Tradespace Investigation of a Telescope Architecture for Next-generation Space Astronomy and Exploration

Humanity’s endeavor to further its scientific understanding of the celestial heavens has led to the creation and evolution of increasingly powerful and complex space telescopes. Space telescopes provide a view of the solar system, galaxy, and universe unobstructed by Earth’s atmosphere and have profoundly changed the way people view space. In an effort to further advance space telescope capability and achieve the accompanying scientific understanding, the Massachusetts Institute of Technology (MIT), specifically, course 16.89 Space Systems Engineering, explored the tradespace of architectural enumerations encompassed within the design of an ultraviolet-optical-infrared (UVOIR) space telescope located at Sun-Earth Lagrangian Point Two (SE-L2). SE-L2 presents several advantages as an operating location for a UVOIR telescope such as a thermally stable environment and an orbit that allows the telescope to maintain a constant orientation with respect to all of the primary sources of heat and light. The main disadvantages associated with SE-L2 are caused by its relatively large distance from Earth, which marginalizes the effectiveness of real-time telerobotics because of latency and increases the cost of communications, launch, and servicing. Course 16.89 believes that, for this UVOIR application, the strengths of this operating location outweigh its weaknesses and therefore decided to explore the family of opportunities associated with SE-L2. This course used appropriate performance and system metrics to quantify the effectiveness of the aforementioned architectures and create a Pareto front of viable architectures. Evaluating the designs along the Pareto front allowed the course to characterize and group architectures and present these group-types to stakeholders for the selection of an optimal space telescope according to stakeholder requirements and resources. This course also developed sensitivity analysis, which allowed for a greater understanding of how architectural decisions affect the performance of the satellite. Segmentation, modularity, assembly, autonomy, and servicing were key aspects of this multidimensional analysis given the 16.8-meter class size and location of the telescope. Within the respective operating environment and for a spacecraft of similar characteristics, this model will allow stakeholders to predict the long-term operational effectiveness of different space telescope architectures and capture the synergistic effects of combining various architectural decisions into a spacecraft design. The following sections step through the aforesaid analysis and design efforts conducted in 16.89 beginning with Section III, which explicitly performs the stakeholder analysis and articulates the requirements of the mission. Section IV gives an overview of past designs and expands upon the architecture enumerations pertinent to this project, while Section V presents the methods and metrics by which those architectures will be evaluated and the system metrics which will be balanced and optimized in the creation of this space telescope. Section VI will present the model validation of this project and Section VII will discuss the results and analyses of the project. Finally, Section VIII will explore the future work opportunities of this project, while Section IX will present the conclusions and recommendations drawn from this project.MIT Department of Aeronautics and Astronautic

Cognitive systems and model-based approaches for spacecraft architecture development

Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, Engineering Systems Division, 2014.Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 230-239).Systems engineering exists as a discipline to enable organizations to control and manage the development of complex hardware and software. These methods are particularly essential in the development of space systems, which feature extremely challenging demands for engineering performance, coupled with extremely limited resources for accomplishing them. Success requires careful attention to the relationships between various components as well as the organizations constructing them. Unfortunately, aerospace organizations routinely struggle with the traditional systems engineering process, and as a result, program managers experience pressure to conclude, curtail or ignore critical elements. The consequence is that cost overruns, slipped schedules and outright failures are a regular feature of the industry. Recent advances in Model-Based Systems Engineering (MBSE) tools and methods provide an opportunity to rectify these issues by better integrating systems engineering capabilities into the engineering development process. By directly networking the engineering models used in the development process to each other and the systems diagrams which describe them, MBSE has the potential to make the development process more responsive to design evolutions and account for changes across the entire space system. In this way, systems engineering could become a more integrated part of the development process and better contribute to successful space systems. Unfortunately, current-generation MBSE tools and methods have yet to fully realize this potential. Critical capability gaps have deterred adoption and relegated their use to academic endeavors. This thesis argues that many of the difficulties encountered in current systems engineering practice - as well as attempts to reform that practice - can be explained with reference to distributed cognition, control theory and the wider field of cognitive systems engineering. Existing tools and techniques, while nominally fulfilling the purposes assigned to them, generally fail to adequately support systems engineers in the cognitive tasks associated with the control and management of development processes. As a result, systems engineers are frequently overburdened in their roles and are unable to fully address the myriad of concerns relevant to the design of good system solutions. A cognitive analysis of the software and hardware devices situated in practical instantiations of development activities can reveal opportunities to improve performance and enhance effectiveness. Such changes would make systems engineering tools easier to use and better tailored to the needs of the system engineering task, encouraging adoption and accomplishing the goals of the MBSE community. A cognitively-informed MBSE approach, in addition to better linking the elements of the engineering effort, can also be used to link the engineering effort to the higher-level needs which drive the engineering process in the first place. One of the biggest challenges any engineering organization faces is managing the "how," "why," and "what" of system development, that is, the engineering logic which determines "how" a given program or system will be built and the business, political or policy logic which determines "why" and "what" system will come into being. Often, these latter concerns are poorly addressed by the space system development process, which can lead to sub-optimal outcomes for the wider organizations involved in the engineering project. Methods which better systematize, quantify and direct the process of stakeholder analysis, concept generation and architecture exploration can aid in the selection of system architectures that better meet the strategic objectives of the organizations which develop and operate space systems. Such methods are demonstrated with respect to an evaluation of possible architectures for a notional large, ultraviolet-visible-near-infrared (UV-VIS-NIR) optical space telescope to succeed Hubble in the late 2020s to early 2030s timeframe. This research draws on MBSE concepts and the legacy of tradespace modeling for system design to extend tradespace modeling to the realm of architectural exploration. Its particular interest is the quantitative treatment of "programmatic factors": the business, policy and political considerations which govern high-level decision-making. Through modeling, these considerations can be directly associated with engineering performance factors, enabling better selection decisions and reinforcing linkages and understanding between the engineering and management levels within an organization. It is intended to leverage existing work in stakeholder modeling, real options, strategic evolution and tradespace exploration to bridge existing divisions between systems engineering and programmatic decision-making processes which can lead to poorly optimized architectures. It is geared towards systems engineers and program managers seeking to account for organizational and higher-level stakeholder needs during the tradespace exploration process and more efficiently and practically integrate these decision frameworks in real-world engineering environments.by Brandon Karlow.S.M. in Technology and PolicyS.M

How Alkyl Halide Structure Affects E2 and S_N2 Reaction Barriers: E2 Reactions Are as Sensitive as S_N2 Reactions

High-level electronic structure calculations, including a continuum treatment of solvent, are employed to elucidate and quantify the effects of alkyl halide structure on the barriers of S_N2 and E2 reactions. In cases where such comparisons are available, the results of these calculations show close agreement with solution experimental data. Structural factors investigated include α- and β-methylation, adjacency to unsaturated functionality (allyl, benzyl, propargyl, α to carbonyl), ring size, and α-halogenation and cyanation. While the influence of these factors on S_N2 reactivity is mostly well-known, the present study attempts to provide a broad comparison of both S_N2 and E2 reactivity across many cases using a single methodology, so as to quantify relative reactivity trends. Despite the fact that most organic chemistry textbooks say far more about how structure affects S_N2 reactions than about how it affects E2 reactions, the latter are just as sensitive to structural variation as are the former. This sensitivity of E2 reactions to structure is often underappreciated