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What Engineers Know and How They Know It. Summary by David E. Goldberg University of Illinois at Urbana-Champaign deg@uiuc.edu. Text.
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What Engineers Know and How They Know It Summary by David E. Goldberg University of Illinois at Urbana-Champaign deg@uiuc.edu
Text • Vincenti, W. G. (1990). What engineers know and how they know it: Analytical studies from aeronautical history. Baltimore, MD: Johns Hopkins University Press.
Engineering is Just Applied Science • 1922: “Aeroplanes are not designed by science, but by art in spite of some pretence and humbug to the contrary.” • Historians of technology have split off from historians of science • View science and technology as two categories, related but distinguishable.
Goal of Engineering: Design • Normal design (by analogy to Kuhn’s normal science). • Versus radical design. • Design of artifacts as social activity
Design and Growth of Knowledge • B-24 airfoil design • Planform and airfoil • Consolidated Aircraft Corp. • Inventor David R. Davis. • Adopted and credited with B-24 long range. • Not in the main stream of airfoil thought.
Air Foil Evolution of Knowledge • Separation of planform and section. • Geometry first • Laminar v. turbulent boundary layer • Prolong laminar BL • Pressure distribution first • Analytical calculations based on conformal mapping.
Drivers of Knowledge • Decrease uncertainty • Increased performance: presumptive anomaly, when science indicates better result is possible • Functional failure: subjected to ever greater demands, applied in new situations. • Process: Selection and variation.
Establishment of Design Requirements • Problem: Flying quality specification. • Longitudinal stability • What stability and control characteristics needed? • How proportion aircraft to obtain? • Early schools of thought: • Chauffeurs vs. airmen • Inherent stability vs. active control.
Early Aircraft • Sopwith Camel, Curtis JN-4, Thomas Morse S-4C, longitudinally unstable. • Qualitative description of early aircraft followed in end by detailed specs.
7 Elements • Familiarization with artifact and recognition of problem. • ID of basic variables & derivation of concepts and criteria. • Development of instruments and technique. • Growth of opinion regarding desirable qualitities. • Development of practical scheme for research. • Measurement of characteristics for cross section of artifacts. • Assessment of results.
Theoretical Tool for Design • Example: Control volume models. • Bernoulli as forerunner. • Karman & Prandtl: Modern usage. • Useful to engineers not physicists. • Creation of artifacts dictates different choice of tools.
Engineering Science v. Science • Similarities: • Conform to same natural laws. • Diffuse by same mechanisms. • Cumulative: facts build on facts. • Differences • ES: create artifacts. S: understand nature • Skolimowski: technological progress = pursuit of effectiveness in producing objects of given kind.
Data for Design • Case: Durand propeller tests at Stanford, 1916-26. • History: • Smeaton: Waterwheel studies of 1759, systematic experiment + scale models. • Froude: testing of ship hulls 1868-1874. • Reynolds: 1883. • Dimensional analysis: Fourier (early 1800s), Rayleigh (late 1800s)
Parameter Variation • Via experimental or theoretical means. • Via experimental means is not peculiar to engineering. • Immediate interest in data for design, longer term interest in establishing a theory. • Produce data in absence of theory. • Indispensable for creation of such data. • Absence of theory a number of causes. • Scale models not necessary. • Optimization often part of the experimentation.
Design and Production • Case: Invention of flush riveting. • Innovation driven by aerodynamics. • Caused changes in production. • Bigger gains first (retractable gear, flaps). • 160,000 to 400,000 rivets per plane.
Dimpled Riveting • Science played no role in the story. • Each company pursued own program. • Different types of knowledge: • Explicit • Tacit
Problems Within Technology • Internal logic of technology: • Physical laws • Practical requirements dictate solution of problems. • Internal needs of design: e.g. quality specs.& design theory. • Need for decreased uncertainty.
Categorization of Engineering Design Knowledge • Fundamental design concepts. • Criteria and specifications. • Theoretical tools. • Quantitative data. • Practical considerations. • Design instrumentalities.
Knowledge Generating Activities • Transfer from science. • Invention • Theoretical engineering research • Experimental engineering research • Design practice • Production • Direct trial
Evolutionary Model of Knowledge Growth • Variation-Selection • Consistent with GAs • Not as detailed in its mechanisms.