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Updated: Jun 11, 2026

Design and Optimization Strategies of a High-Performance Vented Box
14:23

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Published on: June 9, 2023

Knowledge embedded wind tunnel typical pressure vessel design optimization method research.

Yisheng Yang1,2, Sijie Yan1, Qiang Xie2

  • 1State Key Lab of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China.

Plos One
|June 9, 2026
PubMed
Summary
This summary is machine-generated.

A new knowledge-embedded hierarchical Kriging (KEHK) framework enhances wind tunnel pressure vessel design. This method reduces structural weight by 26.2% and improves computational efficiency by 150x.

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Area of Science:

  • Aerospace Engineering
  • Computational Mechanics
  • Structural Optimization

Background:

  • Wind tunnels are critical for aerodynamic research and aerospace development.
  • Pressure vessels are key components in transonic and supersonic wind tunnels.
  • Conventional design methods for pressure vessels are inefficient and overly conservative.

Purpose of the Study:

  • To develop an advanced framework for optimizing wind tunnel pressure vessel design.
  • To overcome limitations of conventional and data-driven design approaches.
  • To improve generalizability across variable operating conditions.

Main Methods:

  • Proposed a knowledge-embedded hierarchical Kriging (KEHK) framework.
  • Integrated pressure vessel design specifications with adaptive multi-fidelity modeling.
  • Introduced innovations including a knowledge-embedded sequential sampling method, adaptive hierarchical Kriging architecture, and a condition-mapping protocol.

Main Results:

  • Achieved a 26.2% structural weight reduction in a transonic wind tunnel acceleration section.
  • Demonstrated a 150x computational efficiency improvement over traditional finite element analysis.
  • KEHK model showed prediction error <15% across operational pressure ranges (0.1-2.0), outperforming other methods.

Conclusions:

  • The KEHK framework offers a robust solution for next-generation wind tunnel engineering.
  • Successfully bridges the gap between computational efficiency and operational reliability in pressure vessel design.
  • Enhances structural optimization for aerospace applications through improved generalizability and accuracy.