In recent decade, industry had started to use intensively 3D simulation in turbine flow path and its components design. At the same time, this remains a very labor- and time-consumable process that sufficiently hampers its usage, whereas unidimensional and axisymmetric analyses are still widely used in the industry practice. A comparison of the data obtained from experiments conducted on a single stage air turbine test model with the results of 1D and 2D modeling and 3D simulation using a CFD solver was performed. The results were analyzed to validate a judgement of the authors that along with 3D CFD methods the low-fidelity models can be successfully used for turbine flow path optimization with the help of DoE methods. The forthcomings and advantages of different models are also discussed.

Key words: Modeling/Experiment Data Comparison, 1D/2D/ 3D Analysis, Optimization.

INTRODUCTION

The validation of the computations remains a subject of meticulous attention in the industry. Some authors introduce the results of comparison of the turbine rig test data with 2D computations [1, 2]. An objective of this study was to correlate the results of 1D, 2D and 3D aerodynamic computations with the proven test data extracted from experiments on several designs of a single stage test air turbine.

It was shown that proper unidimensional and axisymmetric models based on validated empiric methods of loss computation provide an accuracy of the flow path parameters estimation sufficient for solving a bulk of practice valuable optimization problems.

The turbine multidisciplinary optimization problems are the topic of different authors’ research (see, for example, the list of references presented in [3]).

It was proposed to perform optimization on parameterized geometrical 3D models of blade rows or stages utilizing the design of numerical experiment (DoE) technique [4, 6], earlier applied to optimization on 1D and 2D models. Aside from the problems of aerodynamic optimization, this permits to solve the problems of multidisciplinary optimization with regard to, for instance, strength, vibration and other limitations.

NOMENCLATURE AND GLOSSARY