Evidence map›Paper›PMID 36661782›Full record

ArticleGels (Basel, Switzerland)2022

Study of Thixotropic Characteristics of a Kerosene Gel Propellant by Bayesian Optimization.

Hao Zhou, Cai Chen, Feng Feng, Changsheng Zhou, Wenling Zhang, Wei-Tao Wu

Abstract read
In one paragraph

Article in Gels (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Hao ZhouSchool of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China.ORCID 0000-0002-0212-1741
Cai ChenSino-French Engineer School, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, China.
Feng FengSchool of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China.
Changsheng ZhouSchool of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China.
Wenling ZhangSchool of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China.
Wei-Tao WuSchool of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China.

Funding

National Natural Science Foundation of China 51901101
6 · The paper itself

Abstract

The rheological behavior of gel propellants is crucial for their practical applications, especially in the rocket engine and ramjet fields. The thixotropic characteristics of gel propellants are an important component of their rheological properties and have a notable impact on their flow and injection process. However, most gel propellants contain rich, dynamic cross-linked network structures, which impart complex non-Newtonian fluid properties, and it is difficult to establish a unified mathematical model. In view of this, this study addresses the thixotropy of a prepared RP-3 kerosene gel and determines the mathematical model and model parameters describing its thixotropy. Experiments show that the kerosene gel exhibits shear-thinning properties as well as thixotropy. To describe the microstructural changes in the gel, three thixotropic constitutive models are introduced to analyze the rheological data, and the constitutive equation parameters are optimized. The three models are all structural dynamic models, which can be used to describe microstructural changes within the material. In addition, the fitting of the constitutive equation is a multiparameter optimization problem, and an appropriate optimization method must be used for parameter fitting. Therefore, the Bayesian optimization method combined with Gaussian process regression and the upper confidence bound (UCB) acquisition function is used in the multiparameter fitting of the constitutive models. Both experiments and numerical results show that the thixotropic model, which introduces a pre-factor with shear strain and assumes that the breakdown of the gel structure is related to energy dissipation rather than the shear rate, has a better fitting effect and prediction ability with regard to the gel. Combined with transient experiments at different shear rates, the model parameters of the constitutive law can be determined quickly by applying the Bayesian optimization method.

Indexed as

Bayesian optimizationkerosene gel propellantthixotropic

Identifiers

PMID36661782
PMCPMC9857846

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