David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
In this paper, I would like to show that considering technological models as they arise in engineering disciplines can greatly enrich the philosophical perspective on models. In fluid mechanics, (at least) three types of models are distinguished: mathematical, computer and physical models. Very often, the choice of a particular mathematical, computer or physical model highly affects the type of solutions and the computational time needed for it. Technological models not only aim at a correct description of the physical phenomena, but also for an efficient and accurate simulation. The problem arises how heterogeneous models of an engineering problem can be brought together and be compared to each other as regards their function and technological efficiency. There are two developments in the history of fluid mechanics that have greatly influenced the use of models in the field: The introduction of the concept of the boundary layer by Ludwig Prandtl in 1904 made it possible to apply ideal analytical solutions, which at the time were almost entirely based on Euler’s equations for inviscid fluids, to interesting real cases and to approximate the theoretical Navier-Stokes equations to practical engineering problems, i.e. to cases at high Reynolds numbers. This made it possible to link the empirical tradition of hydraulics with the theoretical tradition of analytical mechanics and therefore lead to a kind of equilibrium in the use of mathematical and physical models. In the 1970s the introduction of the computer has greatly pushed back the importance of both physical and mathematical (analytic) models alike without making them superfluous. There remain, however, three different ways to conceive of physical models in fluid mechanics, and thus of the experimental ingredient, depending on whether they are devised from an analytical, computational or measurement theoretical point of view. Yet even inside the tradition of computer simulation, different practices have formed according to the programming methods used. The choice of method either depends on considerations of efficiency in terms of costs and time, or on historically contingent factors, like availability of instruments and programming packages or the arbitrary choice of a forerunner. Seen from a technological point of view the factors that make models “autonomous agents” and thus (relatively) independent from theory depend on efficiency constraints. Models are means to solve problems in a certain practical perspective by the most efficient means available. To develop a model is a “fast and frugal way” to get to grips with a certain region of reality, whereas the theoretical approach stresses the importance of universal features.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Margaret Morrison (2009). Models, Measurement and Computer Simulation: The Changing Face of Experimentation. Philosophical Studies 143 (1):33 - 57.
Mieke Boon, How Science is Applied in Technology: Explaining Basic Sciences in the Engineering Sciences.
Peter Krebs (2007). Virtual Models and Simulations. Techne 11 (1):42-54.
Michael J. White (1992). The Continuous and the Discrete: Ancient Physical Theories From a Contemporary Perspective. Oxford University Press.
Stephan Hartmann & Roman Frigg (2006). Models in Science. In Ed Zalta (ed.), The Stanford Encyclopedia of Philosophy. Stanford
Eric Winsberg (2006). Models of Success Versus the Success of Models: Reliability Without Truth. Synthese 152 (1):1 - 19.
Frederic Wieber, Theoretical Technologies in an “Experimental” Setting: Empirical Modeling of Proteinic Objects and Simulation of Their Dynamics Within Scientific Collaborations Around a Supercomputer.
Eckhart Arnold, Tools of Toys? On Specific Challenges for Modeling and the Epistemology of Models and Computer Simulations in the Social Sciences.
Susan G. Sterrett (2006). Models of Machines and Models of Phenomena. International Studies in the Philosophy of Science 20 (1):69 – 80.
Michael Heidelberger (2006). Applying Models in Fluid Dynamics. International Studies in the Philosophy of Science 20 (1):49 – 67.
Added to index2009-01-28
Total downloads25 ( #120,994 of 1,726,249 )
Recent downloads (6 months)12 ( #56,985 of 1,726,249 )
How can I increase my downloads?