Presentation on theme: "What Engineers Know and How They Know It Summary by David E. Goldberg University of Illinois at Urbana-Champaign"— Presentation transcript:
What Engineers Know and How They Know It Summary by David E. Goldberg University of Illinois at Urbana-Champaign firstname.lastname@example.org
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.