Near-Earth Object Characterization Priorities For Planetary Defense
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This paper is part of an integrated study by NASA and the NNSA to quantitatively understand the response timeframe should a threatening Earth-impacting near-Earth object (NEO) be identified. The two realistic responses considered are the use of a spacecraft functioning as either a kinetic impactor or a nuclear explosive carrier to deflect the approaching NEO. The choice depends on the NEO size and mass, the available response time prior to Earth impact, and the various uncertainties. Whenever practical, the kinetic impactor is the preferred approach, but various factors, such as large uncertainties or short available response time, reduce the kinetic impactor's suitability and, ultimately, eliminate its sufficiency. Herein we examine response time and the activities that occur between the time when an NEO is recognized as being a sufficient threat to require a deflection and the time when the deflection impulse is applied to the NEO. To use a kinetic impactor for successful deflection of an NEO, it is essential to minimize the reaction time and maximize the time available for the impulse delivered to the NEO by the kinetic impactor to integrate forward in time to the eventual deflection of the NEO away from Earth impact. To shorten the response time, we develop tools to survey the profile of needed spacecraft launches and the possible mission payloads. We further present a vehicle design capable of either serving as a kinetic impactor, or, if the need arises, serving as a system to transport a nuclear explosive to the NEO. These results are generated by analyzing a specific case study in which the simulated Earth-impacting NEO is modeled very closely after the real NEO known as 101955 Bennu (1999 RQ36). Bennu was selected for our case study in part because it is the best-studied of the known NEOs. It is also the destination of NASA's OSIRIS-REx sample return mission, which is, at the time of this writing, enroute to Bennu following a September 2016 launch.
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There has been growing demand for the use of small Unmanned Aircraft Systems (UAS) domestically and globally. The versatility of vehicles to support many use cases and business models with broad advances in technology has created an industry with clear growth and continued growth potential. However, an early barrier to operations at scale has been the lack of a coordinated airspace management approach. To address that barrier, NASA pioneered a revolutionary airspace management paradigm that incorporated a federated, service-based approach to enable fair, safe, and scalable operations of small UAS in the nation’s airspace. This paradigm came to be known as UAS Traffic Management (UTM) [1]. During the UTM Project, NASA worked closely with the Federal Aviation Administration (FAA) and Industry to develop a system and supporting concept that incorporated the needs and perspectives of Industry and balanced them with the regulatory and operational needs of the FAA. Through development and rigorous testing, NASA evolved and strengthened the UTM concept and associated system architecture hand-in-hand with partners and stakeholders, which has gone on to take hold globally and move forward toward dedicated implementation in the US through rulemaking and standards bodies.
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This is a three-paper dissertation that responds to a call for empirical research on stakeholder influence in the implementation of evaluation recommendations. The literature is consistent but mostly theoretical: stakeholder participation should positively influence and increase the probability of implementation (see e.g., Christie, 2011; Cousins & Earl, 1992; Fetterman, 2004). In response, I use two Goddard Space Flight Center engineering technical training and development programs to build a causal argument supporting my proposition that stakeholder influence in the logic modeling process impacts organizational decision-making and change. I first introduce the logic modeling process as a tool to involve stakeholders in program and evaluation design (paper 1); then use the data collection process to implement the collaboratively constructed logic model outcomes (paper 2); and, finally, I triangulate the data using the Toal (2009) Evaluation Involvement Scale (paper 3). I do this to “increase the validity of the findings” (Noble & Heale, 2019, p. 67) and to garner potential mechanisms. I found that stakeholder participation in the logic modeling process positively influenced the use of data to inform recommendations which in turn positively influenced implementation of evaluation recommendations. I identified the process itself as the mechanism and the key decision points as links or influencers. Potential factors included power and interest (measured by role) and time-on-job.
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