Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-26T08:17:40.720Z Has data issue: false hasContentIssue false
  • English
  • Français

Intelligent automated quality control for computational simulation

Published online by Cambridge University Press:  27 February 2009

Andrew Gelsey
Andrew Gelsey
Affiliation:
Computer Science Department, Rutgers University, New Brunswick, NJ 08903, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

Computational simulation of physical systems generally requires human experts to set up a simulation, run it, evaluate the quality of the simulation output, and repeatedly invoke the simulator with modified input until a satisfactory output quality is achieved. This reliance on human experts makes use of simulators by other programs difficult and unreliable, though invocation of simulators by other programs is critical for important tasks such as automated engineering design optimization. Presented is a framework for constructing intelligent controllers for computational simulators that can automatically detect a wide variety of problems that lead to low-quality simulation output, using a set of evaluation methods based on knowledge of physics and numerical analysis stored in a data/knowledgebase of models and simulations. An experimental implementation of this framework in an intelligent automated controller for a widely used computational fluid dynamics simulator is described.

Keywords

Artificial Intelligence (AI)SimulationComputational Fluid Dynamics (CFD)Design Optimization
Type
Articles
Information
AI EDAM , Volume 9 , Issue 5 , November 1995 , pp. 387 - 400
Copyright
Copyright © Cambridge University Press 1995

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Addanki, S., Cremonini, R., & Pemberthy, J.S. (1991). Graphs of models. Artif. Intell. 51, 145–177.CrossRefGoogle Scholar
Andrews, A.E. (1988). Progress and challenges in the application of artificial intelligence to computational fluid dynamics. AIAA Journal 26(1), 40–46.CrossRefGoogle Scholar
Ashby, D.L., Dudley, M.R., Iguchi, S.K., Browne, L., & Katz, J. (1992). Potential Flow Theory and Operation Guide for the Panel Code PMARC_12. NASA Ames Research Center.Google Scholar
Dahlquist, G., & Bjorck, A. (1974). Numerical methods. Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
Dvorak, D., & Kuipers, B. (1991). Process monitoring and diagnosis. IEEE Expert 6(3), 67–74.CrossRefGoogle Scholar
Ellman, T., Keane, J., & Schwabacher, M. (1992). The Rutgers CAP Project Design Associate. Technical Report CAP-TR-7, Department of Computer Science, Rutgers University.Google Scholar
Ellman, T., Keane, J., & Schwabacher, M. (1993). Intelligent model selection for hillclimbing search in computer-aided design. Proc. Eleventh Nat. Conf. on Artif. Intell., Washington, D.C., 594–599.Google Scholar
Fatunla, S.O. (1988). Numerical methods for initial value problems in ordinary differential equations. Academic Press, Boston.Google Scholar
Forbus, K.D. & Falkenhainer, B. (1990). Self-explanatory simulations: An integration of qualitative and quantitative knowledge. Proc. Eighth Nat. Conf. Artif. Intell., AAAI-90. Boston, MA, 380–387.Google Scholar
Gear, C.W. (1971). Numerical initial value problems in ordinary differential equations. Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
Gelsey, A. (1991). Using intelligently controlled simulation to predict a machine's long-term behavior. Proc. Ninth Nat. Conf. on Artif. Intell., pp. 880–887. Cambridge, MA.Google Scholar
Gelsey, A. (1992). Modeling and simulation for automated yacht design. AAAI Fall Symp. on Design from Physical Principles, 44–49.Google Scholar
Gelsey, A. (1995). Automated reasoning about machines. Artif. Intell. 74(1), 1–53.CrossRefGoogle Scholar
Gelsey, A.Knight, D.D., Gao, S., & Schwabacher, M. (1995). NPARC simulation and redesign of the NASA P2 hypersonic inlet. Proc. 31st Joint Propulsion Conf., San Diego, CA, AIAA-95–2760.Google Scholar
Gelsey, A., & Smith, D. (1995 a). A computational environment for exhaust nozzle design. Proc. Comput. in Aerospace 10, San Antonio, TX, 531–539, AIAA-95–1016.Google Scholar
Gelsey, A., Smith, D. (1995 b). A search space toolkit. Proc. 11th IEEE Conf. on Artif. Intell. Applications, Los Angeles, CA, 117–123.Google Scholar
Golub, G., & Ortega, J.M. (1993). Scientific computing: An introduction with parallel computing. Academic Press, Bosten, MA.CrossRefGoogle Scholar
Jambunathan, K., Lai, E., Hartle, S.L., & Button, B.L. (1991). Development of an intelligent front-end for a computational fluid dynamics package. Artif. Intell. Eng. 6(1), 27–35.CrossRefGoogle Scholar
Katz, J. & Plotkin, A. (1991). Low-speed aerodynamics: From wing theory to panel methods. McGraw-Hill, New York.Google Scholar
Letcher, J.S. Jr, (1975). Sailing hull hydrodynamics, with reanalysis of the Antiope data. Transactions of the Society of Naval Architects and Marine Engineers, 83, 22–40.Google Scholar
Letcher, J.S. Jr, Cressy, C.P., Olivier, J.C. III, & Fritts, M.J. (1987). Hydro-numeric design of winglet keels for Stars & Stripes. Marine Technol. 24(4), 265–285.Google Scholar
Newman, J.N. (1977). Marine hydrodynamics. MIT Press, Cambridge, MA.CrossRefGoogle Scholar
Newman, J.N. & Wu, T.Y. (1973). A generalized slender-body theory for fish-like forms. J. Fluid Mechanics, 57(4), 673–693.CrossRefGoogle Scholar
Sacks, E.P. (1991). Automatic analysis of one-parameter ordinary differential equations by intelligent numeric simulation. Artif. Intell. 48(1), 27–56.CrossRefGoogle Scholar
Shampine, L.F., Watts, H.A., & Davenport, S. (1976). Solving non-stiff ordinary differential equations – the state of the art. Siam Rev. 18, 376–411.CrossRefGoogle Scholar
Thompson, J.F.Warsi, Z.U.A., & Mastin, C.W. (1985). Numerical grid generation: Foundations and applications. North-Holland, Amsterdam.Google Scholar
Weld, D.S. (1992). Reasoning about model accuracy. Artif. Intell. 56, 255–300.CrossRefGoogle Scholar
Yao, K.-T. & Gelsey, A. (1994). Intelligent automated grid generation for numerical simulations. Proc. 12th National Conf. Artif. Intell., Seattle, Washington, 1224–1230.Google Scholar
Yao, K.-T. & Gelsey, A. (1995). Intelligent automated surface grid generation. Proc. Workshop on Surface Modeling, Grid Generation, and Related Issues in CFD Solutions, NASA, Cleveland, Ohio, 581–595.Google Scholar
Yip, K (1991). Understanding complex dynamics by visual and symbolic reasoning. Artif. Intell. 51(1–3), 179–221.CrossRefGoogle Scholar
Zhao, F. (1991). Extracting and representing qualitative behaviors of complex systems in phase space. Proc. 12th Int. Joint Conf. Artif. Intell., 1144–1149.CrossRefGoogle Scholar