Technological Concepts and Mathematical Models in the Evolution of Modern Engineering Systems: Controlling • Managing • OrganizingMario Lucertini, Ana Millàn Gasca, Fernando Nicolò Springer Science & Business Media, 27. nov. 2003 - 246 strani M. LUCERTINI, A. MILLAN GASCA, AND F. NICOLO 1 Technology as Knowledge: The Case of Modern Engineering Systems In recent years scholars coming from the fields of history and philosophy of sci ence and technology have devoted much attention to the problem of "technology as knowledge" and to the emergence of an autonomous engineering science in the Industrial Agel. This interest echoes a growing awareness among engineers of the independence of their conceptual approach with respect to other forms of knowl edge, linked to the consolidation of autonomous academic engineering research in th the 20 century. A careful examination of the nature of technological knowledge appears particularly valuable in view of the pervasive presence of technology in contemporary life and culture, not only as a result of its impressive achievements, but through the less obvious influence of its concepts and viewpoints as well. The activity of engineers and technicians has been traditionally based on the practical ability to cope with specific situations and to attain the corresponding specific goal by means of the design and realization of an artifact or structure, on the basis of past experience handed down by tradition and applied by means of trial-and-error and rule-of-thumb procedures. But the existence of a theoreti cal background and of principles underpinning this activity can be traced back to classical antiquity. |
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1 Mathematical Methods in Preindustrial Technology and Machines | 3 |
11 Renaissance ArchitectsEngineers | 4 |
12 Steps Towards Scientific Technology and Technical Mechanics | 9 |
13 The Achievements of Leonardo da Vinci | 12 |
14 Engineers of the 16th Century | 15 |
A Look into the Prehistory of Industrial Engineering | 21 |
21 A New Branch of Engineering Science | 22 |
the Early Attempts between the 18th and 19th Centuries | 28 |
56 Stochastic and SampledData Signals | 117 |
57 State Space Models and Optimal Control | 119 |
58 System Identification | 121 |
59 Conclusion | 122 |
A Technique and a Tool for Thought | 129 |
Were their Inventors Aware of Such a Structure? | 131 |
63 Feedback Loops in Mathematics and Computer Science | 136 |
64 The Role of Feedback in Explanatory Models | 139 |
23 Rationality and Mathematization | 32 |
The Birth of Industrial Engineering | 38 |
van der Pol and the Birth of Nonlinear Dynamics | 52 |
32 From Radio to Limit Cycles | 54 |
33 The Contribution of the Soviet School | 63 |
34 The Heartbeat Model | 66 |
35 Concluding Remarks | 72 |
Two Unpublished Letters Written by Balthasar van der Pol to Vito Volterra | 77 |
4 Transferring Formal and Mathematical Tools from War Management to Political Technological and Social Intervention 19401960 | 79 |
41 Operational Research and Mathematicians Mobilization in World War II | 80 |
42 Mathematical Tools for Managing Social andor Complex Systems | 83 |
The RAND Corporation | 86 |
44 On a Few Characters of these New Scientific Modes | 89 |
45 Three Short Remarks as Way of Conclusion | 92 |
5 Technological Concepts and Mathematical Models in the Evolution of Control Engineering | 103 |
52 Models in Control Engineering | 104 |
53 Model Representations | 108 |
54 Determination of Stability of a System | 110 |
Impulse and Frequency Response | 112 |
65 Concluding Remarks | 153 |
7 Adequacy of Mathematical Models in Control Theory Physics and Environmental Science | 156 |
71 Mathematical Models of Technological Processes | 158 |
Fluctuations in the Level of the Caspian Sea | 163 |
73 Mathematical Simulation in the Civil Engineering Design of the Leningrad Dam | 172 |
Concepts of Knowledge as Seen from Western and Eastern Perpsective | 189 |
Hard Versus Soft Systems Science | 190 |
Megatrends and Challenges | 196 |
83 Diverse Concepts of Knowledge | 199 |
84 The Importance and Typical Forms of Mathematical Models Expressing Knowledge | 201 |
Computerized Decision Support Systems | 204 |
9 Coping With Complexity in the Management of Organized Systems | 221 |
91 Forms of Complexity | 222 |
93 The Forms of Simplification | 224 |
94 Decentralized Management of Complex Organizations | 229 |
95 Open Systems | 232 |
239 | |
Authors | 246 |
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20th century achievement function algorithms analysis applications approach aspects basic behavior Brunelleschi Cambridge Mass Caspian Sea Caspian Sea level changes coefficients complex concept considered construction context contribution control engineering control system cultural cybernetic decision support described differential equations dynamic economic example field flow frequency game theory Gulf of Finland idea important industrial engineering input interaction intuition John von Neumann knowledge limit cycle linear programming machines mathematical models mathematicians mechanics megatrend methods Mishchenko modern Neumann Neva Bay nonlinear objectives obtained operations research optimization organization oscillations output parameters Paris period phenomena physical phytoplankton Pol's problems production RAND rational reference point relaxation oscillations represented result role scientific sector servomechanism signal simulation social solutions statistical structure studies systems science systems thinking techniques theoretical tion triode typical University Press variables Vito Volterra Wiener York