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Published: 10 December 2022

Last updated: 29 January 2023

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1

Chen, Cheng-Wu. "Modeling and Fuzzy PDC Control and Its Application to an Oscillatory TLP Structure." Mathematical Problems in Engineering 2010 (2010): 1–13. http://dx.doi.org/10.1155/2010/120403.

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An analytical solution is derived to describe the wave-induced flow field and surge motion of a deformable platform structure controlled with fuzzy controllers in an oceanic environment. In the controller design procedure, a parallel distributed compensation (PDC) scheme is utilized to construct a global fuzzy logic controller by blending all local state feedback controllers. The Lyapunov method is used to carry out stability analysis of a real system structure. The corresponding boundary value problems are then incorporated into scattering and radiation problems. These are analytically solved, based on the separation of variables, to obtain a series of solutions showing the harmonic incident wave motion and surge motion. The dependence of the wave-induced flow field and its resonant frequency on wave characteristics and structural properties including platform width, thickness and mass can thus be drawn with a parametric approach. The wave-induced displacement of the surge motion is determined from these mathematical models. The vibration of the floating structure and mechanical motion caused by the wave force are also discussed analytically based on fuzzy logic theory and the mathematical framework to find the decay in amplitude of the surge motion in the tension leg platform (TLP) system. The expected effects of the damping in amplitude of the surge motion due to the control force on the structural response are obvious.

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2

JESSOP, ALAN. "MODELS AND DESIGN IN ENGINEERING." Civil Engineering and Environmental Systems 16, no. 1 (March 1999): 1–17. http://dx.doi.org/10.1080/02630259908970248.

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3

EMORI, Ichiro. "Scale Models and Engineering Design." Journal of the Society of Mechanical Engineers 91, no. 833 (1988): 303–7. http://dx.doi.org/10.1299/jsmemag.91.833_303.

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4

THOMPSON, G. "Mathematical Models and Engineering Design." Water and Environment Journal 7, no. 1 (February 1993): 18–23. http://dx.doi.org/10.1111/j.1747-6593.1993.tb00805.x.

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5

Smoak, Robert A., and Xavier R. Ollat. "Construction of control design models from engineering simulation models." Mathematical and Computer Modelling 14 (1990): 413–17. http://dx.doi.org/10.1016/0895-7177(90)90218-c.

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6

Parkinson, A. "Robust Mechanical Design Using Engineering Models." Journal of Mechanical Design 117, B (June 1, 1995): 48–54. http://dx.doi.org/10.1115/1.2836470.

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This paper examines how engineering models can be used to develop robust designs—designs that can tolerate variation. Variation is defined in terms of tolerances which bracket the expected deviation of model variables and/or parameters. Several methods for robust design are discussed. The method of transmitted variation is explained in detail and illustrated on a linkage design problem and a check valve design problem.

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7

Parkinson, A. "Robust Mechanical Design Using Engineering Models." Journal of Vibration and Acoustics 117, B (June 1, 1995): 48–54. http://dx.doi.org/10.1115/1.2838676.

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This paper examines how engineering models can be used to develop robust designs—designs that can tolerate variation. Variation is defined in terms of tolerances which bracket the expected deviation of model variables and/or parameters. Several methods for robust design are discussed. The method of transmitted variation is explained in detail and illustrated on a linkage design problem and a check valve design problem.

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8

Hershenson, M. "Models of good design." IEE Review 49, no. 6 (June 1, 2003): 38. http://dx.doi.org/10.1049/ir:20030612.

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9

Rodd, M. G. "Logic models of design." Engineering Applications of Artificial Intelligence 1, no. 4 (December 1988): 335. http://dx.doi.org/10.1016/0952-1976(88)90059-0.

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10

Maffin, David. "Engineering Design Models: Context, theory and practice." Journal of Engineering Design 9, no. 4 (December 1998): 315–27. http://dx.doi.org/10.1080/095448298261462.

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11

Maarten Bonnema, G., and Fred J. A. M. Van Houten. "Use of models in conceptual design." Journal of Engineering Design 17, no. 6 (December 2006): 549–62. http://dx.doi.org/10.1080/09544820600664994.

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12

Tuffley, David. "Engineering Organisational Behaviour with Design Research." International Journal of Sociotechnology and Knowledge Development 3, no. 2 (April 2011): 1–14. http://dx.doi.org/10.4018/jskd.2011040101.

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Can Design Research be used to develop process models of organisational behavior? The question is significant given the desirability of finding ways to optimise organisational performance. It is also significant because the precursor of such process models have been largely restricted to the software engineering domain. This paper examines (a) whether Design Research is an effective tool for developing such models, and (b) asks, can process models be more broadly defined to include organisational behavior generally? The study concludes that Design Research is an excellent tool for developers of process models in general, and that there appears to be no good reason why such models cannot be used to describe optimal organisational behavior in a broad range of domains.

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13

Kondusov, D. V., and V. B. Kondusova. "Deriving Design Knowledge from 3D Models." Russian Engineering Research 41, no. 10 (October 2021): 913–15. http://dx.doi.org/10.3103/s1068798x21100142.

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14

Rowles, C. D., and C. Leckie. "A design automation system using explicit models of design." Engineering Applications of Artificial Intelligence 1, no. 4 (December 1988): 258–68. http://dx.doi.org/10.1016/0952-1976(88)90044-9.

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15

Wynn, David C., and P. John Clarkson. "Process models in design and development." Research in Engineering Design 29, no. 2 (July 12, 2017): 161–202. http://dx.doi.org/10.1007/s00163-017-0262-7.

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16

Stewart, Mark G., and Robert E. Melchers. "Checking Models in Structural Design." Journal of Structural Engineering 115, no. 6 (June 1989): 1309–24. http://dx.doi.org/10.1061/(asce)0733-9445(1989)115:6(1309).

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17

Zare, Shokrollah, and Amund Bruland. "Comparison of tunnel blast design models." Tunnelling and Underground Space Technology 21, no. 5 (September 2006): 533–41. http://dx.doi.org/10.1016/j.tust.2005.09.001.

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18

Pohlmann, Lawrence D. "The Engineering Design of Systems - Models and Methods." INSIGHT 3, no. 1 (April 2000): 58–59. http://dx.doi.org/10.1002/inst.20003158a.

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19

Jong, Wan Hu, and Woong Park Ji. "Composite Joints Design - Migrating from Finite Element Models to Component Models." Advanced Materials Research 716 (July 2013): 620–25. http://dx.doi.org/10.4028/www.scientific.net/amr.716.620.

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When developing innovative structural systems, designers are faced with a difficult problem when addressing connection design. While the provisions for the design of members and their failure modes are well understood and codified, the design and performance of the connections are not. Current specifications require designers to provide evidence, through either experiments or analysis and combinations thereof, that these connections will perform as intended. In this paper, the design of an innovative type of connection to concrete-filled tube columns is described. These connection are partially-restrained, contain a new type of material (shape memory alloys), and are geared for high seismic loads making their design a very challenging proposition without the aid of physical testing. The design is developed based on detailed finite element analyses of the connection region and elements which lead to simplified spring models suitable for design of entire frames. The results indicate that through careful and rigorous analyses, robust simplified connection models can be developed even for complex connections.

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20

Rajeev, A. C., Swarup Mohalik, and S. Ramesh. "Design Verification of Automotive Controller Models." SAE International Journal of Passenger Cars - Electronic and Electrical Systems 6, no. 2 (April 8, 2013): 419–24. http://dx.doi.org/10.4271/2013-01-0428.

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21

Malyugin, V. D. "Design and Verification of Logical Models." Automation and Remote Control 65, no. 6 (June 2004): 997–1006. http://dx.doi.org/10.1023/b:aurc.0000030909.45643.3a.

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22

van Eck, Dingmar. "Incommensurability and Rationality in Engineering Design." Techné: Research in Philosophy and Technology 15, no. 2 (2011): 118–36. http://dx.doi.org/10.5840/techne201115212.

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In engineering design research different models of functional decomposition are advanced side-by-side. In this paper I explain and validate this co-existence of models in terms of the Kuhnian thesis of methodological incommensurability. I advance this analysis in terms of the thesis’ construal of (non-algorithmic) theory choice in terms of values, expanding this notion to the engineering domain. I further argue that the (by some) implicated threat of the thesis to rational theory choice has no force in the functional decomposition case: co-existence of different models of functional decomposition is rational from an instrumental point of view. My explanation covers cases in which different models are advanced as means for the same objective. Such cases cannot be explicated with the explanatory construct of variety in objectives, as advanced in other analyses of co-existing conceptualizations in engineering.

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23

Oxman, Robert, and Rivka Oxman. "Cognitive models in design case libraries." Automation in Construction 3, no. 2-3 (July 1994): 113–22. http://dx.doi.org/10.1016/0926-5805(94)90013-2.

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24

Zuo-Jun Shen. "Integrated Stochastic Supply-Chain Design Models." Computing in Science & Engineering 9, no. 2 (March 2007): 50–59. http://dx.doi.org/10.1109/mcse.2007.34.

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25

Deciu, E. R., E. Ostrosi, M. Ferney, and M. Gheorghe. "Configurable product design using multiple fuzzy models." Journal of Engineering Design 16, no. 2 (April 2005): 209–33. http://dx.doi.org/10.1080/09544820500031526.

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26

Kalyakulin, S. Yu, E. V. Mitin, and S. P. Sul’din. "Classification of Mathematical Models for Process Design." Russian Engineering Research 41, no. 11 (November 2021): 1045–48. http://dx.doi.org/10.3103/s1068798x21110137.

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27

Merz, Kenneth M., Gianni De Fabritiis, and Guo-Wei Wei. "Generative Models for Molecular Design." Journal of Chemical Information and Modeling 60, no. 12 (December 28, 2020): 5635–36. http://dx.doi.org/10.1021/acs.jcim.0c01388.

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28

Reddy, Sudhakar Y. "Learning abstract models for system design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10, no. 2 (April 1996): 167–69. http://dx.doi.org/10.1017/s0890060400001475.

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AbstractThough simulation models are extensively used for detailed design analysis, they find limited role in preliminary design decisions. We have developed a machine learning based approach to enable detailed simulation models to be harvested for supporting early-stage design of engineering systems.

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29

Gibon-Fargeot, A. M., F. Celle-Couenne, and H. Hammouri. "Cascade estimation design for CSTR models." Computers & Chemical Engineering 24, no. 11 (November 2000): 2355–66. http://dx.doi.org/10.1016/s0098-1354(00)00303-3.

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30

Cline, Terry, Harold Abelson, and Warren Harris. "Symbolic computing in engineering design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 3, no. 3 (August 1989): 195–206. http://dx.doi.org/10.1017/s0890060400001220.

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Computer programs that combine traditional numeric methods with symbolic algebra and with specific knowledge of application-based techniques can provide new levels of computational support for engineering design. We illustrate this with a computer-based ‘control engineer’s assistant’. Although this program is focussed on control system design, it demonstrates techniques that should be widely applicable across many engineering disciplines. In particular, we show how, with symbolic computing, a computer-aided design system can usefully simulate engineering models early in the design process, before all (or any) system parameters have been specified numerically. Our system employs a flexible, extensible, object-oriented representation for control systems, which admits multiple mathematical models of designs and provides a framework for integrating tools that operate on diverse representations.

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31

Hoover, Stephen P., James R. Rinderle, and Susan Finger. "Models and abstractions in design." Design Studies 12, no. 4 (October 1991): 237–45. http://dx.doi.org/10.1016/0142-694x(91)90039-y.

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32

Hogg, R. "Agglomeration models for process design and control." Powder Technology 69, no. 1 (January 1992): 69–76. http://dx.doi.org/10.1016/0032-5910(92)85009-k.

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33

Platt, David G. "Building Process Models for Design Management." Journal of Computing in Civil Engineering 10, no. 3 (July 1996): 194–203. http://dx.doi.org/10.1061/(asce)0887-3801(1996)10:3(194).

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34

Wang, Zhonghui, and Chee Kiong Soh. "Managing Design Changes for Multiview Models." Journal of Computing in Civil Engineering 15, no. 2 (April 2001): 102–11. http://dx.doi.org/10.1061/(asce)0887-3801(2001)15:2(102).

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35

Smith, Philip J., William A. Sowa, and Paul O. Hedman. "Furnace design using comprehensive combustion models." Combustion and Flame 79, no. 2 (February 1990): 111–21. http://dx.doi.org/10.1016/0010-2180(90)90037-r.

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36

Hayes, J. "Matching analogue models with reality [IC design]." Electronics Systems and Software 1, no. 5 (October 1, 2003): 32–35. http://dx.doi.org/10.1049/ess:20030506.

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37

Wang, Dong Sheng, and Xin Feng Wang. "Rapid Prototyping Design of 3D Models by Reverse Engineering." Applied Mechanics and Materials 215-216 (November 2012): 648–51. http://dx.doi.org/10.4028/www.scientific.net/amm.215-216.648.

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The digital measurements of the back cover of a telephone receiver were carried out by a three dimensional measuring machine and the data point clouds of the contours were got. Using reverse engineering software, Imageware, the data point clouds were processed, including of data smoothing, data reducing, data point clouds resample, feature lines extracting and curve fitting. Based on the shape features of the research object, the 3D model of the back cover of the telephone receiver were reconstructed with the boundary curves and the scanning data point groups successfully. Precision evaluation was carried out to the 3D model and the error controlled within 0.2mm.

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38

Prior, R. C., D. K. Fowler, and A. M. Mellor. "Engineering design models for ramjet efficiency and lean blowoff." Journal of Propulsion and Power 11, no. 1 (January 1995): 117–23. http://dx.doi.org/10.2514/3.23848.

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39

Herskovits, J., P. Mappa, E. Goulart, and C. M. Mota Soares. "Mathematical programming models and algorithms for engineering design optimization." Computer Methods in Applied Mechanics and Engineering 194, no. 30-33 (August 2005): 3244–68. http://dx.doi.org/10.1016/j.cma.2004.12.017.

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40

Trevisan, Lucile, and Daniel Brissaud. "Engineering models to support product–service system integrated design." CIRP Journal of Manufacturing Science and Technology 15 (November 2016): 3–18. http://dx.doi.org/10.1016/j.cirpj.2016.02.004.

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41

Zwart, Sjoerd D., Johannes Jacobs, and Ibo van de Poel. "Values in engineering models: social ramifications of modeling in engineering design." Engineering Studies 5, no. 2 (June 20, 2013): 93–116. http://dx.doi.org/10.1080/19378629.2013.809349.

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42

Phanikumar, M. S., David W. Hyndman, and Craig S. Criddle. "Biocurtain Design Using Reactive Transport Models." Groundwater Monitoring & Remediation 22, no. 3 (August 2002): 113–23. http://dx.doi.org/10.1111/j.1745-6592.2002.tb00760.x.

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43

Akkermans, J. A. G., M. C. van Beurden, G. J. N. Doodeman, and H. J. Visser. "Analytical models for low-power rectenna design." IEEE Antennas and Wireless Propagation Letters 4 (2005): 187–90. http://dx.doi.org/10.1109/lawp.2005.850798.

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44

Lim, Kyong B. "Robust control design framework for substructure models." Journal of Guidance, Control, and Dynamics 19, no. 1 (January 1996): 181–90. http://dx.doi.org/10.2514/3.21596.

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45

Joshi, S. P., J. R. Umaretiya, and Sanjay B. Joshi. "Decision-making in preliminary engineering design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 5, no. 1 (February 1991): 21–30. http://dx.doi.org/10.1017/s0890060400002511.

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A designer often has to deal with complex and ill-structured situations during specification synthesis and preliminary engineering design. To assist in the development of computer-aided design systems, it is desirable to capture the designers decision-making process during these design states. The research presented in this paper is towards this direction. Based on the conceptual understanding of the process, three postulates are presented. The following two postulates; (1) the decisions are neither optimum nor just satisfying but retain certain characteristics of both, (2) the design is driven by the important objective(s) among all the specified objectives, at the preliminary design, although the remaining objectives do have a weak influence on the preliminary design; are used to develop a compensatory and a non-compensatory model of the decision-making. These models are formulated with the help of fuzzy set theory and they implicitly or explicitly follow the two postulates. These models are suitable for discrete decision situations where the above mentioned postulates apply. Examples of material selection during a preliminary structural design are used to illustrate the effectiveness of these models.

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46

Paredis, C. J. J., A. Diaz-Calderon, R. Sinha, and P. K. Khosla. "Composable Models for Simulation-Based Design." Engineering with Computers 17, no. 2 (July 2001): 112–28. http://dx.doi.org/10.1007/pl00007197.

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47

Flomenhoft, Hubert I. "Brief history of gust models for aircraft design." Journal of Aircraft 31, no. 5 (September 1994): 1225–27. http://dx.doi.org/10.2514/3.46637.

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48

Stenfelt, G., and U. Ringertz. "Design and construction of aeroelastic wind tunnel models." Aeronautical Journal 119, no. 1222 (December 2015): 1585–99. http://dx.doi.org/10.1017/s0001924000011416.

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AbstractThe design and building of accurately scaled aeroelastic wind-tunnel models is difficult, time consuming and very costly. With the increasing usefulness of computational methods for predicting aeroelastic phenomena, few complex models have been built in recent years. New fighter aircraft projects are also becoming more and more scarce, and transport aircraft have essentially the same configuration since half a decade. This also significantly reduces the need for aeroelastic wind-tunnel models. However, there still is an interest in the results from aeroelastic testing. In some cases new and radical configurations may warrant wind-tunnel testing and in other cases complex phenomena arising in fight testing may need carefully performed experiments to resolve problems. However, there is definitely a trend towards building models and performing testing in the support of the development of computational methods.The developments in computer technology do not only improve the computational methods for aeroelasticity. Modern Computer Aided Design and Manufacturing techniques can significantly improve the quality and efficiency of the design and build process for aeroelastic models. There have also been some recent improvements in measurement techniques which have proven very useful for testing of aeroelastic wind-tunnel models. The paper will present some new design and build techniques developed for the manufacturing of a large scale wind-tunnel model of a canard delta wing fighter aircraft configuration. In the build process fiber-reinforced composites will be used, hence, challenges and possible solutions concerning the ability to produce a model with well defined material properties and fiber angles will be discussed. Further challenges arise when both measurement equipment and adjustable control surfaces should be attached inside the model using techniques that are possible to describe with computational methods. In addition, equipment, such as pressure taps, and control surface mechanics need to fit and function in a flexible structure. As a result, the above requirements will lead to necessary compromises in the design, hence, the paper will present the choices taken during the build process and for which reasons. The use of an optical positioning measurement system will also be discussed for both the validation of model properties and non-contact measurement of model deformations during wind-tunnel testing.

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49

Kawase, Y., and M. Moo-Young. "Mathematical models for design of bioreactors: Applications of." Chemical Engineering Journal 43, no. 1 (February 1990): B19—B41. http://dx.doi.org/10.1016/0300-9467(90)80048-h.

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50

Schaible, B., Yung-Chang Lee, and Hong Xie. "Efficient design using fuzzy logic based regression models." IEEE Transactions on Components, Packaging, and Manufacturing Technology: Part A 21, no. 1 (March 1998): 132–41. http://dx.doi.org/10.1109/95.679043.

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