Shape Optimization of Supersonic Turbines Using Global Approximation Methods

Papila, Nilay; Shyy, Wei; Griffin, Lisa W.; Dorney, Daniel J.

doi:10.2514/2.5991

Cited by 62 publications

(22 citation statements)

References 12 publications

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“…For a simple preliminary design optimization, the use of an inexpensive 1-D solver may be sufficient. However, for the final detailed design, more complex solvers may be needed [11,12]. The choice of a model has an important bearing on the computational expense of evaluating designs.…”

Section: Framework Detailsmentioning

confidence: 99%

“…The surrogates provide fast approximations of the system response making optimization and sensitivity studies possible. Response surface approximations, neural network techniques, spline, and kriging are examples of methods used to generate surrogates for simulations in the optimization of complex flows involving applications such as engine diffusers [8], rocket injectors [17], and supersonic turbines [11,12]. The major benefit of surrogate models is the ability to quickly obtain any number of additional function evaluations without resorting to more expensive numerical models.…”

Section: Model Objectives Using Surrogate Modelsmentioning

confidence: 99%

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Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design

Mack

Goel

Shyy

et al. 2007

Studies in Computational Intelligence

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Summary. Surrogate-based optimization has proven very useful for novel or exploratory design tasks because it offers a global view of the characteristics of the design space, and it enables one to refine the design of experiments, conduct sensitivity analyses, characterize tradeoffs between multiple objectives, and, if necessary, help modify the design space. In this article, a framework is presented for design optimization on problems that involve two or more objectives which may be conflicting in nature. The applicability of the framework is demonstrated using a case study in space propulsion: a response surface-based multi-objective optimization of a radial turbine for an expander cycle-type liquid rocket engine. The surrogate model is combined with a genetic algorithm-based Pareto front construction and can be effective in supporting global sensitivity evaluations. In this case study, due to the lack of established experiences in adopting radial turbines for space propulsion, much of the original design space, generated based on intuitive ideas from the designer, violated established design constraints. Response surfaces were successfully used to define previously unknown feasible design space boundaries. Once a feasible design space was identified, the optimization framework was followed, which led to the construction of the Pareto front using genetic algorithms. The optimization framework was effectively utilized to achieve a substantial performance improvement and to reveal important physics in the design. IntroductionWith continuing progress in computational simulations, computational-based optimization has proven to be a useful tool in reducing the design process duration and expense. Numerous methods exist for conducting design optimizations. Popular methods include gradient-based methods [4,14], adjoint methods [6,10], and surrogate model-based optimization methods such as the response surface approximation (RSA) [9]. Gradient-based methods rely on a

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Section: Framework Detailsmentioning

confidence: 99%

Section: Model Objectives Using Surrogate Modelsmentioning

confidence: 99%

Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design

Mack

Goel

Shyy

et al. 2007

Studies in Computational Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

“…Madsen et al [7], Papila et al [8,9], Shyy et al [10,11] and, Vaidyanathan et al [12,13] used RSAs as design evaluators for the optimization of propulsion components including a turbulent flow diffuser, supersonic turbine, swirl coaxial injector element and liquid rocket injector designs. Redhe et al [14,15] and Craig et al [16] used RSAs in design of vehicles for crashworthiness.…”

Section: T Goel Et Almentioning

confidence: 99%

“…For each dataset, the vector (1) was evaluated using Equation (9), and the actual response vector y was calculated using Equation (25). The estimated coefficients vector b was then recomputed for cases 2-4 to account for inconsistencies between the data and the assumed true model and is referred as b .…”

Section: Selection Of Design Points and Generation Of Response Vectormentioning

confidence: 99%

Generalized pointwise bias error bounds for response surface approximations

Goel

Haftka

Papila

et al. 2005

Numerical Meth Engineering

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SUMMARYThis paper proposes a generalized pointwise bias error bounds estimation method for polynomialbased response surface approximations when bias errors are substantial. A relaxation parameter is introduced to account for inconsistencies between the data and the assumed true model. The method is demonstrated with a polynomial example where the model is a quadratic polynomial while the true function is assumed to be cubic polynomial. The effect of relaxation parameter is studied. It is demonstrated that when bias errors dominate, the bias error bounds characterize the actual error field better than the standard error. The bias error bound estimates also help to identify regions in the design space where the accuracy of the response surface approximations is inadequate. It is demonstrated that this information can be utilized for adaptive sampling in order to improve accuracy in such regions.

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“…Other approaches suggest implementing a neural network (NN) and/or a response surface method (RSM) to fit curves through the design space [45,52,53,54]. This enables an approximate answer to be obtained for a design rather than having to call a CFD solver for a computationally expensive fitness evaluation.…”

Section: Previous Work On Cfd-based Shape Optimizationmentioning

confidence: 99%

An interactive design environment for coal piping system

Huang

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Shape Optimization of Supersonic Turbines Using Global Approximation Methods

Cited by 62 publications

References 12 publications

Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design

Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design

Generalized pointwise bias error bounds for response surface approximations

An interactive design environment for coal piping system

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