Review of performance optimization techniques applied to wind turbines

Response surface approximation of Pareto optimal front in multi-objective optimization

To further improve the efficiency of turbomachinery, an advanced aerodynamic optimization system has been developed for the turbomachinery blade optimization design. The system includes parametric modeling, evaluation system, and optimization strategy modules. The nonuniform rational B-spline technique is successfully used for parametric modeling of different blade shapes. An in-house viscous flow code, which combines the lower-upper symmetric-Gauss-Seidel Gaussian elimination (LU-SGS-GE) implicit scheme and the modified fourth-order monotone upstream-centered schemes for conservation laws total variation diminishing (MUSCL TVD) scheme, has been developed for flow field evaluation, which can be replaced by other computational fluid dynamics codes. The optimization strategy is defined by different cases in the system. Parallel optimization technique was used to accelerate the optimization processes. Three test cases were optimized to improve the efficiency by using the system. These cases are the annular turbine cascades with a subsonic turbine blade, a transonic turbine blade, and a subsonic turbine stage. Reasonably high efficiency and performance were confirmed by comparing the analytical results with those of the previous ones. The advanced aerodynamic optimization system can be an efficient and robust design tool to achieve good blade optimization designs in a reasonable time.

Advanced Aerodynamic Optimization System for Turbomachinery

The role of uncertainty in information rich design systems is critical to the development of advanced propulsion systems. Future turbine engines would have lower lifetime operating costs similar to current evolving automotive systems. Detailed multi-physics models (thermo-fluid, structural and mechanical systems) and an operational environment are enablers of rapid correction and model-based predictive analyses. Bayesian machine learning paradigms are developed to identify the behavior of turbo-machinery components for preliminary design. The embedded models approach enables systematic evolution from individual components level to the advanced engine. The embedded models approach enables systematic evolution from individual components level to the advanced engine. A rapid response strategy is proposed, for design of turbine disks by using multidisciplinary optimization and neural networks. iSIGHT optimization software is interfaced with ANSYS to find optimum designs for a given set of design boundary conditions (rpm, live rim load, thermals, etc.). The optimum designs obtained from iSIGHT for different set of design conditions are used for machine learning and design knowledge recognition using the neural network technique. The trained network is used to predict responses for design boundary conditions. Responses predicted by the neural network are validated using ANSYS. Discrete design points are chosen from the wide design space of turbine disks. A hierarchical neural network approach provides an ability to quickly train the network and predict responses (weight, stresses, burst margin, etc.) for applied design conditions. This basic building process involves four steps starting from identifying design boundary conditions to the prediction of design shape for the disk. Sensitivity-based scaling rules are developed, to accommodate different materials for the disk. The technique is developed in RAPIDDISK, which provides an optimal preliminary shape and design attributes for a turbine disk.

Optimal rapid multidisciplinary response networks: RAPIDDISK

A new transonic turbine cascade model that accurately produces infinite cascade flow conditions with minimal compressor requirements is presented. An inverse design procedure using the Favre-averaged Navier-Stokes equations and k- turbulence model based on the method of steepest descent was applied to a geometry consisting of a single turbine blade in a passage. For a fixed blade geometry, the passage walls were designed such that the surface isentropic Mach number (SIMN) distribution on the blade in the passage matched the SIMN distribution on the blade in an infinite cascade, while maintaining attached flow along both passage walls. An experimental rig was built that produces realistic flow conditions, and also provides the extensive optical access needed to obtain detailed particle image velocimetry measurements around the blade. Excellent agreement was achieved between computational fluid dynamics (CFD) of the infinite cascade SIMN, CFD of the designed double passage SIMN, and the measured SIMN. DOI: 10.1115/1.1929810

/pdf/inverse-design-of-and-experimental-measurements-in-a-double-4rg1710p3f.pdf

Inverse Design of and Experimental Measurements in a Double-Passage Transonic Turbine Cascade Model

The development of an optimization environment for multidisciplinary turbomachinery blade design is presented. The optimization environment simplifies the use of various optimization and evaluation methods. An advanced blade parameterization scheme is developed, in order to perform particular blade modifications with a limited number of optimization parameters. The generation of invalid designs is avoided a priori, which is particularly important in turbomachinery design with expensive 3D flow computations. The parameterization includes several components, such as profile, blade and grid generation tools. A CPD solver computes various turbomachinery stage designs in parallel. A preevaluation tool extracts necessary data and processes objectives and constraints. These objectives and constraints are assessed in an evaluator indicating the design quality. Interfaces between all tools of the optimization environment guarantee an automated, closed data flow. It is shown that this optimization environment enables easily sensitivity analyses as well as optimization runs. Investigations are made in order to derive reasonable 3D flow assessment criteria for the objectives and constraints. Results of performing multidisciplinary turbomachinery blade design optimizations on a turbine stage are presented.

MULTIDISCIPLINARY TURBOMACHINERY BLADE DESIGN OPTIiMIZATION

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MULTIDISCIPLINARY TURBOMACHINERY BLADE DESIGN OPTIiMIZATION