DATA ENGINEERING IN AEROSPACE SYSTEMS DESIGN & FORECASTING ERIC HANEY Presented to the Faculty of the Graduate School of The University of Texas at Arlington in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSPHY IN AEROSPACE ENGINEERING THE UNIVERSITY OF TEXAS AT ARLINGTON May 2016 Copyright © by Eric Haney 2016 All Rights Reserved ACKNOWLEDGEMENTS I have not reached this point alone. Although I can never truly repay those that have supported me through this journey, it is my honor to pay tribute to their contributions. First, to my parents, Janice and Rusty Haney, who have given me every opportunity to pursue my passions, both academically and personally. You have understood my potential from an early age and always expected excellence. I owe my work ethic and drive to you both – I would not have achieved this degree without you. Next, to my colleagues at the Aerospace Vehicle Design Laboratory, who have exposed to me to a new world of ideas and concepts it takes most engineers a lifetime to experience. It has been a privilege to work with Lex Gonzalez, Amit Oza, Amen Omoragbon, Dr. Gary Coleman and the other great minds at the AVD Lab – the intersection of challenging, meaningful problems and passionate, talented engineers has been a fantastic environment to grow as a professional. To my colleagues at Lone Star Aerospace, who have patiently supported the final leg of this degree. Your constant encouragement has invigorated me through the finish line. To my friends and family who have, at times, wondered why I am so often absent or so slow to return phone calls, I hope you know you always been in my thoughts. I look forward to seeing more of you all in the coming years. Finally, to my wife, Kate. You didn’t know what you were signing up for when we moved to Arlington together – and you probably wouldn’t have if you had understood the realty. You have been on the front lines with me the entire time. I’m not sure I would have had the strength of character to complete this dissertation by myself, but thankfully I didn’t have to. Your sacrifices to make this a reality will never be forgotten. I love you and congratulations on your PhT! iii ABSTRACT Data-Engineering in Aerospace Systems Design & Forecasting Eric Haney The University of Texas at Arlington, 2016 Supervising Professor: Dr. Bernd Chudoba Although engineers spend the majority of their time finding, managing, and transforming data, the process of how to handle data remains under-educated & under-researched. The alignment of engineering education & proficiency along an analysis-dominated mindset is ill-conditioned for the growing reliance on data-driven processes and holistic decision-making requirements. Although a majority of driving decisions in the aerospace industry rely heavily on utilizing existing organizational data-stores and built-in knowledge bases to make informed decisions, formalized development & implementation of data within existing engineering processes is seldom addressed. The current research defines a complementary discipline, Data-Engineering, which seeks to capitalize on the exponential growth in availability of technical and socio-technical data to better inform aerospace decision-making. In particular, those design & forecasting decisions made early in the product development life-cycle are selected as the most impactful to the aerospace industry, as well as presenting the most under-exposed opportunity for improvement. This class of decisions presents unique challenges for forecasting situations with highly coupled technical & non-technical considerations. Adequately capturing market, environmental, political, social, & political issues alongside technical considerations is essential to advance the process of forecasting the total system capability & benefit of aerospace vehicle systems. The driving problems in aerospace for current and future generations of engineers will all require significant up-front investments of time, fiscal & human capital, and are therefore beholden to quality forecasting. The primary contribution of this research has been the specification & development of Data- Engineering software system, designed to support aerospace design & forecasting efforts. Specification for the Data-Engineering System has extracted best-practices from existing fields – data and data system standards from information sciences, data-model integration from systems engineering, and vehicle system analysis from aerospace engineering. The result has been a holistic iv perspective of the features and requirements for a Data-Engineering System capable of adapting to a wide range of aerospace design & forecasting problems. The end-product provides the engineer a single software environment to access previous analyses, catalogue research source documents, store supplementary datasets, characterize data systems, execute multi-disciplinary design analysis, and visualize information deliverables – all of which are performed, stored and catalogued in an organizationally-linked environment for future reference. When applied under a controlled experiment case study, the Data-Engineering System produces accurate results while substantially reducing required engineering resources – an assessment of re-entry capsule solution space showed a reduction in effort from 2 man-weeks to 2 man-days to produce similar outputs. By automating repetitive non-cognitive data tasks, the Data- Engineering System reduces the overall time required to execute a forecasting effort and enables the forecasting engineer to spend more time on higher level cognitive tasks, i.e. assessing multi- disciplinary analysis approaches; communicating results towards decision-makers. Additionally, the standardization and integration of all data tasks in an organizationally-shared environment has been leveraged to produce intelligent design feedback features which automatically identify and define tangentially systems, system analyses, and information deliverables for considerations. These features provide the foundation for artificially-intelligent design systems in future development. v TABLE OF CONTENTS Table of Contents ............................................................................................................. vi List of Illustrations ......................................................................................................... xiv List of Tables ............................................................................................................... xviii Chapter 1 Introduction ..................................................................................... 22 1.1 Aerospace Design & Forecasting ......................................................................... 22 1.2 The Creation of Decision-Making Information .................................................... 24 1.3 Need for Data & Knowledge Engineering in Aerospace Forecasting .................. 25 1.4 Formalization of Data-Engineering as a Required Discipline .............................. 27 1.5 Summary & Outline .............................................................................................. 31 Chapter 2 Introduction to Information & Information Systems . 34 2.1 Introduction to the Decision-Making Process ...................................................... 34 2.1.1 Military Decision-Making Process ........................................................... 35 2.1.2 Engineering Decision-Making Process ..................................................... 36 2.1.3 Representative Decision-Making Process ................................................ 37 2.1.4 Data-Information-Knowledge Distinction ................................................ 39 2.2 Classification of Data Systems ............................................................................. 42 2.2.1 Components of Data ................................................................................. 42 2.2.2 Steps of Data Processing .......................................................................... 43 2.2.2.1 Collection ............................................................................................................ 45 2.2.2.2 Storage................................................................................................................. 45 2.2.2.3 Organization ........................................................................................................ 45 2.2.2.4 Recall................................................................................................................... 46 2.2.2.5 Analysis ............................................................................................................... 46 2.2.2.6 Visualization ....................................................................................................... 46 2.3 Understanding Data-Engineering Performance .................................................... 47 2.3.1 Dimensionality of Performance Metrics ................................................... 48 2.3.2 Metrics of Data Quality ............................................................................ 48 2.3.2.1 Scope ................................................................................................................... 48 2.3.2.2 Density ................................................................................................................ 49 2.3.2.3 Meta-Data Ratio .................................................................................................. 49