Generating Exploration Mission-3 Trajectories to a 9:2 NRHO using Machine Learning

The purpose of this thesis is to design a machine learning algorithm platform that provides expanded knowledge of mission availability through a launch season by improving trajectory resolution and introducing launch mission forecasting. The specific scenario addressed in this paper is one in which data is provided for four deterministic translational maneuvers through a mission to a Near Rectilinear Halo Orbit (NRHO) with a 9:2 synodic frequency. Current launch availability knowledge under NASA’s Orion Orbit Performance Team is established by altering optimization variables associated to given reference launch epochs. This current method can be an abstract task and relies on an orbit analyst to structure a mission based off an established mission design methodology associated to the performance of Orion and NASA\u27s Space Launch System. Introducing a machine learning algorithm trained to construct mission scenarios within the feasible range of known trajectories reduces the required interaction of the orbit analyst by removing the needed step of optimizing the orbit to fit an expected translational response required of the spacecraft. In this study, k-Nearest Neighbor and Bayesian Linear Regression successfully predicted classical orbital elements for the launch windows observed. However both algorithms had limitations due to their approaches to model fitting. Training machine learning algorithms off of classical orbital elements introduced a repetitive approach to reconstructing mission segments for different arrival opportunities through the launch window and can prove to be a viable method of launch window scan generation for future missions

Monte Carlo Tree Search Applied to a Modified Pursuit/Evasion Scotland Yard Game with Rendezvous Spaceflight Operation Applications

This thesis takes the Scotland Yard board game and modifies its rules to mimic important aspects of space in order to facilitate the creation of artificial intelligence for space asset pursuit/evasion scenarios. Space has become a physical warfighting domain. To combat threats, an understanding of the tactics, techniques, and procedures must be captured and studied. Games and simulations are effective tools to capture data lacking historical context. Artificial intelligence and machine learning models can use simulations to develop proper defensive and offensive tactics, techniques, and procedures capable of protecting systems against potential threats. Monte Carlo Tree Search is a bandit-based reinforcement learning model known for using limited domain knowledge to push favorable results. Monte Carlo agents have been used in a multitude of imperfect domain knowledge games. One such game was in which Monte Carlo agents were produced and studied in an imperfect domain game for pursuit-evasion tactics is Scotland Yard. This thesis continues the Monte Carlo agents previously produced by Mark Winands and Pim Nijssen and applied to Scotland Yard. In the research presented here, the rules for Scotland Yard are analyzed and presented in an expansion that partially accounts for spaceflight dynamics in order to study the agents within a simplified model, while having some foundation for use within space environments. Results show promise for the use of Monte- Carlo agents in pursuit/evasion autonomous space scenarios while also illuminating some major challenges for future work in more realistic three-dimensional space environments

Issues and Challenges in Teaching Secondary School Quantum Physics with Integrated STEM Education in Malaysia

The emphasis on STEM education in the physics curriculum moves toward addressing the 21st-century demands, but its implementation is fraught with issues and challenges. This paper exposes teachers’ and students’ concerns and problems with integrated STEM education implementation and relates them to the anticipated problem in quantum physics (QP) learning and facilitation (L&F) in secondary school. The QP L&F challenges include the odd ontological worldview and abstractness of concepts, which have created serious misconceptions among teachers and students. A solution is proposed to address this difficulty, including applying an interactive simulation and a hands-on experiment. This paper also proposes a theoretical framework for developing an instructional module to cater to meaningful QP learning with integrated STEM elements. The proposed theoretical framework has several advantages, including guidance in planning an instructional module applicable to classroom activities and explaining the topic using an inquiry-based learning (IBL) approach with learning activities coordinated using the 5E Instructional Model. Nonetheless, further research is necessary to study the instructional module’s development, usability, and L&F effectiveness in the classroom

SciTech News Volume 71, No. 2 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division 9 Aerospace Section of the Engineering Division 12 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 14 Reviews Sci-Tech Book News Reviews 16 Advertisements IEEE

Kuhn Losses Regained: Van Vleck from Spectra to Susceptibilities

We follow the trajectory of John H. Van Vleck from his 1926 Bulletin for the National Research Council (NRC) on the old quantum theory to his 1932 book, The Theory of Electric and Magnetic Susceptibilities. We highlight the continuity of formalism and technique in the transition from dealing with spectra in the old quantum theory to dealing with susceptibilities in the new quantum mechanics. Our main focus is on the checkered history of a numerical factor in the Langevin-Debye formula for the electric susceptibility of gases. Classical theory predicts that this factor is equal to 1/3. The old quantum theory predicted values up to 14 times higher. Van Vleck showed that quantum mechanics does away with this "wonderful nonsense" (as Van Vleck called it) and restores the classical value 1/3. The Langevin-Debye formula thus provides an instructive example of a Kuhn loss in one paradigm shift that was regained in the next. In accordance with Kuhn's expectation that textbooks sweep Kuhn losses under the rug, Van Vleck did not mention this particular Kuhn loss anywhere in his 1926 NRC Bulletin (though he prominently did flag a Kuhn loss in dispersion theory that had recently been regained). Contrary to Kuhn's expectations, however, he put the regained Kuhn loss in susceptibility theory to good pedagogical use in his 1932 book. Kuhn claimed that textbooks must suppress, truncate, and/or distort the prehistory of their subject matter if they are to inculcate the exemplars of the new paradigm in their readers. This claim is not borne out in this case. Because of the continuity of formalism and technique that we draw attention to that Van Vleck could achieve his pedagogical objectives in his 1932 book even though he devoted about a third of it to the treatment of susceptibilities in classical theory and the old quantum theory in a way that matches the historical record reasonably well.Comment: This paper will be published in: Massimiliano Badino and Jaume Navarro (eds.), Research and Pedagogy: A History of Early Quantum Physics through its Textbooks, Berlin: Edition Open Access, forthcoming. This volume is part of a larger project on the history of quantum physics of the Max Planck Institute for History of Science in Berli