Non-linear finite element model for dynamic analysis of high-speed valve train and coil collisions

Gu, Zewen; Hou, Xiaonan; Keating, Elspeth; Ye, Jianqiao

doi:10.1016/j.ijmecsci.2020.105476

Cited by 11 publications

(19 citation statements)

References 19 publications

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“…These results will be discussed in further detail in Section 4.7. Typical numerical models on springs show that they have failure behaviour under dynamic transient loads [85,86]. As seen on Figure 10, there are different locations of the damages to the helical spring reinforcement, which the authors identified as "damage sites".…”

Section: Results Of Helix Localised Damagementioning

confidence: 99%

“…On the other hand, the spring has been modelled fully because the study's goal is to investigate spring behaviour so that spring load and the number of spring layers (one or two helical springs) can be considered in the model's conceptualisation. Other methods of numerical models for spring models exist in the literature [84][85][86]. The parameters used in constructing the helix geometry considered in the numerical model are listed in Table 7.…”

Section: Helix Reinforcementmentioning

confidence: 99%

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Numerical Modelling on the Local Design of a Marine Bonded Composite Hose (MBCH) and Its Helix Reinforcement

et al. 2022

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With the exploration of oil trending deeper, from shallow waters to deep waters, there is a corresponding increase in the need for more sustainable conduit materials for production purposes. Secondly, there is an increasing demand for more energy from fossil fuels that are excavated with less expensive technologies. As such, short-service hoses are applied in the offshore industry. The industry has utilised composites to improve the material and solve different offshore issues. This study analyses a current problem facing the oil and gas industry at present regarding hose usage. This paper presents results from the local design and analyses of a marine bonded composite hose (MBCH), to present its result visualisations and nephographs. In this paper, the local design of a 1 m section of an MBCH was carried out in ANSYS under different loading conditions. Some design criteria were set, and other load conditions were used to simulate the model using the finite element model (FEM) approach. From this study, composites could be considered to improve conventional marine hoses. The findings of the study include the identification of linear wrinkling and damage sites on the helix reinforcement. An experimental investigation and proper content test are recommended for the bonded hose. Additionally, highly reinforced hose ends are recommended in the ends of the MBCH, as they had maximum stress and strain values. It is recommended that hose operations like reeling must be conducted under operational pressure and not design pressure, as the study shows that the design pressure could be high on the hose model.

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Section: Results Of Helix Localised Damagementioning

confidence: 99%

Section: Helix Reinforcementmentioning

confidence: 99%

Numerical Modelling on the Local Design of a Marine Bonded Composite Hose (MBCH) and Its Helix Reinforcement

et al. 2022

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“…The meshing strategy and boundary conditions are the same as the ones used in the author's previous study. 4 The coils 02 to 06 are meshed by hexahedron elements to reduce the computing time. The end coils 01 and 07 are meshed using tetrahedron elements for their unique shapes.…”

Section: Parametric Fe Modelmentioning

confidence: 99%

“…Besides, the spring pitches of these three types are constant as well. However, it has been proved in previous research 4 that the existing linear theories fail to accurately describe the behaviour of specific high performance helical springs with nonlinear properties. Especially, when coils clash exists, the actual magnitude of spring forces can be 3 times larger than that calculated by the analytical models based on these linear spring theories.…”

Section: Introductionmentioning

confidence: 99%

“…In an internal combustion (IC) engine, valve springs are the most flexible components, which must guarantee the precise and high-efficient return motions of valves. Good designs can bring many benefits, such as high spring strength, low fuel consumption and enhanced high speed performances, etc., [1][2][3][4] while poor designs of valve springs may lead to severe spring damage and malfunction of engine.…”

Section: Introductionmentioning

confidence: 99%

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Design and analysis method of nonlinear helical springs using a combining technique: Finite element analysis, constrained Latin hypercube sampling and genetic programming

Hou

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Helical springs have been widely used in various engineering applications for centuries. For many years, there is no significant development in the design methods of helical springs. Recently, a renewed interest is raised from the industry in exploring new designs for the helical springs with novel configurations due to the requirements of customised properties, such as specific spring stiffness and natural frequency for better performance of valve train systems. In this paper, an innovative method which combines the techniques of Finite Element Analysis (FEA), constrained Latin Hypercube sampling (cLHS) and Genetic Programming (GP) is developed to design and analyse helical springs with arbitrary shapes. cLHS method is applied to generate 300 sets of spring samples within a constrained design domain, and FE analysis is conducted on these spring samples. Two meta-models are developed from the 300 sets of FE results by using GP. They successfully describe the relationships between the design parameters and the overall mechanical performances including compression force and fundamental natural frequency of helical springs. The results show that the developed models have robust abilities on designing helical springs with arbitrary shapes, which significantly expands the design domain of the engineering design methods and potential for precise optimization of helical springs.

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A Novel Self‐Updating Design Method for Complex 3D Structures Using Combined Convolutional Neuron and Deep Convolutional Generative Adversarial Networks

Hou

Saafi

et al. 2022

Advanced Intelligent Systems

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Mechanical design is one of the essential disciplines in engineering applications, while inspirations of design ideas highly depend on the ability and prior knowledge of engineers or designers. With the rapid development of machine learning (ML) techniques, artificial intelligence (AI)‐based design methods are promising tools for the design of advanced engineering systems. So far, there have been some studies of 2D patterns and structural designs based on ML techniques. However, a particular challenge remains in allowing complex 3D mechanical designs using ML techniques. Herein, a novel and experience‐free method to equip ML models with 3D design capabilities by combining a convolutional neuron network (CNN) with a deep convolutional generative adversarial network (DCGAN) is developed. The model directly receives 2D image‐based training data that define the complex 3D structures of a specific machine part. After the training process, an infinite number of new 3D designs can be generated by the proposed model, with their geometric and mechanical properties being accurately predicted at the same time. Moreover, the generated new designs can be fed back to expand the original input datasets for further ML model training and updating.

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Non-linear finite element model for dynamic analysis of high-speed valve train and coil collisions

Cited by 11 publications

References 19 publications

Numerical Modelling on the Local Design of a Marine Bonded Composite Hose (MBCH) and Its Helix Reinforcement

Numerical Modelling on the Local Design of a Marine Bonded Composite Hose (MBCH) and Its Helix Reinforcement

Design and analysis method of nonlinear helical springs using a combining technique: Finite element analysis, constrained Latin hypercube sampling and genetic programming

A Novel Self‐Updating Design Method for Complex 3D Structures Using Combined Convolutional Neuron and Deep Convolutional Generative Adversarial Networks

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