scholarly journals Surrogate Assisted Design Optimization of an Air Turbine

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Rameez Badhurshah ◽  
Abdus Samad
Keyword(s):  
Design Space ◽  
Hybrid Genetic Algorithm ◽  
Navier Stokes ◽  
Cfd Analysis ◽  
Impulse Turbine ◽  
Optimal Point ◽  
Ocean Wave Energy ◽  
Design Variables ◽  
Air Turbine ◽  
Full Factorial

Surrogates are cheaper to evaluate and assist in designing systems with lesser time. On the other hand, the surrogates are problem dependent and they need evaluation for each problem to find a suitable surrogate. The Kriging variants such as ordinary, universal, and blind along with commonly used response surface approximation (RSA) model were used in the present problem, to optimize the performance of an air impulse turbine used for ocean wave energy harvesting by CFD analysis. A three-level full factorial design was employed to find sample points in the design space for two design variables. A Reynolds-averaged Navier Stokes solver was used to evaluate the objective function responses, and these responses along with the design variables were used to construct the Kriging variants and RSA functions. A hybrid genetic algorithm was used to find the optimal point in the design space. It was found that the best optimal design was produced by the universal Kriging while the blind Kriging produced the worst. The present approach is suggested for renewable energy application.

Design Optimization of Micro-Channel for Micro Electronic Cooling

2007 ◽  
Author(s):  
Afzal Husain ◽  
Kwang-Yong Kim
Keyword(s):  
Numerical Optimization ◽  
Conjugate Heat Transfer ◽  
Electronic Cooling ◽  
Navier Stokes ◽  
Micro Channel ◽  
Pumping Power ◽  
Design Variables ◽  
Energy Equations ◽  
Full Factorial ◽  
Surrogate Method

A numerical optimization of a rectangular micro-channel has been performed. Navier-Stokes and energy equations for laminar flow and conjugate heat transfer are solved using a finite volume solver. Solutions are first carefully validated with available analytical and experimental results; the shape of the micro-channel is then optimized using surrogate method. Ratios of the micro-channel width to depth and the width of the fin to the depth are selected as design variables. Design points are selected through a four-level full factorial design. A single objective function thermal resistance, formulated using pumping power as a constraint, is optimized. Mass flow rate is adjusted by the constant pumping power constraint. Kriging method is applied to construct surrogate and the optimum point is searched by sequential quadratic programming.

Efficiency Enhancement of Bidirectional Impulse Turbine Using Artificial Neural Network

2014 ◽  
Author(s):  
R. Badhurshah ◽  
A. Samad
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Stokes Equations ◽  
Hybrid Genetic Algorithm ◽  
Rotor Blades ◽  
Efficiency Enhancement ◽  
Impulse Turbine ◽  
Optimal Point ◽  
Artificial Neural

The work represents a systematic numerical optimization methodology using artificial neural network and hybrid genetic algorithm for a bi-directional axial impulse turbine used in wave energy harvesting system. Reynolds-averaged Navier-Stokes equations with k-ε turbulence model were discretized and solved for unstructured tetrahedral grid elements for flow analyses. Efficiency enhancement of the turbine was chosen as an objective. The design variables chosen were numbers of stator and rotor blades. The responses obtained from CFD analysis were used to train the neural network. The optimal point search from the network by hybrid genetic algorithm produced 13% increase in turbine efficiency. Detailed description of the methodology and analysis of the results has been presented in this paper.

Multi-Objective Optimization of an Autonomous Underwater Vehicle

2009 ◽  
Vol 43 (2) ◽  
pp. 48-60 ◽  
Author(s):  
M. Martz ◽  
W. L. Neu
Keyword(s):  
Design Space ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Design Problems ◽  
Single Measure ◽  
Design Variables ◽  
Complex Design ◽  
Final Design ◽  
Undersea Vehicle ◽  
Hierarchy Process

AbstractThe design of complex systems involves a number of choices, the implications of which are interrelated. If these choices are made sequentially, each choice may limit the options available in subsequent choices. Early choices may unknowingly limit the effectiveness of a final design in this way. Only a formal process that considers all possible choices (and combinations of choices) can insure that the best option has been selected. Complex design problems may easily present a number of choices to evaluate that is prohibitive. Modern optimization algorithms attempt to navigate a multidimensional design space in search of an optimal combination of design variables. A design optimization process for an autonomous underwater vehicle is developed using a multiple objective genetic optimization algorithm that searches the design space, evaluating designs based on three measures of performance: cost, effectiveness, and risk. A synthesis model evaluates the characteristics of a design having any chosen combination of design variable values. The effectiveness determined by the synthesis model is based on nine attributes identified in the U.S. Navy’s Unmanned Undersea Vehicle Master Plan and four performance-based attributes calculated by the synthesis model. The analytical hierarchy process is used to synthesize these attributes into a single measure of effectiveness. The genetic algorithm generates a set of Pareto optimal, feasible designs from which a decision maker(s) can choose designs for further analysis.

Aerodynamic Efficiency Optimization of the 1st Stage of Transonic High Pressure Turbine through Lean and Sweep Angles

2019 ◽  
Vol 36 (3) ◽  
pp. 245-256
Author(s):  
Yoonki Kim ◽  
Sanga Lee ◽  
Kwanjung Yee ◽  
Young-Seok Kang
Keyword(s):  
High Pressure ◽  
Surrogate Model ◽  
Design Space ◽  
Latin Hypercube Sampling ◽  
Expected Improvement ◽  
Total Efficiency ◽  
High Pressure Turbine ◽  
Sweep Angle ◽  
Design Variables ◽  
Optimal Latin Hypercube Sampling

Abstract The purpose of this study is to optimize the 1st stage of the transonic high pressure turbine (HPT) for enhancement of aerodynamic performance. Isentropic total-to-total efficiency is designated as the objective function. Since the isentropic efficiency can be improved through modifying the geometry of vane and rotor blade, lean angle and sweep angle are chosen as design variables, which can effectively alter the blade geometry. The sensitivities of each design variable are investigated by applying lean and sweep angles to the base nozzle and rotor, respectively. The design space is also determined based on the results of the parametric study. For the design of experiment (DoE), Optimal Latin Hypercube sampling is adopted, so that 25 evenly distributed samples are selected on the design space. Sequentially, based on the values from the CFD calculation, Kriging surrogate model is constructed and refined using Expected Improvement (EI). With the converged surrogate model, optimum solution is sought by using the Genetic Algorithm. As a result, the efficiency of optimum turbine 1st stage is increased by 1.07 % point compared to that of the base turbine 1st stage. Also, the blade loading, pressure distribution, static entropy, shock structure, and secondary flow are thoroughly discussed.

Multi-Objective Design and Optimisation of Bypass Outlet-Guide Vanes

2003 ◽  
Author(s):  
Shahrokh Shahpar ◽  
David Giacche ◽  
Leigh Lapworth
Keyword(s):  
Design Space ◽  
Guide Vane ◽  
Automated Design ◽  
Analysis Procedure ◽  
High Fidelity ◽  
Cfd Analysis ◽  
Optimization System ◽  
Multi Objective ◽  
Cluster Of Workstations ◽  
Stagger Angle

This paper describes the development of an automated design optimization system that makes use of a high fidelity Reynolds-Averaged CFD analysis procedure to minimize the fan forcing and fan BOGV (bypass outlet guide vane) losses simultaneously taking into the account the down-stream pylon and RDF (radial drive fairing) distortions. The design space consists of the OGV’s stagger angle, trailing-edge recambering, axial and circumferential positions leading to a variable pitch optimum design. An advanced optimization system called SOFT (Smart Optimisation for Turbomachinery) was used to integrate a number of pre-processor, simulation and in-house grid generation codes and postprocessor programs. A number of multi-objective, multi-point optimiztion were carried out by SOFT on a cluster of workstations and are reported herein.

Shape Optimization of Forward-Curved-Blade Centrifugal Fan with Navier-Stokes Analysis

2004 ◽  
Vol 126 (5) ◽  
pp. 735-742 ◽  
Author(s):  
Kwang-Yong Kim ◽  
Seoung-Jin Seo
Keyword(s):  
Response Surface ◽  
Stokes Equations ◽  
Computing Time ◽  
Relative Size ◽  
Three Dimensional ◽  
Navier Stokes ◽  
Centrifugal Fan ◽  
Navier Stokes Equations ◽  
Design Variables ◽  
Curved Blade

In this paper, the response surface method using a three-dimensional Navier-Stokes analysis to optimize the shape of a forward-curved-blade centrifugal fan is described. For the numerical analysis, Reynolds-averaged Navier-Stokes equations with the standard k-ε turbulence model are discretized with finite volume approximations. The SIMPLEC algorithm is used as a velocity–pressure correction procedure. In order to reduce the huge computing time due to a large number of blades in forward-curved-blade centrifugal fan, the flow inside of the fan is regarded as steady flow by introducing the impeller force models. Four design variables, i.e., location of cutoff, radius of cutoff, expansion angle of scroll, and width of impeller, were selected to optimize the shapes of scroll and blades. Data points for response evaluations were selected by D-optimal design, and a linear programming method was used for the optimization on the response surface. As a main result of the optimization, the efficiency was successfully improved. Effects of the relative size of the inactive zone at the exit of impeller and momentum fluxes of the flow in scroll on efficiency were further discussed. It was found that the optimization process provides a reliable design of this kind of fan with reasonable computing time.

An Automated CFD Design and Analysis Tool for Inlet-Bleed Systems

1998 ◽  
Author(s):  
Tom I-P. Shih ◽  
Yu-Liang Lin ◽  
Andrew J. Flores ◽  
Mark A. Stephens ◽  
Mark J. Rimlinger ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Three Dimensional ◽  
Incident Shock ◽  
Navier Stokes ◽  
Oblique Shock Wave ◽  
Design Parameters ◽  
Analysis Tool ◽  
Cfd Analysis ◽  
Circular Holes ◽  
Embedded Grids

Abstract A pre-processor was developed to assist CFD experts and non-experts in performing steady, three-dimensional Navier-Stokes analysis of a class of inlet-bleed problems involving oblique shock-wave/ boundary-layer interactions on a flat plate with bleed into a plenum through rows of circular holes. With this pre-processor, once geometry (e.g., hole dimensions and arrangement) and flow conditions (e.g., Mach number, boundary-layer thickness, incident shock location) are inputted, it will automatically generate every file needed to perform a CFD analysis from the grid system to initial and boundary conditions. This is accomplished by accessing a knowledge base established by experts who understand both CFD and the class of problems being analyzed. For experts in CFD, this tool greatly reduces the amount of time and effort needed to setup a problem for CFD analysis. It also provides experts with knobs to make changes to the setup if desired. For non-experts in CFD, this tool enables reliable and correct usage of CFD. A typical session on a workstation from data input to the generation of all files needed to perform a CFD analysis involves less than ten minutes. This pre-processor, referred to as AUTOMAT-V2, is an improved version of a code called AUTOMAT. Improvements made include: (1) multi-block structured grids can be patched in addition to being overlapped; (2) embedded grids can be introduced near bleed holes to reduce the number of grid points/cells needed by a factor of up to four; (3) grid systems generated allow up to three levels of multigrid; (4) CFL3D is supported in addition to OVERFLOW, two well-known and highly regarded Navier-Stokes solvers developed at NASA’s Langley and Ames Research Centers; (5) all files needed to run RONNIE for patched grids and MAGGIE for overlapped grids are also generated; and (6) more design parameters can be investigated including the study of micro bleed and effects of flow/hole misalignments.

Beyond the Known: Detecting Novel Feasible Domains Over an Unbounded Design Space

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Wei Chen ◽  
Mark Fuge
Keyword(s):  
Real World ◽  
Adaptive Sampling ◽  
Design Space ◽  
Fundamental Problem ◽  
Domain Boundary ◽  
Sampling Technique ◽  
Standard Test ◽  
Worst Case ◽  
Trade Offs ◽  
Design Variables

To solve a design problem, sometimes it is necessary to identify the feasible design space. For design spaces with implicit constraints, sampling methods are usually used. These methods typically bound the design space; that is, limit the range of design variables. But bounds that are too small will fail to cover all possible designs, while bounds that are too large will waste sampling budget. This paper tries to solve the problem of efficiently discovering (possibly disconnected) feasible domains in an unbounded design space. We propose a data-driven adaptive sampling technique—ε-margin sampling, which learns the domain boundary of feasible designs and also expands our knowledge on the design space as available budget increases. This technique is data-efficient, in that it makes principled probabilistic trade-offs between refining existing domain boundaries versus expanding the design space. We demonstrate that this method can better identify feasible domains on standard test functions compared to both random and active sampling (via uncertainty sampling). However, a fundamental problem when applying adaptive sampling to real world designs is that designs often have high dimensionality and thus require (in the worst case) exponentially more samples per dimension. We show how coupling design manifolds with ε-margin sampling allows us to actively expand high-dimensional design spaces without incurring this exponential penalty. We demonstrate this on real-world examples of glassware and bottle design, where our method discovers designs that have different appearance and functionality from its initial design set.

The Influence of Shroud and Cavity Geometry on Turbine Performance — An Experimental and Computational Study: Part II — Exit Cavity Geometry

2007 ◽  
Author(s):  
Budimir Rosic ◽  
John D. Denton ◽  
Eric M. Curtis ◽  
Ashley T. Peterson
Keyword(s):  
Computational Study ◽  
Experimental Program ◽  
Leakage Flow ◽  
Cfd Analysis ◽  
Stage Model ◽  
Low Aspect Ratio ◽  
Turbine Performance ◽  
Air Turbine ◽  
End Wall ◽  
Dominant Influence

The geometry of the exit shroud cavity where the rotor shroud leakage flow re-enters the main passage flow is very important due to the dominant influence of the leakage flow on the aerodynamics of low aspect ratio turbines. The work presented in this paper investigates, both experimentally and numerically, possibilities for the control of shroud leakage flow by modifications to the exit shroud cavity. The processes through which the leakage flow affects the mainstream aerodynamics identified in the first part of this study were used to develop promising strategies for reducing the influence of shroud leakage flow. The experimental program of this study was conducted on a three-stage model air turbine, which was extensively supported by CFD analysis. Three different concepts for shroud leakage flow control in the exit cavity were analysed and tested: a) profiled exit cavity downstream end-wall, b) axial deflector, and c) radial deflector concept. Reductions in aerodynamic losses associated with shroud leakage were achieved by controlling the position and direction at which the leakage jet re-enters the mainstream when it leaves the exit shroud cavity. Suggestions are made for an optimum shroud and cavity geometry.

Aerodynamic Automobile Shape Optimization by Incorporating Reverse Shape Design Method With CFD Analysis

2017 ◽  
Author(s):  
Kisun Song ◽  
Kyung Hak Choo ◽  
Jung-Hyun Kim ◽  
Dimitri N. Mavris
Keyword(s):  
Design Optimization ◽  
Aerodynamic Drag ◽  
Design Method ◽  
Shape Design ◽  
Cfd Analysis ◽  
Simple Arithmetic ◽  
Design Variables ◽  
3D Geometry ◽  
Geometric Entity ◽  
Aerodynamic Drag Reduction

In modern automotive industry market, there have been a lot of state-of-art methodologies to perform a conceptual design of a car; functional methods and 3D scanning technology are widely used. Naturally, the issues frequently boiled down to a trade-off decision making problem between quality and cost. Besides, to incorporate the design method with advanced optimization methodologies such as design-of-experiments (DOE), surrogate modeling, how efficiently a method can morph or recreate a vehicle’s shape is crucial. This paper accomplishes an aerodynamic design optimization of rear shape of a sedan by incorporating a reverse shape design method (RSDM) with the aforementioned methodologies based on CFD analysis for aerodynamic drag reduction. RSDM reversely recovers a 3D geometry of a car from several 2D schematics. The backbone boundary lines of 2D schematic are identified and regressed by appropriate interpolation function and a 3D shape is yielded by a series of simple arithmetic calculations without losing the detail geometric features. Besides, RSDM can parametrize every geometric entity to efficiently manipulate the shape for application to design optimization studies. As the baseline, an Audi A6 is modeled by RSDM and explored through CFD analysis for model validation. Choosing six design variables around the rear shape, 77 design points are created to build neural networks. Finally, a significant amount of CD reduction is obtained and corresponding configuration is validated via CFD.

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