g. a CFD-anchored CBAERO model) that enables rapid estimation of aerothermal environments for a range of candidate trajectories. This capability will enable rapid feasibility trades and design assessments for early stage mission concept evaluation for ADEPT Drag Modulated Aerocapture applications. Results of using TPSizer provided an ADEPT carbon fabric design thickness of the required number of fabric layers needed for predicted aerothermal environments and still have sufficient fabric remaining for overall vehicle integrity. This resulted in aerothermal design closure of the ADEPT drag skirt. In addition, the similar methodology for Venus DMA was used to assess a similar DMA operation at Earth as part of a possible Technology Demonstration Mission (TDM) for ADEPT DMA. This analysis showed that there is a close similarity of design approaches for an Earth DMA TDM that would be applicable to Venus DMA mission designs.
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Estimation of radioactive waste inventory is a key initial step in the development of an environmental fate and transport model for performance assessment (PA) of radioactive waste sites. PA model output is often sensitive to parameters that represent inventory as a source term, yet estimation is a difficult problem. Inventory estimation for a probabilistic performance assessment (PPA) is related to, but distinct from, that performed for waste excavation, characterization, and offsite disposal. The latter appropriately focuses on obtaining the most detailed information possible on specific hazards and locations; however, these questions cannot be fully answered without intrusive site characterization efforts. This type of characterization is often not feasible when assembling information regarding a source term in a PPA model. Development of waste inventory for a PPA is one piece of a larger hierarchical model. The physical process model represented by Goldsim computer code represents one level of the hierarchy, and the second level of the hierarchy involves estimation of inputs such as inventory, transport, and risk parameters. The estimation of inputs is not a straightforward problem. The parameters of a computer model often do not have an equivalence to the physical quantities they describe, and estimation of the physical quantities themselves can be difficult due to limitations of available information. Inventory information for the West Valley Site in upstate New York is based on data from historical shipping and disposal forms. In the 1990's, the West Valley Demonstration Project (WVDP) compiled information from existing databases and historical waste records into one complete modern database. A conceptual model was then applied and used with recorded information to characterize the waste mass and radionuclide composition for each waste burial. Information was ultimately aggregated over all waste records to estimate the mass of each radionuclide within each trench, hole, or caisson. Three decades later, we revisit the master database, make updates with newly-identified information, and incorporate the conceptual model previously used for inventory estimation in the hierarchical modeling framework. We characterize uncertainty using probability distributions for waste mass and radionuclide composition at the individual shipment or individual burial level. Uncertainty incorporated at the lowest level of the hierarchy is ultimately carried through to uncertainty in PPA model endpoints. Sensitivity analysis of early model results is then applied to identify influential inputs and guide subsequent efforts towards parameters that drive risk. (authors)
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