On Aerothermal Optimization of Low-Pressure Steam Turbine Exhaust System

This thesis addresses two challenges in the aerothermal optimization of Low-Pressure Steam Turbine Exhaust System (LPES). The first one is the high computational cost due to the complexity of LPES geometry. To make things worse, designers have to consider extra cost caused by change of constraints, multi-objective and multi-disciplinary optimization. The second is the vulnerability of optimization due to the lack of comprehensive validation of numerical simulation. Sparse experimental data from LPES rig can only give limited and sometimes misleading information for validation. To reduce the computational cost, a commonly used way is to build a surrogate model. However, manual parametrization of high-dimensional geometries like LPES is unreliable. Thus, a Non-Parametric Surrogate Model (NPSM) is developed, which directly builds a mapping relationship between surface mesh and two-dimensional distribution of fluid variables. It can select sensitive geometric features from surface meshes by Graph Neural Networks (GNNs) encoder according to the back-propagated prediction error, which reduces uncertainties caused by manual parameterization and gains the ability to process designs defined by different geometry generation methods. Based on NPSM, a non-parametric sensitivity analysis is conducted, which can calculate the distribution of sensitivity on the surface meshes. It can help users to identify important geometric features and redistribute the control points of geometry. Furthermore, a design classifier is built to detect predictable designs for NPSM, thereby preventing compromises to robustness of optimization. To enhance the robustness of optimization, validation of numerical simulation by exper iment is essential, but the sparsity of experimental data due to the large volume of LPES prevents a comprehensive comparison. This thesis demonstrates a Physical-Informed Neural Networks (PINNs)-based method to reconstruct sparse data, which has much better perfor mance than interpolation. In addition, it can be used to detect anomalies, which prevents data contamination due to mistakes in experiment. A Non-Uniform Rational B-spline (NURBS)-based optimization algorithm is also pre sented in this thesis. It generates surface meshes for NPSM and volume meshes for CFD solver based on control points given by optimizer. The conversion process is achieved by the evaluation of NURBS surfaces and parabolic mesh generator, which provides more degrees of freedom and keep mesh generation robust.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2023

Author dc:creator

• Cao, Jiajun

Advisor dc:contributor.advisor

• Xu, Liping

• graph neural networks
• low-pressure steam turbine exhaust system
• optimization
• physics-informed neural networks
• surrogate model

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/07e7b074-1f3f-4d6f-a9da-c5bb4ec41ffe/download
• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.107513

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/366692