Thermoset coupling structure dynamics order reduction model method suitable for thermodynamic elasticity analysis

A reduced-order model and elastic analysis technology, applied in the field of aerodynamics, can solve problems such as inapplicable nonlinear problems, insufficient accuracy, and high computational cost, and achieve the effect of reducing sample requirements, improving accuracy, and reducing computational cost.

Pending Publication Date: 2020-10-30
AERODYNAMICS NAT KEY LAB +1
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Benefits of technology

This patented technology reduces the order modelling problem used during analyzation methods such as phase equilibrium molecular dynamic simulations or finite element models. By creating a small number of smaller structures that represent different parts of an object undergoing physical processes like heat transfer, pressure waves, etc., they become more accurate than traditional approaches without requiring large amounts of data storage. Additionally, this approach simplifies calculations while still allowing for precise control over factors affecting these phenomena. Overall, this innovative technique improves efficiency and effectiveness in various fields related technologies including science researches and engineering applications.

Problems solved by technology

Technologies aim towards improving the performance of structures exposed to severe environmental factors like airflow or wind. These improvements involve modifying their design variables to match certain constraints imposed upon those environments' behavior characteristics. One approach involves simplification of complex mathematical expressions involving multiple models representing various aspects of the environment, but this also reduces computation costs compared to traditional approaches due to its complexity. Another aspect includes optimizing the model coefficients themselves to minimize energy consumption while maintaining accurate phase response prediction.

Method used

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  • Thermoset coupling structure dynamics order reduction model method suitable for thermodynamic elasticity analysis
  • Thermoset coupling structure dynamics order reduction model method suitable for thermodynamic elasticity analysis
  • Thermoset coupling structure dynamics order reduction model method suitable for thermodynamic elasticity analysis

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Embodiment 1

[0056] In this embodiment, a thermo-aeroelastic analysis applicable thermo-solid coupled structural dynamics reduction model method is provided. The self-encoder algorithm in deep learning is used to reduce the temperature field of the structure, and a multi-layer neural network is used to perform Structural dynamic modeling of thermo-solid coupling to simplify the dynamic equations of heated structures in the prior art to reduce calculation difficulty and improve calculation accuracy.

[0057] Such as Figure 6 As shown, the method specifically includes:

[0058] S1: Reduce the temperature field of the structure

[0059] S101: Construct a structural temperature field reduction model based on a multi-layer neural network. The structural temperature field reduction model is realized based on an autoencoder neural network, and the autoencoder neural network in deep learning is used for reduction. The autoencoder The neural network is divided into encoding part and decoding par...

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Abstract

The invention discloses a thermoset coupling structure dynamics order reduction model method suitable for thermodynamic elasticity analysis and belongs to the technical field of aerodynamics. The method comprises the steps of S1, performing order reduction on a structure temperature field, constructing a structure temperature field order reduction model, and obtaining a structure temperature fieldlow-order vector after order reduction of the structure temperature field at any moment; S2, selecting a modal shape of a reference structure as a reference modal shape phi ref, and performing orderreduction on a deformation field to obtain a low-dimensional vector q based on phi ref; s3, establishing a kinetic equation of the heated structure; s4, establishing a thermosetting coupling kinetic model of the heated structure; sampling is carried out in the q and the formed L + N-dimensional space; a series of sample working conditions with different deformation field and temperature field loads are obtained, M, D and K (1) in a formula (7) are identified through a system, and nonlinear structure dynamics analysis under a time-varying temperature load is carried out after training is completed, so that a thermosetting coupling dynamics model based on a neural network is a nonlinear model and has generality.

Description

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Claims

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Application Information

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Owner AERODYNAMICS NAT KEY LAB
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