1. 0. Objective and Scope 0.1 Third need in science and engg education 0.2 Intent of book 0.3 Applications 0.4 Assumed background of student 1Chap 0-Data Analysis-Reddy

2. 0.1 Third Need in Science and Engg Education Historically engg education was predominantly - process oriented: focusing on basics (ex. heat transfer equations) - while engg practice was system oriented (ex. Design of heat exchangers) - this led to capstone or senior design class requirements Third need emerging- to analyze, interpret and model data from actual systems; ex. Operation of heat exchanger Driven by fact that sensor and data acquisition are cheap, reliable and part of the system itself Major societal problem: most of the major infra-structure systems are in dire need of repair and maintenance 2Chap 0-Data Analysis-Reddy

3. 3 American Society of Civil Engineers Chap 0-Data Analysis-Reddy

4. 4 2009 Grades AviationAviation D BridgesBridges C DamsDams D Drinking WaterDrinking Water D- EnergyEnergy D+ Hazardous WasteHazardous Waste D Inland WaterwaysInland Waterways D- LeveesLevees D- Public Parks and RecreationPublic Parks and Recreation C- RailRail C- RoadsRoads D- SchoolsSchools D Solid WasteSolid Waste C+ TransitTransit D WastewaterWastewater D- America's Infrastructure GPA: D Estimated 5 Year Investment Need: $2.2 Trillion

5. 0.2 Intent of Book To provide: a basic and unified perspective (or road map) of the various techniques of data based mathematical modeling and analysis Strong appreciation of concepts behind different analysis and modeling strategies and methods- advantages and disadvantages an understanding of the “process” (i.e., framing objectives, making assumptions, setting up an analysis methodology) along with the tools (identify suitable tools, interpret results,…) a review of classical statistics and probability concepts well-conceived examples and problems involving real-world data that would illustrate these concepts within the purview of specific areas of application 5Chap 0-Data Analysis-Reddy

6. Focus here is on engineering systems (as against economics or social sciences, or even medicine) which are characterized by: - low epistemic uncertainty (associated with the process) fairly strong relationships or causality - ease in gathering large amounts of data - data quality is generally good Class is: -More hands-on approach, less theory and mathematics -Scope of analysis techniques primarily for thermal, energy-related, environmental and industrial systems Does not focus on software implementation- merely being able to operate a software package does not provide insight into suitable model structure or system behavior embedded in data 6Chap 0-Data Analysis-Reddy

7. 0.3 Applications Forward models- used for system simulation and design Data driven models or inverse models- used for system operation, control or for acquiring insights into internal behavior of unknown systems Numerous applications: various engineering disciplines, business, medical, and physical, natural and social sciences. Though the basic concepts are the same, the diversity in these disciplines results in rather different focus and differing emphasis of the analysis methods. This diversity may be in the process itself, in the type and quantity of data, and in the intended purpose of analysis 7Chap 0-Data Analysis-Reddy

8. Two types of data collection schemes: (a) Experimental data: observer can perform tests on system (b) Observational data: observer cannot interfere with system functioning once a system is designed and built: -how to evaluate its condition in terms of design intent -if possible, operate it in an “optimal” manner under variable operating conditions (minimize cost or carbon footprint, …) 8Chap 0-Data Analysis-Reddy

9. Data analysis and data driven modeling methods are meant to achieve certain practical ends: (a) verifying stated claims of manufacturer; (b) product improvement or product characterization from performance data of prototype; (c) health monitoring of a system, i.e., how does one use quantitative approaches to reach sound decisions on the state or “health” of the system based on its monitored data? (d) controlling a system, i.e., how best to operate and control it on a day-to-day basis? (e) identifying measures to improve system performance, and assess impact of these measures; (f) verification of the performance of implemented measures, i.e., are the remedial measures implemented impacting system performance as intended? 9Chap 0-Data Analysis-Reddy

10. 0.4 Assumed Background of Student -Undergraduate level knowledge of traditional topics such as physics, mathematics (linear algebra and calculus), fluids, thermodynamics and heat transfer, design and optimization methods, -Some exposure to experimental methods, probability, statistics and regression analysis (taught in lab courses at the freshman or sophomore level). -Some basic familiarity with important energy and environmental issues facing society today (so as to provide context) 10Chap 0-Data Analysis-Reddy