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
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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
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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
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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
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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
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Fundamental design concepts.
Criteria and specifications.
Theoretical tools.
Quantitative data.
Practical considerations.
Design instrumentalities.
Knowledge Generating Activities
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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.