Open Access

ARTICLE

Thermomechanical Optimization Design of TGV Weight Respecting Restrictive Condition and Highly Sensitive Variables

Peng Guan^*, Ming-Ran Li, Si-Bo-Wen Wang

Faculty of Aviation Engine, Shenyang Aerospace University, Shenyang, China

* Corresponding Author: Peng Guan. Email:

(This article belongs to the Special Issue: Heat and Mass Transfer in Aero-Engines and Gas Turbines)

Frontiers in Heat and Mass Transfer 2026, 24(4), 11 https://doi.org/10.32604/fhmt.2026.082595

Received 18 March 2026; Accepted 05 June 2026; Issue published 31 August 2026

Abstract

This paper develops a thermomechanical optimization method for turbo guide vane (TGV) weight reduction under restrictive conditions and highly sensitive variables. The proposed method integrates a flow-thermo-structural model, orthogonal experimental design (OED), and an optimization framework based on response surface methodology (RSM) and a genetic algorithm (GA). To address both the plastic limit and temperature distribution of the TGV, a new parameter termed the stress ratio is introduced as a constraint during optimization. Six highly sensitive variables were selected from ten cooling channel diameters using OED. Simulation results based on the thermal-fluid coupling model were validated against NASA reference data for the Mark-II TGV. The predicted and measured temperature distributions on the mid-span plane showed good agreement, with a maximum error of less than 7.5%. Compared with the original model, the optimized masses were reduced by 16.80% and 14.47% at stress ratios of 0.95 and 0.9, respectively. The stress ratio results from the RSM-based surrogate model were consistent with the simulations, demonstrating the accuracy of the thermomechanical optimization method. Furthermore, the location of the maximum stress ratio in the two optimized structures differed from that of the maximum stress, highlighting the necessity of introducing the stress ratio. The introduced stress ratio parameter and the proposed method for extracting highly sensitive factors provide a new approach for high-efficiency TGV optimization.

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Keywords

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