Aerodynamic Optimization of the Low-Pressure Turbine Module: Exploiting Surrogate Models in a High-Dimensional Design Space

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Lieven Baert ◽  
Emmanuel Chérière ◽  
Caroline Sainvitu ◽  
Ingrid Lepot ◽  
Arnaud Nouvellon ◽  
...  
Keyword(s):  
Data Mining ◽  
Design Space ◽  
Low Pressure ◽  
Surrogate Models ◽  
High Dimensional ◽  
Efficiency Gain ◽  
Aerodynamic Optimization ◽  
Component Design ◽  
Efficient Data ◽  
Lp Turbine

Abstract Further improvement of state-of-the-art low-pressure (LP) turbines (LPTs) has become progressively more challenging. LP design is more than ever confronted to the need to further integrate complex models and to shift from single-component design to the design of the complete LPT module at once. This leads to high-dimensional design spaces and automatically challenges their applicability within an industrial context, where computing resources are limited and the cycle time is crucial. The aerodynamic design of a multistage LP turbine is discussed for a design space defined by 350 parameters. Using an online surrogate-based optimization (SBO) approach, a significant efficiency gain of almost 0.5pt has been achieved. By discussing the sampling of the design space, the quality of the surrogate models, and the application of adequate data mining capabilities to steer the optimization, it is shown that despite the high-dimensional nature of the design space, the followed approach allows to obtain performance gains beyond target. The ability to control both global as well as local characteristics of the flow throughout the full LP turbine, in combination with an agile reaction of the search process after dynamically strengthening and/or enforcing new constraints in order to adapt to the review feedback, not only illustrates the feasibility but also the potential of a global design space for the LP module. It is demonstrated that intertwining the capabilities of dynamic SBO and efficient data mining allows to incorporate high-fidelity simulations in design cycle practices of certified engines or novel engine concepts to jointly optimize the multiple stages of the LPT.

Aerodynamic Optimisation of the Low Pressure Turbine Module: Exploiting Surrogate Models in a High-Dimensional Design Space

2019 ◽  
Author(s):  
Lieven Baert ◽  
Ingrid Lepot ◽  
Caroline Sainvitu ◽  
Emmanuel Chérière ◽  
Arnaud Nouvellon ◽  
...  
Keyword(s):  
Data Mining ◽  
Design Space ◽  
Low Pressure ◽  
Surrogate Models ◽  
High Dimensional ◽  
Efficiency Gain ◽  
Component Design ◽  
Efficient Data ◽  
Complex Models ◽  
Lp Turbine

Abstract Further improvement of state-of-the-art Low Pressure (LP) turbines has become progressively more challenging. LP design is more than ever confronted to the need to further integrate complex models and to shift from single component design to the design of the complete LPT module at once. This leads to high-dimensional design spaces and automatically challenges its applicability within an industrial context, where CPU resources are limited and the cycle time crucial. The aerodynamic design of a multistage LP turbine is discussed for a design space defined by 350 parameters. Using an online surrogate-based optimisation (SBO) approach a significant efficiency gain of almost 0.5pt has been achieved. By discussing the sampling of the design space, the quality of the surrogate models, and the application of adequate data mining capabilities to steer the optimisation, it is shown that despite the high-dimensional nature of the design space the followed approach allows to obtain performance gains beyond target. The ability to control both global as well as local characteristics of the flow throughout the full LP turbine, in combination with an agile reaction of the search process after dynamically strengthening and/or enforcing new constraints in order to adapt to the review feedback, illustrates not only the feasibility but also the potential of a global design space for the LP module. It is demonstrated that intertwining the capabilities of dynamic SBO and efficient data mining allows to incorporate high-fidelity simulations in design cycle practices of certified engines or novel engine concepts to jointly optimise the multiple stages of the LPT.

High-Dimensional Constrained Multiobjective Optimization of a Fan Stage

2011 ◽  
Author(s):  
Marcel Aulich ◽  
Ulrich Siller
Keyword(s):  
Design Space ◽  
Surrogate Models ◽  
Expected Improvement ◽  
High Dimensional ◽  
Process Chain ◽  
Fast Evaluation ◽  
New Members ◽  
Stall Margin ◽  
Advantages And Disadvantages ◽  
The Right

A high-dimensional design space, different objectives, many constraints and a time-consuming process chain result in a complex task for any optimization tool. This paper shows methods and strategies used at DLR, Institute of Propulsion Technology, to handle this kind of problem. The present optimization task is a rotor-stator configuration with more than two hundred free design variables, two objective functions (efficiency, stall margin) and mechanical and aerodynamic constraints (mass flow, eigenfrequencies, etc.). The process chain consists of geometry and mesh generation, FEM-and 3D-CFD calculations for different operating points. After defining the setup and explaining the initial already 3-D-preoptimized configuration, the CFD/FEM optimization tool is described. This tool calculates the complete CFD/FEM process chain and creates new designs (also called members) by using an evolutionary algorithms. Parallel to the CFD/FEM optimization a program based on surrogate models is running. By using surrogate models a fast evaluation of new members is enabled. So a database of new members can be created quickly. Based on this database a set of new members is built. This is send to the CFD/FEM optimization tool, where the complete CFD/FEM process chain is applied. After the CFD/FEM evaluation process, these member are used to train the surrogate models again. This procedure repeats until the optimization goals are reached. In the next part of this paper the implemented surrogate models are discussed. Both neural networks and Kriging models have advantages and disadvantages compared to each other. It is important to understand them to choose the right model at the right time of optimization. The main focus of this paper is on the selection criterion for new members. This criterion has two targets: push the performance of the fan stage and enhance the surrogate models. At first sight these targets seem to be contrary, but the surrogate models do not predict a single mean value for an objective. They offer a density distribution of the potential objective values. That allows calculation of the Paretofront enhancement (ParetoEnSet) for a set of new members. ParetoEnSet is the expected area gain of a set of members to the current Paretofront. This criterion based on the already known expected improvement. It is shown, that ParetoEnSet can rise, when the uncertainty of an prediction increases. The uncertainty is estimated by a surrogate model. So new members tend to explore the design space, where the predicted uncertainty is huge. These members are favorable for improving the surrogate models. In addition, it is easy to couple constraints with ParetoEnSet. In the last section the results of the optimization are illustrated. Compared to baseline design the optimized stage accomplishes a notable improvement in efficiency by obtaining the stall margin and fulfilling multi aerodynamical and mechanical constraints.

scholarly journals An approximate high-dimensional optimization method using hierarchical design space reduction strategy

2021 ◽  
Vol 39 (2) ◽  
pp. 292-301
Author(s):  
Pengcheng Ye ◽  
Congcong Wang ◽  
Guang Pan
Keyword(s):  
Design Space ◽  
Optimization Method ◽  
Surrogate Models ◽  
Black Box ◽  
High Dimensional ◽  
Hierarchical Design ◽  
Reduction Strategy ◽  
Space Reduction ◽  
Ensemble Of Surrogates ◽  
Dimensional Optimization

To overcome the complicated engineering model and huge computational cost, a hierarchical design space reduction strategy based approximate high-dimensional optimization(HSRAHO) method is proposed to deal with the high-dimensional expensive black-box problems. Three classical surrogate models including polynomial response surfaces, radial basis functions and Kriging are selected as the component surrogate models. The ensemble of surrogates is constructed using the optimized weight factors selection method based on the prediction sum of squares and employed to replace the real high-dimensional black-box models. The hierarchical design space reduction strategy is used to identify the design subspaces according to the known information. And, the new promising sample points are generated in the design subspaces. Thus, the prediction accuracy of ensemble of surrogates in these interesting sub-regions can be gradually improved until the optimization convergence. Testing using several benchmark optimization functions and an airfoil design optimization problem, the newly proposed approximate high-dimensional optimization method HSRAHO shows improved capability in high-dimensional optimization efficiency and identifying the global optimum.

CLASSIFICATION OF HIGH-DIMENSIONAL MICROARRAY DATA WITH A TWO-STEP PROCEDURE VIA A WILCOXON CRITERION AND MULTILAYER PERCEPTRON

2011 ◽  
Vol 10 (01) ◽  
pp. 1-14
Author(s):  
VLADIMIR NIKULIN ◽  
TIAN-HSIANG HUANG ◽  
GEOFFREY J. MCLACHLAN
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
High Dimensional ◽  
Second Step ◽  
Support Vector ◽  
Step Procedure ◽  
Leave One Out ◽  
Natural Combination ◽  
Feature Selection Techniques

The method presented in this paper is novel as a natural combination of two mutually dependent steps. Feature selection is a key element (first step) in our classification system, which was employed during the 2010 International RSCTC data mining (bioinformatics) Challenge. The second step may be implemented using any suitable classifier such as linear regression, support vector machine or neural networks. We conducted leave-one-out (LOO) experiments with several feature selection techniques and classifiers. Based on the LOO evaluations, we decided to use feature selection with the separation type Wilcoxon-based criterion for all final submissions. The method presented in this paper was tested successfully during the RSCTC data mining Challenge, where we achieved the top score in the Basic track.

scholarly journals Data Mining Usage in Corporate Information Security: Intrusion Detection Applications

2017 ◽  
Vol 8 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Masoud Al Quhtani
Keyword(s):  
Data Mining ◽  
Information Security ◽  
Intrusion Detection ◽  
Mining System ◽  
Systematic Analysis ◽  
New Methods ◽  
Use Of Data ◽  
Efficient Data ◽  
Abuse Risk ◽  
Corporate Information

AbstractBackground: The globalization era has brought with it the development of high technology, and therefore new methods of preserving and storing data. New data storing techniques ensure data are stored for longer periods of time, more efficiently and with a higher quality, but also with a higher data abuse risk. Objective: The goal of the paper is to provide a review of the data mining applications for the purpose of corporate information security, and intrusion detection in particular. Methods/approach: The review was conducted using the systematic analysis of the previously published papers on the usage of data mining in the field of corporate information security. Results: This paper demonstrates that the use of data mining applications is extremely useful and has a great importance for establishing corporate information security. Data mining applications are directly related to issues of intrusion detection and privacy protection. Conclusions: The most important fact that can be specified based on this study is that corporations can establish a sustainable and efficient data mining system that will ensure privacy and successful protection against unwanted intrusions.

Redesign of High-Lift LP-Turbine Airfoils for Low Speed Testing

2010 ◽  
Author(s):  
Michele Marconcini ◽  
Filippo Rubechini ◽  
Roberto Pacciani ◽  
Andrea Arnone ◽  
Francesco Bertini
Keyword(s):  
Separated Flow ◽  
Low Pressure ◽  
High Lift ◽  
Low Speed ◽  
Low Pressure Turbine ◽  
Wide Range ◽  
Flow Configurations ◽  
Artificial Neural Network Ann ◽  
Lp Turbine ◽  
Turbine Airfoils

Low pressure turbine airfoils of the present generation usually operate at subsonic conditions, with exit Mach numbers of about 0.6. To reduce the costs of experimental programs it can be convenient to carry out measurements in low speed tunnels in order to determine the cascades performance. Generally speaking, low speed tests are usually carried out on airfoils with modified shape, in order to compensate for the effects of compressibility. A scaling procedure for high-lift, low pressure turbine airfoils to be studied in low speed conditions is presented and discussed. The proposed procedure is based on the matching of a prescribed blade load distribution between the low speed airfoil and the actual one. Such a requirement is fulfilled via an Artificial Neural Network (ANN) methodology and a detailed parameterization of the airfoil. A RANS solver is used to guide the redesign process. The comparison between high and low speed profiles is carried out, over a wide range of Reynolds numbers, by using a novel three-equation, transition-sensitive, turbulence model. Such a model is based on the coupling of an additional transport equation for the so-called laminar kinetic energy (LKE) with the Wilcox k–ω model and it has proven to be effective for transitional, separated-flow configurations of high-lift cascade flows.

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