Boundary element methods for optimization of distributed parameter systems

Meriç, R. A.

doi:10.1002/nme.1620200709

Cited by 22 publications

(3 citation statements)

References 10 publications

(1 reference statement)

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“…Koda [4][5][6] further developed this approach and outlined a numerical algorithm for the computation of functional derivatives. Meric [7,8] treated optimal control problems governed by parabolic and elliptic partial differential equations and solved them numerically using variational method. In their effort to compare the gradients obtained by ''implicit" and ''variational" approaches, Shubin and Frank [9] implemented VM to optimize the shape of a nozzle of a variable cross-sectional area for steady one-dimensional Euler equations.…”

Section: Variational Sensitivity Analysismentioning

confidence: 99%

Variational sensitivity analysis and design optimization

Tiwari

2009

Computers & Fluids

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Section: Variational Sensitivity Analysismentioning

confidence: 99%

Variational sensitivity analysis and design optimization

Tiwari

2009

Computers & Fluids

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“…In the following (see Reference 26 for more details) we consider a generalized response function written as G(U(Q,), Q,), (7) where Q, is the design parameter vector. For the coupled flow problem the response U consists of the velocity, temperature, pressure, stress, velocity gradient, internal energy, heat flux and temperature gradient fields, while the design parameters in CP may be used to describe the shape, material response and load data.…”

Section: Direct Diferentiation Sensitivity Analysismentioning

confidence: 99%

Sensitivity Analysis and Optimization of Coupled Thermal and Flow Problems With Applications to Contraction Design

Wang

Tortorelli

Dantzig

1996

Int. J. Numer. Meth. Fluids

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The finite element method and the Newton–Raphson solution algorithm are combined to solve the momentum, mass and energy conservation equations for coupled flow problems. Design sensitivities for a generalised response function with respect to design parameters which describe shape, material property and load data are evaluated via the direct differentiation method. The efficiently computed sensitivities are verified by comparison with computationally intensive, finite difference sensitivity approximations. The design sensitivities are then used in a numerical optimization algorithm to minimize the pressure drop in flow through contractions. Both laminar and turbulent flows are considered. In the turbulent flow problems the time‐averaged momentum and mass conservati on equations are solved using a mixing length turbulence model.

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“…Significant contributions have been made in the areas of sensitivity analysis for nonlinear structural mechanics, [11,12,13] vibration control, [14,15] and geometrically nonlinear systems. [16] Additionally, sensitivity methods have been formulated for linear [17,18,19] and nonlinear [20,21] thermal systems. Previous researchers also have developed semianalytical [22,23] and fully analytical [24] methods for computing fluid-flow sensitivities without free surfaces [24] and for Hele-Shaw flows with filling.…”

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confidence: 99%

Fluid flow in casting rigging systems: Modeling, validation, and optimal design

McDavid

Dantzig

1998

Metall Mater Trans B

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In this work, a methodology for the optimal design of flow in foundry casting rigging systems is discussed. The methodology is based on a novel, fully analytical design sensitivity formulation for transient, turbulent, free-surface flows. The filling stage of the casting process is modeled by solving the time-averaged form of the Navier-Stokes equations via a turbulent mixing-length model, in conjunction with the volume-of-fluid (VOF) method for modeling the free surface. The design of the runner and gating system of a simple block casting is presented as an example application for using sensitivity information in design. The analytical sensitivity routine is coupled to a numerical optimizer to yield an automated method for optimal design of the casting rigging system. The finite element model of the filling process is verified through physical experimentation. Solutions of glycerol and water are used to perform laminar and turbulent flow filling experiments, which are used to verify independently the VOF algorithm and the adequacy of the mixing-length model. The results show that the numerical model accurately reproduces the main features of the flow, and filling times agree with acceptable accuracy. Finally, the analytical techniques are used to obtain an optimal runner shape, which eliminates air entrainment, and the design is verified experimentally.

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Boundary element methods for optimization of distributed parameter systems

Cited by 22 publications

References 10 publications

Variational sensitivity analysis and design optimization

Variational sensitivity analysis and design optimization

Sensitivity Analysis and Optimization of Coupled Thermal and Flow Problems With Applications to Contraction Design

Fluid flow in casting rigging systems: Modeling, validation, and optimal design

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