Turbine Preliminary Design Using Artificial Intelligence and Numerical Optimization Techniques

Tong, Siu Shing; Gregory, B. A.

doi:10.1115/1.2927986

Cited by 18 publications

(9 citation statements)

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“…The benefits of using a GA to design and optimize a model in a set design space have been well documented. A few specific aerospace applications include the design and optimization of propellers 5 , freight truck aerodynamics 6 , wings and airfoils 7,8,9,10 , rockets 11,12 , missiles 13,14,15,16,17,18 , flight trajectories 19 , spacecraft controls 20,21 , and turbines 22,23,24 . The final solution is the member that has the best fitness at the end of the last generation.…”

Section: Mathematical and Engineering Problems In His 1975 Book Adapmentioning

confidence: 99%

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Launch Vehicle Performance Enhancement Using Aerodynamic Assist

McDavid

Hartfield

Burkhalter

2008

26th AIAA Applied Aerodynamics Conference

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A complete preliminary design model of a three-stage solid-fuel launch vehicle has been combined with a genetic algorithm to perform an optimization study with the goal to enhance performance using aerodynamic assist. Three studies have been completed which include improving the suborbital and orbital flights of a modern intercontinental ballistic missile and improving on the orbital flight of a generic threestage launch vehicle. The performance is enhanced by varying the geometric definition of the attached wing structure and the internal propellant geometry while the external geometry remains constant. Initial system weights and propellant mass fractions were found to decrease with the addition of the wing structure. Further enhancement involves a payload increase by 10% with a negligible increase in system weight, and a propellant mass decrease by 5.2% without impairing the flight performance. vi ACKNOWLEDGEMENTS The author would like to thank Dr. Roy Hartfield and Dr. John Burkhalter for their technical and professional guidance and support. The author also would like to thank Dr. Murray Anderson, the author of the 3.1 IMPROVE Genetic Algorithm, without which this thesis would not have been possible. The author would also like to thank his parents Harry and Leslie and sister Amanda for their support and encouragement, and his wife Nicole for all her patience and support provided throughout the graduate school process. vii

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Section: Mathematical and Engineering Problems In His 1975 Book Adapmentioning

confidence: 99%

“…As can be seen from References [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24], the genetic algorithm is effective at altorb…”

Section: Preliminary Design Modelmentioning

confidence: 99%

Launch Vehicle Performance Enhancement Using Aerodynamic Assist

McDavid

Hartfield

Burkhalter

2008

26th AIAA Applied Aerodynamics Conference

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“…Today, several other design methods are available such as gradient methods based on finite difference [3] or more recently based on the sensitivity and the adjoint equation [4], genetic algorithm [5], simulated annealing, response surface methods, inverse design [6] or expert systems [7]. All these methods have advantages and disadvantages and cannot cover the whole field of design problems.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of axial hydro turbine for micro hydropower plants

Derakhshan¹,

Kasaeian²

2012

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. In our country we have enormous low head potation flows in agricultures and aquacultures with almost fix flow rates that can be used as micro hydro power plants for producing energy. But the main problem is the high capital price per kW. Therefore there is needed to design a simple machine with a good runner for covering the various potential flows. In this paper an axial hydro turbine has designed for some low heads micro potential flow with flow rates ranged from 50 lit/sec to 150 lit/sec and heads ranged from 1 m to 5 m. The initial runner designed using classical methods and then the runner geometry has been optimized by evolutionary optimization algorithms. The final design has been simulated by a commercial flow solver in a various blade positions. The results showed a wide range characteristic curve with a wide range high efficiency.

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“…Furthermore, oftentimes these problems are black-box problems, i.e., whose problem class is unknown, and they are possibly mathematically ill-behaved (e.g., discontinuous, non-linear, non-convex). For example, most engineering design problems are of this type (see e.g., [12]). They require experiments and/or simulations to evaluate to what extent the design objective has been met as a function of parameters controlling the design.…”

Section: Introductionmentioning

confidence: 99%

Geometric Generalisation of Surrogate Model Based Optimisation to Combinatorial Spaces

Moraglio

Kattan

2011

Evolutionary Computation in Combinatorial Optimization

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Abstract. In continuous optimisation, Surrogate Models (SMs) are often indispensable components of optimisation algorithms aimed at tackling real-world problems whose candidate solutions are very expensive to evaluate. Because of the inherent spatial intuition behind these models, they are naturally suited to continuous problems but they do not seem applicable to combinatorial problems except for the special case when solutions are naturally encoded as integer vectors. In this paper, we show that SMs can be naturally generalised to encompass combinatorial spaces based in principle on any arbitrarily complex underlying solution representation by generalising their geometric interpretation from continuous to general metric spaces. As an initial illustrative example, we show how Radial Basis Function Networks (RBFNs) can be used successfully as surrogate models to optimise combinatorial problems defined on the Hamming space associated with binary strings.

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Turbine Preliminary Design Using Artificial Intelligence and Numerical Optimization Techniques

Cited by 18 publications

References 0 publications

Launch Vehicle Performance Enhancement Using Aerodynamic Assist

Launch Vehicle Performance Enhancement Using Aerodynamic Assist

Optimal design of axial hydro turbine for micro hydropower plants

Geometric Generalisation of Surrogate Model Based Optimisation to Combinatorial Spaces

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