Research Papers

Technology Characterization Models and Their Use in Systems Design

[+] Author and Article Information
Robert R. Parker

Design Systems Laboratory,
Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77845
e-mail: parker.ro@gmail.com

Edgar Galvan

Design Systems Laboratory,
Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77845
e-mail: e_galvan@tamu.edu

Richard J. Malak

Design Systems Laboratory,
Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77845
e-mail: rmalak@tamu.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 13, 2012; final manuscript received October 24, 2013; published online April 28, 2014. Assoc. Editor: Harrison M. Kim.

J. Mech. Des 136(7), 071003 (Apr 28, 2014) (11 pages) Paper No: MD-12-1155; doi: 10.1115/1.4025960 History: Received March 13, 2012; Revised October 24, 2013

Prior research suggests that set-based design representations can be useful for facilitating collaboration among engineers in a design project. However, existing set-based methods are limited in terms of how the sets are constructed and in their representational capability. The focus of this article is on the problem of modeling the capabilities of a component technology in a way that can be communicated and used in support of system-level decision making. The context is the system definition phases of a systems engineering project, when engineers still are considering various technical concepts. The approach under investigation requires engineers familiar with the component- or subsystem-level technologies to generate a set-based model of their achievable technical attributes, called a technology characterization model (TCM). Systems engineers then use these models to explore system-level alternatives and choose the combination of technologies that are best suited to the design problem. Previously, this approach was shown to be theoretically sound from a decision making perspective under idealized circumstances. This article is an investigation into the practical effectiveness of different TCM representational methods under realistic conditions such as having limited data. A power plant systems engineering problem is used as an example, with TCMs generated for different technical concepts for the condenser component. Samples of valid condenser realizations are used as inputs to the TCM representation methods. Two TCM representation methods are compared based on their solution accuracy and computational effort required: a Kriging-based interpolation and a machine learning technique called support vector domain description (SVDD). The results from this example hold that the SVDD-based method provides the better combination of accuracy and efficiency.

Copyright © 2014 by ASME
Your Session has timed out. Please sign back in to continue.


Buede, D. M., 2000, The Engineering Design of Systems, John Wiley & Sons, New York.
Grady, J. O., 2006, System Requirements Analysis, Academic Press, Burlington, MA.
Sage, A. P., and Armstrong, Jr., J. E., 2000, Introduction to Systems Engineering, Wiley and Sons, New York.
Collopy, P., 2007, “Adverse Impact of Extensive Attribute Requirements on the Design of Complex Systems,” 7th AIAA Aviation Technology, Integration and Operations (ATIO) Conference, Belfast, Norther Ireland, Paper No. AIAA 2007-7820.
Malak, R. J., Jr., 2008, “Using Parameterized Efficient Sets to Model Alternatives for Systems Design Decisions,” Ph.D. thesis, Georgia Institute of Technology, Atlanta, GA.
Malak, R. J., and Paredis, C. J. J., 2010, “Using Parameterized Pareto Sets to Model Design Concepts,” ASME J. Mech. Des., 132(4), p. 041007. [CrossRef]
Malak, R. J., and Galvan, E., 2010, “Using Predictive Modeling Techniques to Solve Multilevel Systems Design Problems,” 13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference, Fort Worth, TX.
Malak, R. J., Tucker, L., and Paredis, C. J. J., 2009, “Compositional Modeling of Fluid Power Systems Using Predictive Tradeoff Models,” Int. J. Fluid Power, 10(2), pp. 45–55. [CrossRef]
Galvan, E., and Malak, R. J., 2012, “A Genetic Algorithm Approach for Technology Characterization,” ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Chicago, IL, Paper No. DETC2012-70465.
Ferguson, S., Gurnani, A., Donndelinger, J., and Lewis, K. E., 2005, “A Study of Convergence and Mapping in Preliminary Vehicle Design,” Int. J. Veh. Syst. Modell. Test., 1(1/2/3), pp. 192–215. [CrossRef]
Gurnani, A., Ferguson, S., Lewis, K. E., and Donndelinger, J., 2006, “A Constraint-Based Approach to Feasibility Assessment in Preliminary Design,” Artif. Intell. Eng. Des., Anal. Manuf., 20(4), pp. 351–367. [CrossRef]
Mattson, C. A., and Messac, A., 2003, “Concept Selection Using S-Pareto Frontiers,” AIAA J., 41(6), pp. 1190–1198. [CrossRef]
Mattson, C. A., and Messac, A., 2005, “Pareto Frontier Based Concept Selection Under Uncertainty With Visualization,” Optim. Eng., 6(1), pp. 85–115. [CrossRef]
Tax, D. M. J., and Duin, R. P. W., 1999, “Support Vector Domain Description,” Pattern Recogn. Lett., 20, pp. 1191–1199. [CrossRef]
Malak, R. J., and Paredis, C. J. J., 2010, “Using Support Vector Machines to Formalize the Valid Input Domain of Predictive Models in Systems Design Problems,” ASME J. Mech. Des., 132(10), p. 101001. [CrossRef]
Beers, W. C. M. v., and Kleijnen, J. P. C., 2004, “Kriging Interpolation in Simulation: A Survey,” Proceedings of the 36th Conference on Winter Simulation, Washington, DC.
Alexander, M. J., Allison, J., Papalambros, P. Y., and Gorsich, D. J., 2011, “Constraint Management of Reduced Representation Variables in Decomposition-Based Design Optimization,” ASME J. Mech. Des., 133(10), p. 101014. [CrossRef]
Scholkopf, B., and Smola, J. A., 2002, “Learning With Kernels,” MIT Press, Cambridge, MA.
Stein, M. L., 1999, Interpolation of Spatial Data: Some Theory for Kriging, Springer, New York.
Cressie, N., 1990, “The Origins of Kriging,” Math. Geol., 22(3), pp. 239–252. [CrossRef]
Lophaven, S. N., Nielsen, H. B., and Sondergaard, J., 2002, “DACE: A Matlab Kriging Toolbox,” Informatics and Mathematical Modelling Department, Technical University of Denmark, Lyngby, Denmark.
Tax, D. M. J., and Laskov, P., 2003, “Online SVM Learning: From Classification to Data Description and Back,” 2003 IEEE 13th Workshop on Neural Networks for Signal Processing, NNSP'03, pp. 499–508.
Roach, E., Parker, R. R., and Malak, R. J., 2011, “An Improved Support Vector Domain Description Method for Modeling Valid Search Domains in Engineering Design Problems,” ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Washington, EC.
Shah, R. K., and Sekulic, D. P., 2003, Fundamentals of Heat Exhanger Design, John Wiley and Sons, Inc., Hoboken, NJ.
Moran, M. J., and Shapiro, H. N., 2004, Fundamentals of Engineering Thermodynamics, John Wiley & Sons, Inc., Hoboken, NJ.
Thurston, D. L., 1991, “A Formal Method for Subjective Design Evaluation With Multiple Attributes,” Res. Eng. Des., 3(2), pp. 105–122. [CrossRef]
Keeney, R., and Raiffa, H., 1993, Decisions With Multiple Objectives: Preferences and Value Tradeoffs, Cambridge University Press, Cambridge, UK.
Parker, R., 2011, “Technology Characterization Models and Their Use in Complex Systems Design,” Master of Science in Mechanical Engineering thesis, Texas A&M University, College Station, TX.
Holmgren, M., 2006, “X Steam for Matlab” http://www.x-eng.com
Incropera, F. P., Dewitt, D. P., Bergman, T. L., and Lavine, A. S., 2007, Fundamentals of Heat and Mass Transfer, John Wiley & Sons, Hoboken, NJ.
McCabe, W. L., Smith, J. C., and Harriott, P., 1993, Unit Operations of Chemical Engineering, McGraw-Hill, Singapore.
Singh, P., Walker, J., Lee, H. S., Gharfeh, S., Thomason, B., and Blumer, D., 2007, “An Application of Vacuum-Insulated Tubing (VIT) for Wax Control in an Arctic Environment,” SPE Drill. Completion, 22(2), pp. 127–136. [CrossRef]
Petukhov, B. S., and Roizen, L. I., 1964, “Generalized Relationships for Heat Transfer in a Turbulent Flow of Gas in Tubes of Annular Section,” High Temp., 2, pp. 65–68.
Kreider, J. F., Curtiss, P. S., and Rabl, A., 2010, Heating and Cooling of Buildings Design for Efficiency (Mechanical Engineering Series), CRC Press, Boca Raton, FL.
Shigley, J. E., and Mischke, C. R., 2001, Mechanical Engineering Design, McGraw-Hill, New York.
Galvan, E., 2012, “A Genetic Algorithm Approach for Technology Characterization,” M.S. thesis, Texas A&M University, College Station, TX.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., 2002, “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II,” IEEE Trans. Evol. Comput., 6(2), pp. 182–197. [CrossRef]
Zadeh, L., 1963, “Optimality and Non-Scalar-Valued Performance Criteria,” IEEE Trans. Autom. Control, 8(1), pp. 59–60. [CrossRef]
Das, I., and Dennis, J. E., 1997, “A Closer Look at Drawbacks of Minimizing Weighted Sums of Objectives for Pareto Set Generation in Multicriteria Optimization Problems,” Struct. Optim., 14(1), pp. 63–69. [CrossRef]
Ben-Hur, A., Horn, D., Siegelmann, H. T., and Vapnik, V., 2002, “Support Vector Clustering,” J. Mach. Learn. Res., 2, pp. 125–137.
Arendt, J., Malak, R. J., and McAdams, D. A., 2012, “Technology Evolution and Decision Making in Design,” ASME J. Mech. Des., 134(10), p. 100904. [CrossRef]
Bily, C., and Malak, R. J., 2012, “Efficient Sampling Methods for Tradeoff Studies Under Uncertainty,” ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Chicago, IL, ASME Paper No. DETC2012-70551. [CrossRef]
Bily, C., and Malak, R. J., 2012, “Composing Tradeoff Studies Under Uncertainty Based on Parameterized Efficient Sets and Stochastic Dominance Principles,” SAE Int. J. Mater. Manuf., 5(2), pp. 418–429.
Malak, R. J., and Paredis, C. J. J., 2009, “Modeling Design Concepts Under Risk and Uncertainty Using Parameterized Efficient Sets,” SAE Int. J. Mater. Manuf., 1(1), pp. 339–352.


Grahic Jump Location
Fig. 1

Illustrative examples of the SVDDFS, KRIGFS and KRIGOS TCM representation methods

Grahic Jump Location
Fig. 2

Nonideal Rankine cycle

Grahic Jump Location
Fig. 3

Systems design problem layout

Grahic Jump Location
Fig. 4

Heat exchanger technologies

Grahic Jump Location
Fig. 5

The (a) mean utility found using each method and (b) pairwise t-test that data with 95% confidence interval. The utility of the TCM methods is that of the feasible design found during discipline-level design.

Grahic Jump Location
Fig. 6

Mean normalized distance between the target attributes and the attributes of the feasible design found during discipline-level design

Grahic Jump Location
Fig. 7

Mean number of system and component level function evaluations for the different methods with 95% confidence interval

Grahic Jump Location
Fig. 8

The (a) mean distance between true points on the frontier to the approximation for the different TCM representation methods and (b) pairwise t-test that data with 95% confidence interval. The test shows statistically significant difference between all the representation methods.



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In